Michael O’Rourke @michaeld7
Founder of Dimension 7(d7) immersivelearning.ai san francisco, ca Joined November 2006-
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Everyone posting about the Dwarkesh interview (including Dwarkesh himself!) is missing this subtle point. When LLMs imitate, they imitate the ACTION (ie the token prediction to produce the sequence). When humans imitate, they imitate the OUTPUT but must discover the action
Everyone posting about the Dwarkesh interview (including Dwarkesh himself!) is missing this subtle point. When LLMs imitate, they imitate the ACTION (ie the token prediction to produce the sequence). When humans imitate, they imitate the OUTPUT but must discover the action
OpenAI have 9x'd their capacity this year, and have a goal to 125x further by 2033, thus exceeding current energy capacity of the whole of India - as per @alexeheath reporting of @sama's internal slack note. One thing to note is that 125x energy capacity increase understates the…
~8 minutes in and i can already sense the difference in the abstraction layers at which Dwarkesh and Dr. Sutton operate
🚨 DeepMind finally dropped the Veo3 paper which shows what we all realize from playing with video-gen models. Just like LLMs, visual reasoning on is an emergent property of training on tons of video. It can solve tasks not explicitly in training data. "Veo 3 is the GPT-3…
.@RichardSSutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training…
Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices.…
Progress
Today we’re introducing GDPval, a new evaluation that measures AI on real-world, economically valuable tasks. Evals ground progress in evidence instead of speculation and help track how AI improves at the kind of work that matters most. openai.com/index/gdpval-v0
Very cool work from Meta Superintelligence Lab. They are open-sourcing Meta Agents Research Environments (ARE), the platform they use to create and scale agent environments. Great resource to stress-test agents in environments closer to real apps. Read on for more:
Google’s dominance of the Pareto frontier for AI has been shattered. And actually wild that Grok 4 Fast *also* has a 2m token context window.
Mustafa Suleyman (CEO of Microsoft AI): Today, LLMs are only one-shot prediction engines. With persistent memory and long-term predictions, they could soon be able to plan entire courses of action like humans. → Actionable AI by the end of 2026 – “breathtaking,” according to…
Grok 4 Fast is available in the xAI API. 💫 2M context window!
I departed Google DeepMind after 8 years. So many fond memories—from early foundational papers in Google Brain (w/ @NoamShazeer @ashVaswani @lukaszkaiser on Image Transformer, Tensor2Tensor, Mesh TensorFlow) to lead Gemini posttraining evals to catch up & launch in 100 days, then…
This is an insanely large world created using our 3D world generation model. It blew my mind!
This is an insanely large world created using our 3D world generation model. It blew my mind!
Is In-Context Learning Learning? The paper says yes, with clear limits. Peak results show up when the prompt includes 50-100 examples, not 2 or 5. The prompt acts like a temporary training set, the model reads examples and predicts the next label without changing its weights.…
If you're curious about how the latest spatial intelligence model is doing @theworldlabs, check out this new blog! I'm so excited by how much progress has been made in 3D world generation - bigger, more consistent, and forever persistent worlds! Moreover, everyone in the world…
If you're curious about how the latest spatial intelligence model is doing @theworldlabs, check out this new blog! I'm so excited by how much progress has been made in 3D world generation - bigger, more consistent, and forever persistent worlds! Moreover, everyone in the world…
I was lucky to work in both China and the US LLM labs, and I've been thinking this for a while. The current values of pretraining are indeed different: US labs be like: - lots of GPUs and much larger flops run - Treating stabilities more seriously, and could not tolerate spikes…
I was lucky to work in both China and the US LLM labs, and I've been thinking this for a while. The current values of pretraining are indeed different: US labs be like: - lots of GPUs and much larger flops run - Treating stabilities more seriously, and could not tolerate spikes…
Today, I’m excited to share with you all the fruit of our effort at @OpenAI to create AI models capable of truly general reasoning: OpenAI's new o1 model series! (aka 🍓) Let me explain 🧵 1/
IMO — Fei-fei is spot on... and this is why #SyntheticData for LLMs is a weak strategy for AGI Consider the following example: - Step 1: take a large LLM training dataset (e.g. "the whole internet") - Step 2: remove every sentence with the words "Kim" or "Kardashian" in it…
IMO — Fei-fei is spot on... and this is why #SyntheticData for LLMs is a weak strategy for AGI Consider the following example: - Step 1: take a large LLM training dataset (e.g. "the whole internet") - Step 2: remove every sentence with the words "Kim" or "Kardashian" in it…
Fei-Fei Li says language models are extremely limited. This @GoogleDeepMind paper makes almost the same point, just in the world of video. The models are just very advanced pattern matchers. They can recreate what looks like reality because they’ve seen so much data, but they…
Fei-Fei Li says language models are extremely limited. This @GoogleDeepMind paper makes almost the same point, just in the world of video. The models are just very advanced pattern matchers. They can recreate what looks like reality because they’ve seen so much data, but they… https://t.co/taTWVBiipH
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