A research team from Google, Google DeepMind, the University of Maryland and the University of Virginia has introduced Dream-RSI, a framework that uses an AI agent’s previous discovery history to improve how it searches. The agent first explores algorithm design, mathematical optimization or GPU-kernel engineering and records its decisions and results as a tree. A separate policy-development loop then tests thousands of alternative exploration strategies against that stored tree, without rerunning the underlying experiments. The strongest strategy returns online for the next round. In the paper’s tests, Dream-RSI reached competitive or better results while reducing discovery cost, including up to 162 times fewer agent calls than SimpleTES on one algorithm task.

The technical shift happens one layer above the model. Dream-RSI leaves the coding agent unchanged and tries to improve the rules that decide where to branch, what to run in parallel and when to stop. That design turns paid-for history into a laboratory for cheaper decisions, yet it also confines imagination to paths the earlier search actually visited. A policy can avoid recorded dead ends while remaining blind to a route no one has tried. For scientific work, the evaluator and the stopping rule therefore carry as much authority as the model producing each proposal. This is early research, far from proof of unrestricted self-improvement or artificial general intelligence. The project page says full code and reproduction scripts are still being prepared. Dream-RSI turns yesterday’s search into tomorrow’s policy; the next audit is whether it can learn from a mistake the old tree never recorded.

An AP-NORC Center for Public Affairs Research poll released Wednesday found that 53% of U.S. adults are extremely or very concerned about artificial intelligence’s environmental impact, up from 41% last year. Roughly six in ten support limiting the number of new data centers that can be built, and a majority express high concern about electricity prices or water supplies in the communities where those facilities operate. The survey arrives as thousands of centers run and more projects move toward rural areas, where the server hall can be a community’s largest new industrial neighbor.

Public opinion does not measure a facility’s emissions or settle a permitting case. It does change the political setting for expansion. A data center’s benefits are usually described in national terms—capacity, jobs, competitiveness—while its costs appear locally as substations, cooling systems, noise and utility bills. The poll gives residents a shared vocabulary for asking who receives the gains, who carries the risk and what evidence a company must provide before construction. The answers will vary by watershed and power market, yet the direction is clear: acceptance can no longer be assumed from the promise of progress. Developers and officials could publish water and power forecasts, disclose how often workloads can be reduced during grid stress, and let communities revisit commitments when conditions change. The machine may run in the cloud; the decision to host it is made at a town meeting.

Bill Gates said Tuesday that governments are unprepared for the changes artificial intelligence will bring, while the Gates Foundation pledged at least $1 billion over the next two years to widen access. The money will support tools for health workers, teachers and small farmers, training data in local languages, and projects intended for communities that commercial systems routinely overlook. The announcement arrived with the foundation's 2026 Goalkeepers report, which asks whether AI will reduce the distance between rich and poor or add to it.

A private foundation is stepping into a gap that public institutions have left open. Its grants can help a clinic in Rwanda, a classroom in India or a farm in Andhra Pradesh use systems built around local needs rather than English-first assumptions. They also give one philanthropic organization a powerful hand in deciding which languages receive data, which services receive pilots and which failures become acceptable. “Access” can sound generous while leaving ownership, maintenance and appeal outside the user's reach. The program would gain public value by publishing dataset licenses, reporting error rates by language and context, and giving local health ministries, schools and farmer groups authority over procurement and withdrawal. A billion dollars can open doors, but the key should not remain with the donor.