Dating apps, booze and clubbing - Jane Austen's Emma comes into the 21st Century And your pushy best friend is trying to sort out your love life. It's Jane Austen's Emma, but not as you know it. For the uninitiated, the 1815 novel follows the charmed life of our protagonist in Regency England as she busies herself interfering in her friends' relationships (or matchmaking, depending on your point of view). In Ava Pickett's fresh adaptation, being staged at London's Rose Theatre, Emma Woodhouse still has all the trademark traits of our beloved original heroine - she's clever, quick-witted, meddling, haughty and occasionally cruel. But instead of navigating society balls and dowries, Pickett's modern Emma is poking her nose into her friends' online dating profiles, having returned home after failing her exams at Oxford University.

Large language models (LLMs) exhibit remarkable performance improvement through in-context learning (ICL) by leveraging task-specific examples in the input. However, the mechanisms behind this improvement remain elusive. In this work, we investigate how LLM embeddings and attention representations change following in-context-learning, and how these changes mediate improvement in behavior. We employ neuroscience-inspired techniques such as representational similarity analysis (RSA) and propose novel methods for parameterized probing and measuring ratio of attention to relevant vs. irrelevant information in Llama-2 70B and Vicuna 13B. We designed two tasks with a priori relationships among their conditions: linear regression and reading comprehension. We formed hypotheses about expected similarities in task representations and measured hypothesis alignment of LLM representations before and after ICL as well as changes in attention. Our analyses revealed a meaningful correlation between improvements in behavior after ICL and changes in both embeddings and attention weights across LLM layers. This empirical framework empowers a nuanced understanding of how latent representations shape LLM behavior, offering valuable tools and insights for future research and practical applications.

Jessamine Chan's debut novel, The School for Good Mothers, is not a domestic manual on keeping house. Nor is it the sort of slog that might make tidying look like an appealing alternative. Yet as I read it over the course of one snowy evening, I repeatedly put it down to complete household tasks normally ignored until morning. Every last sock met its match. This book is a horror story so potent it will fill even the most diligent parent with an itchy impulse to panic-clean, to straighten up, to act like someone's watching.

When AI agents don't align their actions with human values they may cause serious harm. One way to solve the value alignment problem is by including a human operator who monitors all of the agent's actions. Despite the fact, that this solution guarantees maximal safety, it is very inefficient, since it requires the human operator to dedicate all of his attention to the agent. In this paper, we propose a much more efficient solution that allows an operator to be engaged in other activities without neglecting his monitoring task. In our approach the AI agent requests permission from the operator only for critical actions, that is, potentially harmful actions. We introduce the concept of critical actions with respect to AI safety and discuss how to build a model that measures action criticality. We also discuss how the operator's feedback could be used to make the agent smarter.

Visual object recognition, speech recognition, machine translation – these are among the "holy grails" of artificial intelligence research. But machines are now at a level that the benchmark performance for these three areas has reached, and even surpassed, human levels. Moreover, in the space of 24 hours, a single program, AlphaZero, became by far the world's best player in three games – chess, Go, and Shogi – to which it had no prior exposure. These developments have provoked some alarmist reporting in the media, invariably accompanied by pictures of Terminator robots, but predictions of imminent superhuman AI are almost certainly wrong – we're still several conceptual breakthroughs away. On the other hand, massive investments in AI research, several hundred billion pounds over the next decade, suggest further rapid advances are not far away.