Viewpoints, commentary, analysis, interviews, and discussion of trends.
In their conversation, Google DeepMind's Pushmeet Kohli and Biohub's Sal Candido pointed out that AlphaFold's breakthrough is only the beginning, protein structure prediction is far from solved, and protein dynamics and disordered structures remain difficult problems.
Toby Ord's analysis says AI swarms are a new form of inference-time scaling. A four-agent swarm uses about twice the total tokens needed by a single agent, but each agent needs only half as many tokens; running them in parallel could theoretically halve task time.
DeepMind researchers proposed Artificial Symbiotic Intelligence, arguing that AI research should focus on coordinating complex networks of agents, people, and connected systems rather than building isolated machine intelligence.
A former Google DeepMind researcher says an LLM's chain of thought is simply an extended iteration of the same next-token prediction process, not an independent reasoning mechanism like AlphaGo's. He identifies three major limitations: no persistent, inspectable cognitive state; knowledge and reasoning tangled together in the weights; and chains of thought that are often fabricated after the fact. He argues for borrowing AlphaGo's game-tree architecture to give machine reasoning an explicit cognitive state.
Google's Empirical Research Assistance (ERA) uses Gemini to maintain a tree of experimental notebooks, select and mutate candidate approaches using an Upper Confidence Bound rule, and automatically solve scientific problems that can be expressed as scoring functions. It has produced at least ten papers.
