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

Or like “nuked from orbit”

"The only thing preventing you from running a datacenter with consumer hardware is the licensing agreement you accept."

The consumer cards don't use ECC and memory errors are a common issue (GDDR6 running at the edge of its capabilities). In a gaming situation that means a polygon might be wrong, a visual glitch occurs, a texture isn't rendered right -- things that just don't matter. For scientific purposes that same glitch could be catastrophic.

The "datacenter" cards offer significantly higher performance for some case (tensor cores, double precision), are designed for data center use, are much more scalable, etc. They also come with over double the memory (which is one of the primary limitations forcing scale outs).

Going with the consumer cards is one of those things that might be Pyrrhic. If funds are super low and you want to just get started, sure, but any implication that the only difference is a license is simply incorrect.

>Citations needed. Large claims: presumably you can name one example of this, and hopefully it's not a company you work at.

Unfortunately this is all emerging just now and yes, I do work at such a company, but I'm old enough to not be naively excited by some hot fad. There's something profound just starting to happen but everyone is keeping the tech rather secret because it isn't developed/differentiated enough yet to keep a competitor from running off with an idea, yet. Disclosure is probably 1-3 years out of estimate.

>I do know the oil companies are using DL...as their other imaging efforts.

You're correct, and I happen to have experience in this domain - except there are a handful of up and commers courting funds from global majors like Shell and BP, and seismic inversion is near the end of the list of novel applications. Peteoleum is ground zero for a potential revolution right now, if we can come up with something before the U.S. administration clamps down on fossil fuels.

But we're talking complex algorithms which consist of multiple interacting neural networks. We are rapidly moving toward rudimentary reasoning systems which represent conceptual information encoded in vectors. I'm jaded enough that I wouldn't say we're developing AGI, but if the progressing ideas I'm familiar with and Workin on personally pan out, they will be massive baby steps towards something like AGI.

The space is evolving at least as rapidly as the academic side, which I think is an unprecedented pace of development for a novel field of study. I can't help but feel like these are the first steps towards some kind of singularity. There's no question that we are on to something civilization changing with neural networks, what remains to be seen is whether compute scaling will keep up with the needs of this next generation ML. Even if research stopped today, the modern ML zoo has exploded with architectures with fruitful applications across domains. The future is here!

No, also inference is quite expensive. You'll have 100% usage on a $10,000 GPU for 3s per customer image for a decently sized optical flow network. That's 3 hours of compute time for 1 minute of 60fps video.

Now let's say your customer wants to analyze 2 hours = 120 minutes of video and doesn't want to wait more than those 3 hours, then suddenly you need 120 servers with one $10k GPU each to service this one customer within 3 hours of waiting.

Good luck reaching that $1,200,000 customer lifetime value to get a positive ROI on your hardware investment.

When I talk about AI, I usually call it "beating the problem to death with cheap computing power". And looking at the average cleverness of AI algorithm training formulas, that seems to be exactly what everyone else is doing, too.

And since I'm being snarky anyway, there's two subdivisions to AI:

supervised learning => remember this

unsupervised learning => approximate this

Both approaches don't put much emphasis on intelligence ;) And both approaches can usually be implemented more efficiently without AI, if you know what you are doing.

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 time that using their library was easier than writing my own).

So, it's not like I dislike ML. But, saying an investment is an "AI" startup, ought to be like saying it's a python startup, or saying it's a postgres startup. That ought not to be something you tell people as a defining characteristic of what you do, not because it's a secret but rather because it's not that important to your odds of success. If you used a different language and database, you would probably have about the same odds of success, because it depends more on how well you understand the problem space, and how well you architect your software.

Linear models or other more traditional statistical models can often perform just as well as DL or any other neural network, for the same reason that when you look at a kaggle leaderboard, the difference between the leaders is usually not that big after a while. The limiting factor is in the data, and how well you have transformed/categorized that data, and all the different methods of ML that get thrown at it all end up with similar looking levels of accuracy.

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." AI boosters sometimes sound as if they are suggesting that this is no longer true. They're incorrect. ML is, absolutely, a technique that a good programmer should know about, and may sometimes wish to use, kind of like knowing how a state machine works. It makes no great deal of difference to how likely a business is to succeed.