This month we tackle the topic of young people and what AI tools mean for their future. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Michael Littman (Brown University), and Ella Scallan (AIhub). As AI tools have become ubiquitous, we've seen growing concern and increasing coverage about how the use of such tools from a formative age might affect children. What do you think the impact will be and what skills might young people need to navigate this AI world? I met up with a bunch of high school friends when I was last in Switzerland and they were all wondering what their kids should study. They were wondering if they should do social science, seeing as AI tools have become adept at many tasks, such as coding, writing, art, etc. I think that we need social sciences, but that we also need people who know the technology and who can continue developing it. I say they should continue doing whatever they're interested in and those jobs will evolve and they'll look different, but there will still be a whole wealth of different types of jobs.

Hannah Fry: 'AI can do some superhuman things - but so can forklifts' Mathematician Hannah Fry travels to the front lines of AI in her new BBC documentary AI Confidential with Hannah Fry. The chances are that you think about artificial intelligence far more today than you did five years ago. Since ChatGPT was launched in November 2022, we have become accustomed to interacting with AIs in most spheres of life, from chatbots and smart home tech to banking and healthcare. But such rapid change brings unexpected problems - as mathematician and broadcaster Hannah Fry shows in AI Confidential With Hannah Fry, a new three-part BBC documentary in which she talks to people whose lives have been transformed by the technology. She spoke to New Scientist about how we should view AI, its role in modern mathematics - and why it will upend the global economy.

The advent of large language models (LLMs) [Brown et al., 2020] has revolutionized the field of

Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.

InFigure 7 of this Appendix, we show that indeed this is due to a decrease in the robustness slope. Across three different datasets, MNIST, CIFAR10, NewsGroup20, we see that increasing the number of tasks leads to a decrease in the robustness slope. Experiments on other languages For our experiments on multilingual generative models, we decided to use Greek and English because we were looking for a linguistic pair with different morphology,syntaxandphonology. This ensures that any benefits in terms of robustness are not coming from exposure to more data. Asshownin Figure 8,eventhough thetwomodels arestarting from roughly thesame perplexity,thebilingual model exhibits higher structural robustness in the presence of weight deletions.

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However, they depend on scarce experimental data or time-consuming docking simulation, leading to overfitting issues with limited training data and slow generation speed.

High-quality, diverse pre-training corpora form the cornerstone for developing powerful foundation models, enabling AI assistants like ChatGPT [47] to exhibit balanced competencies across a broad spectrum of tasks [11].