I'm glad they're making inroads here, cause gesture and pose calculation and tracking is awesome.
But.. it also seems like more usage of black-box magic math of Neural Networking. I'm glad it gets results and all, but it just seems.. inelegant. What's the algo really doing underneath? Did the algo figure out the joints and their ranges of freedom?
The results are spectacular, indeed. But it also seems a bit not like science. It seems more like an oracle system was able to deduce the results - and we're nowhere near closer to understanding how to do this. Just, we have a trained system that can.
Maybe the problem itself is not elegant (hence why it's not solved in closed form)? Neural networks learn thousands of different algorithms in parallel. It's probably not a single idea behind it, it may be thousands. It's more like experimental science: the system is there, it works, someone else needs to analyze it to figure out how it does what it does.
That's my point. Anybody can shove in gigs of data, clean it up some, provide easily digestible samples, make a whole lot of parameters (or let the system decide), and scrape the data out.
How is it working? What criteria does it work and fail? Does it work for black people? Does it work for women's hands? (Or does it work for anyone out of university?) does it handle people with hand defects or missing digits?
That's right... We don't know unless we test this. And only by adding more data can we even determine those questions. And we haves no clue how its working, what features its using, or anything. Just, that it does work. And it doesn't for conditions were unsure of.
Now this is a great starting plank for determining the underlying math. But even the cost of compute seems high for what it could ideally be, if we understood what was going on.
Crazy brute force idea for analyzing a neural net, from someone who has little experience with neural nets:
Graph the response of the neural network, over the range of the stimuli that you care about. This is going to be a ridiculously huge dataset, but bear with me. Then, use a genetic algorithm to evolve equations that have reasonably similar behavior, perhaps over a much smaller domain.
This collection of equations and their valid input ranges, are the raw material for your program. You would simplify them using algebraic solvers, when possible, and attempt to hand-optimize them for readability. Then, when you are done, the whole thing gets compiled down to big switch-case statement in, say, C. From here on, the process looks sort of like yacc.
So, by adding a whole new layer of magic, we get the system to explain itself in a way that a programmer could understand. Come to think of it, this feels sort of like how I do personal introspection. In fact, I'm doing it right now.
This is kind of close to a relatively new openai paper that uses annealing/genetic algorithms for making a great reinforcement learning algorithm.
I do love the idea of machines figuring out the simplest model with maximum accuracy. When we can do that across different domains using same algo then we can say we have figured out secrets of intelligence.
Deep Neural Nets are inelegant now? Have you tried building effective DL models?
In fact it's more like science than you let on. You have a hypothesis for the data you need, test it, evaluate results, test etc... and result in optimized weighting.
But.. it also seems like more usage of black-box magic math of Neural Networking. I'm glad it gets results and all, but it just seems.. inelegant. What's the algo really doing underneath? Did the algo figure out the joints and their ranges of freedom?
The results are spectacular, indeed. But it also seems a bit not like science. It seems more like an oracle system was able to deduce the results - and we're nowhere near closer to understanding how to do this. Just, we have a trained system that can.