Julian Schrittwieser
@Mononofu
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Member of Technical Staff at Anthropic | AlphaGo, AlphaZero, MuZero, AlphaCode, AlphaTensor, AlphaProof Gemini RL Prev Principal Research Engineer at DeepMind
Joined August 2007
I had a lot of fun chatting with @mattturck from MAD podcast this week! We talked trends in AI, RL and why it unlocks Agents, scaling and so much more: https://t.co/otGWjKGicL links to what we talked about and further reading:
Failing to Understand the Exponential, Again? My conversation with @Mononofu - Julian Schrittwieser (@AnthropicAI, AlphaGo Zero, MuZero) - on Move 37, Scaling RL, Nobel Prize for AI, and the AI frontier: 00:00 - Cold open: “We’re not seeing any slowdown.” 00:32 - Intro —
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I’m extremely excited about this! Accelerating science and unlocking new breakthroughs is what excites me so much about AI; I would love to hear what capabilities and features would be useful to accelerate your research!
We’re building tools to support research in the life sciences, from early discovery through to commercialization. With Claude for Life Sciences, we’ve added connectors to scientific tools, Skills, and new partnerships to make Claude more useful for scientific work.
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Our position has been consistent: AI will deliver enormous benefits and should be developed thoughtfully. We share the administration's goals of maximizing those benefits, managing risks, and advancing America's lead in AI. A statement from our CEO:
anthropic.com
A statement from Anthropic CEO, Dario Amodei, on Anthropic’s commitment to advancing America's leadership in building powerful and beneficial AI
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As a researcher at a frontier lab I’m often surprised by how unaware of current AI progress public discussions are. I wrote a post to summarize studies of recent progress, and what we should expect in the next 1-2 years: https://t.co/B7438Z9lOF
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I strongly recommend signing up - Anthropic is an amazing place to work at, I love the culture, the impact is massive and the pace is incredible! I’ll be at the event, happy to answer your questions there 🤗
We're hosting a social hour in London in early August for quant traders and developers. If you'd like to join us, meet researchers from our London office, and learn about the technical problems we're working on, please sign up at the following form: https://t.co/fWD4QsOPTk
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Someone today accused me of being anti-muslin. Not true at all. I rather like it.
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Anthropic's hosting a social in London in early August for quants & quant engineers interested in a career jump! Especially engineers: if you spend all day optimizing trading system performance, we are excited to chat to you. https://t.co/00YSS1jxTp
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New Anthropic research: Alignment faking in large language models. In a series of experiments with Redwood Research, we found that Claude often pretends to have different views during training, while actually maintaining its original preferences.
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I'm very excited to announce that I'm joining @AnthropicAI this week, after 10 amazing years at @GoogleDeepMind ! Thank you also to all the amazing people I got to meet and work with, and I'm really looking forward to meeting all my new colleagues 💖 ! https://t.co/hcb5YJR2SB
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I was especially excited that it managed to find a proof to P6, the hardest problem this year:
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THIS IS THE MOST UNDERVALUED COMPANY ON NASDAQ, according to Warren Buffett’s analysis. Buffett’s rule is simple: Buy real companies that earn real money at irrationally cheap prices. That description fits NextNRG Inc ($NXXT) perfectly. 📊 Facts: * Market Cap ≈ $200–250
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Our latest work AlphaProof, building on AlphaZero, LLMs and the @leanprover theorem prover, combined with AlphaGeometry 2 managed to solve 4 IMO problems and achieve silver-medalist level! 🚀 More at
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In the normal Turing test, an investigator tries to distinguish a human and an AI. I'm more interested in a stricter version: in each interaction the investigator chooses a preferred response; the AI tries to be chosen as many times as possible. https://t.co/K9dfswkIZw
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I’m joining Scale and we are starting a new safety lab! Hiring researchers interested in trustworthy evaluations, red teaming and scalable oversight. These areas require hands-on interaction with human data, and Scale is an unparalleled place to do it.
scale.com
SEAL: Scale’s Safety, Evaluations and Alignment Lab
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3. Can we significantly decrease the probability of sampling harmful behaviour by systematically filtering pre-training data? A kind of G-rated dataset?
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1. Language models can be thought of as simulators: https://t.co/BYFgaKSn4W 2. The types of agents they can simulate and the likelihood of sampling them depends on the distribution of the training data.
generative.ink
Simulacra and simulation in self-supervised models
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David Van Wie, director of the Johns Hopkins Applied Physics Laboratory—brings more than 40 years of defense-technology experience to students as an instructor in our Engineering for Professionals online master's program in mechanical engineering.
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I also wrote a brief summary of our new AlphaTensor paper in my blog:
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Also check out the neat cover animation, huge props to our design team!
Today in @Nature: #AlphaTensor, an AI system for discovering novel, efficient, and exact algorithms for matrix multiplication - a building block of modern computations. AlphaTensor finds faster algorithms for many matrix sizes: https://t.co/E18DezRPTL & https://t.co/SvHgsa0SNV 1/
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Excited to announce our latest paper, AlphaTensor, out in Nature today! We describe how we can discover new faster matrix multiplication algorithms using AlphaTensor, a new agent based on AlphaZero. https://t.co/2Nz9pws8Iu
https://t.co/iC9H5GE9Dm paper:
nature.com
Nature - A reinforcement learning approach based on AlphaZero is used to discover efficient and provably correct algorithms for matrix multiplication, finding faster algorithms for a variety of...
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Excited to share our latest work on AlphaCode! See our blog post and paper for more: https://t.co/VzOvtehFNw I also wrote up my personal brief summary:
deepmind.google
Solving novel problems and setting a new milestone in competitive programming.
Below is an example of a problem #AlphaCode can successfully solve, using the exact information seen on @codeforces, & the program that #AlphaCode writes. By @liyuajia, David Choi, @junyoungch, @NateKushman, @Mononofu, @RemiLeblond, Tom Eccles, James Keeling, @FelixAxelGimeno 2/
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The appearance of glass in this woman suggests a fragility we women know to be real. But Jesus can and does pick up the pieces and put us back together again.
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