How significant are these top 3 things?
Have you ever built a production model for a large company or have a tier 1 phd? If not, what are your qualifications for labeling a claim ridiculous.
Off the top of my head and in no particular order :
1- Playing human games at superhuman performance from raw pixels
2- Recognizing objects and people in images at human or better performance
3- Recognizing human voice in real world noisy environments and parsing it into text at human or better performance
>How significant are these top 3 things?
They generate billions of dollars of profit and beat human knowledge and expertise that took thousands\millions of years to craft and\or evolve with nothing but a GPU and a lot of electricity.
>Have you ever built a production model for a large company or have a tier 1 phd?
No.
>what are your qualifications for labeling a claim ridiculous.
I have a brain that can spot ridiculous hyperbolic claims, and experience-backed knowledge with neural networks and machine learning that can explain with examples why they are ridiculous and hyperbolic.
If you do have the qualifications you request, start with using them to support your claims. Here's a small challenge that should pose no difficulty to you.
Show me, with derivations and citations, how an RNN is "just" an obscure statistical model. Which statistician first published or applied it ? Is it taught in a statistics course you know of ? Which ones ? Are research statisticians publishing on it right now or ever ? In what sense is it part of the science of statistics like, say, the normal distribution is ?
Number 1 has probably generated ~0$ in profits. It's not immediately clear that number 3, when weighting its contribution apart from everything else in the products it's integrated in, has reached 10-digits in $ profits.
So you're only left with number 2.
I used games as examples just because they are the easiest to understand and most popular applications of RL, and also very general, but that would be like saying that CNNs are useless because recognizing cats is not a bussiness, recognizing cats is just 1 example of the vast array of things CNNs can do. For every optimization algorithm pioneered by RL research in order to play a useless $0-return game, you can bet that it's later implemented and used in other areas to save and generate millions or billions of dollars. Games are just the test playground for new research.
I don't understand how #3 can't be credited with the billions of dollars the products and services based on it generate. According to [2], the global market of offering a speech-to-text API is valued at 1.3 billion dollars in 2019, if you assume that just 20% of that is actual customer value (to account for hype, errors in valuation, etc...), that's about 260 million dollar per year. Even an extremly conservative value estimate tells you the technology generates a billion dollar every 4 year (if it stays constant, the forecast projects the market will reach >3 billions by 2027). Keep in mind that this does not include any value generated from any other component or product, this is just the profits from the APIs alone, the generous 20% discount is to account for hype, analysis errors and bad data.
But you know what ? the broader point is, these are just 3 examples that I pulled off the top of my head while I was making a 5 minute comment without consulting google. And considering the person I'm replying to never provided a single example or any kind of specific clarification till now, or anwered any of my questions or otherwise offered any kind of rebuttal, I think it's pretty fair to say they hold up nicely.
[0] https://developer.nvidia.com/blog/designing-arithmetic-circu...
[1] https://www.nature.com/articles/s41586-021-04301-9
[2] https://www.fortunebusinessinsights.com/speech-to-text-api-m...
No, you used it in exactly this context:
" >How significant are these top 3 things?
They generate billions of dollars of profit "
and the claim was incorrect regarding example 1.
I also don't think you understand what profit is and the difference between profit and revenue. Regardless, this exchange has become pretty pointless.
So by this reasoning, something like Linux has exactly $0 value in monetary terms. Linux is never an application, it never does something that actual users pay for (the "users" who pay for Linux support are just developers and corporations who use it to develop something else).
>I also don't think you understand what profit is and the difference between profit and revenue.
Yes, I don't understand an elementary distinction between terms that anybody with a dictionary understands. This is quite a fair and productive point for you to make.
>this exchange has become pretty pointless.
Probably the one thing you're right about in this, I'm indeed not interested in debating incredibly insecure people fond of accusing others of ignorance (quite often a projection technique) instead of engaging with their arguments or citing specific examples or research.
For the sake of analogy, perhaps I should read some articles on equine veterinary practices and start talking down to practitioners in the field, all for the sake of making my internet ego feel better.
Yeah, 15 years in industry and academia, and you don't know how statistics and probability theory are different.
May you have a nice happy day\night my guy.
Yes, you actually don't understand them. Which is why you said this
>the global market of offering a speech-to-text API is valued at 1.3 billion dollars in 2019
which is about revenue when you previously were talking about profits. It's obvious who is the insecure one in this exchange.
First, statistics is not a small branch of math, proability theory is (not a small branch by the way, not by a long shot, just a branch, one of the fundamental and very large ones). Statistics is the art and science of dealing with data, it uses probability theory like physics uses math, but is far more than probability theory just like physics is far more than math.
Second, Neural nets and machine learning in general is an extremly powerful way of solving problems of certain structure and form, and it solves problems not encountered in statistics. Claiming it's a small branch of statistics is like claiming that the entirety of computer science is a small branch of logic.