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

Akkio

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.

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