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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

#191

Post by killjoywashere »

izendejas wrote: post #165
shoo wrote: post #159

> most people haven’t figured out that ML oriented processes almost never scale like a simpler application would. You will be confronted with the same problem as using SAP; there is a ton of work done up front; all of it custom. I’ll go out on a limb and assert that most of the up front data pipelining and organizational changes which allow for [ML to be used operationally by an org] are probably more valuable than t…

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.

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

#192

Post by joe_the_user »

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.

Basically, the system is massively ad-hoc and driven by this large scale annotation, training and testing.

The big question here is, what happens when the world changes next year? You rebuild the application. I know there are companies that advertise doing continuous updating of deep learning models but it seems like calculating total costs and total benefits is going to be hard here.

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

#193

Post by aSplash0fDerp »

perl4ever wrote: post #153
all_blue_chucks wrote: post #124

Earlier quoted context omitted.

"Web application" came later. In the nineties it was called a "cgi web page" by your webmaster.

In the nineties, there was a huge difference between 1995 and 1998. It wasn't all that apparent to some of us until later, but things moved really fast in that timeframe. The years leading up to 2000 were almost like the imagining of approaching an event horizon or asymptote.

I can recall chugging along with a Pentium 133mhz and 56k dialup between 95-98.

Its fantastic to think that we didn't see 1ghz cpus and 1mb cable/dsl Internet until 2000.

The resourcefulness from that pre-2k era was amazing! It was leaps and bounds!

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

#194

Post by nkassis »

gbba wrote: post #139
nkassis wrote: post #33

Earlier quoted context omitted.

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.

I wouldn't say they don't care about gamers, considering that gaming makes up about half of their revenue: https://www.anandtech.com/show/15513/nvidia-releases-q4-fy20...

Sure sorry I realize now the point I was trying to make doesn't match my wording. They do care about selling to gamers but availability to gamers is not in anyway why they are forcing more expensive models of essentially the same cards on the HPC and server market. It's all because they can and the business market is able to bear the cost. Now if they found a true competitor in that market, I think this pricing model would fall apart fast.

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

#195

Post by TheSpiceIsLife »

wpietri wrote: post #130
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…

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…

I would have assumed the colo provider would offer Remote Hands, so you’d only need to send replacement hardware.

That’s how the DC I used to work in operated.

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

#196

Post by killjoywashere »

joe_the_user wrote: post #192
killjoywashere wrote: post #191

Earlier quoted context omitted.

"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.

Basically, the system is massively ad-hoc and driven by this large scale annotation, training and testing. The big question here is, what happens when the world changes next year? You rebuild the application. I know there are companies that advertise doing continuous updating of deep learning models but it seems like calculating total costs and total benefits is going to be hard here.

Sometimes the mine makes money, sometimes it doesn't make sense to run the mine.

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

#197

Post by joshuaellinger »

wpietri wrote: post #130
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…

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…

Prior to the cloud, I ran at a coloc facility for 15 years. I break stuff much more often than having it actually fail. So... make yourself robust against human error first and you'll probably cover the hardware side as a side-effect. I am more likely to hose a machine during an OS upgrade and not have time to recover than I am to have an SSD fail.

But spare capacity is a good idea, especially if you have real-time traffic.

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

#198

Post by dcl »

I'm not terribly convinced of point 4.

> Machine learning will be most productive inside large organizations that have data and process inefficiencies.

I strongly believe ML is at worst dangerous and at best pointless here. Data and Process inefficiencies => garbage in, garbage out. ML is NOT a silver bullet in large organisations that have these issues*, I've seen managers try to adopt ML to solve issues, but the results are almost always suspect and/or marginally better than simple if else rules but require a multiple people or teams to get all the data and models right.

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

#199

Post by linksnapzz »

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.

Once upon a time, a company I worked for convinced Sun Microsystems that it would be cool to provide us with hardware at a discount. The discount hardware was six UltraSPARC E4000 servers, each with 8 cpus and 8GB of memory, and a creator3d card in the io mezzanine upa slot.

This company was using them as desktop workstations, in an open office.

One was used as a build host. Often, the shopvac wail of E4000 fans would be cut short by some poor dev going berserk and unplugging the thing when nobody was looking...

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

#200

Post by moandcompany »

killjoywashere wrote: post #196
joe_the_user wrote: post #192

Earlier quoted context omitted.

Basically, the system is massively ad-hoc and driven by this large scale annotation, training and testing. The big question here is, what happens when the world changes next year? You rebuild the application. I know there are companies that advertise doing continuous updating of deep learning models but it seems like calculating total costs and total benefits is going to be hard here.

Sometimes the mine makes money, sometimes it doesn't make sense to run the mine.

To extend the mining metaphor, and relate back to the original articles:

People and organizations are chasing what they believe, or are told to believe, is pay dirt.

Many unfamiliar investors have rushed in, possibly fearing missing out, and fund many of the prospectors, yet many of the prospectors and investors aren't really aware of the costs of running a mine, nor the practices required to run them efficiently.

It turns out that there's more aspects to the value creation process than dig/refine/polish (data/train/predict), especially when usefulness in application matters and there are finite resources available for digging.

Companies selling shovels are some of the primary beneficiaries of this, by selling shovels (i.e. renting compute) funded by the malinvestment.

Additional beneficiaries are the refiners (training experts) that are able to charge steep labor premiums, however organizations are starting to figure out that their refiners are expensive to keep idle and often operate the mines poorly in terms of throughput/cost-effectiveness/repeatability/application (see the various threads on "Data Engineers")

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