
Senior AI & ML Instructor. NeuroAI Curriculum Specialist @neuromatch. Ex-Postdoc @amiithinks / PhD in ML & Neuroscience
Liverpool, England
Joined August 2011
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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.
Alex Murphy retweeted
Gang, we're back to reading the code! 🤪 ~ @dexhorthy #AIEngineer
Alex Murphy retweeted
An accompanying News & Views by @Alxmrphi for this paper is now available! nature.com/articles/s43588-0…
🔓rdcu.be/eQ9h9
Alex Murphy retweeted
I'm hiring (another) post doc, this time in collaboration with Natalie Brito @NatalieHBrito at Columbia! We will be exploring some of the characteristics of human development using deep learning models. Email me with questions!
iaejup.fa.ocs.oraclecloud.co…
Alex Murphy retweeted
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:
Alex Murphy retweeted
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"
Alex Murphy retweeted
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
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 (🤍 & ❔)
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 🧠
Alex Murphy retweeted
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:
Alex Murphy retweeted
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

ALT A figure depicting three separate learning scenarios for neural networks. Figure a shows the classic student-teacher model for supervised learning. Figure b: in the RL setting the student moves through states making a series of choices given in response to inputs. Figure c: Example learning dynamics in the RL-perceptron for a problem with T = 12 choices where the reward is given only if all the decisions are correct.
Alex Murphy retweeted
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 👇
Alex Murphy retweeted
🧠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
Alex Murphy retweeted
📢 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!
Alex Murphy retweeted
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*:
Alex Murphy retweeted
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-…
Alex Murphy retweeted
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“
Alex Murphy retweeted
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…
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
































