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Amgen Inc. is an independent biotechnology medicines company that discovers, develops, manufactures and markets medicines for grievous illnesses. The Company focuses solely on human therapeutics and concentrates on innovating novel medicines based on advances in cellular and molecular biology.

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Shares of Amgen Inc. sank 0.08% to $309.91 Tuesday, on what proved to be an all-around mixed trading session for the stock market, with the S&P 500 Index...

Amgen Inc. stock falls Tuesday, underperforms market - MarketWatch

Last week, the that is testing an AI tool that enables news organizations to create content using AI. It is unclear what the exact capabilities of the product are but based on one comment from Google, it helps with tasks like headlines and modifying writing styles. Amidst fears of what impact this could have on journalist jobs, Google quickly put out a : New York Times reported Google clarifying statement Our goal is to give journalists the choice of using these emerging technologies in a way that enhances their work and productivity. Quite simply these tools are not intended to, and cannot, replace the essential role journalists have in reporting, creating, and fact-checking their articles. This has sparked a heated conversation about the future of news with generative AI. The companies making AI products argue that this technology will enable media organizations and journalists to be more effective, while critics contend that it could potentially harm journalist jobs, increase misinformation, and flood the market with low-quality AI-generated content. In this piece, we unpack the by exploring a few things: impact of generative AI on the future of news Types of news articles today by complexity The business of news The anatomy of producing a news article What generative AI can and cannot do well Generative AI and the Future of News — A tale of two worlds Types of news articles today by complexity There are several types of news content on the internet, each with varying levels of complexity in their production. The complexity typically arises from factors like timeliness, the amount of research required, and the story being told. The low-complexity articles include: Factual/data news (eg. article listing mortgage rates in San Francisco, article with numbers from a company’s earnings call) — these are relatively straightforward with minimal subjective opinions or perspectives. Interest-based/informational news (eg. summer recipes published on NY Times Cooking, an article explaining what the Fed interest rate means) — there is some creativity in selecting topics but the focus is more informational and catering to specific interests. Breaking news (eg. an article about a CEO resigning, an article describing an active weather event) — these are typically short articles about a rapidly unfolding event, with limited initial information and emerging facts. The include: high-complexity articles News coverage (with context, research, and facts) — these provide a more detailed explanation of news backed by research, additional context, and often interviews with people, thereby requiring more time and effort to produce; they are also extensively fact-checked. Interpretive news — this includes opinion pieces, op-eds, and analyses (like this one) that provide interpretations/perspectives/opinions on current issues; they are often subjective and require extensive research to back up the perspectives. Feature pieces — these are typically deep dives on topics that might not really be hot news right now but important issues, investigative journalism would fall in this category; these require extensive research and interviews spanning across months, and also creative storytelling. I bring up this categorization because the purpose behind and process involved in producing these two types of articles is very different, and consequently will evolve differently with the use of generative AI. With this in mind, let’s talk about how news makes money. The business of news The business of news is tricky — most news organizations operate on an ad-supported media model, and a small subset have successfully managed to pivot to subscriptions. This has major implications for the type of content a news organization produces. The businesses (NYTimes notably now gets from subscriptions) have a content strategy that’s tied directly to consumer value — focus on to make the subscription worthwhile. For example, of the types of content above, NYT primarily does news coverage (that is well-researched), interpretive news, and feature pieces for their core news offering, backed up by interest-based content like and . Some other publications like the Wall Street Journal and the Washington Post have made strides with subscriptions using a similar approach. subscription-based news ~70% of revenue high-quality content + diverse interest-based content NYT Cooking Wirecutter However, and will continue to be so for the foreseeable future. This means that they are focused on generating high engagement: more eyeballs → more ad inventory → more revenue. The strategy that has been more effective for them is (like news coverage, interpretive news) (like factual/data news, interest-based/informational news). most publications are still ad-supported augmenting high-complexity content with a ton of high volume, low-complexity content This strategy works because high-complexity content helps deliver long-term value to consumers, while the ton of low-complexity content helps . The SEO loop looks something like: high volume of content that gets clicks → search engines think your content is valuable → all of your content gets ranked higher → more eyeballs. get eyeballs as well as win the SEO game This is no criticism of the strategy and the need to play the SEO game is a necessity for ad-supported media. The reality is that — the internet disrupted how content was created and distributed (mostly through Google Search / today), and news organizations have not recovered from the impact of that disruption. There are attempts to fix the imbalance, which are still evolving — Canada/Australia passed laws that force Google/Meta to pay what’s essentially a “disruptor tax” to support good journalism, some organizations like NPR are partially supported by , and some newspapers like the Washington Post are . news media is a terrible business Meta federal funding subsidized by (generally well-meaning) billionaires All this to say — news media/journalism is an absolutely essential public utility for a well-functioning democracy but a not-so-great business. Therefore, the more self-reliant these news businesses can be (without having to depend on regulation or billionaires), the more effective they can be towards achieving their mission. Generative AI can’t solve all of these structural challenges (especially content distribution) but it can definitely make content creation more efficient without necessarily compromising on quality. The anatomy of producing a news article To understand what part of news production will generative AI have the most impact on, it’s helpful to understand the different steps involved in getting a news article published. We can break down the effort into a few sequential components: Research — finding a topic, gathering facts/data, talking to the right people for quotes or perspectives Storytelling — making sense of the research to come up with a compelling storyline Writing — turning the storyline into actual words that make an article, buttoning up the storyline, backing it up with the right data / adding supporting links Editing — improving the writing, coming up with catchier headlines, having better visuals, and fact-checking to meet standards Distribution — publishing, and distributing across channels like social media. Based on some research and also influenced by my writing experience, I estimate the effort of producing an article into something like: . Take the actual numbers with a grain of salt but they are directionally accurate. Research (30%), Storytelling (20%), Writing (20%), Editing (20%), and Distribution (10%) Now, note that — the low-complexity pieces might not need to rigorously go through all steps in the life cycle above but the high-complexity pieces do. not all formats of news articles require all of the steps As an example, this article took me ~8–9 hours to finish (so did most other articles on ), and I would like to think that my articles fall under interpretive news. If I wrote a low-complexity piece, I could probably get it done in ~2 hours. Another data point — says he can typically get a news article done in a couple of hours while a feature piece might take up to 6 months. my Substack newsletter this NYT reporter So, what can generative AI actually help with? It basically comes down to what steps in the news production process can current (and future) products do well. What generative AI can and cannot do well Here’s my take on each step. Spoiler — they are somewhat effective for writing and editing, they can be effective for research if the right products are built, and they will continue to be bad at storytelling. Research — surprisingly low capability Most current generative AI products (like ChatGPT, and Google Bard) have surprisingly low capability when it comes to Research: There are definitely certain capabilities that they are good at. For example, they are good at giving arguments for a particular point of view or providing inspiration for new topics. They are average-to-good at summarizing content and in particular, answering questions based on reading an article. For example, you can ask them to read this article and list out different steps involved in news production. However, they often produce (“hallucinations”), to what they claim is the truth, and have questionably sourced data with potential copyright infringement. See the screenshot below. factually incorrect information do not provide links/sources For a generic use case (say you want to draft an email to a future LinkedIn connection), these issues don’t matter. But when you are writing a news article, not fact-checking can severely impair your brand perception. Though the research tools can be used for directionally useful research (for example, you want to know how much market share Bing and Yahoo search have), writers/journalists still have to do additional work to find a new source to link because current tools neither provide links nor have fully accurate data. for anyone building a research product for writers, and it’s very likely that new companies will emerge here. Using cleanly sourced data + being able to provide answers with trustworthy links is a big opportunity Storytelling — low capability Generative AI’s storytelling capabilities are fairly weak today. Here’s an example: I did a bunch of research for this article, put together the research in an organized format, and asked ChatGPT to give me a storyline. See the screenshot below for results. At first glance, it looks like they “make sense”. But that’s literally all it is — it makes sense at a surface level. In reality, none of these storylines are very compelling and the research notes I provided had much more detail and nuance beyond the surface-level results provided here. You can make the argument that these are still decent, but in the context of writing an article, this was completely useless to me — at best, this was inspiration/ideas, not a storyline based on the research I submitted. If you have a low-complexity story, it will get the job done for you. If you are building a nuanced story, or have the data and need help constructing a story, current products do not get the job done. Given the high amount of subjectivity involved, I am not bullish it will get better and this will continue to be the part where writers can add the most value. Writing — medium capability If you provide current products with a detailed storyline of what you want to say, it can generate a barely decent v1 of the content. The output is still pretty cookie-cutter and it is pretty challenging to instruct the current models to craft the language in a way that it tells a story. See the screenshot for an example output after I fed ChatGPT detailed notes about the storyline for this article. At first glance, you might think it looks okay for a v1 draft. It is not — the tone isn’t right, the story doesn’t flow and it always looks like it’s been written by a bot. It’s very generic and does not articulate the story despite being fed a very specific narrative. If I published that article, you would not read it. And that’s the challenge with the writing capability today — it can work for low-complexity articles but for anything more complex, you essentially have to rewrite the whole draft. — writers don’t want to take terrible cookie-cutter versions and redo the whole thing line by line. What they would like is some form of a user interaction construct that lets a writer feed in a storyline and sequentially craft the article section by section, while giving active feedback to the AI tool. Cherry on top would be being able to customize the writing style by feeding past articles written by the same writer. The big product unlock here would be enabling effective human instruction I think the underlying models today do have the capability to do this and there is , which I believe will happen near-term. innovation needed at the UI layer Editing — medium capability Tools today have a good amount of capability to review articles, find errors and make corrections. These tools are also very good at cosmetic tasks, like coming up with ideas for catchy headlines or section titles that might perform well. However, there is still work needed at the UI layer to make this usable for editing — there are some partial solutions today like which lets you improve language and make sentences shorter / longer from within a Notion page, but it does not capture the full context of the page. ChatGPT does a good job of making edits across a full article but lacks the ability to take instructions to easily edit specific sections and also cannot support links (i.e. I give it a blurb with text that has hyperlinks, I get back text without any links). Notion AI I absolutely hate the editing part of the writing process, and I am sure several writers and journalists do as well — generative AI can most definitely minimize some of this editing grunt work near-term. Distribution — medium capability There are tools coming up here today that for example help you generate social snippets, or identify parts of your story that might go more viral. This will likely continue getting better in the future. Generative AI and the future of News — A tale of two worlds Based on the above analysis, you can see a clear duality emerging: , which require simpler research & storytelling, and can be written & edited easily, will start to be (or heavily AI-assisted). Low-complexity formats more and more AI-generated , which require more complex research and storytelling capabilities that don’t exist today, will but generative AI can bring a good amount of High-complexity formats continue to be primarily created by journalists efficiency by minimizing writing and editing gruntwork. While AI-generated low-complexity content seems bad at first glance, these articles are primarily written for SEO purposes or to augment existing high-quality content, and the race to commoditization had started much before the generative AI wave. For example, the Associated Press has been using since 2014. The upside here — this is not the kind of content journalists want to spend time creating and automating this will free up their time for high-complexity content. bots to publish articles reporting company earnings More and more high-complexity content will emerge. Research and storytelling capabilities with products today are limited, which means that the ability to . This, accelerated by AI writing and editing tools that take the grunt work out of publishing quality content, will be a big boon for journalists. construct a compelling story based on information and story-tell in a unique way will continue to be the biggest currency journalists have What about some of the concerns raised around using AI for news? Some are fair but I believe they are mostly solvable: A wave of junk SEO content — Google has that they won’t penalize AI-generated content and have gotten criticism for opening up a world of junk content, including factually incorrect articles. That’s a somewhat fair critique but I believe Google will crack down on this — not out of the goodness of their hearts but because the crux of a search product is to give users useful results. Google already has penalties for SEO hacking practices (like keyword stuffing, and link farming) and it would be easy to extend this framework to AI content. taken the stance Journalists will lose jobs — Some have brought up the concern that journalists might lose jobs, or be in a less advantageous position (like writers in Hollywood who are currently on strike); the big difference here is that while there are a large number of writers in Hollywood, newsroom employment in the US since 2008, not because we don’t need journalists but because news is a bad business; Generative AI can help fix the economics of the business while continuing to empower journalists do what they love. has fallen 26% Conclusion I am by no means an AI maximalist and I absolutely think there are real risks to AI that need to be addressed as we scale the technology. However, I think it’s important to analyze every AI-impacted market/situation separately and not bundle them into one big AI-impact problem. In the case of news, generative AI can tremendously improve the economics of news businesses. The products are not there today — there is a clear need for AI products that thoughtfully solve writers’ needs without a brute-force, chat-based, language model interface, but I’m confident these will emerge near-term. Low-complexity writing will be more and more AI generated and that’s okay — it can help companies run an efficient SEO machine, while journalists (assisted by AI for grunt work) bring to life a lot more high complexity content that elevates the public discourse. Thank you for reading! Also published here.

Exploring the Impact of Generative AI on the Future of News 

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समाचार के भविष्य पर जेनरेटिव एआई के प्रभाव की खोज

ニュースの未来に対する生成 AI の影響を探る

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探索生成人工智能对新闻未来的影响

The IT market is developing rapidly, and the current trends are changing very quickly, so it is especially important for IT companies to understand what to expect in the coming year in order to plan their recruiting processes effectively. In our first article this year, we will talk about fields within the IT industry, which are currently developing particularly intensively, which specialists are in increasing demand. We'll also talk about which IT professions are in high demand and which are less popular. The fact is that the IT market is still a candidate market. The demand for IT specialists is as high as it was, and companies will continue to compete with each other in hiring candidates. IT specialties in demand in 2022 Recently, the demand for continues to grow: IT specialists of this profile are responsible for the reliability of the site and the creation of functional and high-quality software. According to the international job search portal, Indeed, the average annual salary of an SRE engineer is $131,842. SRE engineers (site reliability engineering) Among the main soft skills that an SRE engineer should have, employers name the ability to work with a large amount of information and to be able to analyze it at a deep level. We share an inspiring fact for those who really want to try themselves in IT and especially love SRE. Jennifer Petoff, head of the SRE training department at Google and co-author of Google's SRE book, has no first education related to IT. Jennifer has a PhD in chemical sciences, but that didn't stop her from becoming a cool SRE specialist :) Therefore, if you are determined to conquer SRE horizons and ready to work hard — go ahead and master a new field. This profession in 2022 will be at the top of demand, so even junior specialists have good chances to build a career. Data specialists are also in demand now — for example, data analytics. The demand for testers has also increased. In 2022, companies will need a lot of QA. It is impossible not to mention that Golang developers are still very popular, and there are still very few of them on the IT market today :) Programming languages such as Java, C++, and Python are not losing their top positions too. Python in 2022 rose from third to first place in the TIOBE ranking, while Java and C++ have 3rd and 4th places respectively, holding positions in the top five. What will change? While certain IT specialties are getting more popular, there are also those for which demand is decreasing. You can observe a similar situation with PHP developers: this is due to the fact that this programming language is now being actively rewritten and changed to meet modern realities. In the TIOBE rating, PHP dropped significantly lower when compared to last year. At the end of 2021, it already lost several positions. As of today, it did not enter the top 10 most popular programming languages, ending up in 11th place. Ahead of the entire planet One of those areas that are actively developing and will continue to develop in 2022 is the biotech industry, which is based on medicine and IT. The biotechnology industry studies the issues of genetic engineering and also considers how biology can be used in solving various technological problems. Among the major biotech corporations are BIOCAD, Amgen, Ginko BioWorks, Bioniq, and others. Biotech, again, primarily requires data specialists — data analysts, big data specialists, biostatistics specialists (biostatistics is applied in the development of statistical methods in biology and medicine). In addition, there is a great demand for cybersecurity engineers. Take into consideration the large amount of data that is currently stored in the digital space. One of the priorities for companies is to ensure their security and protection from cyber-attacks. Up and only up The question that many IT companies are worried about today: What forecasts can we expect on salary forks of candidates? Despite the fact that it is difficult to name exact numbers, the level of salaries of IT specialists, in any case, will continue to increase. The CEO of Lucky Hunter Tatiana Melnichuk advises considering at least a 30% growth. Heading abroad Another trend that has entered with us in 2022 and will not leave the IT market in the near future is the growth in the number of Russian IT specialists, for whom relocation is a high priority. This is partly due to the fact that Western companies have realized the high level of professionalism Russian developers have, and now they are hiring Russian-speaking candidates to strengthen their teams. The countries that most often hire IT specialists from Russia include the United Kingdom, Germany, and the United States. For example, we recently filled a job opening for a senior product designer for a London startup offering business solutions for the effective organization of a telephone support service. In general, we can say with confidence that it will not be boring in 2022 for sure :) Our team wishes both candidates and IT companies a productive and interesting year!

IT Market in 2022: Professions, Industries, Hiring

Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. the visualization by McKinsey Global Institute As businesses realize these technologies are a boon, the demand for machine learning consulting companies is skyrocketing, which predictably influences the supply. In such a way, today’s IT market is literally larded with ML vendors tackling projects of any scope and budget. To help you make your way through this enormous and diverse offering, we assembled a list of today’s most trustworthy vendors of machine learning consulting and development services. Our IT market analysts assessed an extensive range of companies according to the following objective criteria: Proven machine learning expertise Prior projects: size and complexity Diversity of business domains served Client references Speed and quality of customer service As a result, we got a list of 30 companies worthy of attention. For readers’ convenience, we present the vendors in descending order. 1. Itransition A recognized custom software development company, Itransition has delivered 1,500 projects over the span of its 20-year market presence. With more than 2,000 IT experts on board, the company has served 810 clients from 30 countries. Among the ML services the company provides there are data mining, natural language processing, computer vision, robotic process automation, and machine learning consulting. To deliver its ML solutions, Itransition uses such technologies as Python, R, Azure, Google Cloud, and AWS. Location: Denver, CO Established: 1998 Project budgets start at: $10,000 Key clients: Expedia, Xerox, Toyota, eBay, PayPal, Shell, adidas Other, non-ML services: custom software development, CRM consulting, ecommerce development Website: https://www.itransition.com/ 2. Tooploox EU-based Tooploox has around 100 skilled experts on board. For seven years of the company’s operation, they delivered 150+ successful projects. Tooploox is a machine learning consulting company using this technology to deliver computer and 3D vision, sensor fusion, deep learning, natural language processing, reinforcement learning, and other ML-related services. The company has expertise with other emerging technologies, such as blockchain. Location: Wrocław, Poland Established: 2012 Project budgets start at: $25,000 Key clients: CoinDesk, DTS, ING, Spire Health, Group Nine Other, non-ML services: mobile app development, product design Website: https://www.tooploox.com/ 3. Skim Technologies London-based Skim Technologies is an internationally recognized machine learning consulting company delivering a wide range of related services. The company’s data scientists offer chatbot development, custom ML model creation, and NLP engineering. Skim Technologies also delivers AI workshops as well as ML and AI consulting services. Location: London, United Kingdom Established: 2014 Project budgets start at: $10,000 Key clients: Jones Lang-Lasalle, Breast Cancer Care, Quant Insight Other, non-ML services: business intelligence consulting, custom software development Website: https://www.skimtechnologies.com/ 4. ThirdEye Data US-based ThirdEye Data has been delivering its ML development services for nine years. The company specializes in chatbot development, custom ML algorithms, and machine learning consulting. Among the data science technologies the company works with, there are Spark, TensorFlow, Caffe, and others. Location: Santa Clara, CA Established: 2010 Project budgets start at: $25,000 Key clients: Microsoft, Amazon, Nokia, Ford Direct, Getty Images, Amgen Other, non-ML services: business intelligence consulting, cloud consulting Website: https://thirdeyedata.io/ 5. Chatbots.Studio As its name implies, Chatbots.Studio is a company focusing on AI-powered chatbot development. It builds multichannel conversational platforms, automates business processes, and designs conversations. The company’s technology stack includes React, Redux, Angular 6, Node.js, Express.js, Botkit, and others. Location: Lviv, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Visa, Unibot, Aldi, Digital Flex Other, non-ML services: custom software development Website: https://chatbots.studio/ 6. Azati Software Since 2001, Azati's experts have cooperated with 330 clients globally. Among the ML competencies of the company there are supervised and unsupervised learning, distributed computing, natural language processing, digital signal processing, and machine learning consulting. Azati’s tech toolkit includes CuDNN, Scikit Learn for Python, and Tensor Flow. The company mainly targets clients in the Insurance domain. Location: Livingston, NJ Established: 2001 Project budgets start at: $1,000 Key clients: MedPro Group, SequenceBase, Casio, Thomson Reuters Other, non-ML services: mobile app development, web development Website: https://azati.ai/ 7. Hidden Brains Since its foundation in 2003, Hidden Brains has cooperated with clients from 107 countries. The company delivers AI, ML, deep learning, and data science services. It also specializes in developing natural language processing software. Hidden Brains also delivers chatbots, including those for Facebook, Telegram, WhatsApp, and Slack. Location: Ahmedabad, India Established: 2003 Project budgets start at: $10,000 Key clients: McAfee, Raymond, Maxtor, SanDisk Other, non-ML services: mobile app development, web development Website: https://www.hiddenbrains.com/ 8. InData Labs Established in 2014, InData Labs houses 50+ data scientists, engineers, and analysts. The company develops ML algorithms and natural language processing solutions. Besides, InData Labs specializes in computer vision and predictive modeling. The company's stack includes Python, SpaCy, TensorFlow, Keras, and more proven languages and frameworks. Location: Minsk, Belarus Established: 2014 Project budgets start at: $5,000 Key clients: Captiv8, Flo Health, Wargaming Other, non-ML services: business intelligence consulting Website: https://indatalabs.com/ 9. Arcanys Since 2010, Philippines-based Arcanys has been delivering its ML and AI services to clients from all over the world. The company specializes in sentiment analysis, recommender systems, and image recognition. As one of its cooperation models, the company provides dedicated teams of highly experienced data scientists and developers. Location: Cebu City, Philippines Established: 2010 Project budgets start at: $100,000 Key clients: General Electric Korea, Enfo, Payment Logic, L’Oreal, Spinetix Other, non-ML services: custom software development, mobile app development Website: https://www.arcanys.com/ 10. MobiDev Since 2009, MobiDev’s IT experts have delivered 350+ software solutions. While headquartered in the US, the company provides its services globally. MobiDev uses the following machine learning technologies: Python, R, C++, Go, TensorFlow, ONNX, and Keras. MobiDev’s mobile ML toolkit includes Core ML, TensorFlow Lite, and Cafe2Go. On top of that, the company provides machine learning consulting. Location: Atlanta, GA Established: 2009 Project budgets start at: $10,000 Key clients: Verizon Media, Comcash, Quality Bridge, SmartTab, SMS Group Other, non-ML services: IoT development, web development Website: https://mobidev.biz 11. Spire Digital For over 20 years that Spire Digital has been providing IT services, it has cooperated with 600+ global customers. Among the ML and AI development services the company offers, there are natural language processing, supervised and unsupervised learning, and sentiment analysis. Also, Spire Digital creates chatbots for Facebook Messenger, Slack, Skype, and Cisco WebEx. Location: Denver, CO Established: 1998 Project budgets start at: Undisclosed Key clients: TomTom, Lockheed Martin, US Navy, USDA, Western Union Other, non-ML services: custom software development, mobile app development Website: https://www.spiredigital.com/ 12. Icreon New York-based Icreon is a digital innovation agency mostly targeting medium-sized businesses. The company uses AI and ML technologies to create innovative applications, platforms, and ecosystems. The company’s technological expertise in ML includes Amazon Web Services, Azure, Caffe2, Google Cloud, Keras, and more. Location: New York, NY Established: 2000 Project budgets start at: $25,000 Key clients: National Geographic Channel, FOX Movies, Harry Winston, Nokia Other, non-ML services: custom software development Website: https://www.icreon.us/ 13. Miquido For eight years, Miquido's team has successfully completed more than 100 projects, including in machine learning. The company offers solutions for AI-based personalization, predictive and behavioral analytics, and churn prediction. Miquido also develops chatbots. Recognized by Deloitte as one the fastest growing companies in Europe, Miquido also offers UI/UX design creation and mobile apps development. Location: Kraków, Poland Established: 2011 Project budgets start at: $25,000 Key clients: Skyscanner, Abbey Road Studios, HelloFresh Other, non-ML services: mobile app development, web development Website: https://www.miquido.com/ 14. Neoteriс Founded in 2005, Europe-based Neoteric offers a wide range of ML solutions, including predictive analytics, recommendation engines, NLP, robotic process automation, and machine learning consulting. The company also specializes in such AI platforms as SaaS Manager, SoapBox, AppOrchid, and RapidSOS. Location: Gdańsk, Poland Established: 2005 Project budgets start at: $10,000 Key clients: LunchBadger, Arvata, IT Trans Other, non-ML services: custom software development, web development Website: https://neoteric.eu/ 15. Unicsoft Established in 2005, Unicsoft employs more than 100 specialists in emerging technologies and software development. Within its ML tech stack, the company offers development of predictive and behavioral analytics solutions, neural networks, and recommendation engines, among other ML applications. The company also delivers consulting services, and maintenance of existing ML platforms. Location: Kiev, Ukraine Established: 2005 Project budgets start at: $5,000 Key Clients: Hoodifood, Executive Eye, GOFORIT, Intellica Group Other, non-ML services: blockchain Website: https://unicsoft.com/ 16. Abto Software With 12 years of its market presence, Abto Software’s has worked with 200+ global customers. Specializing in AI and ML, the company offers custom enterprise solutions for advanced and predictive analytics, virtual assistants, recommendation systems, and intelligent business process automation. Abto Software works with the following tools and frameworks, among others: TensorFlow, Keras, Caffe, OpenCV, and AWS. Location: New York, NY Established: 2007 Project budgets start at: Undisclosed Key clients: Just Answer, Skybow, Medikit, Up Great Other, non-ML services: web development, enterprise app modernization Website: https://www.abtosoftware.com/ 17. Accubits Technologies, Inc. US-located Accubits started in 2012 and has already cooperated with 140+ clients. The company’s ML capabilities include development of conversational and classification tools, video, image and text analytics solutions, as well as predictive analytics software. Besides, Accubits offers machine learning consulting. The company’s experts work with NumPy, PHP, Python, SpaCy, Spark, and several other platforms. Location: Vienna, VA Established: 2012 Project budgets start at: $5,000 Key сlients: Dubai Land Department, Smart Dubai, Eduvalue, Landmark Other, non-ML services: blockchain, custom software development Website: https://accubits.com/ 18. BotsCrew Established in 2016, BotsCrew is a global leader in chatbot development, delivering to mid-sized companies and startups. BotsCrew has its own enterprise chatbot platform that is ready to be customized and optimized according to customers' needs. Besides the proprietary platform, the company offers development of voice assistants and IoT bots. Location: London, United Kingdom Established: 2016 Project budgets start at: $10,000 Key clients: Ailira, IVA, ReVA, Dr. Dean Other, non-ML services: None Website: https://botscrew.com/ 19. STX Next Founded in 2005, STX Next is a custom software development company that specializes in streamlining IT solutions with AI and ML technologies. The company’s main technological advantage is in its exceptionally skilled Python developers. For 14 years on the market, STX Next has delivered over 250 successful projects. Location: Poznań, Poland Established: 2005 Project budgets start at: $25,000 Key clients: Seerene, Verve, Hogarth WW, Pixomondo, Unity Technologies Other, non-ML services: web development, custom software development Website: https://stxnext.com/ 20. Dogtown Media California-based Dogtown Media specializes in delivering mobile applications, enhanced with ML and AI technologies. Besides ML-powered mobile apps, the company provides machine learning consulting. For the last eight years, Dogtown Media has brought to life at least 200 applications. Location: Venice Beach, CA Established: 2011 Project budgets start at: $25,000 Key clients: Google, Lexus, United Nations, Citibank, Red Bull, YouTube Other, non-ML services: mobile app development, IoT development Website: https://www.dogtownmedia.com/ 21. Sigmoidal Sigmoidal is a New-York-based consultancy specializing in machine learning and data science, mostly for companies in Finance, Trading, and Pharma. The company integrates these technologies into web and mobile applications and APIs. It also offers solutions for NLP, computer vision, and predictive analytics. Location: New York, NY Established: 2016 Project budgets start at: $1,000 Key clients: Undisclosed Other, non-ML services: business intelligence consulting Website: https://sigmoidal.io/ 22. Cyber Infrastructure Established in 2003, Cyber Infrastructure is a custom software development company with 100+ clients worldwide. Its ML expertise includes development of virtual assistants and chatbots for a variety of domains. Cyber Infrastructure also provides machine learning consulting and employs Python, Java, C++, Lisp, and R as its primary ML and AI stack. Location: San Francisco, CA Established: 2003 Project budgets start at: $5,000 Key clients: Mannatech, Bell Other, non-ML services: custom software development, web development Website: https://www.cisin.com/ 23. Azilen Technologies US-headquartered Azilen Technologies has 250+ IT experts on board distributed across four offices worldwide. They employ ML technologies to deliver customer service, survey, and informational bots. On top of that, the company delivers facial recognition and predictive analytics solutions. Location: West Bloomfield, MI Established: 2009 Project budgets start at: $25,000 Key clients: Samsung, Pizza Hut, Sodexo, ICICI Bank Other, non-ML services: custom software development Website: https://www.azilen.com/ 24. Stermedia Stermedia has been delivering its ML and data science services since 2007. The company’s dedicated teams of data scientists and software architects design ML algorithms and models for extracting knowledge from big data sets. The company also provides NLP solutions and uses such technologies as TensorFlow and Keras, to name just a few. Location: Wroclaw, Poland Established: 2007 Project budgets start at: $10,000 Key Clients: Family Fabric, WorkService, ISOK Other, non-ML services: custom software development, web development Website: https://stermedia.ai/ 25. Belatrix Having five offices with 600+ professionals all around the world, Belatrix is one of the most fast-evolving machine learning consulting companies on the market today. Its machine learning experts have a proven track record of building various solutions, from predictive modeling to communication automation. Location: Mendoza, Argentina Established: 2003 Project budgets start at: $5,000 Key clients: Disney, Adobe, AOL, FIS, Chatman Financial Other, non-ML services: custom software development, mobile app development Website: https://www.belatrixsf.com/ 26. DataRoot Labs DataRoot Labs is an agency focusing on ML and AI development. It delivers full-cycle ML software development, including project evaluation, machine learning consulting, strategy development, architecture, integration, and support. Besides, DataRoot Labs helps enterprises with digital transformation with AI at the core. Location: Kiev, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Servers.com, Everad, ABM Cloud, Cargofy Other, non-ML services: custom software development, mobile app development Website: https://datarootlabs.com/ 27. Softengi Since 1995, Softengi has completed 900+ projects for clients across six continents. The company’s ML services include chatbot engineering and NLP algorithms. For these purposes, Softengi uses R, Hadoop, and Python, among other tools. Location: Kiev, Ukraine Established: 1995 Project budgets start at: $10,000 Key clients: Boeing, Zeppelin, Enviance, OneRoof Energy Other, non-ML services: custom software development, AR/VR development, IoT development Website: http://softengi.com/ 28. Waverley With 27 years of software development experience, Waverley is a company housing 370K talented engineers. Among other services, they offer ML-centric solutions such as data clustering and unsupervised algorithms. On top of that, the company offers NLP and computer vision solutions and chatbots. To deliver ML products, Waverley’s engineers use Java, Python, Scala, and R. Location: Palo Alto, CA Established: 1992 Project budgets start at: $50,000 Key clients: Mozilla, Qualcomm, Swisscom, American Express Other, non-ML services: custom software development, mobile app development, IoT development Website: https://waverleysoftware.com/ 29. Imaginovation Founded in 2011, Imaginovation is a software development agency that has successfully completed 300+ IT projects of various complexity. As for ML and AI, the company develops recommendation and effectiveness assessment engines as well as solutions for speech and text processing automation. Location: Cary, NC Established: 2011 Project budgets start at: $10,000 Key clients: MetLife, Nestle, UNC Pembroke, Contractor Score Other, non-ML services: custom software development, mobile app development, web development Website: https://www.imaginovation.net/ 30. E2logy Established in 2005, E2logy has delivered 300+ projects to businesses of various sizes. Being a leading Amazon Web Services and Azure AI vendor, the company develops ML algorithms and chatbots. They also offer machine learning consulting. In addition to their ML services, E2Logy brings custom IoT solutions to its global customers. Location: Ahmedabad, India Established: 2005 Project budgets start at: $5,000 Key clients: McMillan Publishers, Polaris Industries, Vodafone Other, non-ML services: custom software development, web development Website: https://e2logy.com/ This was a full list of the top 30 machine learning consulting companies in 2019. If you cooperated with any of the presented vendors, please do get in touch and share your story.

Top 30 Machine Learning Consulting Companies

Thanks to big data, today an organization can quickly obtain the necessary information from an unordered data set and deploy it effectively. The growing popularity of big data analytics has led to a significant increase in the number of companies providing big data solutions and related services. Today, there are in the global market, and this number may be very confusing. Big data skills, experience, and related technical expertise can significantly vary among vendors, which may puzzle anyone looking for a big data service provider. almost 1000 big data analytics companies How to identify the best big data consulting firms I set myself the goal to identify the most reliable big data consulting companies on today’s IT market. To do this, I investigated hundreds of data providers and studied their characteristics. Each company was analyzed according to the set of specifically designed criteria. Among the criteria are big data expertise, major clients, offering strategy, track record, and online reviews. Below you can see the results of the conducted market research. 1. Itransition Founded in 1998, Itransition is a custom software development company that provides big data consulting and platform engineering services. The company also offers business intelligence (BI) portals development and boosts them with machine learning (ML) and other technologies. The company offers data store design, data migration, performance optimization, and support services. The company’s client base includes IBM, Expedia, Toyota, eBay, Xerox, PayPal, and Adidas. With more than 150 completed projects and 810 clients worldwide, IAOP recognizes the company as one of The Global Outsourcing 100. Denver, CO Headquarters: Expedia, Xerox, Toyota, eBay, IBM, PayPal, Adidas Core clients: $25 — $49 Pay per hour: 1000–9999 Number of employees: Website: https://www.itransition.com/ 2. Saviant Consulting Saviant is a consulting company with more than 120 experts in its team and more than 100 clients in various countries, including UK, Canada, and the US. The company specifically focuses on Energy, Logistics, and Retail. Among the company’s big data services are big data analytics and management, full-cycle big data solution development, and more. Pune, India Headquarters: JJ Food Service, BuilderMT, Transparent Technologies, Inc Core clients: Not Revealed Pay per hour: 50–249 Number of employees: Website: https://www.saviantconsulting.com/ 3. Squadex Headquartered in California, USA, Squadex is a technology consulting and engineering company that provides big data services. It offers big data analytics, consulting, and big data platform engineering. Squadex is an AWS certified consulting partner that works with companies of different size, from small startups to large enterprises. Palo Alto, CA Headquarters: Model N, NinthDecimal, Nitrio Core clients: Not Revealed Pay per hour: 250–999 Number of employees: Website: https://squadex.com/ 4. CBIG Consulting Founded in 2002, CBIG Consulting is an American company offering business intelligence and big data analytics solutions. Besides analytics and consulting, the company provides big data solutions architecture. For this purpose, CBIG Consulting employs Hadoop distributions, Hive, Sqoop, etc. The company’s industrial focus is on Finance, Healthcare, and Retail. Chicago, IL Headquarters: Abbott, Astellas, Baxter, Cigna, DePaul University Core clients: $150 — $199 Pay per hour: 50–249 Number of employees: Website: https://www.cbigconsulting.com/ 5. Clavax Technologies Founded in 2011, Clavax is a software development company headquartered in San Jose, California. It delivers big data consulting and implementation. By employing big data technologies, the company provides services for data warehousing, data processing, and data integration. Clavax’ client list includes PWC, Football.com, Bookfari, and Honeywell. San Jose, CA Headquarters: PWC, Football.com, Bookfari, Honeywell, Jabil Core clients: $25 — $49 Pay per hour: 50–249 Number of employees: Website: https://www.clavax.com/ 6. ThirdEye Data ThirdEye Data is a big data services company that delivers big data consulting, analytics and engineering while using artificial intelligence, deep learning, and machine learning. ThirdEye Data has served such companies as Nokia, Ford, Amgen, GettyImages, Actian, and others. The company focuses on Finance, Government, IT, Retail, and Manufacturing. Santa Clara, CA Headquarters: Microsoft, Nokia, Ford, Amgen, GettyImages, Actian, Google Core clients: $50 — $99 Pay per hour: 50–249 Number of employees: Website: https://thirdeyedata.io/ 7. Signity Solutions Established in 2009, Signity Solutions is a company that delivers custom software development and big data solutions. The company helps with big data analysis and brings data solutions empowered by machine learning. Signity works for the Commerce, Education, Media, and other industries. Rajiv Gandhi IT Park, India Headquarters: Samsung, Grupio, The Project Factory Core clients: $25 — $49 Pay per hour: 50–249 Number of employees: Website: https://www.signitysolutions.com 8. Caserta Founded in 2001, Caserta is a NY-based IT company that delivers a wide range of data-related services. The company offers big data consulting and solution architecture/development. Caserta also provides data visualization, data warehousing services, and more. The company has worked with such clients as New York Times, Capital One, Equinox, and HBO. New York, NY Headquarters: New York Times, Capital One, Equinox, HBO Core clients: Not Revealed Pay per hour: 10–49 Number of employees: Website: https://caserta.com/ 9. RapidOps, Inc RapidOps is a custom software development company based in the USA. The company’s expertise includes big data analytics, consulting, and visualization. The company’s team of 60+ professional developers delivers its services to the Manufacturing, Retail, and IT industries. Charlotte, NC Headquarters: YoDish, Zurn Industries, DassaultSystemes Core clients: $25 — $49 Pay per hour: 50–249 Number of employees: Website: https://www.rapidops.com/ 10. Aegis Soft Tech Since 2003, Aegis Soft Tech has been providing software development to Healthcare, Finance, Retail, Manufacturing, and other industries. Aegis provides big data consultation, management, and analysis. Among that, Aegis helps with data migration processes. The company’s pool of developers expertizes in Hadoop, Cloudera, and Hortonworks platforms. Rajkot, India Headquarters: Sunzinet, Zydus, PointRF, Efacec Core clients: < $25 Pay per hour: 50–249 Number of employees: Website: https://www.aegissofttech.com/ 11. GetInData GetInData is a Poland-based company that unites more than 30 professionals. The company has carried out more than 30 projects. GetInData delivers the following big data services: big data infrastructure development, analytics, consulting, engineering, and maintenance. GetInData delivered its services to such companies as Spotify, Allegro, Agora, and Freshmail. Warsaw, Poland Headquarters: Spotify, Allegro, Agora, Freshmail, Truecaller, GoEuro Core clients: $50 — $99 Pay per hour: 10–49 Number of employees: Website: https://getindata.com/ 12. Kavi Global Established in 2008, Kavi Global is an analytical company that delivers a whole spectrum of big data services. They include consulting, big data solution architecture, and platform implementation. The company also offers data mining and data management services. Kavi Global’s industrial expertise includes Transportation, Manufacturing, Education, and Healthcare. Barrington, IL Headquarters: General Electric, TEAMHealth, Thomson Reuters, Ryder Core clients: $150 — $199 Pay per hour: 10–49 Number of employees: Website: https://www.kaviglobal.com/ 13. Indium Software Since 1999, Indium Software has been providing technological solutions for 19 various industries. The company has worked with 750 clients from all over the world. Employing such technologies as Hadoop, Spark, and Apache Kafka, Indium Software delivers big data architecture and consulting services. Princeton, NJ Headquarters: Not Revealed Core clients: < $25 Pay per hour: 250–999 Number of employees: Website: https://www.indiumsoftware.com/ 14. Fayrix Israel-based Fayrix is a custom software development company and big data services provider with 1530 IT experts on board. The company deals with various projects, from initial data analysis and consulting to big data infrastructure design, big data software development, and its implementation into a client’s existing technological landscape. Pitu’akh, Israel Headquarters: Biosence Webster, SCR, S7 Airlines Core clients: < $25 Pay per hour: 1000–9999 Number of employees: Website: https://fayrix.com/ 15. Denologix Established in 2002, Denologix is an analytics and information management company providing a wide range of big data services, including data analysis, consulting, and big data solution implementation and support. The company deploys a large number of technological tools for big data, including Hortonworks Data Platform, Cloudera СВР Hadoop Distribution, and more. Among the company’s clients are Adidas, Allstream, Aviva, Canada Post, and others. Toronto, Canada Headquarters: Adidas, Allstream, Aviva, Canada Post, Bell, Brookfield Core clients: $150 — $199 Pay per hour: 50–249 Number of employees: Website: http://www.denologix.com/ 16. Data Reply Established in 2015, Data Reply is a big data and analytics consultancy based in London, UK. Besides big data consulting and training, the company offers data engineering and data science services. The company focuses on such industries as Retail, Media, Telecommunications, and Commerce. London, United Kingdom Headquarters: Not revealed Core clients: $100 — $149 Pay per hour: 10–49 Number of employees: Website: https://www.datareply.co.uk/ 17. Dexlock Established in 2012, Dexlock is an Indian company that delivers big data services. Among them are large data store development and deep learning/machine learning implementation. The company’s technological expertise includes Hadoop, Apache Spark, Apache Kafka, and more. Dexlock works with the Healthcare, Retail, Insurance, Energy and other industries. Kochi, India Headquarters: FindCharisma, Splash of Colors, StatFuel, MyPeepz Core clients: < $25 Pay per hour: 50–249 Number of employees: Website: http://www.dexlock.com/ 18. 47Billion 47Billion is a bespoke software development company that provides mobile and web development and UI/UX design. Besides these services, the company delivers end-to-end big data analysis, consulting, and engineering. The company’s industrial focus includes IT, Gaming, and Consumer Services. 47Billion’s clients include Cisco, Nazara, and Apsalar. Bangalore, India Headquarters: Cisco, Nazara, Apsalar Core clients: : $25 — $49 Pay per hour 50–249 Number of employees: Website: https://47billion.com/ 19. 51zero 51zero is a UK-based company that specializes in big data services providing. The company delivers big data consulting and solutions development and focuses on the financial service domain. The company’s pool of technological experts deploys such big data technologies as Spark, Apache Kafka, Docker, and others. London, United Kingdom Headquarters: HSBC, The Royal Bank of Scotland, Barclays Core clients: $200 — $300 Pay per hour: 2–9 Number of employees: Website: https://www.51zero.com/ 20. SCI-BI SCI-BI is a company based in India. With more than seven years of experience, the company specializes in big data consulting and engineering. The company also helps with data integration strategy. SCI-BI’s big data technological expertise includes Cloudera and Hortonworks. The company focuses explicitly on Logistics and Retail. Chennai, India Headquarters: Executive Planning Services, USA SaasByDesign Inc, USA Caravel Logistics Core clients: < $25 Pay per hour: 10–49 Number of employees: Website: http://sci-bi.com/

The Top Big Data Consulting Firms

2019 is the year of adoption. We have weathered the storm of early 2018, lived through the ensuing drought and we can see glimpses of the promised land on the horizon. At , we are strongly passionate about overall adoption of blockchain technology as we believe it can benefit many people and industries. As such, we are always on the look-out for news of increasing adoption and collaboration. aelf Last year I wrote an article with a as I found it very difficult to find a single source for this information online. Comprehensive List of Banks using Blockchain tech Although there was a strong interest in blockchain tech over the last 24 months, due to a mixture of issues, including the volatile ‘unproven’ market and rampant ICO scams, many companies avoided public exposure linking them to the blockchain industry. Many would agree we have seen a shift in this mindset as more high profile individuals and companies are now being more open about their association with blockchain. Take for example, the #1 YouTuber, . Pewdiepie, who has recently partnered with Dlive In fact, according to a by Gemalto, adoption of blockchain to help secure IoT doubled in 2018. report Deloitte recently released survey results in regards to . The companies represented by these executives all have an annual revenue of over $100M USD. One point to note, was that over 75% of respondents believe they will lose a competitive advantage if they do not adopt blockchain. Did someone say FOMO? executive’s views on blockchain adoption Also, according to the survey, over 90% of respondents are already participating in or planning to within 12 months, a blockchain consortium or alliance. According to the , global spending on blockchain development will almost double to $2.9 Billion this year, and increase exponentially to reach $12.4 billion by 2022. International Data Corporation The financial sector will lead the charge with an estimated of $1.1 Billion to be spent in this sector alone. The Manufacturing and resources sector will see the fastest growth this year with a growth rate of 77.6%. Notably, of (Statisca.com), over 50% are included in this list; and in particular, 9 out of the top 10 with a combined market value of over $5.6 trillion USD in 2018. the 100 largest companies in the world by market value Surprisingly, the largest industry in this list is Transport, Shipping & Logistics. There could be a few reasons due to this, firstly, there were a few large alliances that have been created specifically to aid in blockchain adoption with members in the hundreds. Secondly, this is one of the biggest industries in which there is a high degree of interdependence and cross company reliance. The finance industry was always going to be in the top of this list, but another exciting development is the high rank that government received with many countries showing a clear positive attitude towards the technology. In light of all this, it is safe to say, blockchain is here to stay and 2019 is the Year of Adoption. So let’s take a look at the enterprises who have already taken the leap. This list is focusing on active enterprises in 2019. For those that are wondering, this list has almost 1,000 Enterprises in it. *There may be early adopters that are not in this list due to having little activity in the first quarter of this year. If there are ones that are active and not on this list, comment below with the links and I’ll aim to add them in **Accomodation** Cheval Blanc The Dolder Grand **Agriculture** Bayer Crop Science Bumble Bee FoodsCargill **Auto Industry** Hyundai BMW Ford General Motors Genuine Parts CompanyGreat DaneHummer Jaguar Karamba Security Volkswagen Waymo Data **Automated Retail Companies**AAEONAR SystemsFastcorp VendingGlobal Vending GroupGreenbox RoboticsInvendaIVMIVSRetail Automated ConceptsSandenVendoThe-Venders and Wemp **Communication** Deutsche Telekom NEC Europe Smart Communications Solace Corporation Swisscomm Broadridge China Mobile Comcast FarEasTone Kakao OrbcommOrange SoftBank Sprint Telefónica **Consultancy**21 Consulting GmbHAccentureAdaptive Financial Consulting Bain & CompanyBCS ConsultingBerkeley Research Group CH & CoCOMPELLIO DNV GL Business Assurance Group Everis InfosysJDX Post Oak LabsProtivitiRaoof Consultancy Limited Scrypt.MediaSynechronSynpulse XLNTEC Thomas ReutersThoughtWorksTigerRiskTomorrow Labs AizoonAONAsset Balance Solutions Cambridge Associates Deloitte enVista EY J.J. Keller & Associates Inc KPMG RevUnit ThreatAdvice Tata Consultancy Services **Data Managemen**Accuity CommodDT EverisInterxion RoyaltyRange Ansonia Credit DataApplya Connected Holdings Docusign Nielsen Ocean Protocol **Digital Security**CrossVerifyGemaltoGuardtimeObjectTechSLIBSovrinUtimacoYoti **Education** RMIT Duke University Harvard University Swinburne University The Air Force Institute of TechnologyThe University of TennesseeUniversity of Central ArkansasUniversity of Denver University of Colorado Boulder University of Memphis University of Michigan University of Nevada Yale University **Finance**ABN AMROAIBAl Rajhi Banking & Investment CorporationAlawwal BankAlfa Bank Allied Irish Bank ANZ Banking GroupATB FinancialB3Banca Intesa SanpaoloBanca MediolanumBanca MifelBanco BICEBanco BisaBanco Bradesco Banco de Crédito del PerúBanco de Investment GlobalBanco de SabadellBanco de Galicia y Buenos AiresBanco IndustrialBanco Mercantil del NorteBanco Mercantil Santa CruzBanco Nacional de BoliviaBanco Popular DominicanoBanco RegionalBanco SantanderBangkok BankBank ABCBank Al Habib LimitedBank BusanBank Frick & CoBank of America | Merrill LynchBank of CyprusBank of Montreal BankinterBanorteBanreservasBanque Internationale de Commerce — BRED (Suisse)Banque ThalerBarclays BCIBladex BNY Mellon Bolsa de MadridBPCE BRAC Bank LimitedBradescoCBH Compagnie Bancaire HelvétiqueChina Banking AssociationChina Merchant BankChina CITIC Bank International LimitedChina Guangfa BankChong Hing Bank LimitedCIBCCIM Banque S.A GeneveCitiBankCLS GroupCommercial Bank of Africa LimitedCommerzBankCommonwealth Bank of AustraliaCooperatieve RabobankCredicorp BankCredit Agricole S.A.Credit Libanais SAL CTBC BankDaiwa Securities GroupDanske BankDavivienda DenizBank A.S. Deutsche BörseDigiledgeDianrongDNB Ernst & Young European Central Securities Depositories Association First America BankFulton Financial CorporationGazprombank Habib Metropolitan Bank LimitedHo Chi Minh City Development JS CommercialHQLA ICICI Bank LimitedIndovina Bank Ltd Intesa SanpaoloItaú Joint Stock Commercial Bank for Investment and Development of VietnamJoint-Stock Company BCS-Investment BankJPMorgan ChaseJS Bank Limited KASIKORNBANK Public Company LimitedKB Financial GroupKEB Hana BankKuveyt Turk Katilim Bankasi Macquarie Bank MauBank LimitedMizuho Bank National Bank of CanadaNational Bank of EgyptNational Bank of Kenya LimitedNational Bank of KuwaitNatixis Norddeutsche Landesbank GirozentraleNordeaNorthern TrustOmidyar NetworkOP Financial Group Paris Europlace PKO Bank PolskiPortal FinancePrime Bank Ltd.Privatbank IHAG Zurich PT Bank Central Asia TbkPT Bank CIMBNiaga TbkQiwiRaiffeisen BankRBCRBSRCBCRCI Bank & ServicesResona Bank, Ltd. Reyl & Cie SA Satoshi Systems ScopusSEB Group Shinhan BankShuttle HoldingsSIA SMBCSnapCheck SRI Capital Stock Exchange of ThailandSuMi TRUSTSunTrust Synchrony Bank Temasek Holdings The First International Bank of Israel Ltd The Toronto-Dominion Bank TradeCloudTradeIXTradeWindTurkiye Finans Katilim BankasiUBSUniCreditUnion Bank Of The PhilippinesUSBancorpVietnam Joint Stock Commercial Bank for Industry and Trade Volksbank Mittweida Wells FargoWestpac Woori Bank Alipay Ant Financial Banco BTG Pactual Bank MellatBank Melli IranBank Pasargad BBVA Bloomberg BNP Paribas Boerse Stuttgart Digital Ventures BP PLC Credit Suisse DBS Deutsche Bank DTCC Eastern Caribbean Central Bank Employees’ Retirement System eToro Fidelity Investments Goldman Sachs HSBC ING International Chamber of Commerce International Securities Services Association Jamica Stock Exchange Julius Baer Linx Partners London Metal Exchange London Stock Exchange Luxembourg Stock Exchange Mastercard Morgan Creek MUFG NASDAQ Nomura Pakistani Central Bank Paypal Proof of Capital Revolut Santander SBI Holdings SGX Signature Bank Silvergate Bank SIX Group Société Générale SoFi Square Standard Chartered Bank Swissquote SWIFT Telenor Microfinance Bank Thai Stock Exchange The National Payments Corporation of India Thomson Reuters Eikon Tora Virginia’s Police Officer’s Retirement System Visa WEG Bank Western Union **Government** Caisse des dépôts et consignations CSIRO United NationsU.S. Department of Agriculture Australian Federal Government AUSTRAC Brazil’s Ministry of Planning, Budget & Management Canadian Municipality of Innisfil City of Denver Colorado State Government Colorado Water Institute Commerce Committee of Connecticut General Assembly County Auditors’ Association of Ohio Department of Homeland Security Government of Kyrgyzstan Government of Rwanda Illinois Department of Financial & Professional Regulation Illinois Department of Transportation Luxembourg Government Malta Financial Services Authority Ministry of Land & Natural Resources, ZambiaMunicipal Government of Tulum, Mexico Municipal Government of Tsukuba, Japan New York City Economic Development Corporation Pennsylvania’s Economic Development Office (Venture Capital Arm) Seoul Digital Foundation Singapore Government South Korea’s Defence Acquisition Program Administration South Korea Internet & Security Agency Thailand’s Ministry of Science & Technology UK Parliament U.S. Department of Energy U.S. Department of State U.S. House of Representatives West Virginia State Government Wyoming State Government **Insurance** MetLife Ping An Insurance Aegon AIA Allianz AXA Generali Health Care Service Corporation MAPFRE RE Munich Re State Farm Swiss ReTokia Marine TrueNorth XL Catlin ZhongAn Zurich **Internet** Google Twitter Baidu Digital Garage Facebook Opera **IT Solutions**7CommAKonceptsAmazon Web ServicesAOS SASAppwayAt.cashaXpireBillon Group LtdBlanc LabsBoleroBRQ Digital SolutionsCalypso TechnologyCanonical Česká fintech asociace, z.s. CIeNETCMACOCUS AGCodeITCognizantcoIB Eclipse FoundationEPAMEvernym FinastraFinFabrik GFT TechnologiesGiant MachinesGoogle CloudGuildOne Hewlett Packard EnterpriseIBM Inacta AGInfosys Finacle iSiDLG CNSMagiaDigitalMicrosoftNec NTT Data PCCW SolutionsRedhat Vyoma SoftwareWiproZirconTech Capgemini Cass Information Systems Inc. CISCO Collabrian EXL Fujitsu Technology Solutions HCL Imaginnovate Intel Neuro Red Oracle Salesforce SAP SE VMWare **Legal**ACCORD ProjectCOALA National Council of Clerks (France) Linklaters Orrick Taylor Associates **Medical** COCIR European Federation of Nurses Associations Abbvie AnthemBig Blue Cardinal Health Ciox Health CVS Health Genentech HumanaMultiplan NMC Healthcare Optum Pfizer Quest DiagnosticsUnited Healthcare **Media**Les Échos _CBS_Fox Business Network **Mining** BHP Billiton GoldcorpKutcho CopperLG CorpOcean Partners USAWheaton Precious Metals **Non-Profit** Oxfam Freshwater Trust UNICEF **Online Marketplace** Moonlighting **Pharmaceuticals**Amgen MerckPfizer Sanofi Boehringer Ingelheim Roche **Real Estate** Convexity Properties Emaar Group Greenbrier Management Growth Capital ServicesICP Medici Land Governance St. Regis Aspen Resort **Retail & Manufacturing** Acqua di Parma Ardbeg BelvedereBenefit CosmeticsBerlutiBodega Numanthia BulgariCaffe-Pasticceria CovaCape Mentelle CelineChandonCharles & KeithChâteau Cheval BlancChâteau d’YquemChaumetCloudy BayDFSDiorDom PerignonEmilio PucciFendiFenty Beauty by Rihanna FREDFresh (Cosmetics & FragrancesGivenchyGlenmorangie GuerlainHennessyHouse of BijanHublotKalaKat Von D BeautyKenzo KrugLa Grande Epicerie LoeweLoro pianaL’Oreal Maison Francis KurkdjianMarc JacobsMercierMoet & ChandonMoynat Naja Newton VineyardNicholas Kirkwood RimowaRuinartSephoraStarboard Cruise ServicesTAG Heuer Terrazas de los AndesThomas Pink Veuve ClicquotVolcan di mi tierra WenjunZenith A. Favre & Fils AB InBev Anheuser-Busch InBev Albertsons Companies BASF Bridgestone Carrefour Foxconn Golden State Foods Kroger Levi Strauss Louis Vuitton Naked Brand Group Nestle Nike Overstock Parfums Christian Dior Rakuten Target Unilever Walmart **Sports Teams** U.S. LA Kings (Ice Hockey) **Technology** Dell HuaweiLenovo T-Mobile Aidant Technologies AMD Apple Avnet Bosch Foxconn Fujitsu Hewlett PackardHitachi HTC LG NASA Nvidia PitneyBowes RCS Global Samsung Sapphire Technology Seagate TechnologySiemens Sony SweetSense Tencent TSMC **Transport, Logistics & Shipping** CMA CGM ERTICO Feadship Penske 10–4360dataABAQ.USAble FreightAdventAGForceAITAgistixAltriaAmaltoAmerican Global LogisticsAmerican Short Line & Regional Railroad AssociationAMTRAMXAnhueser-BuschApex APL ArcBest CorporationAriaLogisticsAriesArrive LogisticsArrowATSAxle LogisticsBCGBest TransportBlueGraceBlue HorseshoeBourque LogisticsBP BNSF Railway BoyleBoxLockBreakthrough FuelBucklandBurris LogisticsC.H. RobinsonC & K TruckingC.R. EnglandCargoChainCarrier DirectCDL MarketingCenturyCerasisChopTankCitizens ReserveClearDestinationCJ Logistics CNColumbia TransportConvoyCovenant TransportCowan SystemsCoyoteCRST InternationalCSXCTSI GlobalCustoms DirectDaimlerDAMCODasekeDATDDCDecker Truck Line Inc.DeringerDescartesDexFreightDicomDleveredDLT LabsDMTBDreamOrbitDriverReachEagle TransportationEast Coast TransporteCapitalEchoEdge LogisticsEnergy Transportation GroupEnvio360Eroad eShipGlobaleVansEXPAK Logistics FedExFirstFleetFleetCompleteFleetPalFLSFMIFontinalis PartnersForward AirFourKitesFR8StarFraightFreight RoverFreight SourceFullbayGateHouse LogisticsGebruder WeissGE TransportationGlobalTranzGT SolutionsHill Bros Hyundai Merchant Marine IMCInnovativosIntelligent AuditIrvingITLIts LogisticsJ.B. HuntJD LogisticsKamionKansas City Southern LinesKencoKingsgate LogisticsKion GroupKleinschmidtKochKonexial Kuehne & Nagel LabelmasterLandStarLanehubLazer SpotLDILiquid FreightLivingstonLoad OneLoadblockLoadDeliveredLogiflexLogistics+Logistyx TechnologiesLotte Global LogisticsLyncAmericaMach1 Maersk Manhattan AssociatesMatch FactorsMaven MachinesMaverickMcLeod SoftwareMedallion Transport & LogisticsMercerMercuryGateMMM ExpressMode TransportationMorpheus.NetworkMSTSNascentNavistarNBTGNFINGLNorfolk SouthernNT LogisticsNTGNVision GlobalOAROmnitracsOpenPortOOIDAOrigintrailP & GP & S TransportationP.B. IndustriesPackygePAMPanalpinaPantos LogisticsPaschall Truck LinesPavestone CapitalPegasus TransTechPeir Phillips Connect TechnologiesPhoenix Capital GroupPilot Freight ServicesPitt OhioPlatform SciencePLMPolarisPrecisionPrime3Project44ProTransPTIPurolatorPWCQITR & R ExpressRaider ExpressRail IncRay-Mont LogisticsRedwoodReliance PartnersRIMRoadrunner Transportation SystemsRoadSyncRouteiqueRSI LogisticsRTWAirRUANRyderSAIASaint John Capital Schneider Trucking SCLogicScopelitisSeaboard MarineSharelogisShawSheerShipwellSimpleLogisticsSinglePoint CapitalSkyBitzSmartHopSMC3SolarisSouthEastern Freight LinesSource Alliance NetworkSpireonSuddathSunsetSupplyOnSyfan LogisticsTadmoreTAFSTeam WorldwideThe Home DepotThird Coast LogisticsTideworks TechnologyTMCTMW SystemsTRAC IntermodalTraffixTransCardTransfixTransfloTransLytixTransPlaceTransportationInsightTransProTranzlogixTranzportTrinity LogisticsTrinium TechnologiesTriumphPayTrucker PathTruckstopTruck ToolsUber FreightUNISUnion Pacific UPS U.S. XpressUSA TruckVelocity TransportVHI TransportVitesseVnomicsWataco Supply Chain ServicesWELWernerWhirlpool CorporationWhite ArrowWiseTech GlobalWolf DirectWOLTWorldwide ExpressWWS TradingXLNTECYRC WorldwideZoof-It **Utilities** ENGIE Repsol WASSHA (DIGITAL GRID Corporation) Concentric Gazprom Petroteq TEPCO Wien Energie **Noteworthy Individuals** — Russian Opposition Leader — U.S. Presidential Candidate — South Korean Actor — Inventor of World Wide Web — Founder SpaceX & Tesla — French President — CEO Twitter & Square — Retired NBA Basketball Player — №1 Youtube Channel — LinkedIn co-founder — Professional Tennis Player — CEO Berkshire Hathaway & Billionaire investor Alexei Navalny Andrew Yang Bae Yong-joon Tim Berners-Lee Elon Musk Emmanuel Macron Jack Dorsey Kobe Bryant Pewdiepie Reid Hoffman Serena Williams Warren Buffet <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

A Comprehensive List of Enterprises Partnering with blockchain in 2019

The State of The Market — February 14, 2019 : $3,611.84 (-0.52%) : $121.70 (-0.93%) : $0.302635 (-0.38%) BTC ETH XRP The market is in negative today, as Bitcoin is testing its support at $3,600. It is not a strong support yet, and if bears have it their way, Bitcoin could once again crash to $3,400 over the weekend when the volumes are low. The total market cap lost $1 Billion in the last 24 hours, and all of the top 10 cryptocurrencies have posted small losses. The market is following Bitcoin’s price movement right now and Bitcoin dominance remains unchanged. In other news, . The function adds a new attack vector to the platform and allows a smart contract to be created at an address that can be determined beforehand by different parties. The new function is designed to allow interaction with a smart contract that does not yet exist on the blockchain and it would be included in EIP-1014. Also, . Hyundai Pay has developed a blockchain platform and a cryptocurrency wallet. The partnership is to promote the growth of fintech startups and jobs in Busan. Hyundai Pay will relocate its headquarters to Busan and build the needed infrastructure. Ethereum co-founder Vitalik Buterin has suggested that a new smart contract function called “Create2” be integrated into the upcoming Constantinople upgrade Busan, South Korea’s second-most populated city after Seoul has signed a Memorandum of Understanding (MoU) with blockchain company Hyundai Pay According to Intel, the blockchain suite is designed for businesses desiring to launch their own blockchain fast and effectively. The platform will allow users to develop blockchain-based applications and it uses Xeon processors along with other Intel hardware. Intel says the suite includes all the necessary components to launch a hyperledger-driven blockchain and Intel says that the package is “enterprise-ready”. ( ) 1) Market materials from Intel show that the company has launched a commercial blockchain package based on Hyperledger’s ecosystem. Read More XBT launched the world’s first Bitcoin tracker, Bitcoin Tracker One in 2015 and subsequently launched Bitcoin exchange-traded note (ETN). XBT said it needs more knowledge about hard forks and different communities before listing coins. XBT’s CEO Laurent Kssis said, “If we get it wrong, these assets will drop and if they’re part of the basket we can’t go back because it’s in the final term-sheet.” XBT wants the crypto market to settle and give further clarity before revisiting its plans. For now, it has been shelved without a launch date. ( ) 2) Sweden’s XBT Provider has canceled the launch of its cryptocurrency basket due to volatility. Read More The company is hoping to improve trust, transparency, patient safety, and patient improvement in clinical trials using blockchain. Currently, clinical trial processes and records are often erroneous or incomplete. Boehringer will test the integrity of blockchain in this sector with IBM. Early last year, Pfizer, Amgen and Sanofi were considering blockchain for developing and testing new drugs and another pharma firm Merck filed a patent to use blockchain in manufacturing medicines. Also, the Indian government plans on using blockchain to prevent counterfeit drugs. ( ) 3) German pharmaceutical multinational Boehringer Ingelheim has announced that it is set to trial IBM Blockchain in clinical trials. Read More Subscribe to the Berminal Brief Newsletter Download Berminal App for Free The Berminal Website Berminal Official Telegram Berminal Twitter

Intel Launches A Commercial Blockchain Suite, And XBT Postpones The Launch Of Its Crypto Basket

Real world Use Cases of Blockchain in Pharma and Healthcare The blockchain technology is well suited for use cases involving track-and-trace, product provenance and supply chain governance. The pharmaceutical industry is actively exploring the blockchain technology to help with several real world use cases. Many of these explorations already have proof-of-concept implementations that have generated tractable interests in the Pharma industry. In this post we will review the top 5 of these use cases. Use Case 1: Verifying the Authenticity of Returned Drugs In the Pharma industry, drugs are frequently returned to the pharmaceutical manufacturers. For instance, wholesalers may have ordered excess inventory and accordingly may need to return unsold stock to the pharmaceutical manufactures. While the proportion of the returned drugs is small compared to the sales (about 2–3% of sales), the per year volume is in the range of $7- 10 billion. In 2016, the US pharmaceutical manufacturer sales were . The 2018 forecast revenue for the top ten global pharmaceutical companies (which represent half of the sales of the top fifty companies) is . Based on these estimates, the saleable returns market at 2–3% of total sales is 7–10 billion. $323 billion $355 billion Instead of destroying these perfectly good drug shipments, pharmaceutical companies instead opt to . However before they can re-sell these returned drugs, the pharmaceutical companies have a re-sell them legal obligation to verify the authenticity of the returned drugs. In the US, the Drug Supply Chain Security Act (DSCSA) stipulates that all US manufacturers must implement serialization or barcoding of drugs at a package level by November 2018. Also, by the same time next year these serial numbers must be used to verify the authenticity of the returned drugs. Europe has a similar regulatory enactment called the Falsified Medicine Directive (FMD), requiring all drugs to be serialized or barcoded by February 2019. While the EU has opted for a (allowing manufacturers to upload serial numbers to a centralized EU regulator’s database, and distributors to connect to this centralized database to verify drug authenticity), centralized approach in the US there is no centralized database regulator. Without such a centralized database, one option is for every distributor to integrate data with every pharmaceutical manufacturer, which is logistically prohibitive! A far better and recommended approach is to have pharmaceutical manufacturers record the serial numbers of their packages on a blockchain, which serves as a decentralized and distributed ledger. Wholesalers and customers can then verify the authenticity of a drug package by connecting to the blockchain. Industry Proof-of-Concept Currently, Merck in partnership with SAP has developed the SAP Pharma Blockchain POC app for this Use Case. SAP launched this first Proof of Concept in Q3 2017, and a second one in early 2018. The PoC for the Use Case works as follows, SAP’s existing solution (unrelated to blockchain) called ATTP (Advanced Track and Trace for Pharmaceuticals), generates unique identifiers for a drug package. When a manufacturer ships a package they register the item on the SAP Pharma POC blockchain, with the four pieces of information generated by the ATTP; the item number (based on GS1 standard), a serial number, a batch number, and an expiration date. The distributor can extract the four pieces information from the packaging’s barcode, using a simple scanner mobile app, allowing them to verify returns . Counterfeiter copies of barcodes can be avoided, since SAP has the added ability to track every time a package changes hands. A map view within the mobile app also helps ensure that the drugs are in the expected geographical region. Use Case 2: Prevention of Counterfeit Drugs and Medical Devices Blockchain’s ability to establish provenance of data makes it especially suited for this supply chain use case. ​Drug companies that manufacture, ship and deliver products have a difficult time keeping track of their products, thereby allowing counterfeiters to introduce fake drugs into the system. The problem of counterfeiting is not just limited to drug manufactures, but extends to medical instruments manufactures. The World Health Organization (WHO) estimates that 8 percent of the medical devices in circulation today are counterfeit copies. Counterfeit drugs and medical devices pose a major risk to consumers, and also lost revenue for the legitimate manufacturers. When the mandate goes into effect by November 2018 every drug package will be uniquely serialized or barcoded. This will provide a unique product identifier for each drug package, and allow the verification of the authenticity of every product sold. Drug Supply Chain Security Act (DSCSA) As the drug moves through the supply chain the transactions can be recorded on a blockchain, thereby providing a distributed provenance ledger. This will make it possible for all parties to track drugs through the entire supply chain life-cycle. This will make it harder for counterfeit drugs from being introduced into the supply chain, and distributed to unsuspecting consumers. Industry Proof-of-Concept Novartis is experimenting with blockchain to identify counterfeit medicines and track temperature with real-time visibility for all participants in the supply chain, using Blockchain and IoT. The initiative is led by Fritz, the Novartis’ Domain Architect Supply Chain, and Cuomo, Novartis’ Applied Technology Innovation Manager. Novartis is also currently engaged in developing a consortium blockchain network between the European pharmaceutical industry and the EU, called the Innovative Medicine Initiative(IMI) Blockchain Enabled Healthcare program. The consortium will comprise SME blockchain companies, universities, clinical labs, hospitals, patient representatives and others; and aims at exploring use cases in counterfeit drug detection, supply chain, patient data, and clinical trials. Use Case 3: Compliance in Pharma supply chain As drugs move through the supply chain, logistics companies need to adhere to drug handling, transport and storage guidelines. Several operating constraints may need to be tracked. These may include maintaining temperature range, humidity, air quality within specified limits etc. Environmental conditions within the supply chain may have a direct impact on the quality and efficacy of the drug. For example temperature sensitive products like vaccines need to be properly monitored throughout the supply chain. Today drugs are monitored throughout the supply chain using smart IoT devices and temperature, humidity and other factors can be recorded using smart devices throughout the supply chain life-cycle. However since each participant of the supply chain i.e. the manufacturer, the logistics company, the stores and the pharmacies etc, maintain their own separate ledger, a problem within any particular segment of the supply chain is difficult to track. Blockchain technology provides the pharmaceutical supply chain a better way to add compliance and governance within the supply chain. Due to its inherent transparency, immutability and distributed nature, Blockchain technology provides a mechanism that allows any participant in the supply chain to ensure that the supply chain logistics and transportation guidelines (including handling and storage conditions of the drugs), were adhered to. Additionally smart contracts can be programmed that automatically execute when compliance conditions are not met, there by alerting the relevant parties in the supply chain. So for example suppose, if there is a temperature excursion during a transportation, with blockchain technology the consumer can easily see at what point the event occurred. Additionally, smart contracts may be automatically invoked within the blockchain, which will reduce the invoice amount due to the logistics company, based on its level of non compliance of storage conditions. Use Case 4: Transparency and traceability of consent in Clinical Trials Informed patient consent involves making the patient aware of each step in the Clinical Trial process including any possible risks posed by the study. Clinical trial consent for protocols and their revisions need to be transparent for patients and traceable for stakeholders. However, in practice the informed consent process is difficult to handle in a rigorous and satisfactory way. The FDA reports that almost 10% of the trials they monitor feature some issues related to consent collection. These include: failure to obtain written informed consent, unapproved forms, invalid consent document, failure to re-consent to a revised protocol and missing Institutional Review Board approval to protocol changes; among others. Frequently also there are reported cases of document fraud such as issues of backdating consent documents. Blockchain protocols in clinical trials can provide transparency and traceability of consent. Blockchains provide a mechanism for unfalsifiable time-stamping of consent forms, storing and tracking the consent in a secure, and publicly verifiable way, and enabling the sharing of this information in real time. Additionally, smart contract can be bound to protocol revisions, such that any change in the clinical trials protocol requires the patient consent needing renewal. Use Case 5: Improving the quality and reliability of Clinical Trials data Since the passage of the Prescription Drug User Fee Act in 1992 (which allowed the FDA to receive funding from pharmaceutical companies), the FDA has collected $7.67 billion in user fees from pharmaceutical manufacturers. This trend of annual funding to the FDA from drug manufacturers is expected to continue to increase. Patients and physicians have begun to question the current standards for Clinical Trial funding, leading to growing concerns due to the conflict of interest and the high stakes involved. Blockchain with its decentralized, immutable ledger and a mechanism to ensure transparency provides a solution. Clinical trials data can be stored in a secure, unfalsifiable and publicly verifiable manner on the blockchain. This prevents tampering with clinical trials results, thus improving the reliability of the clinical trials data. Blockchain technology can also be used to directly increase the quantity and quality of patients recruited for clinical trials in a number of ways. Using blockchain individual patients can store and control access to their medical data, and make it visible to trial recruiters, who could then reach out to the patients if their data qualifies for the clinical trial. The decentralized nature of blockchain gives to the patients, control over their data, and consent and its revocation. Industry Proof-of-Concept Pfizer, Amgen, and Sanofi are currently working side-by-side to leverage to blockchain technology. Use cases range from storing patient health data safely, to speeding up clinical trials, and ultimately lowering drug development costs. Exochain (a blockchain Pharma startup) manages secure storage of patient health information on the blockchain. Exochain allows individuals to control how clinical trial researchers may interact with their medical data. This can potentially lead to increased quantity and quality of patients recruited for clinical trials, while at the same time giving individuals precision control over their medical information. Enjoyed reading the article? for related articles and the latest updates! Follow me on Medium 👏 Found this post useful? Hit the button to show how much you liked it :) Related articles: Read Next: Is your Healthcare App HIPAA compliant? Other Articles: 5 Common Myths about Blockchain and Bitcoin How to Innovate like a Startup!

Top 5 Blockchain Use Cases in Pharma and Healthcare — that you should know about!

Generative Artificial Intelligence for Software Engineering: Research Agenda

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Kiriaki Frangias;
(2) Andrew Lin;
(3) Ellen Vitercik;
(4) Manolis Zampetakis. Table of Links Abstract and Introduction Warm-up: Agents with known equal disutility Agents with unknown disutilites Experiments Conclusions and future directions, References A Summary of notation B Omitted proofs from Section 2 C Omitted proofs from Section 3 D Additional Information about Experiments 3 Agents with unknown disutilites We now significantly expand our model to study heterogeneous agents with differing, unknown disutilities. The full proofs of all results in this section are in Appendix C. 3.1 Model and notation Some agents might experience high disutility when exerting effort, so it may not be in the principal’s best interest to incentivize all agents to exert effort. Therefore, the principal must set the payment so that the number of agents incentivized is utility maximizing for the principal. For the rest of this section, we denote by g the number of agents that the principal aims to incentivize; in Theorem 3.6, we describe an efficient optimization problem that solves for the optimal value of g, denoted g∗. The principal controls the number of incentivized agents only through the payment function. Hence, the realized number of agents that exert effort, denoted ˆg, is a random variable with a distribution that depends on the payment. Under this model, the principal will: 1. Calculate g∗, the optimal number of agents to incentivize. 2. Announce the payment pg∗ ∈ R+ (based on g∗). 3. Verify a subset of comparisons assigned to the agents. 4. Assign each comparison to a total of r agents. 5. Pay pg∗ to all agents not identified as “bad.” 3.2 Payment function 3.3 Correctness of CrowdSort Next, we bound the redundancy required to ensure that CrowdSort returns the groundtruth ordering. Proof. We know from Lemma 3.4 that if then with high probability, the algorithm CrowdSort(T, s, v, r, δ) identifies all “bad” agents. Therefore, it suffices to assign each comparison to at least one “good” agent for the true value of the comparison to be returned. By Remark 3.3, we know that for any agent ai, So the probability that all r agents assigned to a single comparison are “bad” is at most: Taking the union bound over all comparisons, we have that the probability there exists a comparison such that all agents assigned to it are “bad” is at most Upper-bounding the above probability by δ/2, we get that: Finally, notice that the above inequality and Inequality (6) hold simultaneously with probability at least 1 − δ/2. 3.4 Principal’s incentive We combine the results presented above in the following theorem, in which we additionally prove that it is possible for the principal to efficiently compute the optimal number of agents to incentivize. Based on Lemma 3.5, we use the notation and This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. Table of Links Abstract and Introduction Abstract and Introduction Warm-up: Agents with known equal disutility Warm-up: Agents with known equal disutility Agents with unknown disutilites Agents with unknown disutilites Experiments Experiments Conclusions and future directions, References Conclusions and future directions, References A Summary of notation A Summary of notation B Omitted proofs from Section 2 B Omitted proofs from Section 2 C Omitted proofs from Section 3 C Omitted proofs from Section 3 D Additional Information about Experiments D Additional Information about Experiments 3 Agents with unknown disutilites We now significantly expand our model to study heterogeneous agents with differing, unknown disutilities. The full proofs of all results in this section are in Appendix C. 3.1 Model and notation Some agents might experience high disutility when exerting effort, so it may not be in the principal’s best interest to incentivize all agents to exert effort. Therefore, the principal must set the payment so that the number of agents incentivized is utility maximizing for the principal. For the rest of this section, we denote by g the number of agents that the principal aims to incentivize; in Theorem 3.6, we describe an efficient optimization problem that solves for the optimal value of g, denoted g∗. The principal controls the number of incentivized agents only through the payment function. Hence, the realized number of agents that exert effort, denoted ˆg, is a random variable with a distribution that depends on the payment. Under this model, the principal will: 1. Calculate g∗, the optimal number of agents to incentivize. 2. Announce the payment pg∗ ∈ R+ (based on g∗). 3. Verify a subset of comparisons assigned to the agents. 4. Assign each comparison to a total of r agents. 5. Pay pg∗ to all agents not identified as “bad.” 3.2 Payment function 3.3 Correctness of CrowdSort Next, we bound the redundancy required to ensure that CrowdSort returns the groundtruth ordering. Proof. We know from Lemma 3.4 that if then with high probability, the algorithm CrowdSort(T, s, v, r, δ) identifies all “bad” agents. Therefore, it suffices to assign each comparison to at least one “good” agent for the true value of the comparison to be returned. By Remark 3.3, we know that for any agent ai, So the probability that all r agents assigned to a single comparison are “bad” is at most: r Taking the union bound over all comparisons, we have that the probability there exists a comparison such that all agents assigned to it are “bad” is at most Upper-bounding the above probability by δ/2, we get that: Finally, notice that the above inequality and Inequality (6) hold simultaneously with probability at least 1 − δ/2. 3.4 Principal’s incentive We combine the results presented above in the following theorem, in which we additionally prove that it is possible for the principal to efficiently compute the optimal number of agents to incentivize. Based on Lemma 3.5, we use the notation and This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv Arxiv

Algorithmic Contract Design: What to Know About Agents With Unknown Disutilites 

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Kiriaki Frangias;
(2) Andrew Lin;
(3) Ellen Vitercik;
(4) Manolis Zampetakis. Table of Links Abstract and Introduction Warm-up: Agents with known equal disutility Agents with unknown disutilites Experiments Conclusions and future directions, References A Summary of notation B Omitted proofs from Section 2 C Omitted proofs from Section 3 D Additional Information about Experiments 2 Warm-up: Agents with known equal disutility As a warm up to our main result, in this section, we propose a contract assuming that (a) the principal knows the agents’ disutilites and (b) every agent experiences the same value of per-comparison disutility (assumptions we relax in Section 3). We begin with a formal model in Section 2.1 and define the optimal payment in Section 2.2. In Sections 2.3 and 2.4, we present our main algorithm CrowdSort for computing the correct sorting. Section 2.5 identifies conditions under which the principal gets higher utility when implementing our proposed contract than when performing all pairwise comparisons herself. The full proofs of all results in this section are in Appendix B. 2.1 Model and notation The interaction proceeds as follows: 1. The principal fixes a payment p∗ ∈ R+ that she will pay agents in order to incentivize them to exert effort. 2. The principal verifies a subset of the comparisons and announces the size of this set to the agents. 3. Agents choose whether to exert effort, which fixes their type upfront. 4. Agents are assigned verified and unverified comparisons. 2.2 Agents’ incentives 2.3 Peer grading algorithm 2.4 Allocation of items 2.4.1 Assigning verified comparisons. Lemma 2.3. If each item in V appears in exactly one pair, then no pair in V is redundant. 2.4.2 Assigning unverified comparisons. We now define findWset. First, we bound the number of agents each comparison must be assigned to in order to retrieve the ground-truth sorting. Details about the social golfer problem. In the social golfer problem, the goal is to determine whether m golfers can play together in g groups of p (where m = gp) for w days in such a way that (1) no two golfers play in the same group more than once, and (2) each golfer plays exactly once each day. Thus, an instance of this problem is defined by integers (g, p, w). For us, the golfers correspond to the items, the days correspond to the partitions, and the groups of golfers correspond to the sets in the partitions. Thus, we aim to solve this problem with g, p, and w all approximately equal to √n. 2.5 Principal’s incentives We synthesize our results in the following theorem, in which we additionally prove that if the principal’s disutility ψ¯ is sufficiently larger than the agents’ disuility ψ, then it is in the principal’s best interest to use our mechanism rather than perform all pairwise comparisons herself. This is a natural assumption, which implies that the principal would have to be compensated sufficiently more per comparison than the agents for their work to be profitable to the principal. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. Table of Links Abstract and Introduction Abstract and Introduction Warm-up: Agents with known equal disutility Warm-up: Agents with known equal disutility Agents with unknown disutilites Agents with unknown disutilites Experiments Experiments Conclusions and future directions, References Conclusions and future directions, References A Summary of notation A Summary of notation B Omitted proofs from Section 2 B Omitted proofs from Section 2 C Omitted proofs from Section 3 C Omitted proofs from Section 3 D Additional Information about Experiments D Additional Information about Experiments 2 Warm-up: Agents with known equal disutility As a warm up to our main result, in this section, we propose a contract assuming that (a) the principal knows the agents’ disutilites and (b) every agent experiences the same value of per-comparison disutility (assumptions we relax in Section 3). We begin with a formal model in Section 2.1 and define the optimal payment in Section 2.2. In Sections 2.3 and 2.4, we present our main algorithm CrowdSort for computing the correct sorting. Section 2.5 identifies conditions under which the principal gets higher utility when implementing our proposed contract than when performing all pairwise comparisons herself. The full proofs of all results in this section are in Appendix B. 2.1 Model and notation The interaction proceeds as follows: 1. The principal fixes a payment p∗ ∈ R+ that she will pay agents in order to incentivize them to exert effort. 2. The principal verifies a subset of the comparisons and announces the size of this set to the agents. 3. Agents choose whether to exert effort, which fixes their type upfront. 4. Agents are assigned verified and unverified comparisons. 2.2 Agents’ incentives 2.3 Peer grading algorithm 2.4 Allocation of items 2.4.1 Assigning verified comparisons. Lemma 2.3. If each item in V appears in exactly one pair, then no pair in V is redundant. Lemma 2.3. If each item in V appears in exactly one pair, then no pair in V is redundant. 2.4.2 Assigning unverified comparisons. We now define findWset. First, we bound the number of agents each comparison must be assigned to in order to retrieve the ground-truth sorting. Details about the social golfer problem. In the social golfer problem, the goal is to determine whether m golfers can play together in g groups of p (where m = gp) for w days in such a way that (1) no two golfers play in the same group more than once, and (2) each golfer plays exactly once each day. Thus, an instance of this problem is defined by integers (g, p, w). For us, the golfers correspond to the items, the days correspond to the partitions, and the groups of golfers correspond to the sets in the partitions. Thus, we aim to solve this problem with g, p, and w all approximately equal to √n. Details about the social golfer problem. 2.5 Principal’s incentives We synthesize our results in the following theorem, in which we additionally prove that if the principal’s disutility ψ¯ is sufficiently larger than the agents’ disuility ψ, then it is in the principal’s best interest to use our mechanism rather than perform all pairwise comparisons herself. This is a natural assumption, which implies that the principal would have to be compensated sufficiently more per comparison than the agents for their work to be profitable to the principal. This paper is available on Arxiv under CC 4.0 license. Arxiv Arxiv

Algorithmic Contract Design for Crowdsourced Ranking: Warm-up: Agents With Known Equal Disutility

Biden-⁠Harris Administration Commends Federal Agencies for Completing Key AI Actions in 180 Days

বিডেন-হ্যারিস প্রশাসন 180 দিনের মধ্যে মূল এআই অ্যাকশনগুলি সম্পূর্ণ করার জন্য ফেডারেল এজেন্সিগুলির প্রশংসা করেছে

Biden-Harris-Regierung lobt Bundesbehörden für die Umsetzung wichtiger KI-Maßnahmen in 180 Tagen

La administración Biden-⁠Harris elogia a las agencias federales por completar acciones clave de IA en 180 días

L’administration Biden-⁠Harris félicite les agences fédérales pour avoir mené à bien des actions clés en matière d’IA en 180 jours

बिडेन-हैरिस प्रशासन ने 180 दिनों में प्रमुख एआई कार्रवाइयों को पूरा करने के लिए संघीय एजेंसियों की सराहना की

バイデン・ハリス政権、連邦政府機関が180日以内に主要なAI対策を完了したことを称賛

Biden-⁠해리스 행정부, 180일 만에 핵심 AI 작업을 완료한 연방 기관 칭찬

Administração Biden-⁠Harris elogia agências federais por concluírem ações importantes de IA em 180 dias

Администрация Байдена-Харриса благодарит федеральные агентства за завершение ключевых действий в области искусственного интеллекта за 180 дней

Biden-⁠Harris Yönetimi, Temel Yapay Zeka Eylemlerini 180 Günde Tamamlayan Federal Kurumları Övdü

Chính quyền Biden-⁠Harris khen ngợi các cơ quan liên bang đã hoàn thành các hành động quan trọng về AI trong 180 ngày

拜登-哈里斯政府赞扬联邦机构在 180 天内完成关键人工智能行动

Authors:
(1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Abstract This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) - ChatGPT, Bing Chat, Bard, and Claude - in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case study approach, we scrutinised interaction logs between the AI systems and a simulated student persona during Chemistry learning simulations, incorporating Content Analysis methodology to delve deeper into the discourse. Our findings underscore these tools' potential as "agents-to-think-with", enhancing critical thinking, problem-solving, comprehension, creativity, and tailored learning. Especially noteworthy is their ability to stimulate learners through Socratic-like questioning, aligning with constructionist principles. The research emphasises the pivotal role of prompt crafting to coax desired responses from GenAIbots, engendering iterative reflections. It also highlights the need for robust educator training to infuse these technologies into educational settings. Conclusively, while ChatGPT, Bing Chat, Bard, and Claude are poised to enrich Chemistry education by fostering dynamic, inclusive learning experiences, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in-depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Keywords: Chemistry education, ChatGPT, Bing Chat, Bard, Claude, Artificial Intelligence in Education, agents-to-think-with Introduction Chemistry, a core Science Education subject, clarifies matter's properties and transformations, thus critically shaping our daily lives (Dunlop et al., 2020). However, Chemistry is also a complex and dynamic field that necessitates a profound grasp of fundamental concepts and principles, and it is sometimes difficult for students to associate real-life circumstances with abstract chemistry concepts (Dewi et al., 2021). Research in Chemistry Education seeks effective strategies to alleviate these learning difficulties (Permatasari et al., 2022; Timilsena et al., 2022; Tümay, 2016). Timilsena et al. (2022) identified difficulties in understanding the abstract nature of chemical reactions and factors such as inadequate teaching materials and curriculum complexity, emphasising the need for effective teaching strategies and tools. Tümay (2016) discussed students' struggles in understanding fundamental chemistry concepts and emphasised the importance of addressing misconceptions and learning difficulties. Dewi et al. (2021) underscored the need for critical thinking skills and the integration of digital technology to enhance the quality of Chemistry education for Generation Z students. Dunlop et al. (2020) proposed introducing philosophical dialogue in higher education to address undergraduate Chemistry students' challenges, suggesting that it can stimulate new ways of thinking. In 2023, Castro Nascimento and Pimentel undertook a study to evaluate the proficiency of the ChatGPT model by having it respond to five distinct tasks across various subfields of chemistry (Castro Nascimento & Pimentel, 2023). These tasks included converting compound names to their SMILES chemical representation and vice versa, procuring information on the octanol-water partition coefficient of chemical compounds, extracting structural information on coordination compounds, determining the water solubility of polymers, and identifying the molecular point groups of simple molecular compounds. The unsatisfactory outcomes highlighted potential limitations in the model's ability to adequately address these specific chemistry-related queries. Notably, the suboptimal performance observed might be attributed to utilising an antiquated version of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). In a separate study conducted in the same year, Leon and Vidhani (2023) explored the reliability of ChatGPT responses within the framework of an introductory college-level Chemistry course. Their findings indicated a significant reliability concern, with ChatGPT failing to secure a score above 37%. Such a performance implies that learners relying on this tool for study support would predominantly receive incorrect responses, with the variability of the tool leading to different responses for individual learners. Although the authors didn't specify which version of ChatGPT they utilised in their study, their reference to "ChatGPT's free original version" and the citation of Floridi and Chiriatti's (2020) research on ChatGPT-3 hint at the likelihood that they employed this version. Additionally, Pimentel et al. (2023) delved into the efficacy of ChatGPT versions 3 and 4 in responding to intricate questions spanning six topics in Chemistry. While they concluded that both versions were currently inadequate in addressing the nuances of complex topics, they also observed noteworthy advancements from ChatGPT-3 to ChatGPT-4. Such progress signals promising potential for the tool to aid scientists in future literature reviews, surveys, and educational endeavours. Generative AI-powered chatbots (GenAIbots), including ChatGPT, Bing Chat, Bard, and Claude, have been introduced as innovative solutions to persistent challenges in Chemistry education (Baidoo-Anu & Owusu Ansah, 2023; Taylor et al., 2022). Their emergence offers a dynamic, inclusive educational environment, transforming how complex concepts are conveyed and understood. These GenAIbots simplify intricate topics, promote self-reflection, engage users in stimulating dialogues, facilitate personalised learning, and enhance critical thinking, collaboration, and cognitive development (Okonkwo & Ade-Ibijola, 2021). As a result, they have played a pivotal role in revolutionising Chemistry education. In the discourse on agency, traditional theories, such as the ones of Anscombe and Davidson, attribute actions to entities based on representational mental states encompassing desires, beliefs, and intentions. However, alternative perspectives, drawing from the works of Heider and Simmel, Dennett, Davidson, and Barandiaran et al., challenge this representation dependent view, suggesting potential agency without such mental representations (Schlosser, 2019). When considering GenAIbots within these frameworks, the entity's ability to respond based on training contrasts with its lack of self-awareness, intentions, and proactive behaviour— characteristics central to the agency. Consequently, while dominant philosophical and cognitive models would not grant ChatGPT agency, more expansive interpretations, as offered by these cited thinkers, could accept it. Building on this foundation and inspired by Melanie Swan's suggestion, we have expanded upon Papert's (1980) concept of "objects-to-think-with" to introduce “agents-to-thinkwith.” This positions GenAIbots as integral participants in the educational path, echoing Turkle's (1984) concept of "metacognitive machines," in the sense that they assist in enhancing one's awareness of their own cognitive processes, i.e., "thinking about thinking" as described by Flavell (1976). In Latour's (1991) perspective, we can perceive GenAIbots as "hybrids," which blur the boundaries between humans and non-human entities. Such a perspective further aligns with Swan's (2015) vision of a future where augmented humans and AI collaborate and thrive symbiotically. While GenAIbots posit themselves as powerful metacognitive educational tools, promoting critical thinking, problem-solving, and deep comprehension of concepts, it's imperative to acknowledge their limitations. For instance, there's the possibility of generating nonsensical or inaccurate content, as OpenAI (2023) highlighted. Nevertheless, the unique ability of GenAIbots to provide instant feedback, introduce diverse perspectives, and foster an interactive engagement with complex ideas solidifies their position as influential agents-to-think-with in the realm of Chemistry education. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Authors: Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Abstract and Introduction Materials And Methods Materials And Methods Results and Analyses Results and Analyses Prompts and generated texts Prompts and generated texts Conceptualizing chemical reactions Conceptualizing chemical reactions Deepening on understanding of chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Deepening on the concept of mole Question about changing of state Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about plasma, a state of matter Question about chemical bondings Question about chemical bondings Question about illustration of chemical bonds Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Question about the essence of the type of chemical bonding Further analysis Further analysis Conclusions Conclusions Limitations of the study and possible future studies Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Author Contributions, Conflicts of interest, Acknowledgements, and References Abstract This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) - ChatGPT, Bing Chat, Bard, and Claude - in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case study approach, we scrutinised interaction logs between the AI systems and a simulated student persona during Chemistry learning simulations, incorporating Content Analysis methodology to delve deeper into the discourse. Our findings underscore these tools' potential as "agents-to-think-with", enhancing critical thinking, problem-solving, comprehension, creativity, and tailored learning. Especially noteworthy is their ability to stimulate learners through Socratic-like questioning, aligning with constructionist principles. The research emphasises the pivotal role of prompt crafting to coax desired responses from GenAIbots, engendering iterative reflections. It also highlights the need for robust educator training to infuse these technologies into educational settings. Conclusively, while ChatGPT, Bing Chat, Bard, and Claude are poised to enrich Chemistry education by fostering dynamic, inclusive learning experiences, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in-depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Keywords: Chemistry education, ChatGPT, Bing Chat, Bard, Claude, Artificial Intelligence in Education, agents-to-think-with Keywords: Introduction Chemistry, a core Science Education subject, clarifies matter's properties and transformations, thus critically shaping our daily lives (Dunlop et al., 2020). However, Chemistry is also a complex and dynamic field that necessitates a profound grasp of fundamental concepts and principles, and it is sometimes difficult for students to associate real-life circumstances with abstract chemistry concepts (Dewi et al., 2021). Research in Chemistry Education seeks effective strategies to alleviate these learning difficulties (Permatasari et al., 2022; Timilsena et al., 2022; Tümay, 2016). Timilsena et al. (2022) identified difficulties in understanding the abstract nature of chemical reactions and factors such as inadequate teaching materials and curriculum complexity, emphasising the need for effective teaching strategies and tools. Tümay (2016) discussed students' struggles in understanding fundamental chemistry concepts and emphasised the importance of addressing misconceptions and learning difficulties. Dewi et al. (2021) underscored the need for critical thinking skills and the integration of digital technology to enhance the quality of Chemistry education for Generation Z students. Dunlop et al. (2020) proposed introducing philosophical dialogue in higher education to address undergraduate Chemistry students' challenges, suggesting that it can stimulate new ways of thinking. In 2023, Castro Nascimento and Pimentel undertook a study to evaluate the proficiency of the ChatGPT model by having it respond to five distinct tasks across various subfields of chemistry (Castro Nascimento & Pimentel, 2023). These tasks included converting compound names to their SMILES chemical representation and vice versa, procuring information on the octanol-water partition coefficient of chemical compounds, extracting structural information on coordination compounds, determining the water solubility of polymers, and identifying the molecular point groups of simple molecular compounds. The unsatisfactory outcomes highlighted potential limitations in the model's ability to adequately address these specific chemistry-related queries. Notably, the suboptimal performance observed might be attributed to utilising an antiquated version of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). In a separate study conducted in the same year, Leon and Vidhani (2023) explored the reliability of ChatGPT responses within the framework of an introductory college-level Chemistry course. Their findings indicated a significant reliability concern, with ChatGPT failing to secure a score above 37%. Such a performance implies that learners relying on this tool for study support would predominantly receive incorrect responses, with the variability of the tool leading to different responses for individual learners. Although the authors didn't specify which version of ChatGPT they utilised in their study, their reference to "ChatGPT's free original version" and the citation of Floridi and Chiriatti's (2020) research on ChatGPT-3 hint at the likelihood that they employed this version. Additionally, Pimentel et al. (2023) delved into the efficacy of ChatGPT versions 3 and 4 in responding to intricate questions spanning six topics in Chemistry. While they concluded that both versions were currently inadequate in addressing the nuances of complex topics, they also observed noteworthy advancements from ChatGPT-3 to ChatGPT-4. Such progress signals promising potential for the tool to aid scientists in future literature reviews, surveys, and educational endeavours. Generative AI-powered chatbots (GenAIbots), including ChatGPT, Bing Chat, Bard, and Claude, have been introduced as innovative solutions to persistent challenges in Chemistry education (Baidoo-Anu & Owusu Ansah, 2023; Taylor et al., 2022). Their emergence offers a dynamic, inclusive educational environment, transforming how complex concepts are conveyed and understood. These GenAIbots simplify intricate topics, promote self-reflection, engage users in stimulating dialogues, facilitate personalised learning, and enhance critical thinking, collaboration, and cognitive development (Okonkwo & Ade-Ibijola, 2021). As a result, they have played a pivotal role in revolutionising Chemistry education. In the discourse on agency, traditional theories, such as the ones of Anscombe and Davidson, attribute actions to entities based on representational mental states encompassing desires, beliefs, and intentions. However, alternative perspectives, drawing from the works of Heider and Simmel, Dennett, Davidson, and Barandiaran et al., challenge this representation dependent view, suggesting potential agency without such mental representations (Schlosser, 2019). When considering GenAIbots within these frameworks, the entity's ability to respond based on training contrasts with its lack of self-awareness, intentions, and proactive behaviour— characteristics central to the agency. Consequently, while dominant philosophical and cognitive models would not grant ChatGPT agency, more expansive interpretations, as offered by these cited thinkers, could accept it. Building on this foundation and inspired by Melanie Swan's suggestion, we have expanded upon Papert's (1980) concept of "objects-to-think-with" to introduce “agents-to-thinkwith.” This positions GenAIbots as integral participants in the educational path, echoing Turkle's (1984) concept of "metacognitive machines," in the sense that they assist in enhancing one's awareness of their own cognitive processes, i.e., "thinking about thinking" as described by Flavell (1976). In Latour's (1991) perspective, we can perceive GenAIbots as "hybrids," which blur the boundaries between humans and non-human entities. Such a perspective further aligns with Swan's (2015) vision of a future where augmented humans and AI collaborate and thrive symbiotically. While GenAIbots posit themselves as powerful metacognitive educational tools, promoting critical thinking, problem-solving, and deep comprehension of concepts, it's imperative to acknowledge their limitations. For instance, there's the possibility of generating nonsensical or inaccurate content, as OpenAI (2023) highlighted. Nevertheless, the unique ability of GenAIbots to provide instant feedback, introduce diverse perspectives, and foster an interactive engagement with complex ideas solidifies their position as influential agents-to-think-with in the realm of Chemistry education. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Enhancing Chemistry Learning with ChatGPT, Bing Chat, Bard, and Claude as Agents-to-Think-With

ChatGPT, Bing Chat, Bard, এবং Claude-এর সাথে এজেন্ট-টু-থিঙ্ক-এর মাধ্যমে রসায়ন শেখার উন্নতি করা

Verbesserung des Chemielernens mit ChatGPT, Bing Chat, Bard und Claude als Denkagenten

Mejora del aprendizaje de química con ChatGPT, Bing Chat, Bard y Claude como agentes con los que pensar

Améliorer l'apprentissage de la chimie avec ChatGPT, Bing Chat, Bard et Claude en tant qu'agents avec lesquels penser

चैटजीपीटी, बिंग चैट, बार्ड और क्लाउड के साथ रसायन विज्ञान सीखने को बेहतर बनाना, एजेंट-टू-थिंक-विथ

ChatGPT、Bing Chat、Bard、Claude をエージェントとして利用して化学学習を強化する

ChatGPT, Bing Chat, Bard 및 Claude를 생각하는 에이전트로 사용하여 화학 학습 강화

Aprimorando o aprendizado de química com ChatGPT, Bing Chat, Bard e Claude como agentes para pensar

Улучшение изучения химии с помощью ChatGPT, Bing Chat, Bard и Claude в качестве агентов для размышлений

Birlikte Düşünülecek Aracılar Olarak ChatGPT, Bing Chat, Bard ve Claude ile Kimya Öğrenimini Geliştirme

Nâng cao việc học hóa học với ChatGPT, Bing Chat, Bard và Claude với tư cách là Đại lý để suy nghĩ

使用 ChatGPT、Bing Chat、Bard 和 Claude 作为思考代理来增强化学学习

In today’s fast-paced job market, Staffing agencies are becoming key players in the future of work. This is because as jobs change and new skills are constantly required, traditional hiring methods are rethought. According to the American Staff Association (ASA), nearly three million temporary and contract employees work for America’s staffing companies during an average week. It represents a broad trend toward flexible business processes. This shift is being driven by staffing companies offering innovative solutions that not only meet the immediate needs of staff but also prepare organizations for tomorrow’s challenges. Understanding the Evolving Role of Staffing Agencies Traditionally, they were seen as street saviors who jumped in whenever companies needed to fill vacancies quickly or there was a shortage of temporary workers, and today, their role has changed dramatically. Staffing agencies are now building relationships with companies where they help these organizations anticipate recruitment needs, manage talent pipelines, and staff the changing market dynamics. One of the most important functions of modern personnel agencies is the ability to provide exceptional talent. As technology advances, for example, the demand for specialized roles in AI, cybersecurity, and blockchain has increased. Staffing agencies have revolutionized internationally by expanding their search and recruitment but also by using sophisticated technology to better match the right candidate with the right job. How Staffing Agencies Enhance Workforce Flexibility? The concept of ‘working for life’ is becoming obsolete. Today’s workforce, especially millennials and Gen Z, value work-life balance, flexibility, and the opportunity to work on their own terms. According to the American Staffing Association (ASA), during the course of a year, America’s staffing companies hire over 14.5 million temporary and contract employees. Staffing agencies are facilitating this new employment arrangement by offering contract positions, part-time roles, and temporary work offering flexible staffing and recruitment services for both employers and employees. For businesses, the flexibility offered by staffing companies means they can increase or decrease their staff based on the demands of the business without being burdened with long-term commitments. This flexibility is important, especially in projects and events such as IT, where the scope and timeline of the project can change dramatically. Leveraging Global Talent In a globalized world, geographical boundaries no longer limit talent. Staffing agencies have become important links connecting global talent pools with local and international companies. This globalization is especially useful for areas where specific skills are lacking. By leveraging a broader pool of talent, companies can not only better address skills gaps but also increase diversity within their teams. Teams bring diverse perspectives and tend to be highly innovative and robust in problem-solving, qualities critical to competitive advantage in today’s marketplace. Navigating Regulatory Landscapes Employment laws and regulations can vary greatly from region to region. Working groups help companies navigate these complex legal issues, ensuring compliance and reducing the risk of legal challenges. Whether understanding new labor laws, managing contracts, or enforcing fair labor practices, staffing agencies provide a valuable service that protects both employer and employee. Impact of Technology on Employees Staffing solutions combining artificial intelligence and machine learning are transforming the hiring landscape. This technology simplifies, manages, and automates the recruitment process, as well as recruiters are able to focus more on engaging with prospective clients and understanding the needs of the companies they serve. Conclusion: The Role of Systemart in Shaping a Successful Future As we look to the future, the role of staffing agencies in successful businesses becomes more apparent. Systemart professionals stand out as leaders in the industry, providing tailored solutions that help companies remain agile, competitive, and forward-thinking. With a commitment to innovation and in-depth knowledge of market trends, Systemart is geared to not only help your business meet immediate employee needs but also provide a flexible workforce, and they are active and ready for tomorrow’s challenges. In partnership with Systemart, business In today’s fast-paced job market, staffing agencies are becoming key players in the future of work. In today’s fast-paced job market, Staffing agencies are becoming key players in the future of work. This is because as jobs change and new skills are constantly required, traditional hiring methods are rethought. According to the American Staff Association (ASA), nearly three million temporary and contract employees work for America’s staffing companies during an average week. three million It represents a broad trend toward flexible business processes. This shift is being driven by staffing companies offering innovative solutions that not only meet the immediate needs of staff but also prepare organizations for tomorrow’s challenges. Understanding the Evolving Role of Staffing Agencies Understanding the Evolving Role of Staffing Agencies Traditionally, they were seen as street saviors who jumped in whenever companies needed to fill vacancies quickly or there was a shortage of temporary workers, and today, their role has changed dramatically. Staffing agencies are now building relationships with companies where they help these organizations anticipate recruitment needs, manage talent pipelines, and staff the changing market dynamics. One of the most important functions of modern personnel agencies is the ability to provide exceptional talent. As technology advances, for example, the demand for specialized roles in AI, cybersecurity, and blockchain has increased. Staffing agencies have revolutionized internationally by expanding their search and recruitment but also by using sophisticated technology to better match the right candidate with the right job. How Staffing Agencies Enhance Workforce Flexibility? How Staffing Agencies Enhance Workforce Flexibility? The concept of ‘working for life’ is becoming obsolete. Today’s workforce, especially millennials and Gen Z, value work-life balance, flexibility, and the opportunity to work on their own terms. According to the American Staffing Association (ASA) , during the course of a year, America’s staffing companies hire over 14.5 million temporary and contract employees. American Staffing Association (ASA) 14.5 million Staffing agencies are facilitating this new employment arrangement by offering contract positions, part-time roles, and temporary work offering flexible staffing and recruitment services for both employers and employees. staffing and recruitment services For businesses, the flexibility offered by staffing companies means they can increase or decrease their staff based on the demands of the business without being burdened with long-term commitments. This flexibility is important, especially in projects and events such as IT, where the scope and timeline of the project can change dramatically. Leveraging Global Talent In a globalized world, geographical boundaries no longer limit talent. Staffing agencies have become important links connecting global talent pools with local and international companies. This globalization is especially useful for areas where specific skills are lacking. By leveraging a broader pool of talent, companies can not only better address skills gaps but also increase diversity within their teams. Teams bring diverse perspectives and tend to be highly innovative and robust in problem-solving, qualities critical to competitive advantage in today’s marketplace. Navigating Regulatory Landscapes Employment laws and regulations can vary greatly from region to region. Working groups help companies navigate these complex legal issues, ensuring compliance and reducing the risk of legal challenges. Whether understanding new labor laws, managing contracts, or enforcing fair labor practices, staffing agencies provide a valuable service that protects both employer and employee. Impact of Technology on Employees Staffing solutions combining artificial intelligence and machine learning are transforming the hiring landscape. This technology simplifies, manages, and automates the recruitment process, as well as recruiters are able to focus more on engaging with prospective clients and understanding the needs of the companies they serve. Conclusion: The Role of Systemart in Shaping a Successful Future As we look to the future, the role of staffing agencies in successful businesses becomes more apparent. Systemart professionals stand out as leaders in the industry, providing tailored solutions that help companies remain agile, competitive, and forward-thinking. With a commitment to innovation and in-depth knowledge of market trends, Systemart is geared to not only help your business meet immediate employee needs but also provide a flexible workforce, and they are active and ready for tomorrow’s challenges. In partnership with Systemart, business In today’s fast-paced job market, staffing agencies are becoming key players in the future of work.

The Role of Staffing Agencies: A Game Changer in the Future of Work

স্টাফিং এজেন্সিগুলির ভূমিকা: কাজের ভবিষ্যতের একটি গেম চেঞ্জার৷

Die Rolle von Personalagenturen: Ein Wendepunkt in der Zukunft der Arbeit

El papel de las agencias de empleo: un punto de inflexión en el futuro del trabajo

Le rôle des agences de placement de personnel : un facteur qui change la donne dans l'avenir du travail

स्टाफिंग एजेंसियों की भूमिका: कार्य के भविष्य में एक बड़ा परिवर्तन

人材派遣会社の役割: 仕事の未来を変えるゲームチェンジャー

채용 대행사의 역할: 미래 업무 방식의 판도를 바꾸는 역할

O papel das agências de recrutamento: uma virada de jogo no futuro do trabalho

Роль кадровых агентств: меняет правила игры в будущем сферы труда

İşe Alma Kurumlarının Rolü: İşin Geleceğinde Oyun Değiştirici

Vai trò của các cơ quan cung cấp nhân sự: Người thay đổi cuộc chơi trong tương lai của công việc

人力资源代理机构的作用：未来工作格局的改变者

X Corp. v. Center for Countering Digital Hate, INC. Court Filing, retrieved on March 25, 2024 is part of . You can jump to any part in this filing . This part is 12 of 19. HackerNoon’s Legal PDF Series here 2. Constitutional Law CCDH’s second argument about damages relies on constitutional law—specifically, the principle that the First Amendment bars plaintiffs from using non-defamation causes of action “to avoid the strict requirements for establishing a . . . defamation claim.” MTD&S at 17 (citing cases, including Cohen, 501 U.S. at 671). That principle arose out of the Supreme Court’s holding in Hustler Magazine, Inc. v. Falwell, 485 U.S. 46, 56 (1988), that a public figure could not recover publication damages on a non-defamation claim without showing actual malice. This order will discuss some of the relevant cases before applying their holdings to this case. a. Relevant Case Law i. Cohen In Cohen, decided just a few years after Hustler Magazine, a confidential source brought suit for breach of contract and misrepresentation against newspaper publishers after the publishers—having promised the source confidentiality—published his name. 501 U.S. at 665–66. “The same day the stories appeared, Cohen was fired by his employer.” Id. at 666. The Supreme Court concluded that Cohen had not brought his contract claim “to avoid the strict requirements for establishing a libel or defamation claim.” Id. at 671. Cohen could not have brought a defamation claim because the information the publishers had disclosed was true. Id.[17] The Court also rejected the idea that Cohen was “seeking damages for injury to his reputation or his state of mind,” holding that he was seeking damages for “breach of a promise that caused him to lose his job and lowered his earning capacity.” Id. (distinguishing Hustler Magazine). ii. Food Lion The Fourth Circuit wrestled with both Hustler Magazine and Cohen in Food Lion, Inc. v. Capital Cities/ABC, Inc., 194 F.3d 505, 522 (4th Cir. 1999). In that case, Food Lion, a grocery store, had been the subject of an undercover investigation about its food handling practices. Id. at 510–11. Food Lion brought suit against the news network that aired the video. Id. at 511. It sought to recover “broadcast (publication) damages for matters such as loss of good will, lost sales and profits, and diminished stock value.” Id. The Fourth Circuit recognized that “an overriding (and settled) First Amendment principle precludes the award of publication damages in this case.” Id. at 522. It held that Food Lion had “attempted to avoid the First Amendment limitations on defamation claims by seeking publication damages under non-reputational tort claims, while holding to the normal state law proof standards for these torts” but that “[t]his is precluded by [Hustler Magazine].” Id. It further explained that “What Food Lion sought to do . . . was to recover defamation-type damages under non-reputational tort claims, without satisfying the stricter (First Amendment) standards of a defamation claim,” which was “an end-run around First Amendment strictures.” Id. iii. Planned Parenthood and National Abortion Federation Two recent cases decided by Judge Orrick, Planned Parenthood and National Abortion Federation, illustrate a district court’s conscientious analysis of this same issue. In Planned Parenthood, 402 F. Supp. 3d at 632–33, defendants, who were part of a group called the Human Capital Project (HCP), infiltrated Planned Parenthood conferences and facilities “to surreptitiously record conversations with the conference attendees” and staff. Judge Orrick, at summary judgment,[18] assessed defendants’ argument that “the First Amendment . . . precludes any award of . . . damages . . . because plaintiffs’ damages flow solely from the actual or feared response of third parties to the publication of the HCP videos.” Id. at 641–42. He then noted that the defendants, relying on Food Lion, argued that the damages the plaintiffs were seeking also “stem[med] from the public’s reaction (or feared or expected reaction) to the contents of the HCP videos and should have been sought through a defamation claim.” Id. at 642. Judge Orrick recognized that it was “difficult” given the lack of a defamation claim “to draw the line between impermissible defamation-like publication damages that were caused by the actions and reactions of third parties to the HCP videos and permissible damages that were caused by [defendants’] breaches of contract” and other acts. Id. at 643 (emphasis in original). He concluded that some of the damages the plaintiffs were seeking were “more akin to publication or reputational damages that would be barred by the First Amendment” but that others were “economic damages that are not categorically barred.” Id. at 644. He also explained that “[t]hose that fall in the latter category”—the permissible damages—“result not from the acts of third parties who were motivated by the contents of the videos, but from the direct acts of defendants—their intrusions, their misrepresentations, and their targeting and surreptitious recording of plaintiffs’ staff.” Id. at 644–45 (emphasis in original). The Ninth Circuit later affirmed that decision, stating that “two categories of compensatory damages permitted by the district court, infiltration damages and security damages, were awarded by the jury to reimburse Planned Parenthood for losses caused by Appellants’ violations of generally applicable laws.” Newman, 51 F.4th at 1134. The court deemed Appellants “incorrect in arguing that the infiltration and security damages awarded by the jury are impermissible publication damages.” Id. The court declared that the infiltration and security damages were “for economic harms suffered by Planned Parenthood, not the reputational or emotional damages sought in Hustler Magazine.” Id. It continued by explaining that Planned Parenthood could have recovered its infiltration and security damages “even if Appellants had never published videos of their surreptitious recordings. Regardless of publication, it is probable that Planned Parenthood would have protected its staff who had been secretly recorded and safeguarded its conferences and clinics from future infiltrations.” Id. In Judge Orrick’s related case, National Abortion Federation, 2018 WL 5879786, at *1, the defendants allegedly “used false identification and a ‘phony’ corporation to” get into National Abortion Federation (NAF) conferences and meetings. The defendants argued in support of a motion to dismiss that “because NAF ha[d] not asserted a defamation claim, its claim for ‘damages’ stemming from all remaining causes of action are barred by the First Amendment.” Id. at *5. Judge Orrick stated that “[t]he line demarking permitted damages from purely reputational damages (that stem only from truthful, non-misleading publication of material), as I noted in the Planned Parenthood case, cannot be determined on a motion to dismiss.” Id. He then commented that the defendants had “mischaracterize[d] the FAC as only seeking damages caused by the publication of the recordings and the resulting actions of third parties.” Id. at *6. And he explained that just because the plaintiff had “only learned of the infiltration after publication began does not mean the damages are from the publication.” Id. He continued: “At least some of the damages pleaded are the result of the infiltration and would have been incurred even if none of the recordings were published and [the plaintiff] learned of the infiltration through some other means.” Id. The Ninth Circuit also affirmed Judge Orrick’s National Abortion Federation decision, holding that the plaintiff had “frame[d] its theory of damages as compensation for non-reputational harm—namely its increased expenditures on security measures—which is a cognizable theory of damages.” National Abortion Fed’n v. Ctr. for Med. Progress, No. 18-17195, 793 Fed. Appx. 482, 485 (9th Cir. Nov. 15, 2019) (citing Planned Parenthood Fed’n of Am., Inc. v. Ctr. for Med. Progress, 735 F. App’x 241, 247 (9th Cir. 2018)). b. Takeaways from Relevant Case Law In both Planned Parenthood and National Abortion Federation, Judge Orrick held that plaintiffs could recover damages based directly on the defendant’s breach (the infiltration and security losses that they suffered from the defendants’ entering and filming in their facilities) without running afoul of Hustler Magazine. See Planned Parenthood, 402 F. Supp. 3d at 644–45; National Abortion Federation, 2018 WL 5879786, at *5–6. Such damages would have existed regardless of the publication of the inflammatory videos. See Newman, 51 F.4th at 1134. Judge Orrick held that the plaintiffs could not recover reputational damages that stemmed from publication—specifically, “acts of third parties who were motivated by the contents of the” publication. See Planned Parenthood, 402 F. Supp. 3d at 644–45. This is consistent with Food Lion, where the plaintiff was trying to recover damages based on third parties’ reactions to watching the news broadcast, see Food Lion, 194 F.3d at 522, and also with Cohen, although the latter is less obvious. In Cohen, 501 U.S. at 671, even though Cohen’s damages were based in part on the actions of a third party—the employer who fired him after seeing his identity revealed—the Court recognized that a defamation claim was unavailable to Cohen because the speech at issue was undeniably true, and that the damages represented “breach of a promise that caused him to lose his job and lowered his earning capacity.” The conduct that gave rise to the damages in Cohen was the publishers’ speech: that speech is what breached the publishers’ contract with Cohen and so it was not reputational. See id. The conduct that gave rise to the permissible damages in Judge Orrick’s cases was the defendants’ infiltration and security conduct— not the later publication of the videos, which would have given rise to impermissible reputational harm. Judge Orrick found it “difficult” “to draw the line” in his cases “between impermissible defamation-like publication damages that were caused by the actions and reactions of third parties to the HCP videos and permissible damages that were caused by [defendants’] breaches of contract” in part because there were so many different types of damages claimed in those cases. See Planned Parenthood, 402 F. Supp. 3d at 643–45 (listing the myriad categories of damages plaintiff sought) (emphasis in original). Judge Orrick determined that he was not well-positioned to sort those numerous types of losses into permissible/impermissible buckets on a motion to dismiss. See Planned Parenthood, No. 16-cv-236-WHO (Order on Motions to Dismiss and Strike) at 36; National Abortion Federation, 2018 WL 5879786, at *5. c. Application Here Here, X Corp. is not seeking some complicated mix of damages—some caused by the reactions of third parties and some caused directly by the alleged breach. The Court can say, as a matter of law, whether the single type of damages that X Corp. seeks constitutes “impermissible defamation-like publication damages that were caused by the actions and reactions of third parties to” speech or “permissible damages that were caused by [CCDH’s] breaches of contract.” See Planned Parenthood, 402 F. Supp. 3d at 643. The breach that X Corp. alleges here is CCDH’s scraping of the X platform. FAC ¶ 77. X Corp. does not allege any damages stemming directly from CCDH’s scraping of the X platform.[19] X Corp. seeks only damages based on the reactions of advertisers (third parties) to CCDH’s speech in the Toxic Twitter report, which CCDH created after the scraping. See FAC ¶¶ 70, 78; see also ACLU Br. at 12 (“The damages X Corp. seeks . . . are tied to reputational harm only, with no basis in any direct physical, operational or other harm that CCDH U.S.’s alleged scraping activities inflicted on X Corp.”). That is just what the Fourth Circuit disallowed in Food Lion, 194 F.3d at 522. The speech was not the breach, as it was in Cohen. And X Corp.’s damages would not have existed even if the speech had never occurred, as in Newman, 51 F.4th at 1134. Here, there would be no damages without the subsequent speech. Accordingly, the Court can hold as a matter of law that the damages alleged are impermissible defamation-like publication damages caused by the actions of third parties to CCDH’s report. X Corp.’s main response to all of this is that it has alleged “tangible, economic losses.” See Opp’n at 18 (citing Cohen, Newman, National Abortion Federation). But that is not the question. See Reply at 8 (“X Corp. never explains why core constitutional protections would turn arbitrarily on whether harm to a plaintiff’s reputation from a defendant’s speech happens to have manifested in a way where the dollars and cents can easily be counted before a complaint is filed.”). The “lost sales” in Food Lion, 194 F.3d 505, could presumably have been quantified. Numerous quantifiable expenses in Planned Parenthood (like “grants for security enhancements to affiliates experiencing increased security threats as a result of CMP’s videos”) were disallowed. See Planned Parenthood, 402 F. Supp. 3d at 645. Judge Orrick properly drew the line not between tangible and intangible losses, but between losses from direct harms and losses from reputational harms. See id. at 644–45. Drawing the same line here leads to the conclusion that the complaint fails to allege recoverable damages for the breach of contract under constitutional law. X Corp. therefore fails to state a claim for breach of contract. Continue Reading . Here [17] At the motion hearing, X Corp. seemed to argue that the cases where courts have found that a plaintiff was making an end-run around a libel or defamation claim are “cases where the Court said you couldn’t have brought a defamation claim.” Tr. of 2/29/24 Hearing at 62:13–18. The Court does not know what cases X Corp. had in mind, as it did not cite any, but Cohen is an example of the opposite. One of the reasons the Court held that Cohen was not making an end-run around a defamation claim is that he could not have brought a defamation claim. See Cohen, 501 U.S. at 671. [18] At the motion to dismiss stage, Judge Orrick had held that “the First Amendment does not impose heightened standards on plaintiffs’ tort claims as long as plaintiffs do not seek reputational damages . . . stemming from the publication conduct of defendants.” Planned Parenthood Fed’n of Am., Inc. v. Ctr. for Med. Progress, No. 16-cv-236-WHO (Order on Motions to Dismiss and Strike, dkt. 124) (Sept. 30, 2016) at 36 (emphasis in original). He concluded that “discovery will shed light on the nature of the damages for which plaintiffs seek recovery” and that the issue “is more appropriately addressed at summary judgment or trial.” Id. [19] This order will discuss in the following section X Corp.’s proposed amendment to the complaint. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 25, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Analyzing Constitutional Law in Breach of Contract Claims: Damages and First Amendment Principles

X Corp. v. Center for Countering Digital Hate, INC. Court Filing, retrieved on March 25, 2024 is part of . You can jump to any part in this filing . This part is 13 of 19. HackerNoon’s Legal PDF Series here 3. Amendment The Court must next decide whether to allow X Corp. leave to amend its breach of contract claim. The Court is mindful that “[t]he anti-SLAPP statute was enacted to allow early dismissal of meritless first amendment cases aimed at chilling expression through costly, time-consuming litigation,” Metabolife Int’l, Inc. v. Wornick, 264 F.3d 832, 839 (9th Cir. 2001), but also that “granting a defendant’s anti-SLAPP motion to strike a plaintiff’s initial complaint without granting the plaintiff leave to amend would directly collide with Fed. R. Civ. P. 15(a)’s policy favoring liberal amendment,” Verizon Delaware, Inc. v. Covad Commc’ns Co., 377 F.3d 1081, 1091 (9th Cir. 2004). To be clear, the operative complaint here is not X Corp.’s “initial complaint”—X Corp. initially filed suit on July 31, 2023, see Compl., and amended about a week later, adding ECF as a defendant, see FAC. Nevertheless, the Court agrees with X Corp. that it “is not as if [the defendants] have been dragged through multiple, multiple rounds” of amendments. See Tr. of 2/29/24 Hearing at 34:8–18;[20] but see Gardner v. Martino, 563 F.3d 981, 991 (9th Cir. 2009) (“Appellants had already filed the first amended complaint as a matter of right and Verizon specifically held that a first amended complaint is subject to anti-SLAPP remedies.”) (citing Verizon Del., Inc., 377 F.3d at 1091). The question is whether X Corp. should receive leave to amend under Rule 15. See Mahoney v. Meta Platforms, Inc., No. 22-cv-02873-AMO, 2024 WL 68550, at *5–6 (N.D. Cal. Jan. 6, 2024) (holding that plaintiff failed to state a claim, but because amendment was “not clearly futile,” it was appropriate to defer consideration of defendant’s antiSLAPP motion pending filing of amended complaint). A court should “freely give leave” to amend “when justice so requires.” Fed. R. Civ. Proc. 15(a)(2). It may deny leave to amend due to “undue delay, bad faith or dilatory motive on the part of the movant, repeated failure to cure deficiencies by amendment previously allowed, undue prejudice to the opposing party by virtue of allowance of the amendment, [and] futility of amendment.” Leadsinger, 512 F.3d at 532. The Court believes that amendment would be futile, and that X Corp.’s desire to amend may well be based on a dilatory motive. At the motion hearing, X Corp. argued for the first time that there were damages that it had “not pleaded in the complaint,” but could add upon amendment, “which [are] in direct response to this scraping incident.” Tr. of 2/29/24 Hearing at 8:18–20. X Corp. described these damages as “dozens, if not over a hundred personnel hours across disciplines such as engineering and security that were spent trying to figure out not only what data was gained via access to the Brandwatch systems”—time not relevant to the breach of contract claim—“but what data was scraped, how it was done.” Id. at 8:7–11. It further referenced “anti-scraping measures implemented, again, at great expense from engineering time in tens of thousands, if not over a hundred thousand dollars to deal with this security risk that was identified via this incident.” Id. at 8:21–24; see also id. at 9:3–6 (“I’m sure we had, from what I understand, cost for the servers dealing with the load of just receiving all these requests. So expensive electricity cost at data centers, as well as loss of computing power.”). Damages stemming from X Corp.’s response to CCDH’s alleged scraping—rather than advertisers’ responses to CCDH’s reports—do not present the same constitutional concerns that the current damage allegations do. But they must still have been “within the contemplation of the parties when the contract was entered into or at least reasonably foreseeable by them at that time,” under state contract law. See Erlich, 21 Cal. 4th at 550. The Court is unpersuaded that the proposed allegations clear that hurdle. Fundamentally, neither of the two main reasons that X Corp. asserted for having to spend time and money responding to CCDH’s alleged scraping—(a) “safety and security” and (b) harm to X Corp.—make very much sense. a. Safety and Security First, X Corp. argued that it needed to expend resources responding to CCDH’s alleged breach in order to safeguard its users’ data. Over and over again, X Corp. played up its obligation to “keep [its users’] data safe,” Tr. of 2/29/24 Hearing at 7:13–14, called scraping a “security risk,” id. at 8:23–24, and declared that this case was “about” “the importance of data security,” id. at 12:10–11; see also id. at 14:9–10 (arguing that the CCDH report acknowledging scraping “raised a potential security issue”); id. at 35:13–15 (“we put up anti-scraping measures to stop this kind of thing so that users feel as if their data is safe.”); id. at 39:22–24 (“This is about the security of data and whether or not we’re going to honor ways that people try to keep it safe contractually and otherwise.”). While security and safety are noble concepts, they have nothing to do with this case. The Toxic Twitter report stated that CCDH had used the SNScrape tool, “which utilizes Twitter’s search function,” to “gather tweets from” “ten reinstated accounts,” resulting in a “dataset of 9,615 tweets posted by the accounts.” Toxic Twitter at 17. There is no allegation in the complaint, and X Corp. did not assert that it could add an allegation, that CCDH scraped anything other than public tweets that ten X platform users deliberately broadcast to the world.[21] No private user information was involved—no social security numbers, no account balances, no account numbers, no passwords, not even “gender, relationship status, ad interests etc.” See Meta Platforms, Inc. v. BrandTotal Ltd., 605 F. Supp. 3d 1218, 1273 (2022). When asked why the collecting of public tweets implicates users’ security interests, X Corp. insisted that “this all goes to control over your data,” and that users expect that they will be able to take down their tweets later, or to change them—abilities they are robbed of when the “data” is scraped. See Tr. of 2/29/24 Hearing at 40:17–24. But even assuming that it is “very important” to a user that he be able to amend or remove his proneo-Nazi tweets at some point after he has tweeted them, see id. at 40:24–25, a user can have no expectation that a tweet that he has publicly disseminated will not be seen by the public before that user has a chance to amend or remove it. While scraping is one way to collect a user’s tweets, members of the public could follow that user and view his tweets in their feeds, or use the X platform’s search tool (as SNScrape did) and view his tweets that way. X Corp.’s assertion that the scraping alleged here violates a user’s “safety and security” in his publicly disseminated tweets is therefore a non-starter. CCDH would not, and should not, have foreseen at the time of contracting that X Corp. would need to expend money to address “safety and security” issues following the scraping of ten users’ public tweets. b. Harm to X Corp. Second, X Corp. argued that it was entitled to expend resources responding to CCDH’s scraping because that scraping might have caused harm to X Corp.’s systems. X Corp. maintained that it was entitled to “dive in and understand what was done, how it was done, what data was taken, whether there was any damage,” and to conduct “a large investigation and then remedial action,” Tr. of 2/29/24 Hearing at 9:16–20, adding that “[i]t is well known in this space that if you scrape,” the scrapee will need to investigate and “put up anti-scraping measures,” id. at 35:9–14. X Corp. conceded that it is not able to prevent scraping “100 percent” of the time, but stated that it can make scraping more difficult, and should be compensated for trying to do so. Id. at 35:21–36:7.[22] The problem with this argument is that it is at odds with what X Corp. has alleged. Although social media platforms do not like it, scraping, for various ends, is commonplace. See, e.g., ACLU Br. at 7 (“Researchers and Journalists Use Scraping to Enable Speech in the Public Interest and Hold Power to Account.”); see also id. at 8–9 (collecting sources); Andrew Sellars, “Twenty Years of Web Scraping and the Computer Fraud and Abuse Act,” 24 B.U. J. Sci. & Tech. L 372, 375 (2018) (“Web scraping has proliferated beneath the shadow of the [CFAA].”); hiQ 2022 Circuit opinion, 31 F.4th at 1202 (“HiQ points out that data scraping is a common method of gathering information, used by search engines, academic researchers, and many others. According to hiQ, letting established entities that already have accumulated large user data sets decide who can scrape that data from otherwise public websites gives those entities outsized control over how such data may be put to use . . . the public interest favors hiQ’s position.”); see also id. at 1186 (“LinkedIn blocks approximately 95 million automated attempts to scrape data every day”). Some scraping activity can be harmful not only to the users of a website but also to the website itself. See, e.g., hiQ 2022 district opinion, 639 F. Supp. 3d at 954 (scraping can burden servers and inhibit a website’s performance); Ryanair DAC v. Booking Holdings Inc., 636 F. Supp. 3d 490, 503 (D. Del. 2022) (scraping can “greatly increase[] the quantifies of queries” on a website, “impair[] the . . . availability and/or usability” of a website, “and cause [a] website’s response times to deteriorate.”). Scraping can harm a website because a bot “can make many requests automatically and much more rapidly than any human could,” “request[ing] a huge amount of data from the target’s server.” See Compulife Software Inc. v. Newman, 959 F.3d 1288, 1299 (11th Cir. 2020). X Corp. has another case pending in this district, not before this Court, involving allegations of that kind of scraping. See X Corp. v. Bright Data Ltd., No. 23-cv-3698-WHA, First Amended Complaint (dkt. 36) (N.D. Cal. July 26, 2023) ¶ 1 (defendant “scrapes and sells millions of records from X Corp.’s X platform”), ¶ 98 (such actions “have diminished the server capacity that X Corp. can devote to its legitimate users, and thereby injured X Corp. by depriving it of the ability to use its personal property”), ¶ 102 (alleging “damage in the form of impaired condition, quality, and value of its servers, technology infrastructure, services, and reputation”); see also X Corp. v. John Doe 1, No. DC-23-09157, Original Petition (Tex. Dist. Ct., Dallas Co., July 6, 2023) ¶¶ 21–22, 24 (alleging “widespread unlawful scraping,” “flooding Twitter’s sign-up page with automated requests,” “severely tax[ing] X Corp.’s servers,” “impair[ing] the user experience for millions of X Corp.” users). This is not such a case. Here, CCDH is alleged to have used Twitter’s own search tool to collect 9,615 public tweets from ten Twitter users, see FAC ¶ 77; Toxic Twitter at 17, and then to have announced that it did so in a public report, see id. Assuming for present purposes that this conduct amounts to the “scraping” barred by the ToS,[23] the extent of CCDH’s scraping was not a mystery. As CCDH asked at the motion hearing, “What CCDH did and the specific tweets that it gathered, what tool it used, how it used that tool and what the results were are documented explicitly in its public report. So what is it that they’re investigating?” Tr. of 2/29/24 Hearing at 22: 3–7. Nor was this the kind of large-scale, commercial scraping—as in hiQ, as alleged in Bright Data—that could conceivably harm the X platform or overburden its servers. It is not plausible that this small-scale, non-commercial scraping would prompt X Corp. to divert “dozens, if not over a hundred personnel hours across disciplines,” see Tr. of 2/29/24 Hearing at 8:7–11, of resources toward the repair of X Corp.’s systems. Nor would such expenditures have been foreseeable to CCDH in 2019. In 2019, if CCDH had thought about the no-scraping provision in the ToS at all, it would have expected X Corp. to incur damages only in response to breaches of that provision that could actually harm the X platform. It would not have expected X Corp. to incur damages in connection with a technical breach of that provision that involved the use of Twitter’s search tool to look at ten users and 9,615 public tweets. It is clear to the Court that if X Corp. was indeed motived to spend money in response to CCDH’s scraping in 2023, it was not because of the harm such scraping posed to the X platform, but because of the harm it posed to X Corp.’s image. CCDH’s data collection allowed it to claim knowledge of what was occurring on the X platform in a cumulative sense. According to CCDH, its scraping revealed that X Corp. was generating millions of dollars in advertising revenue from previously banned accounts. See FAC ¶ 49; Kaplan Decl. Ex. B. X Corp.’s desire to keep entities like CCDH from drawing such unflattering conclusions about the X platform is entirely reasonable from a business point of view. But that motivation would not have been foreseeable to CCDH in 2019. In 2019, CCDH would not have understood, and should not have understood, that Twitter would wish to prevent scraping in order to keep cumulative information about the platform from the public view.[24] CCDH would certainly not have known that Twitter would change course on some of its content-moderation policies, reinstating what CCDH later described as “tens of thousands of accounts, including neo-Nazis, white supremacists, misogynists and spreaders of dangerous conspiracy theories,” see Toxic Twitter at 3, and then desire to shield the financial ramifications of that policy shift from the public. That is the kind of theoretical, speculative harm that California contract law rejects. See Erlich, 21 Cal. 4th at 550. Neither the “safety and security” rationale, nor the “harm to X Corp.” rationale for X Corp. spending time and money in response to CCDH’s scraping make any sense given the scraping alleged in this case. Such damages simply were not foreseeable when CCDH entered into the ToS in 2019. Accordingly, allowing X Corp. to amend in order to add the proposed new allegations about damages in connection with its response to CCDH’s scraping would be futile. The Court notes, too, that X Corp.’s motivation in bringing this case is evident. X Corp. has brought this case in order to punish CCDH for CCDH publications that criticized X Corp.—and perhaps in order to dissuade others who might wish to engage in such criticism.[25] Although X Corp. accuses CCDH of trying “to censor viewpoints that CCDH disagrees with,” FAC ¶ 20, it is X Corp. that demands “at least tens of millions of dollars” in damages—presumably enough to torpedo the operations of a small nonprofit—because of the views expressed in the nonprofit’s publications, see, e.g., id. ¶¶ 1, 3–4, 5, 12, 17–24, 38, 41, 43–52, 54–60, 62, 65–70, 77–78, 84–85, 92, 96, 98; see also PPP Br. at 1 (“X Corp. . . . seeks to silence critique rather than to counter it.”). If CCDH’s publications were defamatory, that would be one thing, but X Corp. has carefully avoided saying that they are. Given these circumstances, the Court is concerned that X Corp.’s desire to amend its breach of contract claim has a dilatory motive—forcing CCDH to spend more time and money defending itself before it can hope to get out from under this potentially ruinous litigation. See PPP Br. at 2 (“Without early dismissal, the free speech interests that the California legislature sought to protect will vanish in piles of discovery motions.”). As CCDH argued at the motion hearing, the anti-SLAPP “statute recognizes that very often the litigation itself is the punishment.” Tr. of 2/29/24 Hearing at 33:12–34:5. It would be wrong to allow X Corp. to amend again when the damages it now alleges, and the damages it would like to allege, are so problematic, and when X Corp.’s motivation is so clear. Accordingly, the Court STRIKES the breach of contract claim and will not allow X Corp. to add the proposed new allegations as to that claim. Continue Reading . Here [20] At the motion hearing, CCDH responded, not unreasonably, that “[i]t’s not exactly a[n] earth-shattering rule of law that contract damages have to be foreseeable and comport with the Constitution” and that, in “two shots at this,” X Corp. had only pled publication damages. See id. at 20:18–23. [21] While the Court recognizes that technically, who sees a post might “depend[] on the privacy settings,” see Tr. of 2/29/24 Hearing at 40:9–16, that is a red herring here. The whole reason that CCDH wrote about these ten users is that a large audience viewed their tweets. See Toxic Twitter at 3 (“The analysis shows that the ten accounts have already amassed 2.5 billion tweet impressions”). It is not a reasonable inference that these ten users had selected privacy settings that limited their posts to friends and family. [22] At times, it seemed as if X Corp. was suggesting that whenever a user breaches any term within X Corp.’s nineteen-page ToS, X Corp. is entitled to spend money to prevent further breaches of that term, and then to be compensated for that expenditure. That is not the law. If it was, then a plaintiff could state a claim merely by alleging the existence of a contract, its performance, and the defendant’s breach; there would be no separate requirement of proving foreseeable damages. But there is such a requirement. See Oasis W. Realty, 250 P.3d at 1121; Erlich, 21 Cal. 4th at 550. [23] CCDH argues that it does not. See MTD&S at 10–13. [24] CCDH framed this point thusly at the motion hearing: “the notion that CCDH . . . should have foreseen Twitter’s costs in better hiding white supremacist content on the platform is utterly implausible as a matter of law.” Tr. of 2/29/24 Hearing at 36:8–11. [25] “A recent survey of 167 academics and researchers found that over 100 studies about X Corp. have been diverted, stalled, or canceled, with over half of those interviewed citing a fear of being sued by X Corp. over their findings or data.” ACLU Br. at 14 (citing Sheila Dang, “Exclusive: Elon Musk’s X Restructuring Curtails Disinformation Research, Spurs Legal Fears,” Reuters (Nov. 6, 2023), https://www.reuters.com/technology/elon-musks-xrestructuring-curtailsdisinformation-research-spurs-legal-fears-2023-11-06). About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 25, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Court Decision on Amendment Request in Elon Musk's X Breach of Contract Lawsuit 

At a time when everyone from small businesses to large corporations is making use of Artificial Intelligence technologies to improve the organization's efficiency and productivity, the White House is making headlines as it has issued a notification to the Federal Agencies to adopt new AI safeguards. The White House on Thursday 28th, March 2024, said that it is making it mandatory for federal agencies to adopt concrete AI safeguards by December 1st of this year to protect American citizens' rights and ensure safety as the US Government desires to expand the AI use in a wide range of applications. This notification is issued by the Office of Management and Budget. According to the directive, the federal agencies of the US Government must monitor, assess, and test AI’s impacts "on the public, mitigate the risks of algorithmic discrimination, and provide the public with transparency into how the government uses AI." The data of their observation will be made available to the public as per the issued directive. The Federal agencies are also required to conduct and record "risk assessment" and prepare metrics for the operation and governance of the usage of AI technologies. The White House said agencies "will be required to implement concrete safeguards when using AI in a way that could impact Americans' rights or safety" including detailed public disclosures so the public knows how and when artificial intelligence is being used by the government. The White House is requiring government agencies to release inventories of AI use cases, report metrics about AI use, and release government-owned AI code, models, and data if it does not pose risks. There are numerous federal agencies that are making use of AI technologies for smoother operations. To name a few, the Centre for Disease Control and Prevention of the US Government is using AI technologies to predict and prevent the spread of various diseases as well as to prepare their cure. Federal Emergency Management Agency is using AI technologies to predict hurricanes and other weather phenomena. Among the masses, AI technologies are pointed out as job snatchers and catastrophe bringers which is quite a negative image of a technology with such great potential. As I have said numerous times before, if employed correctly, AI technologies will revolutionize our lives and the very world in which we dwell. The Governments of various countries including India have taken an interest in the implementation of this marvellous technology. Let's see what future awaits for AI techs and its applications. Also published here

US Government to Implement AI Safeguards for Federal Agencies

NEW YORK, New York, March 26th, 2024/Chainwire/--**Syntax takes natural language and converts it into Solidity code, enabling individuals and enterprises to ship production grade smart contracts, arbitrage agents, NFTs, rollups, and more. Syntax marks the manifestation of the Agent Economy in Web3. Today, has launched Syntax, a new LLM enabling people to ship agents that automate onchain projects and tasks, thereby manifesting their Web3 product ideas. While still in beta, Syntax’s vision is to create an open onchain Agent Economy, where developers, individuals, and enterprises can for the first time select the agents they would want to delegate their Web3 tasks, projects, and product development to. These projects and products include production grade smart contracts, arbitrage agents, NFTs, rollups, and more. Spectral Labs Scaling web3 engineering efforts and shipping new onchain products is limited by the number of solidity developers, as well as enterprises’ ability to test and ship smart contract code in a fast and effective way without burning through engineering resources. Spectral has set out to solve this with its new LLM, Syntax, a development environment designed to manifest Web3 product ideas by converting natural language into Solidity code that can be deployed instantly onchain. For professional developers, Syntax can accelerate and optimize bandwidth by writing large libraries and components. Syntax provides an Agentic experience, where users tell Syntax what they want to build, and Syntax writes and deploys the code onchain at the click of a button. Spectral defines an Onchain Agent as a set of instructions and code capable of deploying itself onchain and possessing a dedicated provisioned wallet for doing so. Loosely similar to custom instructions in ChatGPT, Syntax Agents also have their own identity and can autonomously figure out ways to interact with deployment infrastructure. With Syntax, users can compile, debug, and deploy AI-generated solidity code. Syntax gives users and the industry the ability to quickly test, ship and scale new products, as well as determining the usability and functional relevance of the code generated while also giving them control to quickly edit code snippets. Initially, Spectral’s Onchain Agents will be curated and built by Spectral, but soon every user will be able to create their own Agent and monetize it in future Syntax releases. "At Spectral, we're pioneering a future where autonomous Agents redefine what's possible onchain. With Spectral Syntax, we empower these agents to operate independently, making it simpler for everyone to bring their blockchain ideas to life." Sishir Varghese, Founder. What truly sets Syntax apart is its capability to deploy code directly onchain, executing deployments through Foundry by writing instructions that Foundry can process, including code compilation, onchain deployment, and signing transactions. Syntax is pioneering an accessible onchain Agent Economy, inviting users to select agents tailored for their specific Web3 tasks. Users can either interact with the foundational agent or opt for one of the specialized agents, each adept in distinct tasks and operations—like the Code Forker assistant, ideal for those aiming to fork and customize a codebase. Additionally, users can discern an agent's expertise and trustworthiness by reviewing its category listing and the popularity reflected through its user engagement. "Syntax by Spectral is at the forefront of merging AI with web3, pioneering the onchain agent economy and making it easier and more accessible for users to create their own blockchain products," said Josh Rosenthal, Partner, Polychain Syntax produces accurate and gas-optimized Solidity code for a variety of prompts ranging from low to medium complexity, and it outperforms baseline models on Spectral’s Solidity evaluation dataset. As Spectral’s modeling efforts continue to evolve, the company expects Syntax to deliver optimized code for increasingly complex problems. These Solidity models are explicitly designed to assist developers in writing optimized, modern, well-tested, and secure code. About Spectral , a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Our mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. Spectral With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join us in making this vision a reality and explore the potential of autonomous agents with Spectral. For more information, visit /. For more information, users can follow Spectral on and . https://spectrallabs.xyz Twitter Discord Contact Spectral Labs Spectral info@spectrallabs.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Spectral Launches Syntax Enabling Web3 Users To Build Autonomous Agents And Deploy Onchain Products

is a distributed unit of infrastructure. It can provide information about FreeBSD and Linux operating systems. It also supports metrics transmission to Clickhouse, InfluxDB, and Prometheus. Alligator The main interface of alligator is . It connects with spreading Prometheus as a monitoring system in many infrastructures. However, the Prometheus ecosystem has some problems. OpenMetrics First of all - the specialized and simplified agents. Every service on the host needs a specialized exporter. This is the reason for having a lot of exporters per host (like a node_exporter, redis_exporter, sentinel_exporter, and so on). Secondly, simplified agents. A lot of agents can be used for different tasks (like an SNMP, blackbox, etc.), and it inflates infrastructure for such daemons to collect information. Sometimes, a pull model has problems with multi-worker applications and cron jobs - nothing can give metrics from hosts when Prometheus makes a request, or it is connected with problems related to synchronizing results between workers. It forces us to use statsd/pushgateway. Then the question comes up: how to remove outdated metrics? How to make it a high-availability service? The approach of the alligator is different - it is a smart agent for the Prometheus. Alligator consists of a system exporter and an application exporter. It also provides a push model for pushgateway, statsd, and graphite emulation protocol. Metrics Export Alligator supports such sets of metrics: FreeBSD and GNU/Linux system metrics. Filesystem metrics (file’s checksum, sizes, and access level). Certificate metrics (PEM and P12). Some popular applications (ElasticSearch, uwsgi, Druid, RabbitMQ, and others). Ephemeral metrics. It supports pushgateway, statsd, and graphite protocol, and it can collect metrics from file on FS. It also has a cluster mode for centralized installation of pushgateway. Simple JSON to metrics parser. Simple HTTP/TCP checker. Use Cases A part of supported technologies for collecting metrics are reviewed in the . The rest I am describing here. official documentation Application Server Metrics First of all, alligator can collect application server metrics. For instance, alligator can gather metrics from uwsgi instances with this configuration: aggregate {
    uwsgi tcp://localhost:1717;
} and rerun the uwsgi server with the : expose stats port --stats 127.0.0.1:1717 Another example, fastcgi-application: aggregate {
    php-fpm fastcgi://127.0.0.1:9000/stats?json&full;
} and write this option in : php-fpm config pm.status_path = /stats After this, you will have common metrics from the application server. Application Metrics I recommend using synchronization structures to collect metrics in the application internally. Alternatively, you can make only 1 worker per instance and use such an abstraction as orchestration (Kubernetes, opennebula, and others). And next, use alligator as a push server for metrics. Alligator has many collectors to collect it: pushgateway prom-aggregation-gateway statsd graphite file These approaches ensure that metrics will be collected from all workers of the application Applications have to send metrics to alligator. To remove influence on one metric by different workers, you can use two different approaches: mark metric with the worker ID from prometheus_client import Counter, push_to_gateway, CollectorRegistry
    registry = CollectorRegistry()
    c = Counter('my_requests_total', 'HTTP Failures', ['method', 'worker_id'], registry=registry)
    c.labels(method='get', worker_id=uwsgi.worker_id()).inc()
    c.labels(method='post', worker_id=uwsgi.worker_id()).inc()
    push_to_gateway('localhost:1111', job='myapp', registry=registry) use instead of metric type if you can. File collector cannot calculate increment, but others can increment if the counter is a type of metric. You can only send increment value to alligator, and alligator will calculate the sum. Counter Gauge More information about the difference between metric types can be found or on the . here official page What Metrics Do I Recommend to Measure in Applications? Firstly, it is any outgoing requests to external systems. In worker applications, it often is the reason to overflow workers or cause of low speed of query processing. My recommended list of metrics: count of requests to external services grouped by request types request time from external services (avg, or if possible, by quantiles/buckets of time) grouped by request types status of requests grouped by request type (success or errors) It would be nice if you could group not only by request type but more by network layer of requests: DNS resolve count, time, and status connect count, time, and status write/read count, time, and status It is also important to measure internal application metrics: ingoing requests grouped by types processing ingoing request time avg (quantile or buckets if possible) by type status of ingoing request by type cache status (hit/miss if it exists) I guess you can make another list of metrics, but these metrics are the most important to reveal problems and give main observability about application health. If your application is working in podman or docker environment, you can expose the container statistics. Alligator mechanics are similar to Cadvisor's when collecting container statistics. To enable, you have to use option cadvisor in the system context: Container checks. system {
    cadvisor;
} . This is a good way to monitor applications or external sources for reliability. Let’s check the resources on two ports every minute: Blackbox checks aggregate_period 60;
aggregate {
    blackbox http://127.0.0.1:80/check;
    blackbox https://127.0.0.1:443/check;
} Alligator produces network and application response metrics for this resource (response code, response time, and certificate expiration date). If the application has a page with JSON, alligator can also convert JSON to metrics: aggregate {
    jsonparse https://jsonplaceholder.typicode.com/todos;
} The next level of blackbox metrics - checking the process, service, and listening port: system {
	services uwsgi.service;
	process uwsgi;
}


query {
	expr 'count by (src_port, process) (socket_stat{process="uwsgi", src_port="80"})';
	make socket_match;
	datasource internal;
} And in the end: launching external programs and proactive actions (like watchdog): aggregate {
	process 'exec:///usr/local/bin/mycheckscript.sh';
} If the script can produce a metric that can determine the exact application downtime, this metric can be used for proactively restart: query {
    expr 'myapplication_status < 1';
    make app_is_down;
    datasource internal;
    action app_is_down;
}
action {
    name app_is_down;
    type shell;
    expr 'exec://systemctl restart uwsgi;
    datasource internal;
} System Metrics Any application metrics are also important. But without system metrics of servers, you won’t be able to clearly understand what happens in your system. System metrics are a set of metrics that help us gain insight into the health of our systems. This is the basis for making decisions if something goes wrong. An alligator with an empty configuration doesn’t collect any metrics at all. Any system metrics are classified into the following categories: base - CPU, memory, time, logical resources, and other common metrics about the server disk - disk usage and I/O network - interface statistics, network counters, sockets statistics smart - collecting S.M.A.R.T. statistics cpuavg - analog for load average when alligator make by CPU-only load firewall - counters from the system firewall process - check process (uptime, ulimits usage, status, CPU, memory, and disk consumption) packages - metrics with a list of installed packets in the system services - systemd services stats This is configured in a block called “system” in alligator.conf: system {
    base;
    disk;
    network;
    process;
    cpuavg period=5;
} What about cron jobs? How can it be connected to the Prometheus via alligator? There are several ways to do this. The simple way is to output metrics to a file: aggregate {
    prometheus_metrics file:///var/lib/stats/metrics.txt state=save;
} The parameter called ‘state’ enables autosaving at a point of the file when alligator is stopped. It makes us sure that we can stop the alligator and run again, and it rereads only that part of the file that has not been read yet. If we want to reread a file from the start (every 10 sec), we have to use . The third way - is to read only realtime data. To enable this mode, we have to remove any state from the configuration file. state=begin The other way is to use statsd or extended pushgateway protocol (called prom-aggregation-gateway): It only needs to have this section in the configuration: entrypoint {
	tcp 1111;
    metric_aggregation count;
} For instance, we have a code: #!/bin/sh
TASKS=0
do_something() {
        echo 0
        TASKS=$1
}

res=`do_something 1`
echo "amount_of_done_tasks {method=\"do_something\", status=\"done\"} $TASKS
task_exit_code {method=\"do_something\"} $res" | curl --data-binary @- http://localhost:1111/ This script will make two metrics in alligator: amount_of_done_tasks {status="done", method="do_something"} 0.000000
task_exit_code {method="do_something"} 0.000000 Sometimes, we might get a situation when the script is not working, but the metric exists (because alligator doesn’t know that the application doesn't work). To solve this problem, alligator has TTL mechanics. You can specify the lifetime in seconds for a request: curl -H "X-Expire-Time: 30" --data "living_thirty_seconds_metric 4" http://localhost:1111/ Or in the configuration file: entrypoint {
    ttl 30;
    tcp 1111;
    metric_aggregation count;
} The next way is using statsd protocol (udp or tcp). TCP server for statsd is enabled by default. If you want to use udp, specify the UDP port in config: entrypoint {
        ttl 30;
        tcp 1111;
        udp 1111;
} And your job script might have a section like this: from statsd import StatsClient, TCPStatsClient
from time import sleep

statsd = StatsClient(host='localhost',
                     port=1111,
                     prefix=None,
                     ipv6=False)

statsd.incr('baz')
statsd.incr('baz') Graphite protocol is also supported by alligator, and it works like graphite_exporter. Internet Web Server If I have an internet web server, what options do I have? First of all, https is the most popular protocol for web servers today. It forces us to check the certificate expiration time: To check directories with certificates: x509
{
	name nginx;
	path /etc/nginx/ssl/;
	match .crt;
}
x509
{
	name letsencrypt;
	path /etc/letsencrypt/live;
	match .pem;
} Secondly, to check the nginx listening ports: query {
	expr 'count by (src_port, process) (socket_stat{process="nginx", src_port="80"})';
	make socket_match;
	datasource internal;
}
query {
	expr 'count by (src_port, process) (socket_stat{process="nginx", src_port="443"})';
	make socket_match;
	datasource internal;
} To check nginx -t to exclude the situation, when nginx after server reboot cannot be started: aggregate {
	process 'exec:///sbin/nginx -t';
} To check system, web-server process, and firewall counters system {
    base;
    disk;
    network;
    cpuavg period=5;
    services nginx.service;
    process nginx;
    firewall ipset=on;
} We also make alligator port accessible only on local interfaces: entrypoint {
    handler prometheus;
    allow 127.0.0.1;
    allow 10.0.0.0/8;
    allow 172.16.0.0/12;
    allow 192.168.0.0/16;
    tcp 1111;
} or you can use the notation tcp in the tcp directive. <localIP>:<port> Statsd Metric Mapping Simple statsd doesn’t work with labels. To create a label from a statsd metric name, you must use mapping with metric name templates to change new names and create new labels. Due to this, I recommend using an extended format of Statsd, like DogStatsd (alligator also supports this extension), which supports labels in statsd protocol. But, some programs have already made pure statsd protocol without labels. One such example is Airflow. You can catch metrics from Airflow like this: airflow.ti.finish.upload_job.load_data.success:1|c
airflow.ti.finish.upload_job.other_data.success:1|c where the name is the following template: airflow.ti.finish.<job name>.<process name>:<increment by>|type The mapping for such metric name can be: entrypoint {
    tcp 1111;
    udp 1111;
    mapping {
        template   airflow.ti.finish.*.*.*;
        label dag_id "$1;
        label task_id "$2";
        label state "$3";
        name airflow_ti_finish;
        match glob;
    }
} This mapping will create such a metric: airflow_ti_finish {state="success", dag_id="upload_job", task_id="load_data"} 1.000000
airflow_ti_finish {state="success", dag_id="upload_job", task_id="other_data"} 1.000000 Statsd or Pushgateway Cluster Firstly, you can only make some amount of servers statsd or pushgateway and simply proxying to them. The first problem with using this: if you want to use gauges, you will have problems with calculating. If there is a distributed application sending a metric with the same name and labels, the gauge metric will also be replaced by the application. The first way to fix this - is to specify different labels for each application (such as hostname and/or worker_id/process_id). But that is not all. You also have a problem with the same gauge metric: this metric can be a balancer on different machines. Here is what to do with a metric that was sent at different times, but now they are interfering with each other: 10:12 http_request_time_avg {appid="43"} 12
10:14 http_request_time_avg {appid="43"} 22
10:55 http_request_time_avg {appid="43"} 122
11:58 http_request_time_avg {appid="43"} 1232 You can use nginx upstreams with a balancing method called hash: upstream alligators {
    server srv1.example.com:1112;
    server srv2.example.com:1112;
    server srv3.example.com:1112;
    server srv4.example.com:1112;
    server srv5.example.com:1112;
    hash $remote_addr consistent;
} Then one backend will always push all metrics to one server. On the other hand - cluster mode in alligator. Let's set it up! Firstly, we allocated 3 ports: for getting metrics 1111 for replication works 1112 for getting metrics from other applications 80 Imagine that our server is listening to the internet. Then, we need to secure our push metric ports. We can use the directive “return” with arg “empty” to remove any answers outside. We also know that internet browsers don't send POST queries to other domains if CORS doesn't allow this. Thirdly, make an authentication for POST requests. entrypoint {
   allow 127.0.0.1;
   allow 10.0.0.0/8;
   tcp 1111;
}
entrypoint {
   allow 10.0.0.0/8;
   tcp 1112;
   cluster replication;
   instance srv1.example.com:1112;
}
entrypoint {
   return empty;
   allow 0.0.0.0/0;
   ttl 300;
   tcp 80;
   metric_aggregation count;
   auth basic user:setme;
   auth_header Authorization;
   header access-control-allow-headers 'Authorization';
   header access-control-allow-methods POST;
   header access-control-allow-origin *;
} That's good. We annotate that we have a cluster called “replication” and the current instance name. Since this moment, alligator allows to answer collected oplog to anyone from network 10.0.0.0/8 on port 1112. How to command an alligator to join other instances of cluster? For this, we will use the “cluster” directive. We can specify oplog size (the limit of in-memory stored metrics that will wait to sync to other instances), and distribute metrics by its metric name (sharding key in our case, but you can use any other label). The replication factor hides replicated metrics while another primary server is running in the cluster. If the primary server goes down, hidden metrics will be exposed and made available to Prometheus from other instances. cluster {
   name replication;
   size 10000;
   sharding_key __name__;
   replica_factor 2;
   type oplog;
   servers srv1.example.com:1112 srv2.example.com:1112 srv3.example.com:1112 srv4.example.com:1112 srv5.example.com:1112;
} Check the External Sites Status Blackbox mechanics for collecting information about network services have already been mentioned. Nevertheless, that is not all that alligator can be used for. Alligator also has a puppeteer parser for loading external pages with a lot of site resources (which can load a page DOM). puppeteer {
    https://site1.example.com;
    https://site2.example.com;
    https://example.com;
} They might measure domain lookup name in metric: puppeteer_domainLookupEnd - puppeteer_domainLookupStart Connect time: puppeteer_connectEnd - puppeteer_connectStart Time to load DOM: puppeteer_DomContentLoaded Status of loading: puppeteer_eventSourceResponseStatus And even errors in the browser console: puppeteer_eventConsole This is not all that a puppeteer collector can do. However, this engine needs nodejs to be installed on a machine with puppeteer dependencies. Install puppeteer dependency: cd /var/lib/alligator/
npm i puppeteer@14.3.0
npm i ps-node-promise-es6 Conclusion Monitoring systems is a really big challenge despite the amount of tools to solve it. You can choose between a monitoring system or tools in one system of monitoring (such as VictoriaMetrics, Thanos, some amount of exporters, and so on). I guess that the approaches outlined in the article can be applicable to any other tools of monitoring. Alligator is a tool that can simplify it. The main goal of any monitoring is to make your system observable. You must have all measured information from your systems to ensure that it always operates correctly.

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