Hard to believe it’s been 10 years since AlphaGo! It was wonderful to catch up with Lee Sae Dol last week in Korea and join Shin Jin-seo for a special Go match. Great to reminisce about AlphaGo & super interesting to hear how it changed the way players approach the game of Go!

Groq is a Radically Different kind of AI architecture Among the new crop of AI chip startups, Groq stands out with a radically different approach centered around its compiler technology for optimizing a minimalist yet high-performance architecture. Groq's secret sauce is this compiler-first method that shuns complexity in favor of tailored efficiency. At the heart of Groq’s architecture is an almost surprisingly bare-bones design that does away with unnecessary logic in favor of raw parallel throughput. The hardware itself is comparable to an ASIC – an application-specific integrated circuit finely tuned for machine learning. However, unlike a fixed-function ASIC, Groq leverages a custom compiler that can adapt and optimize across different models. It is this combination of a streamlined architecture and an intelligent compiler that sets Groq apart. The key insight is that many AI chips stack components, like GPUs, that bring extraneous hardware and bloat. Groq returns to first principles, recognizing that machine learning workloads are about massive parallelism over simple data types and operations. By eliminating generic hardware and even concepts like locality, the design maximizes throughput and efficiency. This is enabled by Groq’s compiler that sits between software frameworks like TensorFlow and the hardware. The compiler analyzes and optimizes neural network graphs, tailoring and mapping them to the underlying architecture for accelerated execution. It breaks computations into the smallest operations to unlock parallelism. The compiler also enables capabilities like batch size 1 inference that ensures all hardware is usefully leveraged. Critically, Groq built its compiler before even finalizing the hardware design. The software insights directly informed the architecture. This co-design process allowed inference-specific optimization without legacy limitations. The compiler also provides deterministic guarantees of runtimes, enabling reliable scaling. Together, the Groq compiler and architecture form a streamlined, robust engine for machine learning inference. The innovative compiler-first methodology allows custom optimization that balances flexibility with performance. Rather than chasing complexity, Groq realizes less can be more when software and hardware align – a compelling recipe as AI workloads continue evolving.

Stephen Wolfram: AI language models are discovering things about the world in the same way that Aristotle did

10 years ago today, we lost Sir David MacKay FRS. Physicist. Mathematician. Polymath. Gone at 48. I was working on my PhD at Cambridge, and attended some of his last lectures and symposium. He was a reason that attracted me to Cambridge over MIT in 2014. His textbook, Information Theory, Inference, and Learning Algorithms, was the first ML book I ever read — recommended to me by none other than Geoff Hinton. He used that same information theory to build Dasher — a text entry system where users steer through a continuous stream of letters flowing toward them, with a probabilistic language model making likely next letters larger and easier to reach, so that any tiny movement — a finger, a gaze — becomes efficient writing. It was the first ML application that truly blew my mind, and sent me deep into a rabbit hole: arithmetic coding, PAQ8 compression, nonparametric models. A journey I partly owe to his PhD student Christian Steinruecken, who also happened to share my love of Japan. As Chief Scientific Advisor to the UK's Department of Energy & Climate Change, he brought a physicist's clarity to policy. In Sustainable Energy – Without the Hot Air, he ran the numbers on our entire energy diet — and made me confront an uncomfortable truth. One of the biggest single factors? Beef — roughly 1,000 days of cow-time per steak. Hard to argue with the data. Hard to act on it when you were born and raised in Japan. I'm still working on that one, David. At his final symposium in Cambridge — just a few weeks before his passing — the room told the full story. Geoff Hinton and his Caltech PhD advisor John Hopfield — both Nobel Prize winners in Physics 2024 — gave tributes. Environment policy advisers spoke. Dasher users sent video messages of thanks from around the world — people who found their voice because of him. It was extraordinary to witness, in one room, just how many minds and lives a single person had touched. The story of how Hinton first noticed him: at a conference workshop poster session, among everyone who stopped by, it was the young MacKay who asked the sharpest, most penetrating question. Hinton remembered it. That's how it begins. I've always liked physicists who cross into ML — they bring a groundedness, a refusal to hide behind formalism without meaning. David MacKay and Max Welling are the role models I point to. Not just for the mathematics they built, but for how they carried it: with humility, curiosity, and a stubborn insistence on reaching beyond academia. He seemed to know his time was limited, and gave everything anyway. His legacy stays.

Grok will never go to therapy. Never.

Last night I spoke with Brad Smith @ALScyborg, the first person with ALS to have @neuralink implanted. He has his voice back through AI and can even make dad jokes again. Absolutely incredible technology changing lives and bettering humanity. Thank you @elonmusk!