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Andreessen-Horowitz craps on “AI” startups from a great height
101–110 of 256 posts
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#102Earlier 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…
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.
You can stop at “go f* themselves”, plenty of jobs to choose from.
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#103Nice article. The flip side of the coin is that all these “problems” are potential moats for a well tuned ML company to use to defend market share.
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#104Earlier 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…
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.
We would certainly compensate employees. I didn't feel it appropriate to disclose every last detail regarding the arrangement in a thread which is only tangentially-related to the OP.
This was my idea. I am a developer in my company. We are a flat structure. We have a lot of respect for each other. I am on a standup with the CEO every day. We all believe in our product and would happily participate in whatever activity brings it to market more quickly. We do not hire or retain the kind of talent that would flatly refuse to participate in experimental projects like this. At least not without some sort of initial conversation about why it's not a good fit for a particular individual.
I certainly see how someone might share your perspective. I used to work for a souless megacorp and I could have easily found myself telling my former employer to "go fuck themselves" if a proposal similar to this was imposed upon me.
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#105So, 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…
Saying that you're going to "use AI" is more akin to saying "we're going to have a web application" back in 1998.
Back then a lot of startups didn't have websites, because they were making other products (hardware, boxed software, etc). If they had a website it was just a marketing page.
So saying that you were going to make a "web application" did in fact differentiate you, in that it showed your approach was very different from the boxed software folks, but it didn't tell you much beyond that.
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#106I 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)
AI != gold. The market for selling tools to people who are essentially chasing buzz words is much smaller than that of selling tools to people extracting scarce metals from the ground.
Ultimately the value of selling tools is dependent on the riches being mined actually existing. The value of AI/big data to the average business has yet to be determined
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#107Earlier quoted context omitted.
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
Apple does a pretty good job at that, with no compromise in quality.
I believe that just due to memory constraints, running any high-quality neural network on phones is currently impossible.
State of the art optical flow tracking needs about 10 GB of GPU memory to execute on full HD frames. I wouldn't know of any mainstream phone with that much RAM.
That, BTW, is also the reason why autonomous drones usually downsample the images before AI tracking, which has the nasty side effect of making thin branches, fences, telephone wires, etc. invisible.
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#108I 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)
>"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."
A lot of those companies are styled as "AI" companies themselves, aiming to automate the process of labeling.
The main winner here really is Amazon. They get a chunk by serving up infrastructure and in labeling through mechanical turk.
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#109You all know a GTX 1070 with 8GB on a gaming laptop with 32GB is still doing wonders and covering 90%+ business cases when coupled with smart & batch techniques the likes of you learn from fast.ai or under direct pytorch implementation, right??
Re: Andreessen-Horowitz craps on “AI” startups from a great height
#110"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…
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…
And since I'm being snarky anyway, there's two subdivisions to AI:
supervised learning => remember this
unsupervised learning => approximate this
This doesn't make any sense at all.
Both are "remembering" something under some constraint, which forces generalisation.
Supervised learning just "knows" what it is "remembering". Unsupervised learning is just trying to group data into patterns.
Both approaches don't put much emphasis on intelligence
Seems like most "intelligence" relies a lot on pattern recognition.
And both approaches can usually be implemented more efficiently without AI, if you know what you are doing.
The evidence is that you are wrong on this for a number of pretty important problems. I don't know much about optical flow, but in the image and text spaces you can't approach the accuracy of neural network approaches with hand crafted features.