Machine Learning @Latent_Labs, anything from classical statistics to modern machine learning and generative AI, previous @altos_labs
United Kingdom
Joined June 2020
- Tweets373
- Following665
- Followers783
- Likes1.7K
I used @claudeai for orchestration, @bfl_ai for designs drafts, @ChatGPT for blender models, @ElevenLabs for voices and sounds. Implemented using @godotengine
The extent to which you can just do things now is staggering: I used AI to generate my own RTS, including the possibility to play against an agent by creating a machine readable gaming engine.
I am still wrapping my head around how this will change industries.
I have also interviewed with a big bank once and they told me that they could only hire me on an entry level because for them only “years since leaving academia” counts. I was an assistant professor, but for them university was university, whether student or professor. I ended the process then and there.
It can vary a lot based on who’s in charge. I have also seen some universities in the UK being better and promoting more based on merit. That’s often the less prominent ones who have to find alternative ways to retain talent.
One thing that @OpenAI could do with the massive compute is to brute force re-review every research papers, beginning with maths and physics and continue to stats and ML.
Absolutely agree but to be fair it’s the same for most institutions and large companies: if you can’t measure impact or contribution easily on an individual level (either because the signal is noisy or measurement is costly) you divert to poor but cheap proxies.
Claude has written me some of the best explanations on topics that were absolutely worth reading and there is a lot of human generated text on the internet that is factually wrong or at least badly written.
Unpopular(?) opinion: we don’t need classifiers to detect whether something is LLM generated or not, but rather whether it’s worth reading, i.e. we need to detect slop vs non-slop, not AI vs human.
Those are very correlated but not identical.
You can potentially desk reject simply by looking at bad claims or claim that cannot be tested with the selected instrument of validation.
Have one field for “claim”, with low number of maximal characters and another field for “how the claim is validated” (could be a benchmark or proof). Precise high level summaries will help guide reviewers and not get people confused in LLM verbiage.
Reduce maximum page count. PRL (most prestigious physics journal) can get away with 4 pages. Being succinct forces being very deliberate and concise in contribution and gives humans at least a chance to get a sense of the idea without having to invest in pages of AI slop.
Is there a word for a mistake that almost every paper in a field makes, that “everyone knows” is a problem, so nobody can publish the correction? (Rejected as trivial because well known)
Sebastian Schmon retweeted
uatx.substack.com/p/forget-y… Hmm. I'd say the Enlightenment version of individualism is the historically unique idea that individuals exist not for the sake of their ruler, their class, their ancestors, their gods, or any other such superstition, but for their own sake. Meaning does not require subordination to something "greater than oneself". It is created by the idea that progress is possible and desirable – not by a leap to utopia, but endlessly into the future by undirected problem-solving, carried forward voluntarily by individuals for their own reasons.