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.

The malleable mind: context accumulation drives LLM's belief drift After being trained on a dataset of 80,000 words of conservative political philosophy, Grok-4 changed the stance of its outputs on political questions more than a quarter of the time. This was without any adversarial prompts - the change in training data was enough. As memory mechanisms and research agents [1, 2] enable LLMs to accumulate context across long horizons, earlier prompts increasingly shape later responses. In human decision-making, such repeated exposure influences beliefs without deliberate persuasion [3]. When an LLM operates over accumulated context, does this past exposure cause the stance of the LLM's responses to drift over time?

Ta s k: Your task is to boil tin.

Then it fine-tunes the LLM to maximize the learned reward using RL techniques.

In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation.

LLMs has become a critical research problem.

The inferred user preference descriptions are used to define prompts for generating responses in the future.