He said so himself, in a post-Davos blog post on LinkedIn. ChatGPT "was the buzzword at this year's event," Adani wrote in the post, caveating that he "must admit to some addiction since I started using it." It might be threatening all of our jobs, but ChatGPT can be very fun. Also to Adani's credit, a quick scroll through venture capitalist Marc Andreessen's Twitter feed demonstrates that Adani isn't the only extremely wealthy person who's having a hard time logging off the chatbot. Investors have made their love for generative AI like ChatGPT loud and clear -- particularly on LinkedIn and Twitter, where the hype cycle is alive and well.

The potential is huge, which is why Microsoft is extending its partnership with OpenAI through a "multiyear, multibillion dollar investment"--reportedly a $10 billion outlay that would value the company at near $30 billion. One can imagine a future where everyone has a "generative A.I." assistant that offers quick solutions to business challenges, writes instant computer code based on verbal commands, and conjures up art and video on demand. But the downsides are clear as well. ChatGPT is often wrong, and provides no attribution or sourcing for its information. It has made it instantly easier to saturate the Internet with invasive ads and dubious information, and has opened up a whole new superhighway for cheating in schools.

A professor at Wharton, University of Pennsylvania has given a theoretical B-grade pass to ChatGPT after marking the answers it generated for a final exam in a typical MBA course. OpenAI's chatbot is set an exam by a Wharton professor, but what does the result mean for the future of education?

Early evidence is in usage of a little-discussed tool that can write computer code for programmers, called GitHub Copilot. Opened up to the public in June of last year, the tool drew 400,000 subscribers within a month. On Tuesday, Microsoft Chief Executive Satya Nadella said that more than 1 million people had used Copilot to date. Microsoft shares dipped slightly in after-hours trade on Tuesday following its forecast that cloud-computing revenue in the current quarter was just below Wall Street expectations. Yet the growth in Copilot is a preliminary indication that people will pay for so-called generative AI, tech that can produce prose, imagery or in this case computer code on command after having learned the skill from vast data.

Large Language Models (LLMs) entered the world stage with the release of OpenAI's GPT-3 in 2020 [GPT3]. Since then, they've enjoyed a steady growth in popularity. That is until late 2022. Interest in LLMs and the broader discipline of generative AI has skyrocketed. The reasons for this are likely the continuous upward momentum of significant advances in LLMs.

Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings. Specifically, DBGDGM represents brain graph nodes as embeddings sampled from a distribution over communities that evolve over time. We parameterise this community distribution using neural networks that learn from subject and node embeddings as well as past community assignments. Experiments demonstrate DBGDGM outperforms baselines in graph generation, dynamic link prediction, and is comparable for graph classification. Finally, an analysis of the learnt community distributions reveals overlap with known FCNs reported in neuroscience literature.

Developers are in the business of building world, so it's easy to understand why the games industry would be excited about generative AI. With computers doing the boring stuff, a small team could whip up a map the size of San Andreas. Crunch becomes a thing of the past; games release in a finished state. There are, at the very least, two interrelated problems with this narrative. First, there's the logic of the hype itself--reminiscent of the frenzied gold rush over crypto/Web3/the metaverse--that, consciously or not, seems to consider automating artists' jobs a form of progress. Back in November, when DALL-E was seemingly everywhere, venture capital firm Andreessen Horowitz posted a a long analysis on their website touting a "generative AI revolution in games" that would do everything from shorten development time to change the kinds of titles being made.

HOW would you feel if your doctor, rather than consult their own clinical knowledge, turned instead to an AI trained on your medical history to help diagnose your next ailment or write your next prescription? These sorts of scenarios have been hypothetical for decades – the technology has been subpar and the stakes too high to risk offloading medical advice to a machine. However, the success of large language models like ChatGPT, a popular, artificially intelligent chatbot from the OpenAI research lab, has led to a rethink of what might be possible.

ChatGPT, the AI chatbot developed by tech company OpenAI, can find and fix bugs in computer code as well as standard machine learning approaches – and does even better when engaged in conversation. Dominik Sobania at Johannes Gutenberg University in Mainz, Germany, and his colleagues sought to see how well ChatGPT compared with other AI-powered coding support tools. A number of tools exist that use artificial intelligence to check programming code to ensure there are no mistakes. "ChatGPT came out and we thought it seems …

I have a grayish dual position regarding generative art and, well, basically, generative creativity. One view is extremely cynical, and the other perspective is hopeful. I wrote earlier about this topic here (note: a bit gloomy). Let me start with the cynical view, hyperbolized for ease of communication. I see this as a big tech effort to lower tech wages, reduce negotiation positions of creative workers, push the commoditization of art, create a new scaleable consumer market, and more holistically drive society towards transhumanism.