Bradley Love @ProfData
Fellow at Alan Turing Institute and https://t.co/Ja8LHXseQF; Professor of Cognitive and Decision Sciences, University College London. CCN,CogSci. @profdata at the other one bradlove.org London, UK Joined September 2014-
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For AI Researchers, Thinkers, Founders, and VCs: Announcing our Spring Summit on Fundamental Challenges for AI! bit.ly/springsummitai… We discuss core challenges & promising avenues in a day of high-profile keynotes & panels. Lunch & Reception included. April 15th, Bay Area,…
Seminar Alert: Philosophy of Cognitive Science [#CognitiveScienceSeminar] 📅 Date: April 4th ⏰ Time: 10:30 AM CET 🌐 Location: Online (Google Meet, DM for link) Excited to host Prof. Bradley C. Love @ProfData as our guest. Thread 🧵 [1/3]
Here's a recent talk I gave on the BrainGPT.org project, touching on findings from here, arxiv.org/abs/2403.03230. The video is sectioned for those wishing to skip the bits on explanatory vs. predictive approaches. youtu.be/sDt4-Q_jz7g?si…
Brad Love @ProfData from @EP_UCL and @turinginst will deliver this Thu talk at #CitAI_seminars "Taming the neuroscience literature with explanatory and predictive models" March 21; 16:00 GTM cit-ai.net/seminars.html To Zoom in, email organisers or DM @twitemp1
📢 ERC-funded PhD in our team 📢. Curious about the mechanisms underlying learning and perception? Wish to study them via modelling and neuroimaging? This may be for you! Email me qus. Please circulate 🙏 @EP_UCL @WCHN_UCL @ERC_Research findaphd.com/phds/project/c…
Join us for Friday's #BrainMeeting with @ProfData, titled, "Taming the neuroscience literature with explanatory and predictive models." Stick around after for a social with refreshments and cake! 🗓️ Fri 15 Mar, 15:15 - 16:15 📍 12 Queen Square & Zoom 🔗: fil.ion.ucl.ac.uk/event/bl202403…
Neuroscientists should read some psychology to learn that experts are not necessarily the best at prediction. Expertise is more than that.
Neuroscientists should read some psychology to learn that experts are not necessarily the best at prediction. Expertise is more than that.
Minds and Machines Event at UCL next week, March 11th! Thanks to @affectivebrain, @ProfData, and @smfleming for organizing, and @kevinjmiller10 for co-presenting with me on cognitive modeling and neural networks! uclmmi.com/about/
Can't believe I get to share this news: #WhyWeRemember has landed on the @nytimesbooks #bestsellers list in its first week. Thank you again to everyone who made this dream come true!
On Thu, March 21 at 16:00 GTM, on #CitAI_seminars Bradley C. Love @ProfData from @EP_UCL and @turinginst will deliver a talk on "Taming the neuroscience literature with explanatory and predictive models" cit-ai.net/seminars.html To Zoom in, email organisers or DM @twitemp1
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322 Followers 368 Following @Inserm, CRNL, Lyon | implicit cognition | statistical learning | predictive processing | memory consolidation | https://t.co/iVvezrq3IzSeminar Alert: Philosophy of Cognitive Science [#CognitiveScienceSeminar] 📅 Date: April 4th ⏰ Time: 10:30 AM CET 🌐 Location: Online (Google Meet, DM for link) Excited to host Prof. Bradley C. Love @ProfData as our guest. Thread 🧵 [1/3]
@ProfData Brad, thanks for an enticing and revealing talk and for an intellectually stimulating evening.
@ProfData Awesome! Great work [unless it replaces me as a scientist ;) ] Any plans for a prospective approach in the future?
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This call is too hardware oriented for me to apply directly, but if any of my neuromorphic hardware colleagues are thinking of applying and think it would be useful to have a UK-based SNN person involved, get in touch.
📣 Call for proposals! Our first funded programme is now live — Scaling Compute: AI at 1/1000th the cost. Hear more from PD Suraj Bramhavar on how we aim to radically reduce the cost + energy consumption of compute hardware, and open new avenues to reap the benefits of AI 👇
@ProfData Great work! Hopefully, you have room for collaboration. I have over 300 physician-scientists and medical student researchers who would love an opportunity to collaborate at @NeuroSurgGlobal
What! Check this out #BrainGPT
"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. arxiv.org/abs/2403.03230 1/6
It was a huge honor and really cool experience for me to be part of a big team working on this 😉
"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. arxiv.org/abs/2403.03230 1/6
Developing test cases and conducting quality control for this study has been a thrilling endeavor! I'm privileged to contribute to the exceptional BrainGPT team, under the leadership of @ProfData and @ken_lxl, congratulations!
"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. arxiv.org/abs/2403.03230 1/6
Very interesting! Great job @ProfData and colleagues!
"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. arxiv.org/abs/2403.03230 1/6
"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. arxiv.org/abs/2403.03230 1/6
Provocative results! I appreciate the analysis of (lack of) memorization. I am a little surprised that performance is so flat across model families and scales, and is so strongly correlated among models and weakly with humans.
"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. arxiv.org/abs/2403.03230 1/6
LLM predict future (forward-looking) neuroscience results better than current neuroscience experts! Nice to see this work from @ProfData and colleagues. Excited to play a small part in this effort!
"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. arxiv.org/abs/2403.03230 1/6
Thank you, Brad (@ProfData) and Xiaoliang (@ken_lxlxl), for leading this! 🚀🧠✨ Introducing BrainBench, a forward-looking benchmark for predicting neuroscience results: arxiv.org/abs/2403.03230 -- more information at braingpt.org!
"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. arxiv.org/abs/2403.03230 1/6
This is definitely worth a close look!
"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. arxiv.org/abs/2403.03230 1/6
@ProfData No. It does not mean it is wrong at all. but I do think that we should audit the results very carefully before relying on them.
@ProfData @cian_neuro @ken_lxl Hahahaha. Best possible response. I love it. 😂
One important thing: this is not just yet another data leakage, but fine-tuning of an open-source LLM with a relatively low no. of parameters. So here we are, machine learning provides new knowledge.
"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. arxiv.org/abs/2403.03230 1/6
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