This month we tackle the topic of agentic AI. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), and Michael Littman (Brown University). Why is it taking off? Sanmay, perhaps you could kick off with what you noticed at AAMAS [the Autonomous Agents and Multiagent Systems conference]? Sanmay Das: It was very interesting because obviously there's suddenly been an enormous interest in what an agent is and in the development of agentic AI.

There has been a string of articles recently about the end of generative AI hype. Our experts consider whether or not the bubble has burst. Joining the conversation this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Michael Littman (Brown University), and Marija Slavkovik (University of Bergen). Sabine Hauert: There have been a number of recent articles in the mainstream media talking about the fact that AI has not made any money, and that it might be all hype, or a bubble. Marija Slavkovik: There is this article by Cory Doctorow which asks what kind of bubble AI is. I really like his take that a lot of bubbles come and go; some of them leave us something useful and some of them just generate something for a brief moment in time, like excellent revenue for the investment bankers for example.

This month, our trustees tackle the topic of conference reviewing. Joining the conversation are: Sanmay Das (George Mason University), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University) and Carles Sierra (CSIC). Lucy Smith: Our topic this month is the conference reviewing and publication process. It would be good to discuss some of the issues and then consider some possible improvements. Sarit Kraus: Well, where do we start…?! Carles Sierra: I mean, there are so many issues.

Code to Joy: Why Everyone Should Learn a Little Programming is a new book from Michael Littman, Professor of Computer Science at Brown University and a founding trustee of AIhub. We spoke to Michael about what the book covers, what inspired it, and how we are all familiar with many programming concepts in our daily lives, whether we realize it or not. The intended audience is not computer scientists, although I have been getting a very warm reception from computer scientists, which I appreciate. The idea behind the book is to try to help people understand that telling machines what to do (which is how I view much of computer science and AI) is something that is really accessible to everyone. It builds on skills and practices that people already have.

The recent launches of two large language models, ChatGPT and Galactica, have led to much interest and controversy amongst the AI community, and beyond. These models, and in particular their potential use for writing scientific articles (and essays), provided the inspiration for this month's discussion. Joining the discussion this time are: Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), and Lucy Smith (AIhub). Sabine Hauert: Has anyone had a chance to use any of these new models yet? Sarit Kraus: During the summer I played with the previous version of GPT. Have you tried the latest version, Michael?

This month, we tackle the topic of AI-generated art and what this means for artists. Joining the discussion this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), Lucy Smith (AIhub), Anna Tahovská (Czech Technical University), and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: This month our topic is AI-generated art. There are lots of questions relating to the value of the art that's generated by these AI systems, whether artists should be working with these tools, and whether that devalues the work that they do. Lucy Smith: I was interested in this case, whereby Shutterstock is now going to sell images created exclusively by OpenAI's DALL-E 2. They say that they're going to compensate the artists whose work they used in training the model, but I don't know how they are going to work out how much each training image has contributed to each created image that they sell.

This month, we ask if AI can make humans better. Joining the discussion this time are: Joe Daly (AIhub and University of Bristol), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), Lucy Smith (AIhub) and Oskar von Stryk (Technische Universität Darmstadt). Joe Daly: I recently saw this Twitter thread, about how AI has made human players better at the game of Go, then this article about the game of bridge, and more generally about AI's influence on us. People were actually discussing how AI can make us better at stuff, and how we can learn things from AI. What are people's thoughts on that?

This month, we get stuck into AI and consciousness. This topic has long been much-discussed, and especially so recently with one tweet in particular sparking a debate online. Joining the discussion this time are: Tom Dietterich (Oregon State University), Stephen Hanson (Rutgers University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University), Sarit Kraus (Bar-Ilan University) and Michael Littman (Brown University). Stephen Hanson: So, the topic of consciousness has come up a lot recently in discussions on the Connectionists. This area of cognitive science was pretty much wiped out in the first five years of NeurIPS [Conference on Neural Information Processing Systems].

The representation of AI in the media has long been a problem, with blue brains, white robots, and flying maths – usually completely unrelated to the content of the article – featuring heavily. Not too long ago, the team at Better images of AI released a gallery of free-to-use images which they hope will increase public understanding around the different aspects of AI, and enable more meaningful conversations. Joining the discussion this time are: Sabine Hauert (University of Bristol), Michael Littman (Brown University), Carles Sierra (CSIC), Anna Tahovska (Czech Technical University) and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: There are lots of aspects we can consider when thinking about AI images. For example, how can we source or design better images for AI? How should AI be represented pictorially in articles, blogs etc? What's the problem with images in AI?

This month, we take a look at all the things we are thankful for in the AI community. Joining the discussion this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University) and Carles Sierra (Artificial Intelligence Research Institute of the Spanish National Research Council). Holger Hoos: I think one can be really grateful that progress in AI has come at a point where we really need it. I think we've maneuvered ourselves as humankind into a situation where the limitations of our own natural intelligence make it very likely that we're going to drive ourselves against the wall. Issues such as climate change are simply too complex for us to figure out, even if you bring lots of smart people together and give them lots of resources.