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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

#81

Post by fxtentacle »

m0zg wrote: post #74
fxtentacle wrote: post #58

Earlier quoted context omitted.

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…

Some kinds of inference are expensive, yes, not going to dispute that. But 99.95% of it is actually surprisingly inexpensive. Hell, a lot of useful workloads can be deployed on a cell phone nowadays, and that fraction will increase over time, further reducing inference costs or eliminating them outright (or rather moving them to the consumer). For the vast majority of people the main expense is creating the combinati…

The algorithms that run on cell phones tend to be specially optimized and quality-reduced neural networks. For example, https://arxiv.org/abs/1704.04861

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

#82

Post by PeterisP »

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.

Can you elaborate on what you mean by "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.)" ? I know a bunch of labs that apply machine learning to specific tasks, and the parts you list each can easily take up multiple people for years for a single task - not counting data acquisition, because data is definitely not "extremely finite", you need lots of quality data, and improving data is something that always gets improvements and can easily eat up more manpower than you can have budget, no matter what that budget is.

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

#83

Post by fizixer »

m0zg wrote: post #31
fizixer wrote: post #20

Earlier quoted context omitted.

- Or AMD could change their policy of 'never miss an opportunity to miss an opportunity' and offer high-performance OpenCL GPGPU offerings. Then nVidia could have all the stroke they wanted. - Or Tensorflow/Pytorch could've crapped on OpenCL a little less by releasing a fully functional OpenCL version everytime they released a fully functional Cuda version, instead of worshipping Cuda year in and year out. - Or Googl…

As someone who has actually worked on this stuff soup to nuts, it's not as easy as people imagine, because you can't just support some subset of available ops and call it a day. If you want to make OpenCL pie from scratch, you must first make the universe, and support every single stupid thing (among thousands) and even mimic some of the bugs so that models work "the same". This is hard and time consuming, and this f…

I know Tensorflow decided to be cuda-exclusive for the silly reason that the matrix library they were using (eigen) only supported cuda.

I have never recovered from that.

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

#84

Post by fxtentacle »

derefr wrote: post #69
fxtentacle wrote: post #64

Earlier quoted context omitted.

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.

Exactly :)

In most cases, unsupervised learning is nothing more than having the AI try to approximate the solution of your highly non-linear loss function. So if there's any way of solving that loss function directly, it will perform like a well-trained AI.

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

#85

Post by detaro »

harias wrote: post #5

>That’s right; that’s why a lone wolf like me, or a small team can do as good or better a job than some firm with 100x the head count and 100m in VC backing. goes on to say >I agree, but the hockey stick required for VC backing, and the army of Ph.D.s required to make it work doesn’t really mix well with those limited domains, which have a limited market. Choose one? Also assumes running your own data center to be ea…

> Some people don't want to be up 24x7 monitoring their data center or to buy hardware to accommodate the rare 10 minute peaks in usage.

Do you need that for training workloads, and what percentage of a startups workload is training?

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

#86

Post by 7532yahoogmail »

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…

Thank you for the perspective. Now when we talk machine learning are we talking:

L. Pachter and B. Sturmfels. Algebraic Statistics for Computational Biology. Cambridge University Press 2005.

G. Pistone, E. Riccomango, H. P. Wynn. Algebraic Statistics. CRC Press, 2001. Drton, Mathias, Sturmfels, Bernd, Sullivant, Seth. Lectures on Algebraic Statistics, Springer 2009.

Or more like:

Watanabe, Sumio. Algebraic Geometry and Statistical Learning Theory, Cambridge University Press 2009.

My understanding (I do not do AI or machine learning) that AI is distinct from these more mathematical analytic perspectives.

Finally, might we argue that generally AI/ML is more easily suited to data that's already high quality eg. CERN data, trade data, drug trial data as opposed to unconstrained data eg. Find the buses in these 1MM jpegs?

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

#87

Post by dsl »

_bxg1 wrote: post #57
KaiserPro wrote: post #48

Earlier quoted context omitted.

because you are trading speed for cash. Say you have $8M in funding, and you need to train a model to do x You can either: a) gain access to a system that scale ondemand and allows instant, actionable results. b) hire a infrastructure person, someone to write a K8s deployment system. Another person to come in a throw that all away. Another person to negotiate and buy the hardware, and another to install it. Option b…

Why in the world do you need a Kubernetes deployment system to run a single, manual, one-time (or a handful of times), high-compute job?

Because when all you have is a hammer, everything looks like a nail.

We have become so DevOps and cloud dependent that everyone has forgotten how to just run big systems cheaply and efficiently.

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

#88

Post by moksly »

fxtentacle wrote: post #78

I predict a great future for startups that sell pickaxes, err, tools for AI. AI is like the new gold rush. And just like back then, it's not the gold diggers that will get rich. "Most people in AI forget that the hardest part of building a new AI solution or product is not the AI or algorithms — it’s the data collection and labeling." https://medium.com/startup-grind/fueling-the-ai-gold-rush-7a... (from 2017)

Is it the new gold rush though. I work in a large organisation that has a lot of data and inefficient processes, and we haven’t bought anything.

It hasn’t been for a lack of trying. We’ve had everyone from IBM and Microsoft to small local AI startup try to sell us their magic, but no one has come up with anything meaningful to do with our data that our analysis department isn’t already doing without ML/AI. I guess we could replace some of our analysis department with ML/AI, but working with data is only part of what they do, explaining the data and helping our leadership make sound decisions is their primary function, and it’s kind of hard for ML/AI to do that (trust me).

What we have learned though, is that even though we have a truck load of data, we can’t actually use it unless we have someone on deck who actually understands it. IBM had a run at it, and they couldn’t get their algorithms to understand anything, not even when we tried to help them. I mean, they did come up with some basic models that their machine spotted/learned by itself by trawling through our data, but nothing we didn’t already have. Because even though we have a lot of data, the quality of it is absolute shite. Which is anecdotal, but it’s terrible because it was generated by thousand of human employees over 40 years, and even though I’m guessing, I doubt we’re unique in that aspect.

We’ll continue to do various proof of concepts and listen to what suppliers have to say, but I fully expect most of it to go the way Blockchain did which is where we never actually find a use for it.

With a gold rush, you kind of need the nuggets of gold to sell, and I’m just not seeing that with ML/AI. At least no yet.

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

#89

Post by ska »

andreilys wrote: post #46
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…

“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.” Thankfully transfer learning and super convergence invalidates this claim. Using pre-trained models + specific training techniques significantly reduces the amount of data you need, your training time and the cost to create near state of the art models. Both Kaggle and google colab of…

>Thankfully transfer learning and super convergence invalidates this claim.

IME it is nowhere near as universally successful as this suggests.

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