Comment by tonetegeatinst | Hacker News Reader

Comment by tonetegeatinst | Hacker News Reader

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This has always been my stance in basically any field. Be it computer science or material science research or AGI.....I think its so hard to really say when we will hit the next breakthrough in a field. That sort of thing is hard to accurately predict. As in regards to hardware, I kinda agree disagree.

Hardware is a big part of the bottleneck in most computer fields when it comes to AI or computer vision. These things are compute and memory intensive and hardware manufacturers are straddling the line between staying profitable by charging for premium features and just dumping their hardware on the market for cheap.

Am I pretty pissed that most open source researchers and models are stuck using this extremely expensive hardware....yes

I sometimes wish that we would stop using "GPU" for training and we got a dedicated hardware architecture that's open source like riskV but solely for AI and research purposed without costing more tha n my kidneys.

I think its also a matter of discovering new ways to accelerate these workloads so that its feasible for them to be created by hobbyists on a small budget and not by large funded groups or companies.

Were doing really good in some aspects but in other ways....we have much to work on

You can get an ML workstation with 2xRTX4090 for around 10k. A 4xA800 setup tops out at 100k and should last you a few years. I consider that fairly affordable to do bleeding edge research especially compared to other fields.

More like 3k for dual 4090

A single 4090 FE retails for $1,599. Getting two of these in a capable enough workstation is going to cost a fair amount more than $3K.

Yes, compared with equipment for biology, materials science, and so on, this is very cheap.

The $5-$10k mark for a high end home workstation has held pretty firm since the 1980s. That's the buy-in price point for a very early PC, an SGI Indy, a low end Sun workstation, etc.

We went through an amazing period of very cheap computers from 2000-2020. We just didn't need specialty or high end equipment for a while.

Now we do again.

>I sometimes wish that we would stop using "GPU" for training and we got a dedicated hardware architecture that's open source like riskV but solely for AI and research purposed without costing more tha n my kidneys.

At least for the time being there is no magic workaround for the amount of FLOPS and memory bandwidth needed. When you look at neural network architectures they tend to be massively parallel, and building out at that scale in hardware is going to cost a lot.

And, when the hardware does show up, at least with the scaling laws we're seeing at this time, the groups that have massive amounts of this lower powered/accellerated hardware are going to be ahead of those without budgets.

Scientific progress almost always presents as series of iterations, not punctuated breakthroughs. Moreover, many significant advances appear nearly simultaneously. This tends to suggest a measured progress, though obviously still largely unpredictable at any scale.

I don’t see why AGI would follow a different course, one where we are “just some breakthrough away”. Especially not given the current state of the field.

Personally I’d wager neuromorphic computing is the prime candidate to yield AGI.

Why was your comment dead just 5 minutes after posting? Very puzzling.

And it shouldn't be a controversial comment either.

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