Joined September 2014
- Tweets 1,387
- Following 1,568
- Followers 5,904
- Likes 1,810
157 Photos and videos
Pinned Tweet
27 Nov 2024
"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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Jun 2
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
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Bradley Love retweeted
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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Jan 28
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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Jan 28
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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Bradley Love retweeted
24 Nov 2025
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25 Nov 2025
"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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25 Nov 2025
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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25 Nov 2025
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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25 Nov 2025
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
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25 Nov 2025
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
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25 Nov 2025
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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Bradley Love retweeted
4 Nov 2025
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…
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Bradley Love retweeted
27 Oct 2025
🧵🎉 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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Bradley Love retweeted
19 Aug 2025
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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13 Jun 2025
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…
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28 May 2025
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…
ALT https://bradlove.org/blog/prob-llm-consistency
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14 May 2025
"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
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14 May 2025
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
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Bradley Love retweeted
12 Mar 2025
🧠 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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