Andreessen-Horowitz craps on “AI” startups from a great height

Post by [deleted] » Tue, Feb 25, 2020, 10:17 AM UTC

marcus_holmes wrote: post #214

joshuaellinger wrote: post #76

I just spent $50K on coloc hardware. I'm taking a $10K/mo Azure spend down to a $1K/mo hosting cost. But the real kicker is that I get x5 the cores, x20 RAM, x10 storage, and a couple of GPUs. I'm running last-generation Infiniband (56gb/sec) and modern U.2 SSDs (say 500MB/sec per device). I figure it is going to take me about $10K in labor to move and then $1K/mo to maintain and pay for services that are bundled in…

the point of Cloud is that it solves the problem of variable demand. I used to run on-prem back in the 2000's, and we were constantly dealing with demand fluctuation crises. Spinning up new physical servers to deal with new demand, or being massively over-specced when demand dropped, was a real pain. I'm starting a new thing this week, and using the Cloud for it because I have no idea what our demand will be. I can s…

Post by laktak » Tue, Feb 25, 2020, 10:22 AM UTC

Post by supermatt » Tue, Feb 25, 2020, 11:10 AM UTC

throwaway9d0291 wrote: post #222

marcus_holmes wrote: post #214

Earlier quoted context omitted.

the point of Cloud is that it solves the problem of variable demand. I used to run on-prem back in the 2000's, and we were constantly dealing with demand fluctuation crises. Spinning up new physical servers to deal with new demand, or being massively over-specced when demand dropped, was a real pain. I'm starting a new thing this week, and using the Cloud for it because I have no idea what our demand will be. I can s…

I half-agree. The Cloud specifically solves the problem of _highly_ variable demand. If your peak demand is 100x your baseline and only happens for ~1h each day, cloud is almost certainly a good choice. If it happens for ~12h a day or it's only 5x your baseline, the cost of the cloud is such that you're likely to save with dedicated hardware, even though much of your hardware sits around doing nothing part of the tim…

Post by C1sc0cat » Tue, Feb 25, 2020, 1:16 PM UTC

Q6T46nT668w6i3m wrote: post #14

raiyu wrote: post #6

The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small. They are usually tremendously large datasets at gigantic companies, and they probably have to build in house expertise because it's hard to package this up into a product and resell it for various industries, datasets, etc. Certainly something like autonomous driving needs machine learn…

How would you explain the rise (and success) of machine learning in science? A lab that uses some learning-based method will likely be limited to just one or two people (responsible for data acquisition, feature engineering, evaluation, etc.) and extremely finite data.

Post by _laiq » Tue, Feb 25, 2020, 1:55 PM UTC

phreeza wrote: post #152

rossdavidh wrote: post #60

So, way back in the last millenium, I did my Master's thesis (way smaller deal than a Ph.D. thesis) on neural networks. Since then, I have looked in on it every few years. I think they're cool, I like using them, and writing multi-level backpropagation neural networks used to be one of the first things I'd do in a new language, just to get a feel for how it worked (until pytorch came along and I decided for the first…

> There used to be a saying: "If you don't know how to do it, you don't know how to do it with a computer." This is a tautology in the narrow sense, but in the broader sense I think there surely exist things that humans don't "know" how to do without a computer, but know how to do with a computer. And the space of solveable problems is expanding, though AI is only a narrow slice of that.