michael

PhD student at @BoulderNLP @lecslab, intern at @AmazonScience

Studying NLP for rare+endangered languages, multilinguality, automata, synthetic data

[michael @WasianBayesian]

Beyond honored that PolyGloss has won an Outstanding Paper award at #ACL2026

We’re really excited about NLP for endangered language documentation, and it’s inspiring to see that the community is too!!

[michael @WasianBayesian]

Excited to announce that the PolyGloss paper has been accepted to @aclmeeting!

Previously, we trained models to help in endangered language documentation workflows by automatically predicting interlinear glosses. But real-world user studies revealed crucial issues...

[michael @WasianBayesian]

“Coding is solved” is a weird phrase to me

“Coding” is not a math conjecture!

It’s the broad process of translating informal, underspecified requirements into a formal representation. That’s not really something that has a definable “solved” state

[Boris Cherny @bcherny]

@dedene Coding is solved, bugs are not yet solved. Fix incoming

[michael @WasianBayesian]

@alexolegimas @edzitron And what data did you fit that curve on?

[michael @WasianBayesian]

@pourfairelevide I think the Ayn Rand interpretation is pretty surface level and reductive

[michael @WasianBayesian]

@CasinoJoe92 @coldhealing I don’t think music is anywhere close actually!

[michael @WasianBayesian]

@qwts_chris_dev @ericclemmons You don’t think 80% of engineers are incentivized to write code that, eg, doesn’t leak customer data?

[michael @WasianBayesian]

@spaceforceguy @zebassembly Why do you have such a strong emotional response to this

[michael @WasianBayesian]

@JoshSaintJacque @aarondotdev They like to add in fallbacks that hide cases where there’s actually some deeper underlying issues

[michael @WasianBayesian]

@scaling01 Guy who thinks you should make new benchmarks that are already saturated

[michael @WasianBayesian]

@IMakeStupidGame @spirodonfl Yes, new tools tend to face resistance from people who don’t like change.

No, that doesn’t mean every new tool is exactly the same.

[michael @WasianBayesian]

@ConsumerRick @hellosvapnil Boring, try a new response

[michael @WasianBayesian]

@yong_zhengxin I’d push back on the second point; that’s absolutely true for ML conferences but I’m not sure it’s true for math

[michael @WasianBayesian]

@paradite_ None of that will help you understand bespoke idioms where you have to infer the intended meaning, which is one of the big problems.

[michael @WasianBayesian]

@industriaalist Lowkey rl might be all you need

[michael @WasianBayesian]

@repligate That’s a perfectly consistent position

[michael @WasianBayesian]

@ohfault_ It’s probably just like AI mastering (ozone or whatever) which has been around long before generative ai

[michael @WasianBayesian]

@AdrianThonig @Piyush_Goenka_ @karpathy @ChrisJMcCormick For sure! But now you’re relying on your own test coverage, instead of the robust coverage that already exists for torch.

Not to mention, this only works because PyTorch exists—if everyone starts spinning up their own impl with AI, you’d have no definitive source of truth.

[michael @WasianBayesian]

@AdrianThonig @Piyush_Goenka_ @karpathy @ChrisJMcCormick That’s part of training logic, and a derivative could be subtly wrong while not totally blocking training.

[michael @WasianBayesian]

@AdrianThonig @Piyush_Goenka_ @karpathy @ChrisJMcCormick There are so many ways you can mess up training logic without preventing training. It is incredibly common in ML.

[hinterlander @yoltartar]

ai coding skeptic: man this thing seems like a footmachinegun

ai coding fanboy: rookie mistake, you just gotta point another footmachinegun so it shoots the bullets out of the air before they hit your foot