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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

#61

Post by luisfmh »

endorphone wrote: post #54
ThePadawan wrote: post #23

Earlier quoted context omitted.

Datacenter GPUs are mostly identical to the much cheaper consumer versions. The only thing preventing you from running a datacenter with consumer hardware is the licensing agreement you accept.

"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…

Learned a new word today. Pyrrhic.

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

#62

Post by yubozhao »

scottlocklin wrote: post #24
lazzlazzlazz wrote: post #2

Is the misspelling of "Andreessen-Horowitz" and use of "A19H" instead of "a16z" intentional?

I suck at spelling. If I was one of the cool kids I'd claim to be dyslexic.

hi OP. We built an open-source library called, BentoML(https://github.com/bentoml/bentoml) to make model inferencing/serving a lot easier for Data scientists in various serving scenarios.

Love to hear your thoughts on our library

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

#63

Post by aj7 »

“ Embrace services. There are huge opportunities to meet the market where it stands. That may mean offering a full-stack translation service rather than translation software or running a taxi service rather than selling self-driving cars. Building hybrid businesses is harder than pure software, but this approach can provide deep insight into customer needs and yield fast-growing, market-defining companies. Services can also be a great tool to kickstart a company’s go-to-market engine – see this post for more on this – especially when selling complex and/or brand new technology. The key is pursue one strategy in a committed way, rather than supporting both software and services customers.”

Exactly wrong and contradicts most of the thesis of the article - that AI often fails to achieve acceptable models because of the individuality, finickiness, edge cases, and human involvement needed to process customer data sets.

The key to profitability is for AI to be a component in a proprietary software package, where the VENDOR studies, determines, and limits the data sets and PRESCRIBES this to the customer, choosing applications many customers agree upon. Edge cases and cat-guacamole situations are detected and ejected, and the AI forms a smaller, but critical efficiency enhancing component of a larger system.

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

#64

Post by fxtentacle »

pridkett wrote: post #50
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…

There’s also the issue that data scientists often want to go running to hyperparameter optimization and neural architecture search. In most cases improving your data pipelines and ensuring the data are clean and efficient will pay off much more quickly.

But manually improving the data pipeline requires an understanding of the problem, whereas doing a hyperparameter optimized architecture search just needs $$$ hardware and no clue on the side of the operator.

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

#65

Post by aj7 »

nkassis wrote: post #33
OkGoDoIt wrote: post #25

Earlier quoted context omitted.

NVIDIA forces you to buy significantly more expensive cards that perform marginally better if you are using them for datacenter use. They try to enforce not letting businesses use consumer grade gaming cards. I assume this is so cloud providers don't buy up all the supply of graphics cards and make it hard for gamers to get decent cards, like what happened during the bitcoin craze.

No it's just pure price discrimination. They don't care about gamers they just know businesses will pay more if forced to while gamers can't.

Exactly.

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

#66

Post by ggm »

buckminster wrote: post #59
m0zg wrote: post #53

Earlier quoted context omitted.

BTW, the only reason why consumer vacuum cleaners are so loud is because consumers associate loudness with suction power. "Backpack" style commercial vacuum cleaners have more suction, and are barely audible in comparison.

I stand corrected. It was much louder than a consumer vacuum but my analogy skills are weak.

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 noise. If you get my analogy.

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

#67

Post by blt »

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…

If I worked from home and my employer asked me to install a server in my home, I would tell them to go fuck themselves.

It's noisy, it takes up space, and presumably I'm on call to fix it if it breaks.

You should pay them an extra 24x(PSU wattage)x(peak $/Wh in area) per day for the electricity too.

I'm alarmed that someone in your company felt this idea was appropriate enough to propose.

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

#68

Post by ignoramous »

mrkurt wrote: post #38
ignoramous wrote: post #36

Earlier quoted context omitted.

As someone hoping to build a world-wide footprint, say 25 to 50 DCs, of servers to deploy to with unmetered bandwidth, what are some alternatives to the usual suspects? I have come across fly.io, Vultr, Scaleway, Stackpath, Hetzner, and OVH but either they are expensive (in that they charge for bandwidth and uptime) or do not have a wide enough foot-print. I guess colos are the way to go, but how does one work with c…

(Hi, I'm from fly.io) It depends what you need in your datacenters! If you just want servers, and don't care about doing something like anycast, you can find a bunch of local dedicated server providers in a bunch of cities and go to town. But you can't get them all from one provider, really, not with any kind of reasonable budget. You _could_ buy colo from a place like Equinix in a bunch of cities, and then either us…

(thanks mrkurt)

It is anycast that I'm going after. Requirement for unmetered bandwidth (or cheaper than AWS et al) is because of the kind of workloads (TURN relays, proxy, tunnels etc) we'd deal with gets expensive, otherwise. For another related workload, per-request pricing gets expensive, again, due to the nature of the workload (to the tune 100k requests per user per month).

So far, for the former (TURN relays etc), I've found using AWS Global Accelerator and/or GCP's GLB to be the easiest way to do anycast but the bandwidth is slightly on the expensive side. Fly.io matches the pricing in terms of network bandwidth (as promised on the website), so that's a positive but GCP/AWS have a wider footprint. Cloudflare's Magic Transit is another potential solution, but requires an enterprise plan and one needs to bring-your-own-anycast-IP and origin-servers.

For the latter (latency-sensitive workload with ~100k+ reqs / month), Cloudflare Workers (200+ locations minus China) are a great fit though would get expensive once we hit a certain scale. Plus, they're limited to L7 HTTP reqs, only. Whilst, I believe, fly.io can do L4.

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

#69

Post by derefr »

fxtentacle wrote: post #64
pridkett wrote: post #50

Earlier quoted context omitted.

There’s also the issue that data scientists often want to go running to hyperparameter optimization and neural architecture search. In most cases improving your data pipelines and ensuring the data are clean and efficient will pay off much more quickly.

But manually improving the data pipeline requires an understanding of the problem, whereas doing a hyperparameter optimized architecture search just needs $$$ hardware and no clue on the side of the operator.

Or, to put that another way: if you knew what algorithm the AI would be using to discriminate the signal from the noise in your data, why would you need the AI? Just write that algorithm.

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

#70

Post by bob1029 »

buckminster wrote: post #47
bob1029 wrote: post #29

Earlier quoted context omitted.

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…

I once had a borrowed Sun blade server in my home office. The fan in it sounded like an industrial vacuum cleaner. It got moved to a different room and was powered on as little as possible. Your plan makes sense but be mindful of the acoustics or your devs may grow to hate you.

Excellent point. If we are building these rigs by hand (which is a likely option considering the initial usage context), the cooling solution would probably be a Noctua NH-U14S or similar. I already have one of these in my office attached to a 2950X and it is dead silent. You can definitely hear it when every core is pegged, but it's hardly noticeable over any other arbitrary workstation. The sound is nowhere near as intrusive as something like a blower on a GPU (or god forbid a sun microsystems blade).
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