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387 points reaperducer | 1 comments | | HN request time: 0.333s | source
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SubiculumCode ◴[] No.45772210[source]
Given that AI is a national security matter now, I'd expect the U.S.A to step in and rescue certain companies in the event of a crash. However, I'd give higher chances to NVIDIA than OpenAI. Weights are easily transferrable and the expertise is in the engineers, but ability to continue making advanced chips is not as easily transferred.
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embedding-shape ◴[] No.45772241[source]
Why is ML knowledge "in the engineers" while chip manufacturing apparently sits in the company/hardware/something else than the engineers/humans?
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tonyarkles ◴[] No.45772346[source]
First-order: because of the capex and lead times. If you grab a bunch of world-class ML folks and put them in a room together, they're going to be able to start producing world-class work together. If you grab a bunch of world-class chip designers in the same scenario but don't have world-class fabs for them to use, they're not going to be able to ship competitive designs.
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embedding-shape ◴[] No.45772419[source]
> If you grab a bunch of world-class chip designers in the same scenario but don't have world-class fabs for them to use, they're not going to be able to ship competitive designs.

But why such an unfair comparison?

Instead of comparing "skilled people with hardware VS skilled people without hardware", why not compare it to "a bunch of world-class ML folks" without any computers to do the work, how could they produce world-class work then?

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jimbokun ◴[] No.45772760[source]
Much easier and cheaper to source computers than a fab.
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embedding-shape ◴[] No.45773318[source]
Right, but to source a fab you need experience as well, nothing you can just hire a random person to do exactly.
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tonyarkles ◴[] No.45773867[source]
To simplify it down even more:

- For the ML team, you need money. Money to pay them and money to get access to GPUs. You might buy the GPUs and make your own server farm (which also takes time) or you might just burn all that money with AWS and use their GPUs. You can trade off money vs. time.

- For the chip design team, you need money and time. There's no workaround for the time aspect of it. You can't spend more money and get a fab quicker.

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embedding-shape ◴[] No.45773937[source]
> - For the ML team, you need money. Money to pay them and money to get access to GPUs. You might buy the GPUs and make your own server farm (which also takes time) or you might just burn all that money with AWS and use their GPUs. You can trade off money vs. time.

Even if you do those things though, it doesn't guarantee success or you'll be able to train something bigger. For that you need knowledge, hard work and expertise, regardless of how much money you have. It's not a problem you can solve by throwing money at it, although many are trying. You can increase the chances of hopefully discovering something novel that helps you build something SOTA, but as current history tells us, it isn't as easy as "ML Team + Money == SOTA model in a few months".

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1. tonyarkles ◴[] No.45774519[source]
Sure. No guarantees that you could throw money at putting an ML team together and have a new SOTA model in a few months. You might, you might not.

You know what I can guarantee? No matter how much money you throw at it, you will not have a new SOTA fab in a few months.