Anthropic’s paper smells like bullshit

I don’t know anything about him, but if he is running a department at Meta, he as at the very least a political genius and a teenage data labeller

It's a simple heuristic that will save a lot of time: something that seems too good to be true usually is.

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Presumably this is all referring to Alexander Wang, who's 28 now. The data-labeling company he co-founded, Scale AI, was acquired by Meta at a valuation of nearly $30 billion.

But I suppose the criticism is that he doesn't have deep AI model research credentials. Which raises the age-old question of how much technical expertise is really needed in executive management.

Hopefully he isn’t referring to Alex Wang, as it would invalidate anything else he said in his comment

> They hired a teenager to run one of their departments

Except they didn’t. The person in question was 28 when they hired him.

He was a teenager when he cofounded the company that was acquired for thirty billion dollars. But the taste of those really sour grapes must be hard to deal with.

> how much technical expertise is really needed in executive management.

For running an AI lab? a lot. Put it this way, part of the reason that Meta has squandered its lead is because it decided to fill it's genAI dept (pre wang) with non-ML people.

Now thats fine, if they had decent product design and clear road map as to the products they want to release.

but no, they are just learning ML as they go, coming up with bullshit ideas as they go and seeing what sticks.

But, where it gets worse, is they take the FAIR team and pass them around like a soiled blanket: "You're a team that is pushing the boundaries in research, but also you need stop doing that and work on this chatbot that pretends to be a black gay single mother"

All the while you have a sister department, RL-L run by Abrash, who lets you actually do real research.

Which means most of FAIR have fucked off to somewhere less stressful, and more concentrated on actually doing research, rather than posting about how you're doing research.

Wangs misteps are numerous, the biggest one is re-platforming the training system. Thats a two year project right there, for no gain. It also force forks you from the rest of the ML teams. Given how long it took to move to MAST from fblearner, its going be a long slog. And thats before you tackle increasing GPU efficiency.

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> Which raises the age-old question of how much technical expertise is really needed in executive management.

For whomever you choose to set as the core decision maker, you get out whatever their expertise is with minor impact by their guides.

Scaling a business is a skill set. It's not a skill set that captures or expands the frontier of AI, so it's clearly in the realm to label the gentleman's expensive buyout is a product development play instead of a technology play.

I could not imagine being as salty as the original poster seems to be about Alex Wang. To hold that amount of hate for a superior that is more successful than you can’t be good for the soul

I was just watching the Y Combinator interview with Alexandr Wang who I guess may be being referred to https://youtu.be/5noIKN8t69U

The teenage data labeler thing was a bit of an exaggeration. He did found scale.ai at nineteen which does data labeling amongst other things.

You’re taking this a tad too seriously

What other things?

Alexandr

why did they move to fblearner

what is the new training platform

I must know

I watched this interview when I first heard about Alexandr Wang. I'd seen he was the youngest self made billionaire, which is a pretty impressive credential to have under your belt, and I wanted to see if I could get a read on what sets him apart.

Unfortunately he doesn't reveal any particular intelligence, insight, or drive in the interview, nor does he in other videos I found. Possibly he hides it, or possibly his genius is beyond me. Or possibly he had good timing on starting a data labelling company and then leveraged his connections in SV (including being roommates with Sam Altman) to massively inflate Scale AI's valuation and snag a Meta acquisition.

> Comic hyperbole darling.

Even if you say so yourself.

> I know that's hard to understand, especially when you're one of the start up elect, who still believes.

There's a lot of projection going on in that sentence.

Thanks

Semantic tagging.

a superior is kind of a loaded way to say "executive" or "company's leadership".

I got the impression he's intelligent and hard working but to a large extent got lucky. I mean his idea was to kind of do a better version of Mechanical Turk which is ok as an idea but not amazing or anything. But then all these LLM companies were getting billions thrown at them by investors thinking they'd be AGI soon but they didn't work well without lots of humans doing fine tuning and Wang's company provided an outlet to throw the money at to get humans to try to do that.

I don't know how that will go at Meta. At the moment having lots of humans tweek LLMs still seems to be the main thing at the AI companies but that could change.

Meta has been itching to kill FBlearner for a while. Its basically an airflow style interface (much better to use as a dev, not sure about admin, I think it might even pre-date airflow)

They are mostly moved to MAST for GPU stuff now I dpn;t think any GPUs are assigned to fblearner anymore. This is a shame because it feels a bit less integrated into python and feels a bit more like "run your exe on n machines" however, it has a more reliable mechanism for doing multi-GPU things, which is key for doing any kind of research at speed.

My old team are not in the super intelligence org, so I don't have much details on the new training system, but there was lots of noise about "just using vercel" which is great apart from all of the steps and hoops you need to go through before you can train on any kind of non-opensource data. (FAIR had/has thier own cluster on AWS, but that meant that they couldn't use it to train on data we collected internally for research (ie paid studies and data from employees that were bribed with swag)

I've not caught up with the drama for the other choices. Either way, its kinda funny to watch "not invented here syndrome" smashing in to "also not invented here syndrome"

So... Tagging and labeling ok.

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They do testing like 'humanities last exam' and they build custom LLMs for some of the largest companies, the defense dept and other US govt stuff - bit here https://youtu.be/5noIKN8t69U?t=2037

Or maybe, just maybe, becoming a billionaire has way more to do with luck than anything else.

I don't know about any billionaire in the history of billionaires who appears to have gotten there solely based on special abilities. Being born into the right circumstances is all it really takes.

Oprah Winfrey? Still some luck but she didn't start in great circustances.

Surely

> Being born into the right circumstances is all it really takes.

You do still need to do the work. People have squandered golden opportunities because they didn't put in the effort.

> Comic hyperbole darling. I know that's hard to understand, especially when you're one of the start up elect, who still believes.

Please omit patronizing swipes like this from comments on HN. You have no idea what the parent commenter "believes", but we know very well that sneering like this only makes HN worse. Please take a moment to remind yourself of the guidelines and make an effort to observe them in future. https://news.ycombinator.com/newsguidelines.html

> But the taste of those really sour grapes must be hard to deal with

Please don't sneer at fellow community members on HN, and don't reply to a bad comment with a worse one; it just makes HN seem like a more mean and miserable place. The comment would have been fine without that last sentence.

Content is more important to me than tone.

Much of this subthread is nothing more than gossip about someone people are apparently jealous of. Talk about a "mean and miserable place." Techbros upset that they didn't cash out as big.