"Who put Bella down the Wych Elm - Hagley Wood?" A famous unsolved murder mystery memorialized by graffiti in England, I ran across it twice this month. The first instance of this graffiti was seen on the wall in Birmingham Fruit Market, in chalk, on March 30, 1944. Then it morphed into "Who put Luebella in the Wych Elm" on March 31 (reddit source). In 1999, a version appeared on the obelisk on Wychbury Hill as seen above and has remained ever since, even after restoration of the obelisk. The graffiti, by unknown writers, evidently refers to the body of a murdered woman found in an elm in Hagley Wood in April 1943. Robert Hart, of Wollescote, Stourbridge, told the Coroner and jury how at midday on Sunday, 18 April, he and three other lads when birdsnesting in the wood.

The way we think about AI is shaped by works of science-fiction. In the big picture, fiction provides the conceptual building blocks we use to make sense of the long-term significance of "thinking machines" for our civilization and even our species. Zooming in, fiction provides the familiar narrative frame leveraged by the media coverage of new AI-powered product releases. As a result, the dominant view in the popular imagination today is that AI is about creating artificial minds, agents with a will of their own. These agents, since they possess a similar kind of autonomy as their human creators, may decide to pursue their own goals, and eventually turn against humans.

ChatGPT has taken the internet by storm. People have been using it to compose music, understand complex topics, make jokes, write movie scripts, and even debug computer codes. Such is the bot's popularity; it took only five days to score its first million users. This detailed tutorial explains precisely how to use ChatGPT. But before we delve into the details, let's first consider what ChatGPT is and what's causing the huge buzz surrounding the latest AI tool.

As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data.

Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.

Web3 and emerging technologies have been pushing the boundaries of art distribution, ownership and engagement with fans. However, not all of the recent developments are welcomed by the art community, especially when it comes to artificial intelligence (AI). Recently, AI-generated art has sparked a major debate around ownership after a smartphone app went viral which created AI-generated portraits. The debate around ownership of intellectual property (IP) rights is similar to those seen in the film and music industries. However, developers in the emerging tech space say blockchain technology can provide a middle for artists and AI-generated content.

This report is a deep dive into the world of Gen-AI--and the first comprehensive market map available to everybody. We provide an overview of over 160 platforms in the space and their investors, as well as insights from leading thought leaders on the potential of this technology. This hands readers a unique opportunity to gain a comprehensive understanding of the generative AI market and the potential for new players to challenge established players like Google. Please note: The information provided in this piece is based on Antler's day zero investment approach and the support we provide to founders around the world. The platforms featured in our industry mapping are sourced from Crunchbase.

Web3 and emerging technologies have been pushing the boundaries of art distribution, ownership and engagement with fans. However, not all of the recent developments are welcomed by the art community, especially when it comes to artificial intelligence (AI). Recently, AI-generated art has sparked a major debate around ownership after a smartphone app went viral, which created AI-generated portraits. The debate around ownership of intellectual property (IP) rights is similar to those seen in the film and music industries. However, developers in the emerging tech space say blockchain technology can provide a middle for artists and AI-generated content.

The pace of innovation this year has been remarkable--and at times overwhelming. Who could have seen it coming? And how can we predict what's next? Luckily, here at MIT Technology Review we're blessed with not just one but two journalists who spend all day, every day obsessively following all the latest developments in AI, so we're going to give it a go. Here, Will Douglas Heaven and Melissa Heikkilä tell us the four biggest trends they expect to shape the AI landscape in 2023.