Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction.We face a new epoch in computing. Hardware is changing rapidly — not just faster GPUs, but a growing range of chips from different vendors, each with its own architecture and often tailored to specific AI workloads. Software is changing just as fast, and AI coding tools now generate in minutes what took months of effort a few years ago.With so much of computing now centered on AI, GPU kernels are a crucial component of its success. These are the low-level programs that run inside the GPU, and writing efficient ones is far from obvious — it takes years of expertise to get right. Transferring a kernel from one vendor’s hardware to another is harder still, and often means rediscovering the same optimizations from scratch. The CUDA ecosystem, for example, has accumulated decades of hard-won kernel expertise:

.abbel-fig { display: block; text-align: center; margin: 2.4em 0; line-height: 1.4; max-width: 100%;}.abbel-fig img { display: block; margin: 0.65em auto 0; height: auto; max-width: 100%;}/* Image sizes; captions use a narrower measure below */.abbel-fig--wide img { width: 100%; max-width: 100%; }.abbel-fig--wide-90 img { width: 100%; max-width: 90%; }.abbel-fig--wide-lg img { width: 100%; max-width: 100%; }.abbel-fig--chart img { width: 100%; max-width: 82%; }.abbel-fig--chart-sm img { width: 100%; max-width: 64%; }.abbel-fig--portrait img { width: 50%; max-width: 520px; }.abbel-fig--equation img { width: 100%; max-width: 52%; }.abbel-fig--video { width: 110%; max-width: 110%; margin-left: -5%; margin-right: -5%; box-sizing: border-box;}.abbel-fig--video .abbel-frames { max-width: 100%; width: 100%;}.abbel-frames { margin: 0.65em auto 0; max-width: 100%; user-select: none;}.abbel-frames__stage { position: relative; cursor: pointer; border: none; background: transparent; line-height: 0

... government of the people, by the people, for the people ...The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1, and some providers are pushing costs below $0.10. Across benchmarks, inference prices have fallen between 9x and 900x per year, with a median decline near 50x. Even frontier models are getting dramatically cheaper each generation, with open-source models following closely behind. And crucially, even if “Nobel-Prize-winning genius-level” intelligence isn’t here yet, the intelligence that suffices for the vast majority of knowledge work is here today, and getting cheaper by the month. At this rate, we are soon entering the era of virtually free intelligence—the kind that is more than enough for everyday knowledge work.Aditya G. Parameswaran—an Associate Professor of EECS and co-director of the EPIC Data Lab at UC Berkeley—together with his collaborators. It is part landscape survey and

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning.Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare, and much more. Along the way, they have published influential research, built systems with real-world impact, mentored their peers, and shaped the BAIR community for the better.Now they are headed everywhere ideas travel: to faculty and postdoctoral positions, to industry research labs, and to startups of their own founding — and several are still exploring what comes next and would love to hear from you.Please join us in celebrating the achievements of these wonderful graduates. We are proud o

.apr-fig { text-align: center; margin: 1.35em 0; line-height: 1.4; }.apr-fig--wide img { display: inline-block; width: 100%; max-width: 100%; height: auto; vertical-align: middle; }.apr-fig--wide-0-8 { max-width: 80%; margin-left: auto; margin-right: auto; }.apr-fig--tall img { display: inline-block; max-height: 300px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; }.apr-fig--tall-1-2x img { display: inline-block; max-height: 360px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; }.apr-fig--tall-1-5x img { display: inline-block; max-height: 450px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; }.apr-fig--tall-2x img { display: inline-block; max-height: 600px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; }.apr-fig .apr-fig-cap { display: block; text-align: center; font-size: 0.9em; font-style: italic; margin-top: 1.35em;

.grasp-results-table table { font-size: 0.875rem; line-height: 1.35; width: 100%; }.grasp-results-table th,.grasp-results-table td { padding: 0.35rem 0.5rem; }/* Consistent whitespace between major sections (this post is long and hr-heavy) */article.post-content h2 { margin-top: 2.75rem; margin-bottom: 0.75rem;}article.post-content h2:first-of-type { margin-top: 2.25rem;}article.post-content h3 { margin-top: 1.65rem; margin-bottom: 0.5rem;}article.post-content hr { margin-top: 2.5rem; margin-bottom: 2.5rem;} GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshaping gradients so actions get clean signals while we avoid brittle “state-input” gradients through high-dimensional vision models.Large, learned world models are becoming increasingly capab

-->Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution, which isolates the specific input features driving a prediction (Lundberg & Lee, 2017; Ribeiro et al., 2022); data attribution, which links model behaviors to influential training examples (Koh & Liang, 2017; Ilyas et al., 2022); and mechanistic interpretability, which dissects the functions of internal components (Conmy et al., 2023; Sharkey et al., 2025).Across these perspectives, the same fundamental hurdle persists: complexity at scale. Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencie

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects.Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks.What matters in these systems is not how measurements look, but how much useful information they contain. AI can extract this information even when it is encoded in ways that humans cannot interpret.And yet we rarely evaluate information content directly. Traditional metrics like resolution and signal-to-noise ratio assess individual aspects of quality separately, making it difficult to

In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks.We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning.Problem setting: off-policy RLOur problem setting is off-policy RL. Let’s briefly review what this means.There are two classes of algorithms in RL: on-policy RL and off-policy RL. On-policy RL means we can only use fresh data collected by the current policy. In other words, we have to throw away old data each time we update the policy. Algorithms like PPO and GRPO (and policy gradient methods in general) belong to this category.Off-policy RL means we don’t have this restriction: we can use any kind of data, including old experience, human demonstrations, Internet data, and so on. So off-po

What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task. Despite the fact that word2vec is a well-known precursor to modern language models, for many years, researchers lacked a quantitative and predictive theory describing its learning process. In our new paper, we finally provide such a theory. We prove that there are realistic, practical regimes in which the learning problem reduces to unweighted least-squares matrix factorization. We solve the gradient flow dynamics in closed form; the final learned representations are simply given by PCA.Learning dynamics of word2vec. When trained from small initialization, word2vec learns in discrete, sequential steps. Left: rank-incrementing learning steps in the weight matrix, each decreasing the loss. Right: three time slices of the latent embedding space showing how embedding vectors expand into subspaces of increasing dimension at