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

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

#201

Post by foobiekr »

angry_octet wrote: post #177
foobiekr wrote: post #172

Earlier quoted context omitted.

For my own company we built out at two regionally distinct colo facilities. That worked really well and operations was efficient and costs were moderate, clearly tied to CAPEX increments which were predictable. Recent projects have been on AWS. For a project that is roughly on the scale of our colo in terms of instances, though with aggregate lower performance, we are buying one of our colos every year. It’s insane.…

Yes, cloud costs are the cost of having poor internal management, such that inefficiency and incompetence reigns unchallenged. The enormous cost differential is unfortunately borne by the unit doing the work, rather than the one preventing it from being done efficiently.

That is a very accurate summary.

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

#203

Post by MacsHeadroom »

Well, duh. Unless you invent AGI you're always going to be fitting new models for new clients. The best case scenario is getting bought by a client and becoming their full-time ML tailor.

For a pure ML company to IPO they'd have to both solve intelligence and manufacture their own hardware. FOMO screwed a lot of investors who would've been better off buying Google stock.

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

#204

Post by CoolGuySteve »

bob1029 wrote: post #29
m0zg wrote: post #8

"Huge compute bills" usually come from training, or to be more precise, hyperparameter search that's required before you find a model that works well. You could also fail to find such a model, but that's another discussion. So yeah, you could spend one or two FTE salaries' (or one deep learning PhD's) worth of cash on finding such models for your startup if you insist on helping Jeff Bezos to wipe his tears with cris…

I find it fun how the cost of the cloud is forcing people to consider what absolutely must run in the cloud (presumably for stability and compliance reasons) and what can be brought back on-prem. We don't train ML models, but we are in a similar boat regarding cloud compute costs. Building our solutions for our clients is a compute-heavy task which is getting expensive in the cloud. We are considering options such as…

In my experience, teams will rack up thousands in monthly expenses just being parked in a shell on very large On-Demand or Reserved EC2 instances. Basically using them as development boxes without realizing how much they cost.

I've saved a ton of money just giving them dedicated workstations to develop on and then having everyone use a shared EC2 instance to push jobs to a fleet of spot instances for large scale training.

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

#205

Post by artemisyna »

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.

...because previously, the academics would use an army of undergrads to do the same data labeling that ML accomplishes.

(The dis-economy of scale hurts less if you're already starting from a point with the manual labor.)

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

#206

Post by streetcat1 »

killjoywashere wrote: post #191
izendejas wrote: post #165

Earlier quoted context omitted.

That's because, ML and operations-research problems can be simplified to set of optimization problems and the underlying math and statistics are all very similar if not identical in some cases. And the input matters, a lot. So the differentiating factor isn't the models, it's the data and companies like Google figured it out a long time ago. In short, find interesting problems, then the solutions -- not the other way…

"The data" means more than pure computer science people want to admit. In any "advanced" application, that means annotators. Radiologists drawing circles around cancer, attorneys labeling contract clauses as unacceptable, drivers labeling stop signs, etc. ML is a mining problem. Digitizers are the miners. Annotators are the refiners.

This is correct, however, the distinction between labeling and training is artificial, and probably arises from the fact that ML came from academia, where it was not part of the business process.

I.e. a modern ML system should just plug into the business process from day 0, where the ML task should be performed by human and recorded by the machine.

After a while, the machine would train on this recorded data, and start replacing the humans.

Rinse and repeat.

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

#207

Post by [deleted] »

killjoywashere wrote: post #191
izendejas wrote: post #165

Earlier quoted context omitted.

That's because, ML and operations-research problems can be simplified to set of optimization problems and the underlying math and statistics are all very similar if not identical in some cases. And the input matters, a lot. So the differentiating factor isn't the models, it's the data and companies like Google figured it out a long time ago. In short, find interesting problems, then the solutions -- not the other way…

"The data" means more than pure computer science people want to admit. In any "advanced" application, that means annotators. Radiologists drawing circles around cancer, attorneys labeling contract clauses as unacceptable, drivers labeling stop signs, etc. ML is a mining problem. Digitizers are the miners. Annotators are the refiners.

[deleted]

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

#208

Post by ggm »

ColanR wrote: post #119
ggm wrote: post #66

Earlier quoted context omitted.

Your analogy skills were strong, because analogy is rooted in myth, not fact. Achilles did not actually have an Achilles tendon because Achilles did not exist There does not have to be an increadibly loud functional industrial vacuum cleaner, for figuratively everyone to get your analogy, because the Herculean reality of vacuum cleaners is that you cannot clean an augean stable of lego on the floor, without a lot of…

To continue the pedantry, I don't think we know for sure that Achilles didn't exist. Troy certainly did, and we have the Iliad to thank for knowing to look for it.

No, we have Schliemann to thank, for digging through it in his lust for anachronistically Indy-Jonesing the archeology. Troy only exists because Schliemann stole the gold. Otherwise its just some hill in Turkey.

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

#209

Post by sabalaba »

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…

Yea we’re seeing this all over the place at Lambda (https://lambdalabs.com). Most people running consistent GPU training or inference jobs are building on-prem clusters or even groups of workstations.

It just doesn’t make financial sense to use the big the cloud service providers for those with consistent workloads. I always hear stories where folks have saved hundreds of thousands in infrastructure costs with owning + co-lo.

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

#210

Post by kavalg »

latch wrote: post #160
wpietri wrote: post #130

Earlier quoted context omitted.

I'm sure your right for your case. But I'd add one caveat for those less experienced: if you own the hardware, you need to be prepared to go to the colo when something breaks. The various clouds are a much nicer experience when hardware fails. At the very least people should have enough spare capacity that a hardware failure means going sometime in the next couple of weeks, rather than getting up at 3 am and fixing t…

Or take the middle road and just get rent the hardware (aka, dedicated hosting). You pay more than colo but still way less than cloud, get the same level of hardware support as a cloud provider but the same performance as colo.

Yep, for example Hetzner offers bare metal servers as well as cloud instances at a very reasonable price. (Not affiliated in any way. Just a happy customer.)
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