Alex Dimakis (@AlexGDimakis) / X

Alex Dimakis

4,576 posts

Alex Dimakis

@AlexGDimakis

Professor, UC berkeley | Founder @bespokelabsai |

Berkeley, CA

people.eecs.berkeley.edu/~alexdimakis/

Joined April 2009

Alex Dimakis
@AlexGDimakis
Sep 5, 2024
GPT is having a profound effect on how students write. Its verbose style, full of cliches and 'fancy', out of place vocabulary is in every paper and draft I read. A few years back, there were grammar errors and awkwardness -- but at least people had their own voice. Now,
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Alex Dimakis
@AlexGDimakis
Jan 29, 2022
Someone is trying to scam my PhD student. My student asks to verify their identity 1/2
Alex Dimakis
@AlexGDimakis
Aug 16, 2023
I was surprised by a talk Yejin Choi (an NLP expert) gave yesterday in Berkeley, on some surprising weaknesses of GPT4: As many humans know, 237*757=179,409 but GPT4 said 179,289. For the easy problem of multiplying two 3 digit numbers, they measured GPT4 accuracy being only
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Alex Dimakis
@AlexGDimakis
Jun 19, 2024
This paper seems very interesting: say you train an LLM to play chess using only transcripts of games of players up to 1000 elo. Is it possible that the model plays better than 1000 elo? (i.e. "transcends" the training data performance?). It seems you get something from nothing,
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Alex Dimakis
@AlexGDimakis
Jan 31, 2025
Discovered a very interesting thing about DeepSeek-R1 and all reasoning models: The wrong answers are much longer while the correct answers are much shorter. Even on the same question, when we re-run the model, it sometimes produces a short (usually correct) answer or a wrong
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Alex Dimakis
@AlexGDimakis
Feb 4, 2023
Human bilinguals are more robust to dementia and cognitive decline. In our recent NeurIPS paper we show that bilingual GPT models are also more robust to structural damage in their neuron weights. Further, we develop a theory.. (1/n)
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Alex Dimakis
@AlexGDimakis
Sep 6, 2024
Replying to @DimitrisPapail
Thank you for your response, Dimitris. I appreciate your take on the issue. It's true that a request for "a few typos" and fewer "fancy words" may help bring back a sense of authenticity to writing. There’s a delicate balance between polishing a draft and maintaining the writer’s
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Alex Dimakis
@AlexGDimakis
Jan 21, 2025
Most AI researchers I talk to have been a bit shocked by DeepSeek-R1 and its performance. My preliminary understanding nuggets: 1. Simple post-training recipe called GRPO: Start with a good model and reward for correctness and style outcomes. No PRM, no MCTS no fancy reward
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Alex Dimakis
@AlexGDimakis
May 10, 2025
"RL with only one training example" and "Test-Time RL" are two recent papers that I found fascinating. In the "One Training example" paper the authors find one question and ask the model to solve it again and again. Every time, the model tries 8 times (the Group in GRPO), and
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Alex Dimakis
@AlexGDimakis
Dec 17, 2024
Life update: I am excited to announce that I will be starting as a Professor in UC Berkeley in the EECS Department. I spend 12 wonderful years teaching in UT Austin and I am grateful to all my colleagues and students there and extremely proud of what we have achieved in AI in UT
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Alex Dimakis
@AlexGDimakis
Jan 29, 2022
Replying to @AlexGDimakis
2/ Scammer ends up improving our sample complexity bound for StyleGAN inverse problems. They teach them to do chaining arguments instead of just union bounds now, jeez. @giannis_daras
Alex Dimakis
@AlexGDimakis
Oct 9, 2024
For the first (and probably last) time in my life I understand the technical details of both the physics and chemistry Nobel prizes.
The Nobel Prize
@NobelPrize
Oct 9, 2024
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
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Alex Dimakis
@AlexGDimakis
May 24, 2021
Doctor: We used a deep learning algorithm for your MRI reconstruction. Turns out one of your kidneys is a cat.
Alex Dimakis
@AlexGDimakis
Mar 23, 2022
One huge advantage of deep learning (vs classical ML models) that is not often discussed is *modularity*: One can download pre-trained models, glue them like Legos and fine tune them end-to-end because gradients flow through. (1/n)