3 ms·

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 hou

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 within 3 hours of waiting. Good luck reaching that $1,200,000 customer lifetime value to get a positive ROI on your hardware investment. When I talk about AI, I usually call it "beating the problem to death with cheap computing power". And looking at the average cleverness of AI algorithm training formulas, that seems to be exactly what everyone else is doing, too. And since I'm being snarky anyway, there's two subdivisions to AI: supervised learning => remember this unsupervised learning => approximate this Both approaches don't put much emphasis on intelligence ;) And both approaches can usually be implemented more efficiently without AI, if you know what you are doing.

m0zg 7y ago
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 combination of a dataset and model that works for their practical problem, with the dataset being the harder (and sometimes more expensive) problem of the two. The dataset is also their "moat", even though most of them don't realize it, and don't put enough care into that part of the pipeline.
- fxtentacle 7y ago
  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 https://arxiv.org/abs/1704.04861
  - jiofih 7y ago
    Apple does a pretty good job at that, with no compromise in quality.
    - fxtentacle 7y ago
      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.
nl 7y ago
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.
streetcat1 7y ago
I am not sure what you are doing, but can you just compute the similarity between two frames, and analyze only the novel frames? I.e. I think that in one minute video, 95% of your images do not have new information in them
mrpidgeon 7y ago
"supervised learning => remember this unsupervised learning => approximate this" Lol this can't be more wrong lmao. Both areas "remember" and "approximate" things trough training. The difference is that unsupervised learning does not have labeled data, thus it has to search for some pattern. Honestly not even computer science graduates would say something like this.

m0zg 7y ago

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 combination of a dataset and model that works for their practical problem, with the dataset being the harder (and sometimes more expensive) problem of the two. The dataset is also their "moat", even though most of them don't realize it, and don't put enough care into that part of the pipeline.

fxtentacle 7y ago
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 https://arxiv.org/abs/1704.04861
- jiofih 7y ago
  Apple does a pretty good job at that, with no compromise in quality.
  - fxtentacle 7y ago
    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.

nl 7y ago

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.

streetcat1 7y ago

I am not sure what you are doing, but can you just compute the similarity between two frames, and analyze only the novel frames? I.e. I think that in one minute video, 95% of your images do not have new information in them

mrpidgeon 7y ago

"supervised learning => remember this unsupervised learning => approximate this" Lol this can't be more wrong lmao. Both areas "remember" and "approximate" things trough training. The difference is that unsupervised learning does not have labeled data, thus it has to search for some pattern. Honestly not even computer science graduates would say something like this.