Daniel Whettam retweeted
New paper on a long-shot I've been obsessed with for a year:
How much are AI reasoning gains confounded by expanding the training corpus 10000x? How much LLM performance is down to "local" generalisation (pattern-matching to hard-to-detect semantically equivalent training data)?
Daniel Whettam retweeted
Our new ICML 2025 oral paper proposes a new unified theory of both Double Descent and Grokking, revealing that both of these deep learning phenomena can be understood as being caused by prime numbers in the network parameters 🤯🤯
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Daniel Whettam retweeted
After a year of review, our paper is now out in @NatureComms !
I really think our model/theory offers one of the most promising frameworks yet for learning in neocortical circuits - perhaps not in its final form, but the core principle of ssl feels right.
Hoping experimentalists will test it, break it, and computationalists will refine it
#AllModelsAreWrongButSomeAreUseful - I think this one could be very useful😀
Pleased to say that our story of how a theory of self-supervised learning in cortical layers accounts for several experimental observations is now out 🎉
nature.com/articles/s41467-0…
Daniel Whettam retweeted
Good post from @balajis on the "verification gap".
You could see it as there being two modes in creation. Borrowing GAN terminology:
1) generation and
2) discrimination.
e.g. painting - you make a brush stroke (1) and then you look for a while to see if you improved the painting (2). these two stages are interspersed in pretty much all creative work.
Second point. Discrimination can be computationally very hard.
- images are by far the easiest. e.g. image generator teams can create giant grids of results to decide if one image is better than the other. thank you to the giant GPU in your brain built for processing images very fast.
- text is much harder. it is skimmable, but you have to read, it is semantic, discrete and precise so you also have to reason (esp in e.g. code).
- audio is maybe even harder still imo, because it force a time axis so it's not even skimmable. you're forced to spend serial compute and can't parallelize it at all.
You could say that in coding LLMs have collapsed (1) to ~instant, but have done very little to address (2). A person still has to stare at the results and discriminate if they are good. This is my major criticism of LLM coding in that they casually spit out *way* too much code per query at arbitrary complexity, pretending there is no stage 2. Getting that much code is bad and scary. Instead, the LLM has to actively work with you to break down problems into little incremental steps, each more easily verifiable. It has to anticipate the computational work of (2) and reduce it as much as possible. It has to really care.
This leads me to probably the biggest misunderstanding non-coders have about coding. They think that coding is about writing the code (1). It's not. It's about staring at the code (2). Loading it all into your working memory. Pacing back and forth. Thinking through all the edge cases. If you catch me at a random point while I'm "programming", I'm probably just staring at the screen and, if interrupted, really mad because it is so computationally strenuous. If we only get much faster 1, but we don't also reduce 2 (which is most of the time!), then clearly the overall speed of coding won't improve (see Amdahl's law).
AI PROMPTING → AI VERIFYING
AI prompting scales, because prompting is just typing.
But AI verifying doesn’t scale, because verifying AI output involves much more than just typing.
Sometimes you can verify by eye, which is why AI is great for frontend, images, and video. But for anything subtle, you need to read the code or text deeply — and that means knowing the topic well enough to correct the AI.
Researchers are well aware of this, which is why there’s so much work on evals and hallucination.
However, the concept of verification as the bottleneck for AI users is under-discussed. Yes, you can try formal verification, or critic models where one AI checks another, or other techniques. But to even be aware of the issue as a first class problem is half the battle.
For users: AI verifying is as important as AI prompting.
Daniel Whettam retweeted
New paper: a big 90-page intro to AI and its likely effects from ten perspectives, ten camps.
The whole gamut: ML, scientific applications, social applications, access, safety and alignment, economics, AI ethics, governance, and classical philosophy of life.
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Daniel Whettam retweeted
(1) Good news! I've had a paper accepted (with @cian_neuro, @nathanlepora, and Matt W. Jones), and I'll be giving a talk on it at @AAMASconf this year 🥳🥳🥳🧠🤖🐀🥳🥳🥳
Daniel Whettam retweeted
Very excited the EPIC-SOUNDS dataset is finally released! We’ve all worked incredibly hard on this and I can be proud to say that this is my first publication! Looking forward to what this dataset can bring to the deep learning community!
EPIC-SOUNDS is now public available
on ArXiv: arxiv.org/abs/2302.00646
w Trailer: youtu.be/w-Bxat3Cgpk
Annotations: github.com/epic-kitchens/epi…
Challenge: github.com/epic-kitchens/C9-…
Work by @huh_jaesung @JacobChalkie
w @e_kazakos, myself and AZ
Thanks @_akhaliq for sharing
Daniel Whettam retweeted
📢 Now Open For Submissions - all EPIC-KITCHENS Leaderboards for @CVPR #CVPR2023 Challenges. Winners announced at Joint @ego4_d and EPIC workshop: sites.google.com/view/ego4d-…
**Nine** open challenges inc. 4 new ones (see 🧵)
Leaderboards close 1st of June 2023.
🧵 1/7
Daniel Whettam retweeted
For anyone interested in audio-visual learning, this challenge will be of interest! EPIC-100 and EPIC-SOUNDS is a step towards multi-modal methods where one modality does not rely on the timestamps or label set of another! We look forward to seeing what this challenge brings!
Replying to @dimadamen
*New* EPIC-SOUNDS Audio Interaction Recognition.
Recognise sounds of garlic peeling 🔊 or hand mixer. New Audio-Only dataset on #EPIC_KITCHENS [Annotations out - ArXiv soon]
Leads: @huh_jaesung @JacobChalkie
Code: github.com/epic-kitchens/C9-…
Leaderboard: codalab.lisn.upsaclay.fr/com…
5/7
Daniel Whettam retweeted
[RT] Summer Research Visit @BristolUniEng
Apply for a 3-months research visit (June-Sep 2023) at the Machine Learning and Computer Vision group to work with *myself* or *any of my fantastic colleagues*.
DL: 13 Jan 2023
uob-mavi.github.io/Summer@Ma…
Daniel Whettam retweeted
Today, we release EPIC-KITCHENS VISOR: 10M frames of dense segmentations from 271K of manually segmented masks along with 70K of hand-object relations, for long-term reasoning
Watch our 3min VISOR trailer 📽️ [music on]
youtu.be/yGodQAbYW_E
Project: epic-kitchens.github.io/VISO…
1/N
Daniel Whettam retweeted
Congratulations to all winners,
On behalf of myself and this year's challenges team: @GMFarinella @anfurnari @davmoltisanti @DWhettam @adrifragomeni Toby Perrett, Bin Zhu, Michael Wray
Daniel Whettam retweeted
Come talk to us at poster 31b about Ego4D @CVPR or explore the dataset if remotely at ego4d-data.org
Daniel Whettam retweeted
⛳️*5 weeks to go* - Server closes 1 June 2022
Your chance to participate and win🥇 one or more of the EPIC-KITCHENS challenges. Results @CVPR
Action Recognition
Action Anticipation
Action Detection
Multi-Instance Retrieval
UDA for Recognition
Details: epic-kitchens.github.io/2022…
Daniel Whettam retweeted
⛳️📢⏯️🏅
#Ego4D Challenges are OPEN w June 1st deadline
6 challenges with winners announced @CVPR Joint EGO4D&EPIC 2-Day Workshop
Leaderboards on #EvalAI with code&baseline models now public.
#be_a_leader and participate in the first round of #Ego4D challenges
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Daniel Whettam retweeted
📢[Pls RT] Used EPIC-KITCHENS in ur work?
Contribute to our impact anonymous survey:
surveymonkey.co.uk/r/epic_ki…
We are collecting data on usage from academia/industry as well as type of data used: narrations/action segments.
Will u use our upcoming pixel-level segmentations??
LUK!
Daniel Whettam retweeted
📢📢3-year postdoc on video understanding in my group
tinyurl.com/2y6swek7
DM or apply directly by 5 Dec
U can choose your topic-of-focus within multi-modal video understanding
We are *proudly* influential but also a cohesive and supportive environment to work within.
Pls r/w
Daniel Whettam retweeted
📢Working on video understanding:recognition, anticipation,detection,domain adaptation or retrieval?
You'll find 2021 EPIC-KITCHENS Tech report informative w 17 methods
epic-kitchens.github.io/Repo…
@JonathanMunro8 @davmoltisanti @DWhettam @anfurnari @GMFarinella Fragomeni,Perrett,Wray
Daniel Whettam retweeted
"Slow-Fast Auditory Streams for Audio Recognition" by Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, & Dima Damen ieeexplore.ieee.org/document… #ICASSP2021


























