Thanks, edited my post to reference this (lmk if you understand what's happening here better than I do)

Thanks, this is a helpful comment. Fixed the typo

Edit: The sitation has evolved but is still somewhat confusing. There is now a leaderboard of scores on the public test set that Ryan is #1 on (see here). But this tweet from Jack Cole indicates that his (many month old) solution gets a higher score on the public test set than Ryan's top score on that leaderboard. I'm not really sure what's going on here,

•  Why isn't Jack's solution on the public leaderboard?
• Is the semi-pubic test set the same as the old private set?
• If not, is it equal in difficulty to the public test set, or the harder private test set?
• Here it says "New high scores are accepted when the semi-private and public evaluation sets are in good agreement". What does that mean?

One important caveat to the presentation of results in this post (and the discussion on Twitter) is that there are reasons to think this approach may not be SOTA, as it performs similarly to the prior best-performing approach when tested apples-to-apples, i.e. on the same problems.

There are three sets of ARC problems: the public training set, the public eval set, and the private eval set. 

• Buck and Ryan got 71% on the first, 51% on the second, and [we don't know] on the third. 
• The past SOTA got [we don't know] on the first, 52% on the second, and 34% on the third.
• Humans get 85% on the first, [we don't know] on the second, and [we don't know] on the third

My two main deductions from this are:

• It's very misleading to compare human perf on the train set and AI perf on either of the test sets; the test sets seem way harder! Note that 71% is approaching 85%, so it seems like AIs are not far from human perf when you compare apples-to-apples. So graphs from the ARC folks like the one showing little progress towards human-level perf on this page are not scientifically valid.
• Buck and Ryan's approach doesn't exceed the past AI SOTA on the only apples-to-apples comparison we have so far. Unclear if it will beat it on the private test set. 

Apparently, lots of people get better performance on the public test set than the private one, which is a little surprising given that if you read this page from the ARC folks, you'll see the following:

The public training set is significantly easier than the others (public evaluation and private evaluation set) since it contains many "curriculum" type tasks intended to demonstrate Core Knowledge systems. It's like a tutorial level.

The public evaluation sets and the private test sets are intended to be the same difficulty.

Two explanations come to mind: maybe the public and private test sets are not IID, and/or maybe past SOTA method overfit to the public set. Chollet claims it's (accidentally) the latter here, but he doesn't rule out the former. He says the tasks across the two public test sets are meant to be equally hard for a human, but he doesn't say they're divided in an IID manner.

I guess we'll see how the results on the public leaderboard shake out.

(Expanding on a tweet)

"Follow the right people on twitter" is probably the best option. People will often post twitter threads explaining new papers they put out. There's also stuff like:

• News put together by CAIS: https://newsletter.mlsafety.org/ and https://newsletter.safe.ai/ and https://twitter.com/topofmlsafety
• News put together by Daniel Paleka: https://newsletter.danielpaleka.com/ and twitter summaries like https://twitter.com/dpaleka/status/1664617835178631170

I appreciate you transcribing these interviews William!

Did/will this happen?

I've been loving your optimization posts so far; thanks for writing them. I've been feeling confused about this topic for a while and feel like "being able to answer any question about optimization" would be hugely valuable for me.

Thanks, fixed

We're expecting familiarity with PyTorch, unlike MLAB. The level of Python background expected is otherwise similar. The bar will vary somewhat depending on each applicant's other traits, e.g. mathematical and empirical-science backgrounds