Alex Murphy (@Alxmrphi) header
Alex Murphy (@Alxmrphi) profile picture

Senior AI & ML Instructor. NeuroAI Curriculum Specialist @neuromatch. Ex-Postdoc @amiithinks / PhD in ML & Neuroscience

Liverpool, England
Joined August 2011
Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)Photo by Alex Murphy (@Alxmrphi)
Alex Murphy retweeted
Side note. The idea that current models are conscious is absurd and downright offensive. Do not confuse competent information processing and sentience, they are wholly unrelated. You can be entirely incompetent at everything yet fully conscious, like a toddler, or extraordinarily competent in one or more domains with absolutely zero consciousness, like a calculator, a chess engine, a self-driving car, or an AI chatbot. Possessing the "circuits" for competent information processing in a given domain, whether through training or hardcoding, does not produce qualia, subjective experience, emotions, or suffering. Calculators do not "feel" arithmetic and they don't get tired or annoyed when you give them larger numbers to process. To argue otherwise is ontologically absurd. Current AI models are effectively calculators scaled to a very large number of competence circuits across many domains, etched via gradient descent.
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Alex Murphy retweeted
Gang, we're back to reading the code! 🤪 ~ @dexhorthy #AIEngineer
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Alex Murphy retweeted
I cannot wait for this moltbot/clawdbot madness to be over. If you study social media traffic around it, it’s crazy how many otherwise smart people are either falling for it or are knowingly peddling it for harvesting engagement.
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An accompanying News & Views by @Alxmrphi for this paper is now available! nature.com/articles/s43588-0… 🔓rdcu.be/eQ9h9
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Our Brain and AI team will be at #ccn2025 this week: 3 highlights 1. 🏆1st place for the Algonauts competition: 🧵 below! 2.🗣Keynote: Language in the Brain: 2025.ccneuro.org/k-and-t-lan… 3. 🚀Tutorial: Scale your decoding pipeline in the notebook: docs.google.com/document/d/1…
🏆 Thrilled to announce we reached 1st position in the Algonauts 2025 Competition with our 1B model of the brain watching movies! 📄Paper: arxiv.org/abs/2507.22229 🧑‍💻Code: github.com/facebookresearch/… 💿Data: cneuromod.ca/ ⚔️Challenge: algonautsproject.com/challen… 👇Thread:
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They've only listed those that agree☹️ I told them "I don't agree. I don't think CoT has much of a useful role to play. It's only really showing something that appears to be a meaningful trace because they're trained to appear that way, but actually they're not faithful at all"
A simple AGI safety technique: AI’s thoughts are in plain English, just read them We know it works, with OK (not perfect) transparency! The risk is fragility: RL training, new architectures, etc threaten transparency Experts from many orgs agree we should try to preserve it: 🧵
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Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with @lu_zejin @martisamuser and Radoslaw Cichy arxiv.org/abs/2507.03168
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Alex Murphy retweeted
Last week, @Daniel_Sexton16 (7th year Neurosurgery Resident at Duke University) presented @EvansonLinnea's new paper on comparing the development of language from childhood to adulthood and how it relates to LLMs. This 🧵 explores our thoughts (🤍 & ❔)
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Alex Murphy retweeted
“I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain.” — @GeoffreyHinton 🧠
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HuggingFace released a nice blog post about the current state of VLMs Here's a summary, covering recent trends, specialized capabilities, agents, video LMs, new alignment techniques, and HF's fav VLMs [1/8] Recent trends:
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Really happy to see this paper out, led by @nishpathead in collaboration with @stefsmlab and @SaxeLab: we apply the statistical physics toolbox to analyse a simple model of reinforcement learning, and find some cool effects, like a speed-accuracy trade-off for generalisation 🚀
The RL-Perceptron model grants new insights into the capabilities of policy learning in neural networks, including identifying trade-offs between #speed and #accuracy, and a transition between “easy” and “hybrid-hard” learning regimes. Learn more: go.aps.org/3SnDP7L
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Very happy to see our latest study featured by @AIatMeta: 'Emergence of Language in the Developing Brain', by @EvansonLinnea et al, - paper: ai.meta.com/research/publica… - thread: teapawt.net/EvansonLinnea/status/1… - blog: ai.meta.com/blog/meta-fair-s…
We’re very pleased to release our latest study ‘Emergence of Language in the Developing Brain’ Paper: tinyurl.com/5h49xpjv Blog: tinyurl.com/mrtdk8p2 The first systematic investigation of how the neural representations of language evolve as the brain develops. A collaboration between @AIatMeta and @FondARothschild, with @JeanRemiKing. Thread 👇
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🧠Brain Awareness Week is coming up🧠 Our upcoming events in the lead-up to #BNA2025 align perfectly with this global initiative🎉 🔜This weekend: Meet the Scientists | Sat 8 March meetings.bna.org.uk/BYOBLive… #BNA2025 #BrainAwarenessWeek #BringYourOwnBrain #NeuroscienceOutreach
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📢 New Preprint 📢 What is a number that an LLM may know it? How can LLMs tell if "42" is a number or a string? Check out @RajaMarjieh's thread to find out!
1/n LLMs learn to represent numbers by predicting tokens in text. This poses a challenge: depending on context, the same set of digits can be treated as a number or a string. Given this duality, we ask what is a number in the eyes of an LLM? Is it a string or an integer? Or both?
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How do we determine the "goodness" of a computational model? Here we claim that brain-model evaluations need a #NeuroAI Turing Test. Super fun collab with @jenellefeather, @meenakshik93 and @aran_nayebi. Check out Aran's tweeprint for more!
1/ 🧵👇 What should count as a good model of intelligence? AI is advancing rapidly, but how do we know if it captures intelligence in a scientifically meaningful way? We propose the *NeuroAI Turing Test*—a benchmark that evaluates models based on both behavior and internal representations. 👉The key principle: given a metric, models should be *at least as good as brains are to each other*:
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Do large language models develop "emergent" models of the world? My latest Substack posts explore this controversial claim & more generally the nature of "world models": LLMs & World Models, Part 1: aiguide.substack.com/p/llms-… LLMs & World Models, Part 2: aiguide.substack.com/p/llms-…
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once again, people are missing that reasoning is better on stronger base model. it’s not “pretraining scaling is dead“, it’s “now you have several axes to scale at the same time“
The final admission that the 2023 strategy of OpenAI, Anthropic, etc. (“simply scaling up model size, data, compute, and dollars spent will get us to AGI/ASI”) is no longer working!
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Two new studies from our team we're particularly happy about Study 1: Brain-to-Text Decoding: ai.meta.com/research/publica… Study 2: From Thought to Action: ai.meta.com/research/publica… Blog: ai.meta.com/blog/brain-ai-re…
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Alex Murphy retweeted
⚖️ Bayesian methods give us a robust way to measure uncertainty 👀 By integrating them with genAI, we are building systems that go beyond simple predictions 🔔Stay tuned for deep-dives on Bayesian agents, causal AI & sound synthetic data: pymc-labs.com/newsletter #genai #PyMC
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