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What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? How significant are these top 3 things

by jfghi 4y ago

What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? 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.

Banana699 4y ago
>What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? 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 ?
- trention 4y ago
  >They generate billions of dollars of profit 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.
  - jfghi 4y ago
    Except for of course when number 2 fails which is quite often.
  - Banana699 4y ago
    I wouldn't be so quick. #1 is usually implemented by Deep Reinforcement Learning systems, RL is an AI paradigm descended from Control Theory and is used extensively elsewhere in Optimization and Operations Research. Here[0] for example is a Nvidia blog post detailing how they used it to obtain circuits with 25% less area at the same performance metrics. (I didn't search for this post except just to get the link, it's very typical of the kind of research I follow and I read it as soon as it was posted several weeks ago.) In another[1] example, Deepmind researchers used DRL to control a fusion reactor with results that exceed the current state of the art. 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-circuits-with-deep-reinforcement-learning/ https://developer.nvidia.com/blog/designing-arithmetic-circu... [1] https://www.nature.com/articles/s41586-021-04301-9 https://www.nature.com/articles/s41586-021-04301-9 [2] https://www.fortunebusinessinsights.com/speech-to-text-api-market-102781 https://www.fortunebusinessinsights.com/speech-to-text-api-m...

by jfghi 4y ago

Banana699 4y ago

>What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? 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 ?

trention 4y ago
>They generate billions of dollars of profit 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.
- jfghi 4y ago
  Except for of course when number 2 fails which is quite often.
- Banana699 4y ago
  I wouldn't be so quick. #1 is usually implemented by Deep Reinforcement Learning systems, RL is an AI paradigm descended from Control Theory and is used extensively elsewhere in Optimization and Operations Research. Here[0] for example is a Nvidia blog post detailing how they used it to obtain circuits with 25% less area at the same performance metrics. (I didn't search for this post except just to get the link, it's very typical of the kind of research I follow and I read it as soon as it was posted several weeks ago.) In another[1] example, Deepmind researchers used DRL to control a fusion reactor with results that exceed the current state of the art. 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-circuits-with-deep-reinforcement-learning/ https://developer.nvidia.com/blog/designing-arithmetic-circu... [1] https://www.nature.com/articles/s41586-021-04301-9 https://www.nature.com/articles/s41586-021-04301-9 [2] https://www.fortunebusinessinsights.com/speech-to-text-api-market-102781 https://www.fortunebusinessinsights.com/speech-to-text-api-m...