Founded in June of 2019, Akkio is a technology company that makes AI easy enough for anyone to use

Akkio

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Let's learn about via these 60 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Conversational Ai /Learn Repo The automated chatbot that most websites nowadays have as customer service; does basic answer and response tasks. 1. The Social Impact That Deepfake Porn Has On Its Victims Recently, Deeptrance revealed that 15,000 deepfake videos were uploaded daily. 96% were clear pornography of morphed celeb faces onto adult entertainers. 2. Take ChatGPT With You: Introducing Ariana, the ChatGPT Assistant That Lives in WhatsApp Meet Ariana, a ChatGPT AI assistant living in WhatsApp, providing instant answers, translations, and idea generation. 3. Welcome to the World of Artificial Intelligence Programming Discover everything you need to know about artificial intelligence programming 4. Artificial Intelligence That Doesn’t Annoy Customers How to make virtual assistant (conversational AI chatbot), that doesn't get users angry 5. Interview with CEO & Co-founder of Symbl.AI Surbhi Rathore is the cofounder of Symbl, a programmable platform enabling developers to build next-generation AI-powered communication experiences 6. How to Build a Conversational AI bot Using Blenderbot How to build a conversational Bot with the Blenderbot model, an Open Source Language Generation Model made by Facebook AI so that you can have your own Siri. 7. Why is the Education Industry Opting for Conversational AI? As we all know that education is very much important in ones life. Conversational AI can help to improve the  teacher and student interaction. 8. How the Conversational AI Analytics will transform the business? 9. An Essential Guide to Data Collection for Conversational AI With conversational AI being the future of interactions, how can businesses develop and launch rock-solid solutions? 10. As AI Gets More Emotionally Intelligent, So Must We How people behave in solitude is vastly different than how they behave in public, but the foundation of one’s persona remains constant. Dancing around the apartment when nobody’s watching expresses a secret desire to do so on a grand stage, but humans modulate those whims as societal norms dictate. 11. Using ChatGPT to Recover Amounts Owed by Your Client Read how a CEO used ChatGPT to generate a scary email to get $109,500 owed to his company. 12. Is AI Really Disrupting the Way We Live and Work? As artificial intelligence becomes more and more integrated into our workplace and daily life, it will fundamentally disrupt the way we live and work. A recent survey of 5,700 Harvard Business School alumni found that even in the elite group, 52% believe that the number of employees in general companies will decrease in the three years from now. 13. ChatGPT is a Huge Win for Conversational AI Companies (and Their Customers) — Here's Why Find out why we believe ChatGPT is a massive win for conversational AI companies and how this incredible language model can be harnessed to enhance NLU. 14. Improving AI-Assisted Conversation With Zero-Shot Learning Assimilating zero-shot learning with machine learning networks holds many advantages for developers across a wide range of fields. 15. [Infographic] The State of Conversational AI in 2020 Conversational AI was always poised to take off in 2020. In fact, Gartner predicted that 80% of businesses would implement some sort of conversational interface by the end of this year. With the emergence of COVID-19 came compounded growth for the category - and I wanted to capture just how far we’ve come. So for the conversationally curious out there, I created this infographic that offers a clear depiction of where conversational AI stands at this very moment in time. [16. MAKE CONVERSATIONAL AI WORK at JUNCTION 2020 HACKATHON](https://hackernoon.com/make-conversational-ai-work-at-junction-2020-hackathon-pts3egp) 17. The Power of AI in Recruiting: How The Business World is Changing for Good AI drastically improved the world around us in many different ways. However, there are still many notable concerns with regard to overall impact on employment. 18. The Conversational AI Arms Race Has Begun The grown-ups have entered the room. Google and Microsoft have officially entered the conversational AI arms race in search. 19. Voice is the Safest and Most Accurate for Emotion AI Analysis Voice is one of several unique, innate, and immutable biometric identifiers. Other identifiers include retinal scans, iris scans and facial geometry scans. As technology evolves, so does the public’s concern regarding issues of privacy. Consider, for example, the turmoil DNA evidence introduced to our legal system in the late 1990’s, or, the controversial introduction of fingerprint evidence in the 1910 murder trial of Clarence Hiller in Chicago, Illinois. 20. How To Build an AI-Powered Financial Assistant App Find answers to the most common questions related to the development of a financial management application in 2022. 21. Conversational AI in Healthcare: 2 Key Use Cases If there is a silver lining somewhere with the COVID-19 pandemic engulfing the globe, it’s that we have been forced to introspect on the healthcare sector, both public and private, and whether it has the tools it needs – not only to deal with such crises, but to ensure the best possible healthcare outcomes even under normal circumstances. 22. 7 AI-powered Chatbots If you’re a millennial, you’ll know SmarterChild, the first-ever instant messaging bot with natural language comprehension ability. It was developed in 2000 and demonstrated exceptional wit, which most of today’s bot cannot. SmarterChild used to chat with about 2,50,000 humans every day with funny, sad, and sarcastic emotions. Today, we’ve traveled a distance with technologies like AI, ML, NLP, etc. and bots like Xiaocle have passed Turing tests of 10 minutes (i.e. users couldn’t identify that they’re talking to a bot for about 10 minutes). 23. Discussing The Applications of AI Chatbots in Healthcare at the World Economic Forum The World Economic Forum is mostly known for its highly anticipated annual meeting in Davos, Switzerland — a scene of global intrigue and a convergence point for the planet’s most influential politicians, investors, activists, CEOs, and economists. 24. ChatGPT for Office Politics: When is it Ethical? Have you ever stopped to consider the power of language models like ChatGPT and how they can impact our beliefs and opinions? 25. Drive Success with Conversational Marketing: The Feedback-Oriented Approach Conversational marketing can be a solution to achieve faster response times. In this guide, we discuss how to develop effective chatbot applications. 26. What Patients Are Asking Our COVID-19 Virtual Assistant According to a recent Pew Research Center poll, in just one week (March 16–24), the number of Americans who view the coronavirus as a major threat to public health spiked by nearly 20%, from 47% to 66% — a figure that is growing exponentially. 27. An Intro to MedPaLM: ChatGPT's Healthcare-Focused "Cousin" ChatGPT for healthcare? Learn everything you need to know about MedPaLM, a new LLM developed by Google specifically for medical and clinical applications. 28. 7 Ways Banks Can Use Conversational AI to Stay Relevant In Today's FinTech Era Learn how banks can leverage conversational AI and stay relevant in the age of FinTech including the rise of contactless payment methods, omnichannel, and more. 29. How We Built a Voice-Activated Lovecraftian Adventure Game In this article, we will discuss why we decided to create a voice-controlled game, what was the hardest part, and why we decided to open up about the money we make in the emerging market of conversational AI. 30. [Reviewed] 6 Conversational Feedback Tools Chatbots for surveys, surveybots, conversational surveys, feedback chatbots, conversational survey tools, AI survey tools, chatbot questionnaires. The list of names goes on and on. But how do they compare and which one is the best performer? 31. The Quest for Better Music Recommendations Through ChatGPT Prompt Engineering Prompt Engineering leads to an Open Source Obsidian Plugin to help researchers, an imagination-powered music recommendation engine, and some solid playlists. 32. Building A Chatbot On Your Own Might Not Make As Much Sense As You Think Over the past decade plus, chatbots have dominated the conversation (no pun intended) when it comes to digital engagement. You’ve undoubtedly had experiences interacting with them, some helpful while others underwhelming, and perhaps even fiddled around with building one on your own. 33. Amazon QuickSight Q Review Vs. NLSQL: A Comparison New Amazon QuickSight Q AI-powered product for Data Analytics Review and comparison with NLSQL competitors NLP to SQL API software (including pricing details) 34. How to Build Basic Chatbot Without Coding and Deploy to Websites Build best automated AI chat bot using Google Dialog flow 35. 7 Effective Ways to Improve Your Content Marketing with AI Tools Artificial Intelligence (AI) has numerous business use cases and can be applied to customer service, sales, lead generation, and marketing. 36. Understanding Conversational AI: As Chat Enabled Customer Service Technological innovations are necessary to cope up with the customer demands. Customers nowadays use multiple channels to access the services from a business. Thus, they expect multiple channel customer service from companies. 37. Math GPT: Can AI Help Solve Unified Theory ? What if we trained AI to complete equations instead of images of Cats? 38. "AI Can’t 'Think” Like Us Independently," - says Machine Learning Engineer Mani Sarkar In our new blog series, we’re interviewing data scientists and machine learning engineers about their career paths, areas of interest and thoughts on the future of AI. We kick off this week with a 20-year veteran and jack-of-all-trades when it comes to machine learning and data science: Mani Sarkar. Mani is a strategic machine learning engineer based in London, UK, who believes in getting beyond the theoretical and applying AI to real-world problems. [39. Generative AI is Coming for Publishing, But It's Not as Clear-Cut as It Looks](https://hackernoon.com/generative-ai-is-coming-for-publishing-but-its-not-as-clear-cut-as-it-looks) A look at the future of generative AI. 40. Can Artificial Intelligence Claim To Be the Future of Account-Based Marketing? To be honest, I hate phrases like “harness the power of AI” or “use AI to revolutionize your sales”. 41. How to Improve Privacy in Conversational AI Artificial intelligence is a great tool that assists you with fostering your business on the internet. 42. Introducing Conversation.ai This post has been authored by Aakrit Vaish, Co-Founder & CEO of Haptik 43. ChatGPT Writes a Story With Me I started with a story prompt to ChatGPT and then we kept going, I like it. 44. Where are the Chatbots and What is the Progress and Limitation of Virtual Assistants [2020 Edition] Cat's out of the bag: it may have been a long while since you’ve last conversed with a human customer service agent. More and more companies have turned to digital assistants to address the needs of customer inquiries. With messaging being the preferred method of communication, chatbots have taken over online facing services, due to their simplicity, affordability, and accuracy. 45. Data and Analytics Predictions for 2020 [A Top 5 List] It would be no exaggeration to say that the capacity of technology to advance itself is proceeding at a faster rate than our ability to process these changes all at the same time. This is both amazing and alarming in the same breath. 46. Conversational AI is Changing the Way We Interact With Data Large language models like ChatGPT are making it easier to manage data. Akkio has come up with an LLM-based tool to manage tabular data using conversational AI. 47. AWS Chatbot Magic: Building Conversational AI with AWS Services AWS offers a range of services that can be used for chatbot development and deployment. The steps outlined in this article show you how. 48. Why Chatbots Are The Future Of Customer Retention? Customer retention has become a genuinely challenging task for entrepreneurs. In the midst of all this, AI-assisted Chatbots are the real game changers. 49. Can Conversational AI Become The Face of Tomorrow's Business? In recent times, conversational AI has become a tech buzzword in the global market. However, the aggressively growing popularity of AI is leaving many individuals in a great dilemma of whether they should be fear of losing jobs in the future or get excited to leverage the latest edition of the AI-driven applications in businesses. 50. How to Streamline the Landlord-Tenant Relationship Through Conversational AI According to Mashivor, 50% of tenants move out because they are not happy with their landlord. The landlord-tenant relationship is more often than not a contentious one. It features two sides with similar intentions but entirely different priorities. Both parties are interested in peaceful, fluid, and uneventful correspondence and both are wary of being cheated, ill-treated, and misinformed. 51. Conversational AI for President: The Covid-19 Chatbot Use Case Our interactions with government agencies hardly ever find their way into our dinner-table conversations, yet they play a huge role in our everyday lives. 52. LSTM Based Word Detectors This article aims to provide the basics of LSTMs (Long Short Term Memory) and implements a word detector using the architecture. 53. Why Are We Training AI like Dogs Instead of Humans? The fundamental problem of the modern AI is that it tries to create a sophisticated trained dog. This approach is a dead end and needs to be drastically changed 54. Building a Conversational AI Chatbot With AWS Lambda Function and Amazon EFS Amazon announced the general availability of AWS Lambda support for Amazon Elastic File System. Amazon EFS is a fully managed, elastic, shared file system and designed to be consumed by other AWS services. 55. How Conversational AI Tool Can Help You Attract Real Estate Buyers And Residents Step One: Prospecting 56. A Comparison of GPT-3 and Existing Conversational AI Solutions Earlier this year, Elon Musk-backed artificial intelligence laboratory, OpenAI, released its latest, much anticipated autoregressive language model, the Generative Pre-trained Transformer 3 (GPT-3). Emerging to much fanfare and slated as the usherer of a new age of artificial intelligence, the number of articles, blog posts, and news pieces about this language model, perhaps match only the number of parameters the GPT-3 learned; 175 billion (Ok, this may be an exaggeration, but you get my point). 57. The Robots are Coming! - An Interview with Ilan Kasan, CEO at Exceed AI This article talks about automation and artificial intelligence and how it is impacting the sales industry and how the future will look in the age of AI. 58. What We Can Learn From Great Examples of Conversational User Interface Top-notch CUI’s offer a more human-like conversation. This helps in bridging the gap between physical and online conversations, and these brands do it right. 59. New Weapon in the Fight Against COVID-19: Digital Health Passports *Note: This article first appeared in the Los Angeles Tribune by Hyro's Head of Marketing Aaron Bours 60. What Is Conversational AI: Principles and Examples In this article, we will take the time to explain what conversational AI is: principles and examples to have a better idea of ​​how you can implement it. Thank you for checking out the 60 most read stories about Conversational Ai on HackerNoon. to find the most read stories about any technology. 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In the world of spreadsheets and data analysis, has emerged to shake up the game. , the easy-to-use AI company, has launched , a revolutionary machine learning platform that allows users to transform data using ordinary conversational language. a new player Akkio Chat Data Prep Gone are the days of struggling with complicated formulas and formatting commands in Excel. With Akkio's Chat Data Prep, users can simply type in conversational language to make changes to their spreadsheet data. Leveraging AI and large language models, the platform interprets the user's requests and makes the necessary changes to the data. According to Jonathon Reilly, co-founder of , this new method of interacting with data results in a 10-fold reduction in the time it takes to prepare data for analysis. With Chat Data Prep, users can reformat dates, perform time-based math operations, and even fix messy data fields with a simple conversational command. Akkio Large language models like have paved the way for this breakthrough, allowing for natural language processing capabilities that generate human-like text and images. ChatGPT These models will revolutionize how we interact with and analyze data, making it easier and more efficient than ever before. One example of this is Chat Data Prep’s ability to quickly and easily manipulate large data sets with simple commands, instead of struggling to learn complex formulas or macros. This can be especially beneficial for those who are not experienced with data analysis, but still need to work with large amounts of information. Since its public release in November, ChatGPT has led to a rush of business applications. Google has come out with a Chrome extension called GPT for Sheets, which allows users to manipulate data with conversational language, and Microsoft says it will integrate ChatGPT into all of its products. But Akkio's Chat Data Prep is the first solution to utilize this technology for the preparation and transformation of large volumes of data from any database product. People who make their living as Excel jockeys are impressed. “If AI can comprehend and set it up, that’s fantastic,” said , a freelance Excel expert with more than a decade of financial and business analytics experience. “It’s another tool you can use for doing Excel work.” Edward Godesky Data scientists and analysts spend a significant amount of their time on data preparation, with some estimates putting it as high as 80%. Chat Data Prep aims to alleviate this issue by allowing users to clean and transform datasets in minutes, allowing for more time to be spent on insight and modeling. By using natural language prompts, Chat Data Prep can help to make data analysis more intuitive and user-friendly, allowing anyone to gain insights from their data. Additionally, Akkio’s product can improve the accuracy and efficiency of data analysis by quickly and easily identifying patterns and trends in large data sets. This can be particularly useful for industries such as finance and healthcare, where large amounts of data need to be analyzed. By making data analysis more accessible, efficient and accurate, Chat Data Prep has the potential to change almost everything, from how we work with data to how we make important decisions. Akkio's platform is already being used for a wide range of applications, from lead scoring and revenue forecasting to ad spend optimization and custom-built products. With the addition of Chat Data Prep, the platform now offers machine learning for data preparation as well. Also Published Here

Conversational AI is Changing the Way We Interact With Data

Improving the reach and success of your writing is no small feat, and machine learning can play a large role in your process. If you are creating content with the goal of engaging audiences and driving clicks, this article is for you. Modern Machine Learning (ML) tools make it possible to analyze engagement data from past articles so you can make informed decisions about what works and what does not, giving your content a competitive edge. Let’s explore how to before you even hit "publish." utilize to predict the success of articles machine learning Gather Your Data Whether you’re a new writer or an experienced journalist, you’re likely publishing your article on multiple sites, including our beloved Hackernoon. When writing for a publication that’s not your own, you miss out on all the data and brainstorming you typically have available when submitting to your own blog. Without access to data and insights from the publication, it may be difficult to determine which topics are most popular on Hackernoon, the number of links required for maximum exposure, or the reputation needed for a successful article. To get started, . we need to find this data Unless it’s a very small publication, . Let’s use Hackernoon as our main example. any modern will be able to provide you with the top pages SEO tool (a.k.a the top articles) for any given website To figure out the top articles on Hackernoon, you can use something like SpyFu, SEMRush, or Ahrefs. Each tool has its own metrics and will serve you well. Beginners may want to try SpyFu for its affordable entry pricing. I already did that for you and found the . If you want to download the list, you can do so here. top 3000 articles on Hackernoon Download the list ( ) Datasets for Hackernoon - Google Sheets Once you have the source URLs, titles, and engagement statistics, it’s time to move to the next step. Enrich Your Data URLs alone won’t give you much information about an article and won’t be enough to teach you how to write for success in 2023. You need more data. You need enrichment. You can enrich your URLs in any number of ways, but the most valuable ones probably include other , such as word count, backlink profile, and page authority. SEO Metrics Ask yourself these questions to understand what you are trying to get out of the prediction: What are you trying to predict? Is it the title? The length of the article? The number of keywords it will rank for? What data do you have available right now? Any data that's relevant to your engagement objective can help improve the accuracy rate of your prediction Keep in mind that once your ML model is ready, you can use it to predict success on the target publication whenever you want. A Hackernoon Prediction Model today will serve you well for a long time unless Hackernoon undergoes a significant audience change (or a new set of hyper-successful articles skew the data). In this case, our goal is to based on the title, backlinks, and keywords. optimize for the average number of views an article receives on Hackernoon I found all of this data readily available in MajesticSEO, a popular solution for backlink research. You can use some free alternatives if you’re on a budget, like SEOGlass or Ubersuggest. They won’t get granular, but you’ll still be able to create your dataset. After the enrichment, the example dataset now features: Name of the article URL Avg number of backlinks (internal and external) Citation Flow Trust Flow You can download the enriched dataset for free here. ( ) Datasets for Hackernoon - Google Sheets Train Your No-Code Machine Learning Model Machine learning used to be complicated, and you needed a data scientist to come up with any sort of reliable model. Nowadays, that’s no longer the case. The pairing of new technologies like AutoML, ChatGPT, and DALL-E2 with visual interfaces, was recognized in 2022 with the emergence of AI-powered tools. This led to the development of no-code machine learning. and other companies offer automatic machine learning models that can be quickly and easily trained without any coding or data science knowledge. With these tools, anyone around the world can upload their datasets and predict future trends, forecast data, and detect anomalies. Akkio In full disclosure, I’m associated with (and like) , so I'll continue to use it with our example, but there are other tools out there you could also use. Akkio No-code machine learning is now being used by companies worldwide for various purposes, including detecting defects in electronic products like computers, analyzing customer feedback to improve products, streamlining the research process for new pharmaceuticals, and predicting user churn at the time of product registration. That stated, let's get back to the task: we have an enriched database of Hackernoon articles, with the relative success metrics we came up with in step 2. If your success metrics are different, you will have to tweak your initial database accordingly. If you want to follow along and/or train your own model, Akkio and the other tools mentioned above all offer free trials. Upload the dataset You can upload the dataset to Akkio through the Google Sheet integration, a direct CSV upload, or any of the built-in CRM and marketing integrations. Let’s do it with a manual upload for now. This is how the Akkio platform will appear once it’s done: Together with all your columns, aggregations will help you understand some trends in your data from the get-go. For example, you can see the distribution of SEO clicks throughout the articles, clearly outlining how the vast majority of articles get less than 100 SEO Clicks, while some can be hyper-successful. You want to be within the top 5%, right? So, let’s train the model to help you get there! Clean up any discrepancies Proper is essential for successful machine learning model training. If you prefer using Excel, it can be done there. Alternatively, Akkio offers a comprehensive data preparation toolkit that will remind you of ChatGPT. data preparation This allows you to , and the platform's dataset will reflect them. simply type out your desired edits In my example, I asked Akkio’s Chat Data Prep to: Remove all articles with less than 30 views; Remove articles with more than 3200 views; This is to make sure we avoid anomalies in the dataset. There were only a few articles reaching 3200+ views, and a very large number of likely irrelevant articles with less than 30. Removing these outliers helps the model focus its attention on the driving factors that matter most to our outcome. You can clean the data as you see fit for your use case. You might need to combine columns and change the format of certain values to have a uniform format throughout the set (eg. dates are a common one). Run the Model For this use case, we want our machine learning model to predict the success of our new article based on successful articles of the past. This is conveniently referred to as a “prediction” model on the Akkio platform. A simply requires you to pick a column, and the algorithm will do the rest. All you need to do is sit back and watch the model train itself. prediction model At the bottom of the screen, you can decide how fast you want the model to get to the bottom line. I suggest you start with “Fast” (it usually requires less than a minute) and move to a longer training time once you are done with any model iteration. Before you click predict, double-check that you selected the correct value to predict. I want to predict the potential for “SEO Clicks”, so that’s my prediction target. Please note that Akkio, and other no-code tools, usually offer a wide variety of machine learning models. For a number prediction like our example, the tools will use a process called AutoML to pick the best performing model. These platforms also offer time-series models and other ML tools. Depending on which publication you’re pitching to, and what you’re writing about, you might consider other modeling approaches. For example, if you want to perform some in-depth research for a new article, you can use these platforms to detect all anomalies in huge datasets (with even millions of records, maybe coming from Wikipedia!), forecast trends (you can see an example of a website ). Assuming you have some data, the possibilities are pretty much endless. forecasting product success on ProductHunt here If you’re interested in learning more about this, Akkio has a full page with supporting documentation for all the major applications. Spoiler alert: one section discusses Twitter Sentiment Analysis and a feature that’s been accessible to only data scientists for years! Analyze the Results Once the model is done training, you will see an “ ”, detailing all the incredibly interesting patterns in the data that are driving your selected outcome.. Insight Report It will let you know what factors are associated with . You will find many, many golden nuggets here. Reading through the insights report and studying the driving factor graphs will help you create your content marketing strategy for the publication! positive and negative outcomes The report also shows the . Because our dataset has a large range of possible click values and is heavily weighted towards the low end of clicks, we get an accuracy that is usually within 55% - but the statistic to pay attention to here is the “off by” of 88 clicks. accuracy level That's actually pretty good for our purposes! If your model is not accurate enough you usually need to get more examples or more relevant input columns to improve its accuracy - but almost any model will have a lot of directionally correct information you can leverage to improve your results. Deploy the Model You can deploy the model in a variety of ways: API, web app, , and direct integrations of Akkio. Zapier I suggest you start with the web app, as it requires zero setup and can be shipped live in less than 2 minutes. The web app is auto-generated, and allows for bulk predictions of uploaded Excel or CSV files, or you can manually enter the fields to generate a click prediction. This makes it incredibly easy to use the app to estimate the success of all future articles for the publication based on the metrics you defined in the model! . You can test it out here Conclusion Isn’t it great? In , we: less than 2,000 words Generated an awesome dataset of successful articles on Hackernoon; Enriched the data with data from SEO tools such as SpyFu and MajesticSEO; Manipulated the datasets automagically with GPT-3; Shipped a live app that can predict the success of future articles based on the metrics we defined. Something that previously required data engineers, analysts, and a machine learning expert, is now . available to anyone with a computer , and it’s only going to get better! Welcome to the AI revolution In future articles, I will explain even more applications for marketing, sales, and customer service. Machine learning is fascinating and can save you thousands of dollars (and hours!) per week. Give it a shot with your own dataset. I can promise it’s super fun, easy, efficient, and you will be impressed by the end result! . Try it for yourself now

How to Use No-Code Machine Learning to Optimize your HackerNoon Articles

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Artificial Intelligence helped give Democrats a fundraising edge this election season, allowing the blue party to retain control of the Senate and blunt a Republican takeover of the House. While there were many other factors at play, the Democrats relied more heavily on AI in finding donors, according to professional fundraisers, bringing in more money from individual, small-dollar donors than their Republican rivals. “AI will play an increasingly important role in democracy going forward,” said Martin Kurucz, who runs , a Democratic data technology company in the fundraising space. Sterling Data Company Data analytics have been used in political fundraising for more than a decade, and bots – automated systems that post on social media - have long spread political messages on a large scale. But using AI to identify donors and maximize outreach efforts is relatively new. On the left side of the aisle, fundraisers are using artificial intelligence to predict who's likely to donate to their cause, while on the Republican side, fundraisers are using artificial intelligence to maximize the efficiency of outreach to established lists of donors. While the Republican approach may raise more dollars per donor, they have fewer donors to raise from. Consider John Fetterman, the Democrat who won a senate seat in Pennsylvania against Republican Mehmet Oz. , Fetterman raised more than $55 million in 2022 compared with by Mehmet Oz. More than half of Fetterman’s haul came from individual donors giving $200 or less, while only about a third of Oz’s contributions were from such small-dollar donors. According to the Federal Election Commission just over $15 million Mr. Fetterman, whose campaign used AI, was not alone. Most statewide Democrats outraised their GOP counterparts by large multiples in the small-dollar space. from the 2022 election cycle show that Democratic donor organizations such as the Democratic Congressional Campaign Committee, or DCCC, spent around $8.4 million on digital acquisition, while raising $85.6 million from unitemized small dollar contributions – about ten times what they spent. At the same time, Republican organizations such as the National Republican Congressional Committee spent around $32 million on digital acquisition and raised less than double that amount in unitemized small dollar contributions. Federal Elections Commission data While small-dollar donors on the left are typically more affluent than their Republican counterparts, the Democrat advantage is more than just demographics. For most of the past decade, digital ads have been the prevalent means to find donors. But fundraising has shifted to email and mobile acquisition as digital advertising returns withered following that limited the tracking capabilities of digital advertisers. A few firms soon realized that AI-driven donor targeting was the most effective and fastest means of email and mobile acquisition. Apple’s 2021 privacy measures “No data analyst in the world can sort through tens of thousands of potential donors and figure out which is most likely to give money,” said Kurucz, whose firm helped both the DCCC and Fetterman, “but AI can do this.” “We have reached a point where anyone who can use a spreadsheet can use machine learning for data-driven decision-making,” said , cofounder of Akkio. Jonathon Reilly AI models can spot larger trends, predict online donations, and recalibrate targeting in seconds, allowing campaigns to tailor their outreach in near real-time. “We generate more revenue per donor than Democrats across the board,” said Tom Newhouse, vice president of digital marketing at , but added that Apple’s iOS changes have hurt Republicans’ donor prospecting strategies. Convergence Media Sterling Data uses a no-code machine-learning website, , which allows users to drag and drop their data spreadsheets into the site. On the backend, Akkio analyzes the data and gives the user options for what they want to predict. In Sterling Data’s case, that is each person’s likelihood of giving money. Akkio.com Sterling Data has built up a database of about 30 million donors with 500 or more columns of information on everything from the kind of car each donor drives to which Netflix shows they watch. It runs a subset of the data through an Akkio model trained to spot likely donors for a particular candidate. The result is a prospective donor list ranked from most likely to least likely to give, allowing Sterling Data to focus their efforts without wasting time and money on the wrong people. “The ease of using AI-ranked lists means data firms selling donor prospects curated by AI can offer pricing that undercuts the rest of the market,” said Jacob Geers, who until recently served as managing director of Veracity Media, a digital strategy agency. AI tools like Akkio now allow organizations to scale in ways that were once impossible, crunching millions of data points in seconds and prioritizing the actions to take for maximum effect. “We have reached a point where anyone who can use a spreadsheet can use machine learning for data-driven decision-making,” said , cofounder of Akkio. “Our platform enables non-technical users to extract value from their data.” Jonathon Reilly Sterling Data then gives the prioritized contact list to the candidate's team, who contact donors in the most efficient way possible. “The whole point is predicting who will actually donate if my candidate reaches out,” Kurucz said, adding that Akkio’s model builds prospective donor lists that consistently raise twice as much as lists built with other methods. Over the last cycle, AI-driven donor acquisition provided by vendors like Sterling Data has changed industry wide expectations for the speed of a return on investment, said Kevin Massey, a partner in , a digital fundraising agency. In past cycles, he said, it was standard to expect around 6 months or more to go by before an email acquisition paid off in full for a campaign. “But with these new AI-driven acquisitions, we've seen that increase rapidly to one to three months,” Massey said. Momentum Campaigns The faster payoff allows campaigns to react more quickly and be smarter with their acquisition budgets because they're seeing results faster. Sterling Data spends thousands of hours calling potential high-dollar donors. The firm uses another form of AI called natural language processing to read call notes and glean new details about those donors. Emails are reserved for potential small-dollar donors. Sterling Data works with Democrats up and down the ballot, from congressional to city council candidates. “Targeting at scale is really hard and getting a hold of the donor, whether that's through an email or a phone number, is even harder,” Kurucz said. There are many different data sources from which to aggregate information on potential donors. “The more information you get on potential donors, the better the model is,” said Kurucz. Kurucz believes the no-code approach will spread because it puts the power in the hands of a daily practitioner who no longer needs expensive data scientists to achieve results. Kurucz noted that he can create and run his AI models using Akkio on a plane on his laptop. Convergence Media’s Newhouse says they use AI features in an email and SMS platform called to optimize the timing or mode of outreach, whether by email or text message, for example, which leads to higher donations per donor. But he said Republicans have yet to coalesce around a central set of tech tools that are shared across the board. Iterable “To broaden the Republican donor base, Republicans need to embrace artificial intelligence or and data modeling to identify new donors,” he said.

How Artificial Intelligence Swayed The Midterm Elections - And Will Become A Permanent Feature Of Democracy

Cómo la inteligencia artificial influyó en las elecciones intermedias y se convertirá en una característica permanente de la democracia

Comment l'intelligence artificielle a influencé les élections de mi-mandat - et deviendra une caractéristique permanente de la démocratie

आर्टिफिशियल इंटेलिजेंस ने मध्यावधि चुनावों को कैसे प्रभावित किया - और यह लोकतंत्र की एक स्थायी विशेषता बन जाएगा

人工知能が中間選挙をどのように動かしたか - そして民主主義の恒久的な特徴になる

Como a inteligência artificial influenciou as eleições de meio de mandato - e se tornará uma característica permanente da democracia

Trí tuệ nhân tạo đã ảnh hưởng như thế nào đến cuộc bầu cử giữa nhiệm kỳ - và sẽ trở thành một đặc điểm lâu dài của nền dân chủ

人工智能如何影响中期选举——并将成为民主的永久特征

Transformative is an overused word these days, but AI-assisted coding is genuinely a transformative trend, a step toward a future where software writes software. Only about one-half of one percent of the world can code, and there is more code to be written than there are developers to do it. Demand is high for tools that take the struggle out of coding – or remove the need to code altogether - so that the world's limited developers can focus on creative tasks. Now a lot more developers are going to get exposed to AI-assisted coding. In June, GitHub, Microsoft, and OpenAI announced a new AI for code tool Copilot, which they bill as a "virtual pair programmer." It provides AI-powered autocompletion in Microsoft's source-code editor, Visual Studio Code. Copilot is only the latest of a rash of AI-enabled products that save developers time. TabNine, Sourcery.ai, and others have produced AI-powered auto-completion tools.
Diffblue Cover, created by computer scientists at Oxford University, writes software tests using reinforcement learning. And Akkio, an end-to-end no-code AI platform, builds and trains, and deploys machine-learning models automatically. A user needs to drag a data file into the web interface and click a few options. Any business user familiar with their data can use it without help from a developer or engineer. These tools are putting the power of artificial intelligence within reach of even small companies for whom expensive data scientists are in short supply. has revealed 2020 to be the year of highest demand for software developers. LinkedIn data The shortage doesn't just affect the states; it's global. In the report, 58% of hiring managers said hiring software developers was a challenge, with some cities even facing shortages of only semi-qualified applicants. This is exacerbated by the fact that 39% of tech hiring personnel expect to have increasingly more positions to fill on every end of the complexity spectrum. The cities with the highest demand for software developers in 2020 were:  1. San Francisco,  2. Seattle, and  3. New York. There is a silver lining. In the next five years, are coming up. While 58% of these STEM jobs are in computing, only about a third of them can't be hired by non-stem applicants if the company is willing to equip the recruits with assisting software and educate them on the job. However, this strategy takes precious time away from the few seasoned veteran developers companies so desperately need. 149 million+ new tech-orientated jobs Qualified teachers aren't just rare in companies but the entire education sector. This may partially be due to the low salaries offered to them. Personnel skilled enough to teach these subjects consist of veterans that can easily score retainers ranging in the $200000s. Talent drought Given the talent drought, companies need to make the most with what they got. Desperate, some organizations have resorted to extensive talent poaching, recruiting junior developers or even unskilled labor and building them from the ground up. Automation software can help, but developers still spend most of their time building, testing, and deploying software - most of which can't be automated or only partially. Either way, this requires skilled people or putting a lot of person-hours into facing steep learning curves. That being said, there has been a lot of improvement in software development, and a lot of the work can increasingly more be automated or there are new least that make them a lot easier. assisting tools Twenty years ago, a SUN developer created an automated system named Jenkins (originally named Hudson) to simplify software integration and remove many software delivery barriers. Facebook's Getafix learns from an engineer's past code and, much like autocorrect, recommends fixes for bugs. Writing software requires a great deal of skill and patience which often involves painstaking drudgery like reading manuals, writing documentation, and filling out forms. Developers surveyed last year used an average of 3.7 different tools developing software and spent a third of their time testing it. By running unit tests as the code is being written, developers can fix problems on the go and ship finished software much faster. Programmers must manually write, edit, and rerun the tests every time they make a change. This forces them to interrupt their creative work and switch gears between the "left brain" of coding and the "right-brain" of testing and debugging. It also adds time pressure because writing tests often comes after code has been written, but before a deadline is looming. Worse yet, the bugs we find when testing a piece of code often reveal deeper bugs elsewhere in the system. Writing and debugging unit tests is thus akin to using a microscope for hunting for microbes in a dirty petri dish: you're bound to find some, but it takes so long that you'll miss plenty of problems. Fortunately, there are efforts underway to automate away much of this drudgery. Modern software development environments allow developers to click on what they want rather than type it and deploy software with a single command. Source code repositories, bug trackers, continuous integration servers, testing frameworks, and deployment systems are all being automated. Software testing is a industry, but very little of that testing is automated and left in the hands of millions of contract and freelance developers. $12 billion Even an unintelligent algorithm that randomly generates tests can test software exponentially faster than a human can. Deployed are carefully calibrated models which excel automatically generating tests humans can easily understand. This makes it easy to refactor code — to improve the structure of software without breaking existing features. This also represents a remarkable opportunity to save labor and improve the quality and productivity of each remaining engineer at the same time. After all, even a little bit of help snowballs over millions of lines of code. Autocompletion Copilot can autocomplete any code – including unit tests. Based on OpenAI's massive language model, GPT-3 is trained on a blend of text and public repository source code from GitHub. Copilot, a model of GPT-3, does the best it can but leaves it up to the developer to pick the best autocompletion that matches their intent, fix any problems or mistakes, and complete the code. It's designed to be interactive. Diffblue Cover, meanwhile, works 100% autonomously and can write a complete suite of tests that always compile and run (a direct consequence of how it searches the space of possible test programs). Copilot is computationally expensive to run and so must run as a cloud service. Code must be uploaded to Copilot to run the analysis, which many organizations don't like to do. Diffblue Cover, on the other hand, is packaged as software that any developer can run on their laptop. Adopting this assisting software allows for testing that wouldn't have happened before because it's too labor-intensive. Companies have miles of legacy code that never had the chance to be properly tested. As code evolves, backward compatibility becomes an increasingly bigger issue. Companies can't afford to re-build billion-dollar software in a different coding langue or using other modules. It's not a simple matter of translating, and without good tests, it is easy to introduce new bugs when iterating and upgrading software. Those bugs, in turn, require massive, time-consuming effort to find and fix later on, limiting how often updated code can be released.
Organizations that push code more often have a far lower failure rate and are far faster at adapting to trends or recovering from software failure. The less time developers spend solving mind-numbing debugging problems, the more they can focus on facing problems and innovate products. It gives them the necessary room to gain perspective, brainstorm, and deal with unsuspected challenges. Conclusion A considerable portion of code is already developed, delivered, and maintained using a wide array of assisting software. Before long, code will almost write itself, and developers turn from players to referees. In the meantime, executives are left to compromise.

Software Will Mostly Write Itself in the Future

We have been using akka capabilities for some time now, but also we are starting to explore Docker deployments of the actor systems we have today. In my previous two posts, , and we have explored how to use some tools, directly embedded into our build pipeline, to do the deployments into Docker. However, a question arises from our team, Yet another sbt-docker introduction Akka Cluster in Docker, a straightforward configuration should we create a fat JAR and put it on docker or should we do a deployment with all dependencies all together? sbt-assembly Directly from the docs sbt-assembly is a sbt plugin originally ported from codahale’s assembly-sbt, which I’m guessing was inspired by Maven’s assembly plugin. The goal is simple: Create a fat JAR of your project with all of its dependencies. This is a nice tool, very useful for managing dependencies. Using sbt-assembly, we could create a solely JAR and then add it to Docker. Let’s combine sbt-docker and sbt-assembly plugins. lazy val dockerSettings = Seq(    dockerfile in docker := {        val artifact: File = assembly.value        val artifactTargetPath = s"/app/${artifact.name}"                new Dockerfile {          from("java")          add(artifact, artifactTargetPath)          entryPoint("java", "-jar", artifactTargetPath)        }  },  ....} This sbt setting will create the fat JAR, add it to the Docker image and create an entry point so when the container starts up our application runs. There is nothing special here, this should work for any app, except when it doesn’t. The problem is not the plugins, but Akka itself. Akka documentation says that each module relies on its very own configuration which in other words is the same as saying we will need multiple configuration files for Akka. This brings us to the next problem, if Akka is generating config files with the defaults, how will they be merged on the sbt-assembly process? If you follow , you will realize that this has been solved already for us. Still, we want to supply a config file to be used called that is located in directory, let’s see how we can do it. this link application.prod.conf resources/confs/ lazy val dockerSettings = Seq(    dockerfile in docker := {             val dockerFiles = {            val resources = (unmanagedResources in Runtime).value            val dockerFilesDir = resources                .find(_.getPath.endsWith("/confs"))                .get            resources                .filter(_.getPath.contains("/confs/"))                .map(r =>                     dockerFilesDir                        .toURI                        .relativize(r.toURI)                        .getPath -> r)                .toMap          }            val artifact: File = assembly.value        val artifactTargetPath = s"/app/${artifact.name}"                new Dockerfile {          from("java")          add(artifact, artifactTargetPath)          add(dockerFiles(            "application.prod.conf"),            ("/app/application.prod.conf")          )          entryPoint(            "java",             "-Dconfig.resource=/app/application.prod.conf",             "-jar",             artifactTargetPath          )        }  },  ....} In here, we have added our own config and also specify at the entry point that we want to use it as a resource. Now, our Docker image will have the fat JAR and the config we will use as a resource. This approach works nicely, but there was something we didn’t like about the process. The building time associated with each of these containers was long since sbt has to clean everything up, then compile, run the tests, assembly everything (really long time doing this) and the create the Docker images. You might think that this is a small problem, but if you are building a hundred images every time, trust me, it takes quite a long time. To assembly or not to assembly Creating a single JAR from your application makes sense in a lot of scenarios, but is it really worth to go through all this time consuming process if we are going to deploy on Docker? Well, the idea of having a unique JAR with our app is to encapsulate the dependencies which allow us to manage more efficiently our release process. Having only a JAR with everything inside is, ultimately, convenient. However, isn’t that the same idea behind Docker? A Docker image is an encapsulation of our application that can be deployed everywhere and in this context, it replaces (or works as the same) the idea of having a single JAR. These were the ideas we as a team discussed favoring the containerization without fat JARs. Let’s review the required changes. First, we remove from in favor of . sbt-assembly plugins.sbt sbt-native-packager Second, we change how we create the Docker image in our build.sbt lazy val dockerSettings = Seq(    dockerfile in docker := {         val dockerFiles = {            val resources = (unmanagedResources in Runtime).value            val dockerFilesDir = resources                .find(_.getPath.endsWith("/confs"))                .get            resources                .filter(_.getPath.contains("/confs/"))                .map(r =>                     dockerFilesDir                        .toURI                        .relativize(r.toURI)                        .getPath -> r)                .toMap          }

        val appDir: File = stage.value        val targetDir = "/app"            new Dockerfile {          from("java")          add(appDir, targetDir)          add(dockerFiles(            "application.prod.conf"),            ("/app/application.conf"))                    entryPoint(              s"$targetDir/bin/${executableScriptName.value}"          )        }    },  ... } Note, that now we don’t depend on assembly any longer, we use instead. In other words, we add the content generated by into in our container. Also, the entry point has changed. uses a starting scripts instead of command. Remember, we need to add the file to it, but we don’t want to use additional JVM args, we rename it to its default . stage.value stage /app sbt-native-packager java -jar application.prod.conf application.conf Ultimately, the changes towards not creating fat JARs reduces our build time 80% which were, in the first place, the biggest problem we had. Finalizing Sometimes we have not idea what to do, especially in software engineering, but it’s not until we test different approaches to see which one fits best our necessities. There is never a unique solution, most of the time is a mix between ideas where we find our solution. In our case (akka on Docker) , sbt-assembly was nice, but adds too much time to the building process. Docker, on the other hand, was a must have platform and at the same time it provides the same encapsulation of our applications as does in other contexts. sbt-assembly We hope this post gives you some insights into our problems, you might be building systems with similar deployment strategies. Disclaimer: We use sbt-assembly in our applications outside Docker without any problems, we don’t have anything against it, all the contrary. You just need to find the right balance for your use case. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Akka.io, sbt-assembly and Docker

At Hummus Said in Acre, Israel, I always order hummus with Foul, don’t think much about it, just add a bit of salt and digg in with the onion. At Hummus Said, a legendary restaurant in Akko, Israel, I savor their signature dish: creamy hummus with a side of Foul. The taste is always perfect, but on one visit, I brought a giant container, joking, "Can bulk hummus match this experience?" Said, the owner and hummus maestro, chuckled, "The flavor might be the same, but the experience suffers.” Just as the bulk hummus misses the essence of Hummus Said, many leaders get lost in the sheer volume of data.  They dream of overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems.- Lior Barak Forget the data dreamland, leaders! You envision overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems. Think again. Simple KPIs like sessions and revenue are just appetizers, not the full course to success. The harsh reality? Most companies choke on their data because they focus on the wrong ingredients. Data strategy isn't a gourmet buffet; it's a clear, actionable recipe. Time to ditch the data daydream and start cooking! Set Clear KPIs! Many leaders envision a data strategy as a readily available buffet - a vast collection of metrics that can be easily assembled into insights. This approach, however, overlooks a critical first step: defining clear and actionable Key Performance Indicators (KPIs). Just as a chef wouldn't haphazardly throw ingredients together and expect a culinary masterpiece, a data strategy requires a well-defined recipe. Your recipe, in this case, consists of the 3-5 key performance indicators (KPIs) that truly capture the essence of your company's success. These KPIs serve as the foundation for your data strategy, guiding all stakeholders towards the metrics that matter most. Think of KPIs as the flavor profile of your data. In my book, "Data is Like a Plate of Hummus" I promote a funnel methodology for defining KPIs: Identify key questions: What are the most critical questions you need answered about your business? For example: What are the revenues from ad-free subscriptions?
How many users bought more than one item in the same basket? Refine your questions: Focus on the most essential ones. Understand which questions are critical for your company goals and strategy, and which answers will help you evaluate the company's health. Define KPIs: Which metrics can effectively provide answers to those refined questions? Understand the KPIs that will give you these answers following the five W's principles: Who is the controller of the KPI? Who owns the data that will be used for the KPI, and who is responsible for ensuring its accuracy and stability?
What action will it drive me to take? If I see a positive or negative change in the KPI.
How well will it give me answers for the company’s health? What else am I missing?
Where is the issue? Can I receive an answer about which company section the issue is?
When do I need to see it? How frequently are these measurements needed? (For example, a weekly "time-on-site" KPI might be sufficient to gauge the effectiveness of UX changes, not requiring daily monitoring.) Building Scalable and Iterative Infrastructure Now that you know what flavors you need, let's talk kitchen setup. Think fast, efficient deployment. Forget a one-size-fits-all approach. Start with a basic setup that can be easily enhanced. Treat it like a recipe collection, evolving with each new dish. Each iteration focuses on either enhancing the kitchen tools or optimizing costs. As a chef, you care if your kitchen equipment is top-of-the-line or basic, but you will know how to work with both. What matters is delivering a delicious, well-presented dish. Your team needs to think about how to maximize the buy/build approach for the first iteration because they will learn all the time about what enhances the flavors and what doesn't, and where the issues lie. The main purpose of picking kitchen tools should be to enhance the Secret Ingredients you selected and allow for further experimentation by the chefs. It needs to be something that will be easy to switch or optimize. But for me, the more important part of the setup is to launch something I can taste-test with the diners and learn how it complements the dish, whether they notice enhancements or not. So, don’t deploy and forget until it breaks. The data chefs need to go around and collect feedback, understand the flavor profiles of diners, and prioritize them in the recipe book. Building trust is essential. Inconsistent flavors in a dish quickly erode a diner's trust in the chef. Similarly, unreliable or inconsistent data undermine the credibility of your data-driven insights. Establish clear data governance guidelines – outlining data availability timelines, ownership, and issue resolution protocols – to ensure data quality and consistency. Imagine the wasted effort required to validate data if its reliability is questionable constantly! Embrace the Journey, Share the Feast Data strategy is about crafting flavorful dishes for a successful restaurant. By focusing on clear recipes (KPIs), strong governance, and a scalable kitchen setup (infrastructure), you can create your culinary oasis – one that delights your diners (customers) and fuels your restaurant's (company's) growth. Ready to take a bite out of your data woes? Read my book, "Data is Like a Plate of Hummus” Buy My Book On Amazon Dive deeper into the five Ws of effective KPIs, explore data governance best practices, and guide you through building a scalable data infrastructure. Learn how to transform your data into a strategic advantage and unlock the full flavor of your company's success! At Hummus Said in Acre, Israel, I always order hummus with Foul, don’t think much about it, just add a bit of salt and digg in with the onion. At Hummus Said, a legendary restaurant in Akko, Israel, I savor their signature dish: creamy hummus with a side of Foul. The taste is always perfect, but on one visit, I brought a giant container, joking, "Can bulk hummus match this experience?" Said, the owner and hummus maestro, chuckled, "The flavor might be the same, but the experience suffers.” Just as the bulk hummus misses the essence of Hummus Said, many leaders get lost in the sheer volume of data.  They dream of overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems.- Lior Barak Just as the bulk hummus misses the essence of Hummus Said, many leaders get lost in the sheer volume of data.  They dream of overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems. - Lior Barak Forget the data dreamland, leaders! You envision overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems. Think again. Simple KPIs like sessions and revenue are just appetizers, not the full course to success. Forget the data dreamland, leaders! Think again. The harsh reality? Most companies choke on their data because they focus on the wrong ingredients. Data strategy isn't a gourmet buffet; it's a clear, actionable recipe. Time to ditch the data daydream and start cooking! The harsh reality? Time to ditch the data daydream and start cooking! Set Clear KPIs! Set Clear KPIs! Many leaders envision a data strategy as a readily available buffet - a vast collection of metrics that can be easily assembled into insights. This approach, however, overlooks a critical first step: defining clear and actionable Key Performance Indicators (KPIs). Just as a chef wouldn't haphazardly throw ingredients together and expect a culinary masterpiece, a data strategy requires a well-defined recipe. Your recipe, in this case, consists of the 3-5 key performance indicators (KPIs) that truly capture the essence of your company's success. These KPIs serve as the foundation for your data strategy, guiding all stakeholders towards the metrics that matter most. key performance indicators (KPIs) Think of KPIs as the flavor profile of your data. In my book, " Data is Like a Plate of Hummus " I promote a funnel methodology for defining KPIs: flavor profile Data is Like a Plate of Hummus funnel methodology Identify key questions: What are the most critical questions you need answered about your business? For example: What are the revenues from ad-free subscriptions? How many users bought more than one item in the same basket? What are the revenues from ad-free subscriptions? How many users bought more than one item in the same basket? Refine your questions: Focus on the most essential ones. Understand which questions are critical for your company goals and strategy, and which answers will help you evaluate the company's health. Define KPIs: Which metrics can effectively provide answers to those refined questions? Understand the KPIs that will give you these answers following the five W's principles: Who is the controller of the KPI? Who owns the data that will be used for the KPI, and who is responsible for ensuring its accuracy and stability? What action will it drive me to take? If I see a positive or negative change in the KPI. How well will it give me answers for the company’s health? What else am I missing? Where is the issue? Can I receive an answer about which company section the issue is? When do I need to see it? How frequently are these measurements needed? (For example, a weekly "time-on-site" KPI might be sufficient to gauge the effectiveness of UX changes, not requiring daily monitoring.) Who is the controller of the KPI? Who owns the data that will be used for the KPI, and who is responsible for ensuring its accuracy and stability? Who is the controller of the KPI? What action will it drive me to take? If I see a positive or negative change in the KPI. What action will it drive me to take? How well will it give me answers for the company’s health? What else am I missing? How well will it give me answers for the company’s health? Where is the issue? Can I receive an answer about which company section the issue is? Where is the issue? When do I need to see it? How frequently are these measurements needed? (For example, a weekly "time-on-site" KPI might be sufficient to gauge the effectiveness of UX changes, not requiring daily monitoring.) When do I need to see it? Building Scalable and Iterative Infrastructure Building Scalable and Iterative Infrastructure Now that you know what flavors you need, let's talk kitchen setup. Think fast, efficient deployment. Forget a one-size-fits-all approach. Start with a basic setup that can be easily enhanced. Treat it like a recipe collection, evolving with each new dish. Each iteration focuses on either enhancing the kitchen tools or optimizing costs. As a chef, you care if your kitchen equipment is top-of-the-line or basic, but you will know how to work with both. What matters is delivering a delicious, well-presented dish. Your team needs to think about how to maximize the buy/build approach for the first iteration because they will learn all the time about what enhances the flavors and what doesn't, and where the issues lie. The main purpose of picking kitchen tools should be to enhance the Secret Ingredients you selected and allow for further experimentation by the chefs. It needs to be something that will be easy to switch or optimize. But for me, the more important part of the setup is to launch something I can taste-test with the diners and learn how it complements the dish, whether they notice enhancements or not. So, don’t deploy and forget until it breaks. The data chefs need to go around and collect feedback, understand the flavor profiles of diners, and prioritize them in the recipe book. Building trust is essential. Inconsistent flavors in a dish quickly erode a diner's trust in the chef. Similarly, unreliable or inconsistent data undermine the credibility of your data-driven insights. Establish clear data governance guidelines – outlining data availability timelines, ownership, and issue resolution protocols – to ensure data quality and consistency. Imagine the wasted effort required to validate data if its reliability is questionable constantly! Building trust is essential. Embrace the Journey, Share the Feast Embrace the Journey, Share the Feast Data strategy is about crafting flavorful dishes for a successful restaurant. By focusing on clear recipes (KPIs), strong governance, and a scalable kitchen setup (infrastructure), you can create your culinary oasis – one that delights your diners (customers) and fuels your restaurant's (company's) growth. Ready to take a bite out of your data woes? Read my book, "Data is Like a Plate of Hummus” Ready to take a bite out of your data woes? Buy My Book On Amazon Buy My Book On Amazon Dive deeper into the five Ws of effective KPIs, explore data governance best practices, and guide you through building a scalable data infrastructure. Learn how to transform your data into a strategic advantage and unlock the full flavor of your company's success!

The Five W's for Setting KPIs

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 9 CONCLUSION This paper presents ConstitutionMaker, a tool for interactively refining LLM outputs by converting users’ intuitive feedback into principles. ConstitutionMaker’s design is informed by a formative study, where we also collected and classified the types of principles users wanted to write. ConstitutionMaker incorporates three principle-elicitation features: kudos, critique, and rewrite. In a user study with 14 industry professionals, participants felt that ConstitutionMaker helped them (1) write principles that effectively guided the chatbot, (2) convert their feedback into principles more easily, and (3) write principles more efficiently, with (4) less mental demand than the baseline. This was due to ConstitutionMaker supporting their thought processes, including helping them to: identify ways to improve the bot’s responses, convert their intuition into verbal feedback, and phrase their feedback as specific principles. There are many avenues of future work, including supporting users in iterating on and clarifying their principles, organizing larger sets of principles and supporting multiple writers, and helping users test chatbots across multiple user journeys. Together, these findings inform future tools that support interactively customizing LLM outputs. REFERENCES [1] Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy TelleenLawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022. Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073 [cs.CL] [2] Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012. TasteWeights: A Visual Interactive Hybrid Recommender System. In Proceedings of the Sixth ACM Conference on Recommender Systems (Dublin, Ireland) (RecSys ’12). Association for Computing Machinery, New York, NY, USA, 35–42. https://doi.org/10.1145/ 2365952.2365964 [3] Stephen Brade, Bryan Wang, Mauricio Sousa, Sageev Oore, and Tovi Grossman. 2023. Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (UIST). https: //doi.org/10.1145/3586183.3606725 arXiv:2304.09337 [cs.HC] [4] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901. https://proceedings.neurips.cc/paper_files/paper/2020/file/ 1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf [5] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023. Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv:2303.12712 [cs.CL] [6] Ernie Chang, Xiaoyu Shen, Hui-Syuan Yeh, and Vera Demberg. 2021. On Training Instance Selection for Few-Shot Neural Text Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Online, 8–13. https: //doi.org/10.18653/v1/2021.acl-short.2 [7] Qiaochu Chen, Xinyu Wang, Xi Ye, Greg Durrett, and Isil Dillig. 2020. MultiModal Synthesis of Regular Expressions. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (London, UK) (PLDI 2020). Association for Computing Machinery, New York, NY, USA, 487–502. https://doi.org/10.1145/3385412.3385988 [8] Katy Ilonka Gero, Vivian Liu, and Lydia Chilton. 2022. Sparks: Inspiration for Science Writing Using Language Models. In Proceedings of the 2022 ACM Designing Interactive Systems Conference (Virtual Event, Australia) (DIS ’22). Association for Computing Machinery, New York, NY, USA, 1002–1019. https: //doi.org/10.1145/3532106.3533533 [9] Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Human Mental Workload, Peter A. Hancock and Najmedin Meshkati (Eds.). Advances in Psychology, Vol. 52. North-Holland, 139–183. https://doi.org/10.1016/S0166- 4115(08)62386-9 [10] Jeremy Howard and Sebastian Ruder. 2018. Universal Language Model Finetuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 328–339. https://doi.org/ 10.18653/v1/P18-1031 [11] Jiyou Jia. 2009. CSIEC: A computer assisted English learning chatbot based on textual knowledge and reasoning. Knowledge-Based Systems 22, 4 (2009), 249–255. https://doi.org/10.1016/j.knosys.2008.09.001 Artificial Intelligence (AI) in Blended Learning. [12] Ellen Jiang, Kristen Olson, Edwin Toh, Alejandra Molina, Aaron Donsbach, Michael Terry, and Carrie J Cai. 2022. PromptMaker: Prompt-Based Prototyping with Large Language Models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22). Association for Computing Machinery, New York, NY, USA, Article 35, 8 pages. https://doi.org/10.1145/3491101.3503564 [13] Ellen Jiang, Edwin Toh, Alejandra Molina, Aaron Donsbach, Carrie J Cai, and Michael Terry. 2021. GenLine and GenForm: Two Tools for Interacting with Generative Language Models in a Code Editor. In Adjunct Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology (Virtual Event, USA) (UIST ’21 Adjunct). Association for Computing Machinery, New York, NY, USA, 145–147. https://doi.org/10.1145/3474349.3480209 [14] Ellen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson, Claire Kayacik, Aaron Donsbach, Carrie J Cai, and Michael Terry. 2022. Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 386, 19 pages. https://doi.org/10.1145/3491102.3501870 [15] Daniel Kahneman. 2011. Thinking, fast and slow. macmillan. [16] Tae Soo Kim, Yoonjoo Lee, Minsuk Chang, and Juho Kim. 2023. Cells, Generators, and Lenses: Design Framework for Object-Oriented Interaction with Large Language Models. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. ACM. 1–18. [17] Johannes Kunkel, Benedikt Loepp, and Jürgen Ziegler. 2017. A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (Limassol, Cyprus) (IUI ’17). Association for Computing Machinery, New York, NY, USA, 3–15. https://doi.org/10.1145/3025171.3025189 [18] Peter Lee, Sebastien Bubeck, and Joseph Petro. 2023. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. New England Journal of Medicine 388, 13 (2023), 1233–1239. https://doi.org/10.1056/NEJMsr2214184 arXiv:https://doi.org/10.1056/NEJMsr2214184 PMID: 36988602. [19] Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 3045–3059. https: //doi.org/10.18653/v1/2021.emnlp-main.243 [20] Michael Xieyang Liu, Advait Sarkar, Carina Negreanu, Benjamin Zorn, Jack Williams, Neil Toronto, and Andrew D. Gordon. 2023. “What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and CodeGenerating Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 598, 31 pages. https: //doi.org/10.1145/3544548.3580817 [21] Vivian Liu, Han Qiao, and Lydia Chilton. 2022. Opal: Multimodal Image Generation for News Illustration. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22). Association for Computing Machinery, New York, NY, USA, Article 73, 17 pages. https://doi.org/10.1145/3526113.3545621 [22] Vivian Liu, Jo Vermeulen, George Fitzmaurice, and Justin Matejka. 2023. 3DALLE: Integrating Text-to-Image AI in 3D Design Workflows. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, USA) (DIS ’23). Association for Computing Machinery, New York, NY, USA, 1955–1977. https://doi.org/10.1145/3563657.3596098 [23] Aditi Mishra, Utkarsh Soni, Anjana Arunkumar, Jinbin Huang, Bum Chul Kwon, and Chris Bryan. 2023. PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models. arXiv:2304.01964 [cs.HC] [24] Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023. Generative Agents: Interactive Simulacra of Human Behavior. arXiv:2304.03442 [cs.HC] [25] Savvas Petridis, Nediyana Daskalova, Sarah Mennicken, Samuel F Way, Paul Lamere, and Jennifer Thom. 2022. TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22). Association for Computing Machinery, New York, NY, USA, 120–133. https: //doi.org/10.1145/3490099.3511156 [26] Savvas Petridis, Nicholas Diakopoulos, Kevin Crowston, Mark Hansen, Keren Henderson, Stan Jastrzebski, Jeffrey V Nickerson, and Lydia B Chilton. 2023. AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 225, 16 pages. https://doi.org/10.1145/3544548. 3580907 [27] Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. 2023. Automatic Prompt Optimization with "Gradient Descent" and Beam Search. arXiv:2305.03495 [cs.CL] [28] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9. [29] Kiran Ramesh, Surya Ravishankaran, Abhishek Joshi, and K. Chandrasekaran. 2017. A Survey of Design Techniques for Conversational Agents. In Information, Communication and Computing Technology, Saroj Kaushik, Daya Gupta, Latika Kharb, and Deepak Chahal (Eds.). Springer Singapore, Singapore, 336–350. [30] Emily Reif, Minsuk Kahng, and Savvas Petridis. 2023. Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models. arXiv:2305.11364 [cs.CL] [31] Laria Reynolds and Kyle McDonell. 2021. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI EA ’21). Association for Computing Machinery, New York, NY, USA, Article 314, 7 pages. https://doi.org/10.1145/3411763.3451760 [32] Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 4222–4235. https://doi.org/10.18653/v1/2020. emnlp-main.346 [33] Hendrik Strobelt, Albert Webson, Victor Sanh, Benjamin Hoover, Johanna Beyer, Hanspeter Pfister, and Alexander M. Rush. 2023. Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2023), 1146–1156. https://doi.org/10.1109/TVCG.2022.3209479 [34] Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2022. Selective Annotation Makes Language Models Better Few-Shot Learners. arXiv:2209.01975 [cs.CL] [35] Gust Verbruggen, Vu Le, and Sumit Gulwani. 2021. Semantic Programming by Example with Pre-Trained Models. Proc. ACM Program. Lang. 5, OOPSLA, Article 100 (oct 2021), 25 pages. https://doi.org/10.1145/3485477 [36] Sitong Wang, Savvas Petridis, Taeahn Kwon, Xiaojuan Ma, and Lydia B Chilton. 2023. PopBlends: Strategies for Conceptual Blending with Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 435, 19 pages. https://doi.org/10.1145/3544548. [37] Yunlong Wang, Shuyuan Shen, and Brian Y Lim. 2023. RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 22, 29 pages. https://doi.org/10.1145/3544548.3581402 [38] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 24824–24837. https://proceedings.neurips.cc/paper_files/paper/2022/file/ 9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf [39] Sherry Wu, Hua Shen, Daniel S Weld, Jeffrey Heer, and Marco Tulio Ribeiro. 2023. ScatterShot: Interactive In-Context Example Curation for Text Transformation. In Proceedings of the 28th International Conference on Intelligent User Interfaces (Sydney, NSW, Australia) (IUI ’23). Association for Computing Machinery, New York, NY, USA, 353–367. https://doi.org/10.1145/3581641.3584059 [40] Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai. 2022. PromptChainer: Chaining Large Language Model Prompts through Visual Programming. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22). Association for Computing Machinery, New York, NY, USA, Article 359, 10 pages. https://doi.org/10.1145/3491101.3519729 [41] Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022. AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 385, 22 pages. https: //doi.org/10.1145/3491102.3517582 [42] Yu Wu, Wei Wu, Chen Xing, Ming Zhou, and Zhoujun Li. 2017. Sequential Matching Network: A New Architecture for Multi-turn Response Selection in RetrievalBased Chatbots. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 496–505. https://doi.org/10.18653/v1/P17-1046 [43] Anbang Xu, Zhe Liu, Yufan Guo, Vibha Sinha, and Rama Akkiraju. 2017. A New Chatbot for Customer Service on Social Media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 3506–3510. https://doi.org/10.1145/3025453.3025496 [44] Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022. Wordcraft: Story Writing With Large Language Models. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22). Association for Computing Machinery, New York, NY, USA, 841–852. https://doi.org/10.1145/3490099.3511105 [45] J.D. Zamfirescu-Pereira, Heather Wei, Amy Xiao, Kitty Gu, Grace Jung, Matthew G Lee, Bjoern Hartmann, and Qian Yang. 2023. Herding AI Cats: Lessons from Designing a Chatbot by Prompting GPT-3. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, USA) (DIS ’23). Association for Computing Machinery, New York, NY, USA, 2206–2220. https://doi.org/10.1145/3563657.3596138 [46] J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023. Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 437, 21 pages. https://doi.org/10.1145/ 3544548.3581388 [47] Tianyi Zhang, London Lowmanstone, Xinyu Wang, and Elena L. Glassman. 2020. Interactive Program Synthesis by Augmented Examples. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (Virtual Event, USA) (UIST ’20). Association for Computing Machinery, New York, NY, USA, 627–648. https://doi.org/10.1145/3379337.3415900 [48] Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018. Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory. arXiv:1704.01074 [cs.CL] This paper is under CC 4.0 license. available on arxiv

Interactively Critiquing LLMs by Converting Feedback into Principle— Conclusion and References

Literally hundreds of apps have appeared seemingly overnight in the wake of ChatGPT, making it impossible to keep track of them all. But one worth keeping an eye on is , a workflow automation tool that operates in your browser. Bardeen.ai At first glance, Bardeen may look like just another automation tool elbowing its way into a very crowded space. Yet, it introduces a significant wrinkle: context, meaning it builds automations based on what’s open on your computer screen. It also uses AI to build automations for you - making it vastly faster than its competitors. Unlike other automation tools such as , Bardeen runs as an extension in the user’s browser, so users never have to switch windows to use it. And it reads the tabs open in the browser to build automations that are context aware. That means with the click of a button, Bardeen scans what is open on the page and can extract information for use in email drafts, Slack messages, Jira notifications, etc. No more context switching or Saas sprawl. Zapier Carl Ritchie, a salesman at Deel, a fast growing global hiring and payroll platform, says he uses Bardeen every day for prospecting and outreach and automating tasks throughout his day. “It definitely took some time to learn how to use,” said Ritchie, “but for the time invested, a hundred times that has been saved, and now with MagicBox it’s even easier to get started!” Ritchie has become a Bardeen superuser at Deel and rolls out automations to his team using Bardeen’s handy ‘share’ function. One automation that Ritchie built with Bardeen scans competitor pages on Trustpilot, a review site, and sends him a Slack message whenever it spots a negative review. Ritchie then reaches out to the reviewer offering Deel as an alternative. https://vimeo.com/821665636?share=copy&embedable=true Business process automation can be traced back to the 1950s and 1960s, when companies began developing custom built solutions to harness the new advances in computer technology. The 1970s brought more sophisticated tools, including enterprise resource planning systems and workflow management software, which automated everything from inventory tracking to customer service. The rise of the internet and cloud computing led to further innovation that unstuck automation and sent it flying across departments and geographies. But nothing has accelerated automation as much as artificial intelligence, particularly natural language processing and the advent of large language models like OpenAI’s GPT-4. For most repetitive, digital tasks, it’s only a matter of engineering to get an AI system to do it. Knowledge workers spend as much as 40% of their time on repetitive tasks, a significant waste of time and resources that could be better spent elsewhere. Yet, automation is still limited to large corporations or individuals with technical knowledge. As a result, adoption rates remain low. But Bardeen aims to make automation easy and accessible to everyone. Bardeen’s founders, Pascal Weinberger, CEO, and Artem Harutyunyan, CTO, formed the company in early 2020, just before the COVID-19 pandemic shut down much of the global economy. They named their company after John Bardeen, two-time Nobel Prize winner and co-inventor of the transistor. Weinberger is a self-taught computer programmer from Germany who dropped out of college and worked on a series of startups and projects before at Telefonica’s Barcelona-based skunkworks, Alpha Labs. leading the AI team Harutyunyan came out of Mesosphere, now , which builds orchestration software to control applications running across different hardware. One of Mesosphere’s founders and CEO, Florian Leibert, was one of Bardeen’s first investors. D2iQ In many ways, Bardeen is also a kind of orchestration platform, allowing users to mix and match dozens of applications into a single workflow - Weinberger calls it a kind of ‘superglue’ that holds productivity apps together. The robotic process automation market is mature, with recognized dominant players. But Weinberger believes that the industry faces a slow decline because it lacks solutions for automating tasks with modern SaaS apps, such as Hootsuite and Hubspot. Meanwhile, newer automation solutions, such as Zapier and , are ‘trigger-based,’ designed to execute a series of actions in response to a trigger – when a website is updated for example. But these tools do not consider the user’s current context or real-time decision making. UiPath Weinberger and Harutyunyan aimed to provide contextual automations that were easy to build and did not require coding. Their first prototype met their own immediate needs: an automation that scraped personnel data from GitHub pages and wrote recruiting emails to software engineers. “We saw an opportunity between older robotic process automation and modern SaaS apps,” said Weinberger by phone. Indeed, creating an automation with Bardeen is faster and easier than most Saas apps. Mesosphere’s Leibert, who had left to start a venture capital firm, , led Bardeen’s seed round of3.5 million in February last year, which was followed, less than six months later, by it’s $15.3 million series A. The Series A round was led by Insight Partners’ Praveen Akkiraju, an automation software veteran, having served on the Board of robotic process automation giant Automation Anywhere. 468 Capital Bardeen's product is aimed at modern knowledge workers who use the so-called “hipster productivity stack” - tools like Notion, Table, HubSpot, ClickUp, Salesforce, and Gmail. The company’s roadmap reflects feedback from an engaged community of users hundreds of thousands strong. Already, Bardeen works with more than fifty apps, from OpenAI’s GPT-4 to ClickUp, HubSpot, Notion, Google Sheets and . It is adding about one more app per week. more It offers several advantages: Bardeen's automations run locally on the user's device, which makes it more cost-effective and privacy friendly. It is built with AI embedded into the product from the start and the platform learns continuously, improving its functionality. If a user edits the output and the edit makes sense, it is reviewed by Bardeen, and the platform is improved in real-time. “Playbooks” and “MagicBox” are two key features of the Bardeen automation tool. A playbook is a pre-built workflow or template that automates a specific task. Playbooks are designed to be easy to use and customizable, allowing users to quickly automate repetitive tasks without needing any programming skills. Knowledge workers spend as much as 40% of their time on repetitive tasks, a significant waste of time and resources that could be better spent elsewhere. Bardeen offers a vast library of pre-built playbooks for a wide range of tasks, including data entry, report generation, social media management, and more. Users can also create their own custom playbooks using the drag-and-drop interface, which can then be shared with others in their organization. The MagicBox is like an AI enabled search box into which users describe an automation. The interface returns a custom workflow that users can modify by selecting pre-built actions from a list. Its use of generative AI puts it ahead of its competitors, many of whom have promises and demos but long waiting lists for the feature. Others in this category are trying to train generative models to automate tasks, which is very cost and time intensive and has yet to show results, whereas with Bardeen’s approach the generative AI is used to build and suggest an automation template to the user, who can then use the platform’s intuitive no-code builder to adjust it to their needs. When the same task has to be automated multiple times, the user then simply needs to click a button and the automation is triggered. That makes it an easy and reliable way to save time and remove friction from every knowledge worker's daily tasks. Combined, the features are like a for workplace productivity. Anyone can create and deploy automation workflows on the platform with a little training, regardless of their technical expertise. virtual Swiss army knife Bardeen can automate tasks such as sales, recruiting, sourcing work for analysts, and things like even email or meeting management, social media posting, and file organization, allowing users to work more efficiently and effectively from anywhere. Its goal is to help people automate the repetitive and mundane tasks that consume so much of their time and energy, freeing them up to focus on more creative and fulfilling work. As we move further into the digital age, the demand for automation tools like Bardeen is only going to increase. This has raised concerns about job displacement and the erosion of human skills and creativity. But Bardeen offers a glimmer of hope, suggesting that it is possible to harness these powerful technologies in ways that enhance our human productivity by collaborating with AI. Featured image generated using Kadinsky 2 using the following prompt: “A person trying different apps over their phone, from over the shoulder angle.” Also published . here

Bardeen, The Automation App Enhancing Human Productivity

বারডিন, মানুষের উৎপাদনশীলতা বাড়ায় অটোমেশন অ্যাপ

Bardeen, die Automatisierungs-App zur Steigerung der menschlichen Produktivität

Bardeen, la aplicación de automatización que mejora la productividad humana

Bardeen, l'application d'automatisation améliorant la productivité humaine

बार्डीन, मानव उत्पादकता बढ़ाने वाला ऑटोमेशन ऐप

Bardeen、人間の生産性を高める自動化アプリ

Bardeen, 인간 생산성을 향상시키는 자동화 앱

Bardeen, o aplicativo de automação que melhora a produtividade humana

Bardeen, İnsan Verimliliğini Artıran Otomasyon Uygulaması

Bardeen, Ứng dụng tự động hóa nâng cao năng suất của con người

Bardeen，提高人类生产力的自动化应用程序

Thirty-five years ago I made a voyage to the Arctic Seas in what Chaucer calls

The automotive market stands on the verge of a major transformation with the introduction of electric vehicles, addressing the concerns of global greenhouse gas emissions and the use of exhaustible power resources. The accelerated demand for these vehicles comes from the shift in three key areas: consumer behavior, regulations, and integration of AI technology in the process flow. As per , “a total of 10.5 million new BEVs (Battery Electric Vehicles) and PHEVs (Plug-in Electric Vehicles) were delivered during 2022, an increase of +55 % compared to 2021.” EV-Volumes.com “It is clear now that electric vehicles are the future, and that the future is coming faster than many anticipated. California and other states have already declared that no new gas vehicles will be sold after 2035,” said Dave Lewis, CEO of . MoveEV China despite sporadic Covid outbreaks and lockdowns, emerged at the top of the leaderboard with an increment of +82 percent year-over-year EV sales by the end of 2022. The Chinese government's subsidies for electric vehicles, the expansion of the mini-electric vehicle industry, and the emergence of more relatively affordable brands are a few factors contributing to the sector's explosive growth in China. Following closely up with China, Europe made up about 11% of the total revenue generated. Stringent climate laws and regulations drive the European transportation industry, its program defines how laws make “reaching the EU’s climate goal of reducing EU emissions by at least 55% by 2030 a legal obligation.” “Fit For 55” However, the EV market in Europe is in the clutches of an impending energy crisis in the wake of Russia’s invasion of Ukraine. The rising cost of electricity is impacting the affordability of driving electric vehicles, to the point where they may become more expensive to run than their gas-powered counterparts. This surge in electricity prices could potentially hinder the continent's shift towards electric transportation. “When we looked at this(transition to EVs) before the energy crisis, we were looking at a tipping point of around 2023 to 2024. But if you assume you have a tariff going forward of $0.55, the tipping point then moves to 2026,” , a partner at Ernst & Young. said Maria Bengtsson Several countries are framing their policies around the threat of climate change and exhausting renewable sources pool. The Biden administration is pushing America to decarbonize by a 50 percent electric vehicle target by 2030. introducing "Today, nearly all automakers have put zero-emissions vehicles at the heart of their long-term strategies – and, perhaps more importantly, their medium-term investment plans," according to . The report emphasized how EVs are no longer a budding industry but have gained enough traction to be mainstream in markets like China, Germany, France, and the UK. S&P Global Mobility The adoption of EVs is majorly driven by advancements in technology, the integration of AI into automobiles has led to the enhancement of features that have made driving effortless and safe. As per , “ the global Automotive Artificial Intelligence (AI) Market is predicted to thrive at a CAGR of 29.2% and valued at over USD 34.30 billion over the forecast year 2022-2030”. Research and Markets It further noted that the growth in the automotive AI market is a direct reflection of increasing demands “for sophisticated vehicles and the shift of the automotive industry towards electricity.” Challenges like limited driving range, long charging times, and integration with smart grids are being addressed by the introduction of AI Technology. “ The increased sensitization of an electric vehicle leads to more data from the vehicle being available. This data can be used by manufacturers, service representatives, and drivers,” said Cliff Rice from . Rockwell Automation He further explained that manufacturers can use this data to improve driver assistance algorithms, understand driving patterns for maintenance needs, and provide advanced diagnostics and software updates to enhance vehicle performance and increase their lifespan. Predictive analytics is playing a significant role in advancing battery technology. Using predictive maintenance, these technologies are improving the efficiency and reliability of batteries. This is  important as it ensures that batteries operate at optimal levels, delivering high performance and minimizing the risk of breakdowns. “Batteries are highly complex components – chemically, electrically, and in terms of software,” , Data Scientist at Volvo. “But data analytics can help us to understand them better: how they are used, how they are affected by the driver, how they are impacted by other components in the vehicle.” said Rikard Vinge Apart from user experience, engineers are using AI to improve the designs and working efficiency of components. “By using AI in our generative thermal design approach, we are able to create and simulate thousands of iterations of a design in a matter of days, fully autonomously. To fully grasp the scale and speed at play here, in the time it takes a thermal engineer to create one design, we’ve already created 1 000,” said Diabatix CEO, Lieven Vervecken. Several companies are collaborating with big tech names to provide their users with the best of both technologies. Ford has with Google to make use of its cloud services and expertise in artificial intelligence and machine learning. Tesla AI for updating the software over the cloud providing their users with remote services. Volkswagen and Microsoft have also to unleash the power of Azure IoT and PowerBI by offering telematics and productivity solutions. collaborated leverages joined hands The major OEMs(Original Equipment Manufacturers) are convinced that the transition to the e-mobility industry is now inevitable. BYD Auto's sales of BEVs and PHEVs tripled by concentrating its production and sales efforts on electric vehicles, making it the leading manufacturer of PHEVs and BEVs in 2022. Tesla dominates global BEV sales with a 17% market share, while the Volkswagen Group saw only a 10% increase in EV sales in Europe with declining PHEV sales. Established carmakers are reinventing their business models in the hope of adapting to a new world where electricity replaces traditional gasoline and diesel.  Automakers are buying up they can get as factories undergo renovations to build electric vehicles. Some businesses are being forced to find partners due to the high expense of creating electric vehicles, while others are becoming acquisition candidates. every battery The prominent players in the industry are revving up their traditional internal combustion engines to become the frontrunner in eliminating them altogether. Leading names like Tesla, Volkswagen, Toyota, General Motors, and Nissan are introducing new models in the market. The transition to the EV industry is bringing about a lot of opportunities for innovation in the field of battery technology, braking systems, steering systems, and the arrangement of propulsions.  In 2020, more than patent applications were made, with an average grant rate of 50%, according to global patent trends in the Electric Vehicle (EV) sector. 18,000 While the patent filing trends suggest the pace of technology, it also gives a fair picture of the new changes occurring in the industry. As per a published by European Patent Office(EPO) in 2020, patent filings for batteries and other electricity storage technologies saw an annual increment of 14 % worldwide, 88 percent of which offered innovation in battery technology. report Electric vehicles raise the highest demand for Lithium supply for the manufacturing of batteries. If today’s demand is forecasted to 2050, the US market alone would require a three-fold mining of what is currently produced globally, but does it have to be this way? A found that “Lithium demand can be reduced by up to 92 percent in 2050 in comparison to the most lithium-intensive scenarios. This can be done by employing three key policy interventions: decreasing car dependency, right-sizing EV batteries, and creating a robust recycling system.” recent report The existing charging infrastructure for these batteries is far from capable of meeting the growing demand. Although huge investments are underway, skepticism looms owing to cost, choice, and soaring in the areas where battery packs have previously failed to deliver considerable driving range and took long charging times. The fastest vehicle and charger- the Lucid Air can reportedly give of range in 20 minutes, is typical for average EVs. “range anxiety,” about 300 miles 100-200 miles per charging hour To alleviate such concerns, there has been a swift effort to establish fast charging networks throughout the country, with utilities looking to increase electricity sales. More than have joined forces to establish the National Electric Highway Coalition, which aims to develop a nationwide fast-charging network along major highways in the US. The number of fast chargers has already grown significantly, with over available today,  and there are even more 60 utilities 6,500 commitments underway. General Motors Co. and Pilot Co. to partner up and build a network of charging stations across the U.S., set to boost about 20% of the number of fast chargers available to American drivers. plan As the world transitions from a fleet of gasoline-powered vehicles to electric vehicles, it is estimated that millions of people will rely on the electric grid in various ways. This means that the power generation capacity will have to increase to accommodate the growing demand for electricity without causing strain on the grid. , US, there will be a 38 % increase in electricity consumption by 2050, mainly due to the high involvement of electric vehicles. According to the Department of Energy Research conducted at the University of Texas, Austin carried out state-by-state assessment scenarios where every personal car, truck, and SUV is converted to a plug-in electric model. The found that the state energy consumption jumped to 17 % in Wyoming and 55 % in Maine. \ study The ranged from 20-30 percent in most of the states. Some states showed an available capacity to take upon the excess loads whereas, for others, ideal case assumptions for charging times were made to channel the power. The capacity of grids for EV charging depends greatly on the time of the day they are plugged in. The chance of grids overloading falls drastically if charged at when consumption is lesser. consumption off-peak hours With all the speculations and possibilities, the imagined “zero emissions” world doesn’t seem to acknowledge what happens to fossil fuel-driven vehicles running on the roads for now. One idea circulating is the remodeling of traditional engine-driven cars to electric vehicles. At a recent industry event, Toyota's CEO, Akio Toyoda, signaled that the business would enter the conversion market by displaying two vintage Corollas that had been converted to electric and hydrogen power. “The reality is that we cannot achieve zero carbon emissions in 2050 simply by switching all new car sales to EVs,” said CEO Toyoda at the “It is important to provide options for beloved cars that are already owned.” press conference. While some experts support the idea of remodeling, others believe that electrifying cars is unlikely to be a viable option for individuals with limited financial resources. “ If you’re thinking of how do I retrofit an ICE car to be an EV, I don’t think that’ll happen. People will simply buy EVs as replacement cars as usual, but they may straddle the two technologies by having a plug-in hybrid car first,” said Trevor Curwin from . Sheeva.ai The rapid expansion of the EV industry, despite the challenges posed by external factors and limitations, provides a strong indication that electric vehicles are here to stay and will play a significant role in shaping the future of transportation.

Electric Vehicles: Driving Towards a Sustainable Future With Artificial Intelligence

বৈদ্যুতিক যানবাহন: কৃত্রিম বুদ্ধিমত্তা সহ একটি টেকসই ভবিষ্যতের দিকে ড্রাইভিং

Elektrofahrzeuge: Mit künstlicher Intelligenz in eine nachhaltige Zukunft

Vehículos eléctricos: conduciendo hacia un futuro sostenible con inteligencia artificial

Véhicules électriques : vers un avenir durable grâce à l'intelligence artificielle

इलेक्ट्रिक वाहन: आर्टिफिशियल इंटेलिजेंस के साथ एक सतत भविष्य की ओर ड्राइविंग

電気自動車: 人工知能による持続可能な未来への推進

전기 자동차: 인공 지능을 통해 지속 가능한 미래를 향해 나아가다

Veículos Elétricos: Rumo a um Futuro Sustentável com Inteligência Artificial

Elektrikli Araçlar: Yapay Zekayla Sürdürülebilir Bir Geleceğe Doğru Yol Almak

Xe điện: Hướng tới một tương lai bền vững với trí tuệ nhân tạo

电动汽车：借助人工智能迈向可持续发展的未来

THE HEBREW SCRIPTURES AND THE PROPHETS

Has society told you that girls can do this, but girls can’t do that? Math is not for girls, chess is not for girls, and the top level of academia is mostly males. It is embedded in some of our citizens’ subconscious that we have set gender roles. The level of gender roles varies throughout the world. In some areas women have more rights than others. In some areas, they have no rights. Low self-esteem and gender segregation is something that is deeply embedded in some cultures. Thanks to social media, and streaming media in the form of podcasts and , people have bigger platforms to communicate their messages. Women were able to connect with each other on common causes more so than ever before. Subjects that were normally not discussed were getting the spotlight and some taboos were getting dispelled. It started to feel as if we were making some progress. YouTube Then another paradigm shift started to happen, the transition from Web2 to Web3. During the Web2 boom, people were able to connect, but there was always a middle person, someone taking a cut from advertising, aggregating information on their users and the connection between people started to feel not as authentic. In comes Web3 and we are starting to experience a peer-to-peer economy. Artists of all styles and backgrounds can now sell their art direct to consumers. The NFT space in particular became quite the hot commodity. Did it seem like during the transition from Web2 to Web3 that women got lost in the mix? There has been a barrier for a long time in the world of tech between men and women, as in some other industries. But the Crypto Bro Culture is strong. Is it because they got here first and is this a barrier that can be broken? By the numbers It is not just the digital art world. A data analysis of major US art museums found that their collections were . This is an age-old art form too. 87% male There's an apparent gender gap between the number of men compared to the number of women in crypto. Male creators represent of NFT art sales, while only 5% go to women creators. 77% “The study, conducted by a research agency called and reported by , claims at least 77% of the money generated by NFT art sales over the past 21 months went to male artists, with just 5% going to female artists. “ Art Tactic Bloomberg Despite the metaverse being touted as an inclusive and diverse space, the NFT market has been dominated by the likes of the US artist (aka Mike Winkelman), and Canadian-born (the trio take the top three spots). Beeple Pak Mad Dog Jones While Pak’s nationality, gender and age remain undisclosed, the only known woman to make it into the top ten NFT artists is the musician , whose sales total $8.9m to date, compared with Winkelman’s $50.8m. Overall, male artists account for 77% of all primary and secondary sales, at $258.3m. Grimes The highest selling woman artist in NFTs is currently aka Itzel Yard. Whose work “Dreaming at Dusk” sold for 500 ETH or ~ $2 million. The most well-known female NFT artist must be of . Her work, Woman #5672, went for £567,000 at London Evening Sale IX Shells Yam Karkai World of Women Christie’s earlier this year. Women account for just , according to a report published by the research firm , which based its findings on primary and secondary market sales on over the past couple of years. 16% of the NFT art market ArtTactic Nifty Gateway In 32 countries across the world, including India, Egypt and Nigeria, to the internet. In those countries, a third of women were connected to the internet compared with almost half of men. The digital gender gap has barely improved since 2011, dropping just half a percentage point from 30.9% to 30.4%. Globally, men are 21% more likely to be online than women, rising to 52% in the least developed countries, according to a conducted by the in 2021. men are 50% more likely to have access report World Wide Web Foundation Women in certain parts of the world have a hard time getting internet access, which is a problem if you have dreams of making a career online, where there are plenty of opportunities. New opportunities are popping up left and right in the digital world and gasoline has been poured on that fire since the introduction of crypto and especially lately, NFTs. A space filled with mostly men, but is that tide shifting? Ever heard of Seneca? Many may not know by name, but they do know some of her work. A project that took the NFT world by storm was named . The Bored Ape Yacht Club has almost become a household name, especially for those who are NFT savvy. Seneca Bored Ape Yacht Club Seneca does not get a lot of press. In a from January of this year Rolling Stone To be clear, Seneca was not the project’s sole illustrator. “I am the lead artist behind the original collection,” she says. The ape body itself, she adds, is “exactly line-for-line” her drawing. Other production artists — “ , Migwashere, and a couple who chose to remain anonymous,” according to Gargamel — handled the traits and environment. However, she points out, she did develop some of the major traits, like the grinning mouth, the popping eyes, and the beanie. Thomas Dagley “ Not of ton of people know that I did these drawings, which is terrible for an artist,” she says. Word of mouth has been growing, though, and she hopes that will help her find more collaborations. While she’s not able to discuss financial specifics, her compensation, she says, “was definitely not ideal.” However, she insists, she’s grateful for the experience and the entryway to a realm she can no longer imagine living without. In the meantime, she’s focusing on her solo work. In December, Seneca dropped her debut series of NFTs under her own name as part of a collection called Iconoclast at Miami’s Art Basel. She is currently working on follow up series. Women NFT Projects making a mark The world of NFTs took off last year and it did not take too long for women to make their mark in the space. Some of these projects have a big following among female celebrities, most of them are about more than just art, they are about female empowerment, encouraging diversity, education, taking on charitable causes and making a difference in society. It is refreshing to see a movement in the NFT space that aspires to inspire. Here are some of the top projects and collections: World of Women ’s 2022 collection earned Yam Karkai USD 40 million within two weeks of its release. Back in mid-October, became the first from Hollywood to change her Twitter to a blonde-haired, blue-skinned WoW avatar, but the stampede arrived in January. WoW Reese Witherspoon profile picture In the first few days of the new year, both and purchased and paraded their shiny new NFTs; so did beauty influencer and entrepreneur , who has 50 million followers just , where her profile picture is a WoW avatar. Shonda Rhimes Eva Longoria Huda Kattan on Instagram WoW has also been doing monthly “ArtDrops” - airdropping pieces by lesser-known, emerging crypto artists from around the world, many of whom are women, into members’ virtual wallets. The goal, presumably, is to get closer and closer to flooding the playing field with fresh faces, one drop at a time. World of Women is not only making good use of utility NFTs and minting high end NFTs, they are getting behind some good causes as well. These include: She’s the first: an organization that supports local solutions to educate and empower girls across 26 countries. Code to inspire: a coding school for women in Afghanistan. : supports women victims of domestic abuse. La maison des femmes Women Rise The NFT collection was created by the 26-year-old artist, author, and activist who has been focusing on social justice, women's rights, gender equality, and girls' education. @Maliha_z_art Known as the ‘NFT Queen’ in the crypto space, is a global artist, activist, author and NFT creator. She believes the NFT industry will give artists a new space to showcase their collections. She is determined to bring over 100,000 girls and women to the NFT world by the end of 2022. Mahila Abidi Boss Beauties Lisa Mayer, the founder and CEO of said she is using her business selling NFTs as a vehicle to empower young women. The motto and inspiration behind this trending NFT project is, "A woman can be anything she wants." Boss Beauties Associated with , a charitable foundation that has been funding internships and scholarships for women and building a community of Gen Z change-makers for the past decade, Boss Beauties is the epitome of female empowerment in the NFT space. Sold out in just an hour, Boss Beauties also became the inaugural NFT collection to be featured in the New York Stock Exchange. My Social Canvas Remarkable Women NFT collection by is a celebration and ode to all women inspired by the perspectives of fashion, feminism, and cultural diversity. The collection showcases powerful messages of hope, inspiration, and affirmation in 15 languages. The Remarkable Women Rachel Winter Guided by a deep sense of purpose and meaning, Remarkable Women’s mission is to uplift and empower women by focusing on inclusion and representation. 10% of their first drop proceeds (about $50,000) were dedicated to their strategic partner the Fund for Women’s Equality. 10% of all secondary sales will be put into the Remarkable Women’s Impact Fund with proceeds decided and voted upon by their community members towards causes dedicated to women, justice, equality, and inclusion globally. Crypto Chicks Through her human-inspired NFT art, Ms Polly looks forward to encouraging gender and beauty diversity in the real and virtual worlds. Through ’s Women Artist Fund, they dedicate a minimum 5 ETH each quarter to buy unique pieces from up and coming talent. They showcase their art and stories within the community and through their public, media channels, along with raffling the pieces to community members. They also have a launchpad for women designers, creatives, and small retail businesses to develop the skill sets necessary to thrive in the web3 ecosystem and up level their businesses. Crypto Chick Women and Weapons NFT collection that features in the list includes 10,000 diverse, beautiful, and badass women NFTs by artist . The powerful art collection was first released in 2021. The was to speak to the dialectic nature between a weapon's power versus a woman's power and the femininity of a woman versus the “masculinity” of a weapon and create a conversation. If this image makes you uncomfortable, why does it make you uncomfortable? But James Bond holding the weapon doesn't? The weapons symbolize the in their day-to-day. Sara Baumann main purpose weapons that women carry Sara aims to make a difference in society and raise money for charity. She commits to donating 5 percent of the profits to Malala Fund Charity.** Sad Girls Bar “I think it takes courage to show that you’re vulnerable. It is really about the willingness to truly be ourselves. And especially in web3 people shouldn’t be afraid to express how they really feel.” - Glam Beckett Tweet 4/21/22 Proceed of sales supports “Women Side by Side” — a program of peer support for women delivered by Mind and Agenda, the alliance for women and girls at risk. We take emotions seriously and sometimes being sad or upset is OK, but conditions after emotional burnouts, self-harm episodes, or suicide thoughts often need a helping hand, support, and all attention. Flower Girls The Flower Girls counts Reese Witherspoon, Gwyneth Paltrow, Eva Longoria, Brie Larson, America Ferrera, Gary Vaynerchuk, Jimmy Connors, Brooklyn Beckham and President as collectors and community members. TIME Keith Grossman The Flower Girls has passed $15 million in sales. The project donated 20% of primary and secondary sales to children’s charities and 5% of primary and secondary sales to collect children’s NFT art. The project donated over $400,000 to children’s charities in February. Long Neck Ladies The founder, leader, and artist of this NFT project is an inspirational 13-year-old girl named , who started drawing at age four and created her first NFT at the age of nine. Nyla Hayes Last year Nyla created in the style of Long Neckie Ladies and is based on franchise. 1000 unique pieces TIME’s Women of the Year The 100 women featured in this drop include: Coco Chanel, Amelia Earhart, Anna May Wong, Eva Perón, Frida Kahlo, Billie Holiday, China Machado, Dolores Huerta, Gloria Steinem, Indira Gandhi, Tu Youyou, Diana, Princess of Wales, Madonna, Toni Morrison, Michelle Obama, Malala Yousafzai, Pussy Riot, and Beyoncé Knowles-Carter. You can find the full list of women featured . here A couple of female-led projects making moves recently: My BFF It is a new community entirely for women and non-binary people who want to get the NFT or cryptocurrency basics right. MyBFF is a learning-based initiative that also offers the latest industry news and reward systems for participation. There are many celebrities and artists who are a part of the founding team, such as Mila Kunis, Tyra Banks and Gwyneth Paltrow. __ __is co-founded by Brit Morin and Jaime Schmidt. Even key VC investors such as Katie Stanton, founder and general partner at , and Sarah Guo, venture capitalist and company-builder at , are a part of the collaboration. MyBFF Moxxie Ventures Greylock GroupHug Founded by , Mark Zuckerberg’s sister, is ‘a creator accelerator that offers funding and mentorship’ from her and other technology and art industry leaders. Randi Zuckerberg GroupHug GroupHug also promotes brand partnerships and gives PR exposure and institutional fundraising training among other things. HUG's mission of supporting diverse creators. Artist-in-residence program, each of our 1/1 artists are funded with a 4-5 digit grant to support their efforts in taking their artistic pursuit to the next level. They have a no-code database of over 300 projects and artists, with an emphasis on women and diverse creators/founders. GroupHug allows users to search and filter projects by founder attributes (Asian-founded, LBGTQIA-founded, etc) as well as mission/cause. They are looking to help women-led projects or startups with at least one female artist or co-founder. Their Hug Hub allows for creators to find other creators to collaborate with and get discovered by collectors looking for projects or art like yours. Six Degrees of Gary Vanyerchuck Anybody who is in the NFT Space knows who Gary Vee is, Chances are that you may have found out about NFTs from Gary Vayerchuck’s new life mission. He is all-in with NFTs and loves to give back to the community as well as other creators. A number of popular female artists got their break due to Gary Vee. Gary is very supportive of a lot of women getting into Web 3, educating them, making sure that they have the opportunity to have high visibility roles. One needs not to look too far from Gary to see this impact he has had not only in his buying power, but hiring practices. has a woman President in , who has had a successful track record already in the space and Gary could not have chosen a better person for a leadership position. Vayner NFT Avery Akkineni Speaking of buying power and the influence it can have on the buying habits of others in the space. World of Women's first collection sold out in a day after Gary Vaynerchuk promoted the NFTs. Gary Vee liked the concept of Women Rise so much, that he minted 10 NFTs at once, the max allowed per wallet. Women Rise Sara Baumann got her first big break with her Women and Weapons collection. Can you guess who was there to buy the first 3 pieces? Yep, Gary Vee. The rest of her , 10 paintings of women carrying weapons ranging from grenades to flamethrowers, then sold out in 45 seconds. genesis NFT collection Rebels with a Cause While there are some charitable causes in the crypto and NFT Space, women-led NFT projects set a great example with reaching out to help others. Some of the causes were mentioned along with the above projects, it is important to highlight some of the other change-makers. has proceeds earmarked for global causes such as women’s justice, equality, and inclusion, with this “remarkable” difference: It isn’t a board of supervisors that decide where the money goes and who it will impact. Its decides and votes. Remarkable Women’s Impact Fund community When you purchase an , is you gain all-inclusive access to a mental health app curated by the Alpha Girl Club Team. Some features of the mental health app will include their daily check-in system, access to mental health experts, exclusive content that will help you get through your day, and much more. Alpha Girls Club NFT partnered with to present a Artists Who Code exhibition which features NFT art by female and non-binary artists, thus popularizing female and non-binary role models in the space. Another important initiative to follow up on this exposure is to focus on the recruitment of women and other underrepresented groups in the NFT and tech industry, something most female-led collections like, for example, CryptoChicks have openly stated doing in their hiring philosophy.** ** Vellum LA Artsy Not a Conclusion, rather a  beginning... Web3 is allowing for a peer-to-peer economy and marketplace. The NFT art world took off like a rocket last year and for some it is about more than collecting and flipping NFTs, status symbol profile pics or just having too much crypto to spend. It is refreshing to see a movement with a cause, as opposed to ego-gloating personal finance and status. The utility NFT movement is gaining steam. Access to real life events, various resources, future collections, collaborate with like minded artists and also get behind common causes. The winds of change are here, the more that get in early and get involved in this young space of unlimited possibilities have a chance to build not only their own future of dreams, but to help others achieve the same. Women are making their mark in the NFT Space and allowing opportunities for other women to aspire to inspire. It is truly remarkable to see the creative power of women working together to build a better future together. Cover Image: Women and Weapons #0010 by Sara Baumann

Getting Past the Bro Barrier in the NFT Industry

A product designer should identify a market opportunity and develop a solution for the identified problem. Many startup product designers fall into the trap of endless innovation without considering the required marketing efforts. For example, when they use an idea evaluation matrix, their idea screening criteria list does not include marketing efforts. In my journey through life, I worked as a software engineer, a marketing strategist, and a product designer. So in this article, I share my approach to idea evaluation with you. I consider four types of ideas based on the marketability and innovation. Before starting the design of a new product, I discuss this with key people such as my cofounder, client, or our investors. Clone Idea I used the parasite as the symbol of the clone idea because this type of business idea lives on the innovation of others. In countries with closed markets, investors and media favor the startups cloning American/ European businesses. Because in closed markets, foreign companies can not compete usually. For example, in my country, Iran, the secret formula for creating an idea is: Iranian+ a successful western company such as Iranian Youtube I don't think cloning others' innovation is honorable, but investors believe investing in innovative ideas is similar to donating to charities in closed markets. They may pay a little to charities, but they do not invest in charities. Improving a proven idea This approach demonstrates to future investors that there is a market for your new product, but also it means that you are competing with more prominent companies. So your new features should provide cutting-edge technology. An AI-driven word processing application that aims to compete with Microsoft Word can exemplify this type of idea. For the marketing stage, you can use micro-targeting services. Furthermore, your company may need to allocate money for employing more traditional advertising methods to attract conservative customers. Smart Targeting Let me explain it with a real-world example. When canva.com launched, around 10 million graphic designers around the world were using Adobe Photoshop. It implies that there is absolutely a market for graphic design applications; we called it a known market, but entering the competition would be harsh for newcomers. Canva.com and similar projects such as Over (It became GoDaddy Studio) targeted non-graphic designers. Smart Targeted Ideas can benefit from paid and organic media. They can use both traditional and non-traditional advertising techniques, and it would be easier to convince the potential investors in contrast to the previous type. And if you are curious about the total users of Canva.com, I should tell you that 30 million users create graphic works in this app every month.   This is the magic of smart targeting, Pure Innovations Dramatic innovations disrupt the current norms and change human life forever. Technologists like us love to spending time on game-changing ideas. And what will happen if an idea is technically implementable in a short period with minimum costs? Caution, watch for the innovation trap! Launching a disruptive idea is more than technology and knowledge; it requires that a thousand things work in the right direction. As Kotler said : The boldest level of marketing occurs when a company introduces a product or service that nobody asked for and often could not even conceive of. No one in the 1950s asked for a Sony Walkman, a Sony Betamax, or a Sony 31/2-inch disc. Yet Sony, under its brilliant founder and chairman, Akio Morita, introduced those and many other new products that since have become everyday staples. Morita summarized his marketing philosophy in these words: "I don't serve markets. I create them” (Phillip Kotler, Kotler on Marketing). Creating markets is an ambiguous task. You need to have access to venture capitals, you should have access to key influencers in your industry, and have lobbyists to have your back in courts. Your pitch sentence may be irrelevant for most people. So it is essential to be in the right place to find the right people. You may need to travel hundreds of miles to connect with like-minded people. You can not  buy the market with your hard-earned money In other words, the marketing message becomes more complex when we want to present a totally innovative idea. For example, micro-targeting services such as App Store Search ads may work well for launching a new download manager, but they may not be effective in the same way for advertising a dating app for pets! Pure innovation usually faces no direct competitions, and It has enormous growth potential. It would be interesting for editors to publish articles about these ideas for free. But let me tell you a bitter truth: many creative applications will die in the Appstore's blindside; there are many innovative products in the Producthunt that are not featured. Do not underestimate the marketing and PR efforts. Earning organic media is not a straightforward process, and it becomes more challenging when you do not have access to the ecosystem. Idea Evaluation  Matrix There will be a time when we must choose between what is easy and what is right. -Albus Dumbledore Finally, before creating the idea evaluation matrix, you need to discuss the marketing aspects with the stakeholders. If you are an entrepreneur and you wish to launch your own startup, you should ask your self following questions: Can I afford the paid media costs? Do we have enough connections for spreading the word about this idea? How can we do networking to increase earned media? Although I think that each organization should have its own idea screening criteria matrix, I suggest the following factors to be in any idea selection effort: Market Size Technical Difficulty Cost Marketability Innovation Access to earned media Word of Mouth Potenitial Time This process is not limited to initial idea selection. For each iteration, you should use idea evaluation again, because it allows your organization to align marketing effort with development activities

Marketing Centric Idea Evaluation Process for Startups

Since FarmVille, gamers have been jumping from one farming game to the next in pursuit of the highest rewards. Digital tokens called non-fungible tokens (NFTs) have become a game-changer by giving gamers ownership rights to the in-game digital assets on their farm. That’s the allure of a new MMOG game called ’ whose farm animal NFTs are selling for an on the Chinese NFT marketplace Treasureland. The price of an MNA NFT for Berta the cow or Helga the sheep may seem exorbitant but it could be the best investment these gamers ever make in the blockchain. ‘My Neighbor Alice average price of 1,880 USD The social simulation game was designed to introduce gamers to blockchain gaming and the collectible digital tokens behind a global craze in gaming. NFTs turn digital assets into verifiable, tradable tokens that allow gamers to own their cherished digital goods. What they win, create, and buy in one gaming world they can now play, trade, and sell in another. This tamper-proof evidence of ownership in a collectibles market devalued by forgeries is behind a global boom in the trading of NFT-backed digital assets. The Chinese gamers are part of a surge in hobbyists during the COVID pandemic contributing to for newly tokenized collectibles such as sports trading cards. record high prices The throngs of gamers in China and Japan, the two largest online gaming markets, entering the NFT marketplace are pushing blockchain gaming into the mainstream. The infusion of NFTs into Asia’s gaming and consumer culture can be seen from China’s luxury goods market to Japan’s manga culture. The Rising Sun on Global Blockchain Gaming Readying for an onslaught of Asian gamers into blockchain gaming, more Asian crypto businesses are setting up shop in The Sandbox gaming metaverse. Most recently, leading Japanese crypto exchange Coincheck acquired a large number of LANDs in the form of ERC721 non-fungible tokens for its NFT marketplace. is the biggest development in the Japanese NFT market since J-Pop girl band top charter SKE48 instantly sold out its first set of NFTs in the fall. Japan’s beloved J-Pop bands, through the issuance of NFTs and promotion of crypto brands, have been one of several key influencers on gamers in the NFT market. CoinCheck’s NFT marketplace Asian investors, who have become the largest backers of NFT art, are another. Singapore-based investor Vignesh Sundaresan, known as Metakovan, recently for the first crypto art auctioned at Christie’s, Beeple’s ‘EVERYDAYS: THE FIRST 5000 DAYS.’ The runner-up was China’s Justin Sun of Tron and Rainberry (formerly BitTorrent). paid $69.3 million The Emerging Asian NFT Art Scene This year, crypto art will become fully immersed in Asian culture, as open and launch. The comics are being directed by none other than the creative director of Animoca Brands, the parent of The Sandbox, Kevin Abosch. Kevin, whose crypto art has sold for $1 million, is behind blockchain game F1 Delta Time, where tokenized Pirelli tires and engine blocks sell alongside $75k digital racing car collectibles as top-selling gaming NFTs. crypto art exhibits Manga-inspired crypto comic books Perhaps the ultimate status sign that NFTs have arrived in Asia is their presence in the Chinese luxury goods market. A pair of luxury sneakers is becoming a must-have item among some gamers. New Chinese digital brand is selling NFT-tokenized sneakers ranging in price from $1,500–$10,000, with one FEWO shoe by FEWOCIOUS recently . Atari sneakers with a holographic theme are currently best-selling NFTs. RTFKT fetching $28,000 Asia artists, like their counterparts elsewhere, see the opportunity to sell art and find jobs in the crypto economy. Over are listed on China’s BCAEX digital art gallery on the blockchain, which has sold close to half a million dollars in the crypto-collectibles. Meanwhile, Asian voxel artists are quickly making a name for themselves in NFT game art. of The Sandbox's most recent VoxEdit Celebrity Contest were from Japan. Many of them will find jobs creating digital assets for blockchain games. 10,000 tokenized artworks Two of the three winners Winning entry of voxel artist @tak_akkiy to The Sandbox VoxEdit Celebrity Contest Some of these gaming NFTs already have market value before the game releases, such as the charming avatars and fantastical creatures they fight in the ‘Hack the Tao’ game created by Italian artists Sergio Scalet and Nadia Squarci — a project incubated by . The award-winning ‘Hack the Tao’ crypto art has been exhibited globally. The Sandbox's Game Makers Fund Celebrity-backed brands are valued the highest. Digital game collectibles based on some of the more than 60 major IP brands including those of the Smurfs and Care Bears in The Sandbox already sell at a premium. Sandbox theme park owner to bring NFTs for gaming, music, and more to the Atari Metaverse. Rappers Ja Rule and Soulja, football great Rob Gronkowski, and techno-pop artist Grimes are some of the celebrities issuing NFTs, which could end up in games. Atari has partnered with Bondly The Global NFT Gaming Economy With digital wallets stashed full of NFTs, gamers can leave closed gaming worlds and participate in the global economy. Blockchain gaming shifts economic power from gaming owners to the players. The value of NFTs became material for many unemployed workers in developing countries during the COVID pandemic. In The Philippines, an ‘ ’ craze took hold as word spread that players were supplementing and even replacing their salary playing blockchain games. ‘Axi Infinity’ is the number one blockchain game on Ethereum, with close to 24,000 players. Axie Infinity , a community of blockchain gamers from The Philippines emerged to invest in NFT businesses and assets. The guild has set up its headquarters at its Sandbox estate, a recently acquired 144-plot estate, which is in addition to a 6x6 Sandbox estate and 88-plot Savanna estate in ‘Axie Infinity’. In addition to play2earn earnings potential, the guild expects members to benefit from job creation for guild members in open gaming economies. Yield Guild Games Because NFTs can be used across games on interoperable blockchains, the Chinese virtual farmers and Filipino Axie collectors could trade their digital items on other gaming platforms anywhere in the world. The NFT gaming world is rapidly expanding. Playtoearn lists 219 blockchain games with close to 10,000 NFTs already listed — a drop in the NFT bucket. The Sandbox has more than two dozen new games ready to launch in its metaverse. Each digital asset in a game — from a castle or dragon to a mushroom — can be turned into an NFT with the . VoxEdit digital asset and NFT maker

Farm Animal NFTs are Selling for an Average Price of 1,880 USD 

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