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.
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.
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.
I.e. I think that in one minute video, 95% of your images do not have new information in them
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.
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.