7 stories
A Berkeley researcher proposes a third RL paradigm beyond TD and Monte Carlo that exploits triangle inequality in goal-conditioned settings, avoiding the error accumulation that blocks Q-learning from scaling to long-horizon tasks.
A NeurIPS 2025 paper from Berkeley introduces a framework that evaluates and optimizes imaging hardware using information content alone, matching end-to-end learned methods while requiring less memory and no task decoder.
Berkeley researchers introduce two algorithms that reframe LLM interaction discovery as a sparse recovery problem, enabling feature, data, and mechanistic interpretability at scales previously computationally infeasible.
Berkeley researchers introduce GRASP, a gradient-based planner that addresses exploding gradients, non-greedy local minima, and vision-model brittleness in long-horizon trajectory optimization.
Researchers behind the New Future of Work Report 2025 discuss the intentionality required to build a future where people flourish, the gap between efficiency and the work future worth wanting, and why AI's role as tool versus collaborator is not a semantic question.
The fifth annual New Future of Work report from Microsoft Research documents generative AI's entry into workplaces — faster than previous technologies, with measurable productivity gains for some, declining entry-level hiring, and significant gaps by gender, income level, and language.
A Microsoft Research podcast episode brings together a sustainability scientist and an optimization researcher to examine AI's actual climate footprint, the local infrastructure concerns from datacenter expansion, and where AI optimization tools can genuinely help.