In cellular automata, simple rules create elaborate structures. Now researchers can start with the structures and reverse-engineer the rules.
An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
A pilot program allows teachers to use AI to tackle their classroom problems. Researchers found that teachers learned to build and customize tools quickly, but successful integration depended on solving specific problems rather than just seeking efficiency.
Following Altman’s exit, Sutskever is tasked with guiding the team towards developing AI systems that not only push the boundaries of technology but also ensure they align with human values and safety protocols. His leadership style is expected to emphasize collaboration, transparency, and ongoing dialog with various stakeholders, including researchers, policymakers, and the public.
This blog post details the training of 'Chess Llama', a small Llama model designed to play chess. It covers the inspiration behind the project (Chess GPT), the dataset used (Lichess Elite database), the training process using Huggingface Transformers, and the model's performance (Elo rating of 1350-1400). It also includes links to try the model and view the source code.
This article discusses the history of AI, the split between neural networks and symbolic AI, and the recent vindication of neurosymbolic AI through the advancements of models like o3 and Grok 4. It argues that combining the strengths of both approaches is crucial for achieving true AI and highlights the resistance to neurosymbolic AI from some leaders in the deep learning field.
Andrej Karpathy discusses the transformative changes in software development driven by large language models (LLMs) and artificial intelligence, comparing the current era to the early days of computing. The article details Software 3.0 as the latest evolution in software development paradigms, where LLMs are programmable systems that interpret natural language prompts.
The article discusses how AI is forcing institutions like schools, governments, and corporations to re-evaluate their purpose and adapt to a world where machines can increasingly perform cognitive tasks. It argues that institutions must become more adaptive, transparent, and focused on uniquely human values to remain relevant.
PhD student Sarah Alnegheimish is developing Orion, an open-source, user-friendly machine learning framework for detecting anomalies in large-scale industrial and operational settings. She focuses on making machine learning systems accessible, transparent, and trustworthy, and is exploring repurposing pre-trained models for anomaly detection.
Daniel M. Russell's talk will review the successes and failures of past UX research approaches, drawing on decades of experience at the intersection of human experience and intelligent systems, to explore how AI will shape future knowledge practices.
