@ProfData

Senior research scientist at @LosAlamosNatLab. Former prof at @ucl and @UTAustin. CogSci, AI, Comp Neuro, AI for scientific discovery Also @profdata on Bluesky

 London, UK
bradlove.org
Joined September 2014
  • Tweets1.4K
  • Following1.6K
  • Followers5.9K
157Media
"Large language models surpass human experts in predicting neuroscience results" w @ken_lxl and BrainGPT.org. LLMs integrate a noisy yet interrelated scientific literature to forecast outcomes. nature.com/articles/s41562-0… 1/8
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Last month Dawkins declared Claude conscious and was mocked. @GaryMarcus cleverly called it The Claude Delusion. My (and Nagel's?) take: Both are wrong for the same reason. Here's an essay on why machine consciousness will never be settled scientifically. arxiv.org/abs/2606.00226

Consciousness, AI, and the Limits of Scientific Explanation

Science is constitutively third-personal: its findings are in principle reproducible by any observer, independent of perspective, and answerable to measurement. This is the source of its power and...

arxiv.org
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Bradley Love retweeted
Alison Preston@AliPrestonPhDMar 6
Every time you experience something new, your brain faces a decision: Should it update an existing memory or create a new one? In our new paper in @JNeurosci, we isolate that exact decision, moment-by-moment during learning. 🧵
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Are you a graduate student interested in working at Los Alamos National Laboratory (LANL) this summer? LANL has student internships, apply here: lanl.jobs/search/jobdetails/… 1/2
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Personally, I will be looking to mentor projects with Mahindra Rautela on (1) Search and Evaluation for test-time AI Reasoning, and (2) model distillation to compress large physics foundation models. Please feel free to get in touch with questions or to express interest. 2/2
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Energy Department Launches ‘Genesis Mission’

Article · @ENERGY
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"The inevitability and superfluousness of cell types in spatial cognition". Intuitive cell types are found in random artificial networks using the same selection criteria neuroscientists use with actual data. elifesciences.org/reviewed-p…... 1/2
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Intuitive cell types don't necessarily play the ascribed functional role in the overall computation. This is not a message the field wants to hear as it suggests better baselines, controls, and some reflection. elifesciences.org/reviewed-p…... w @ken_lxl , @robmok.bsky.social @ 2/2
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Exciting "new" work illustrating our broken publishing system. @seb_bobadilla presented this work online at neuromatch 2.0 at the height of the pandemic. Then, @xinyazhang_ worked years on addressing reviewer comments, which added some rigor but didn't change the message. 1/2
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Working with monkey data, we found neural representations stretched across brain regions to emphasize task relevant features on a trial-by-trial basis. Spike timing mattered over spike rate. Deep nets did the same. nature.com/articles/s41467-0… 2/2

Adaptive stretching of representations across brain regions and deep learning model layers

Nature Communications - How the brain adapts its representations to prioritize task-relevant information remains unclear. Here, the authors show that both monkey brains and deep learning models...

nature.com
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When AI surpasses human performance, what's left for humans? We find that human judgment boosts performance of human-AI teams because humans and machines make different errors. cell.com/patterns/fulltext/S… 1/2

Confidence-weighted integration of human and machine judgments for superior decision-making

When AI surpasses human performance, what can humans offer? We demonstrate that the performance of teams increases by integrating human judgments with those of machines. Integration is achieved by a...

cell.com
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We developed a straightforward method of combining confidence-weighted judgments for any number of humans and AIs. w @yanezlang, Omar Minero, @ken_lxl 2/2
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Researchers are using LLMs to analyze the literature, brainstorm hypotheses, build models and interact with complex datasets. Hear from Martin Schrimpf @martin_schrimpf, Kim Stachenfeld @neuro_kim, Jeremy Magland, Bradley Love and others. thetransmitter.org/machine-l…

How neuroscientists are using AI

Eight researchers explain how they are using large language models to analyze the literature, brainstorm hypotheses and interact with complex datasets.

thetransmitter.org
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🧵🎉 Our mega-paper is finally published in TMLR! We're "Getting Aligned on Representational Alignment" - the degree to which internal representations of different (biological & artificial) information processing systems agree. 🧠🤖🔬🔍 #CognitiveScience #Neuroscience #AI
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What futures could, and should, we create with advanced AI? Today we are announcing two possible paths – Tool AI and d/acc – with contributors like @VitalikButerin @owocki @AdamMarblestone and @AnthonyNAguirre
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New blog w @ken_lxl, “Giving LLMs too much RoPE: A limit on Sutton’s Bitter Lesson”. The field has shifted from flexible data-driven position representations to fixed approaches following human intuitions. Here’s why and what it means for model performance bradlove.org/blog/position-e…

Giving LLMs too much RoPE: A limit on Sutton’s Bitter Lesson — Bradley C. Love

Introduction Sutton’s Bitter Lesson (Sutton, 2019) argues that machine learning breakthroughs, like AlphaGo, BERT, and large-scale vision models, rely on general, computation-driven methods that...

bradlove.org
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New blog, "Backwards Compatible: The Strange Math Behind Word Order in AI" w @ken_lxl. It turns out the language learning problem is the same for any word order, but is that true in practice for large language models? paper: arxiv.org/abs/2505.08739 BLOG: bradlove.org/blog/prob-llm-c…
https://bradlove.org/blog/prob-llm-consistency

ALT https://bradlove.org/blog/prob-llm-consistency

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"Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies" Oddly, we prove LLMs should be equivalent for any word ordering: forward, backward, scrambled. In practice, LLMs diverge from one another. Why? 1/2 arxiv.org/abs/2505.08739

Probability Consistency in Large Language Models: Theoretical...

Can autoregressive large language models (LLMs) learn consistent probability distributions when trained on sequences in different token orders? We prove formally that for any well-defined...

arxiv.org
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When LLMs diverge from one another because of word order (data factorization), it indicates their probability distributions are inconsistent, which is a red flag (not trustworthy). We trace deviations to self-attention positional and locality biases. 2/2 arxiv.org/abs/2505.08739

Probability Consistency in Large Language Models: Theoretical...

Can autoregressive large language models (LLMs) learn consistent probability distributions when trained on sequences in different token orders? We prove formally that for any well-defined...

arxiv.org
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Bonus: I found it counterintuitive that (in theory) the learning problem is the same for any word ordering. Aligning proof and simulation was key. Now, new avenues open to address positional biases, better training and knowing when to trust LLMs w @ken_lxl, @ramscar1, @XinyiXu6
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🧠 We speak with Prof. Bradley Love about BrainGPT—an AI model helping researchers process neuroscientific data faster than ever. 🔍 How does BrainGPT work? 🚀 Where is AI taking brain research next? 🎧touchneurology.com/podcast/b… @ProfData
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