6 ms·

> I'm increasingly concerned that the impact of ML is going to be limited. You say likely typing on a keyboard with ML predictive algorithms on it, or dictatin

by jjcon 6y ago

> I'm increasingly concerned that the impact of ML is going to be limited. You say likely typing on a keyboard with ML predictive algorithms on it, or dictating with NLP speech to text. On a phone capable of recognizing your face, uploading photos to services that will recognize everything in them.

username90 6y ago
And that seems to be the limits of ML. We might eek out self driving cars, but I don't think we will get much more than that. It is pretty significant, but still limited compared to general purpose AI.
- ChefboyOG 6y ago
  Predictive text and image classification are not the extent of ML's limits, even going by what is currently in production. Recommendation engines, ETA prediction, translation, drug discovery, medicinal chemistry, fraud detection—these are all areas where ML is already very important and present. Sure, it's not artificial general intelligence, but what technological invention in history would compare to the impact of AGI? That's sort of a weird bar.
- jjcon 6y ago
  For the average person that is their greatest exposure - but were already seeing huge movements in medicine and defense and plenty of places where ML is not in average consumer use (the biggest applications are not for consumers). Add in your note on transportation and it is at the front of a huge section of the world economy. That’s all on track today - what will tomorrow’s innovations bring (the question asked by sci-fi)?
  - username90 6y ago
    Image recognition is just image recognition no matter where it is used.
    - jjcon 6y ago
      Wrong sub thread?
      - username90 6y ago
        No, all applications of ML currently are image recognition. The rest are just statistics labelled as ML.
        jjcon 6y ago
        That’s hilariously uninformed - nlp is the biggest counter example but you also have neural nets working in domains like RF signal processing, drug discovery all the way to video games - none of these applications are using just traditional stats.
- PartiallyTyped 6y ago
  One step at a time. As of the previous decade, we have - Learned how to play all atari games [1] - Mastered GO [2] - Mastered Chess without (as much) search [3] - Learned to play MOBAs [4] - Made progress in Protein Folding [5] - Mastered Starcraft [6] Notice that all these methods require an enormous amount of computation, in some cases, we are talking eons in experiences. So there is a lot of progress to be made until we can learn to do [1,2,3,4,6] with as much effort as a human needs. [1] https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark https://deepmind.com/blog/article/Agent57-Outperforming-the-... [2] https://deepmind.com/research/case-studies/alphago-the-story-so-far https://deepmind.com/research/case-studies/alphago-the-story... [3] https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go https://deepmind.com/blog/article/alphazero-shedding-new-lig... [4] https://openai.com/projects/five https://openai.com/projects/five [5] https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery https://deepmind.com/blog/article/AlphaFold-Using-AI-for-sci... [6] https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii https://deepmind.com/blog/article/alphastar-mastering-real-t...
  - username90 6y ago
    AI playing games is cool, but applying those techniques to real world scenarios would require a huge breakthrough. If a huge breakthrough happens then sure, but my point was based on us continuing using techniques similar to what we currently use. Huge breakthroughs happens very rarely so I wouldn't count on it.
    - PartiallyTyped 6y ago
      We can learn a lot by observing what we learned from games. With starcraft in particular, we learned that RL agents can achieve godlike micro play but they are weaker at macro. Dota Five showed that it is possible to coordinate multiple agents at the same time with little information shared between them. This suggests that human theory crafting and ML accuracy should be able to achieve great things. One step at a time.
      - 6y ago

by jjcon 6y ago

username90 6y ago

And that seems to be the limits of ML. We might eek out self driving cars, but I don't think we will get much more than that. It is pretty significant, but still limited compared to general purpose AI.

ChefboyOG 6y ago
Predictive text and image classification are not the extent of ML's limits, even going by what is currently in production. Recommendation engines, ETA prediction, translation, drug discovery, medicinal chemistry, fraud detection—these are all areas where ML is already very important and present. Sure, it's not artificial general intelligence, but what technological invention in history would compare to the impact of AGI? That's sort of a weird bar.
jjcon 6y ago
For the average person that is their greatest exposure - but were already seeing huge movements in medicine and defense and plenty of places where ML is not in average consumer use (the biggest applications are not for consumers). Add in your note on transportation and it is at the front of a huge section of the world economy. That’s all on track today - what will tomorrow’s innovations bring (the question asked by sci-fi)?
- username90 6y ago
  Image recognition is just image recognition no matter where it is used.
  - jjcon 6y ago
    Wrong sub thread?
    - username90 6y ago
      No, all applications of ML currently are image recognition. The rest are just statistics labelled as ML.
      - jjcon 6y ago
        That’s hilariously uninformed - nlp is the biggest counter example but you also have neural nets working in domains like RF signal processing, drug discovery all the way to video games - none of these applications are using just traditional stats.
PartiallyTyped 6y ago
One step at a time. As of the previous decade, we have - Learned how to play all atari games [1] - Mastered GO [2] - Mastered Chess without (as much) search [3] - Learned to play MOBAs [4] - Made progress in Protein Folding [5] - Mastered Starcraft [6] Notice that all these methods require an enormous amount of computation, in some cases, we are talking eons in experiences. So there is a lot of progress to be made until we can learn to do [1,2,3,4,6] with as much effort as a human needs. [1] https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark https://deepmind.com/blog/article/Agent57-Outperforming-the-... [2] https://deepmind.com/research/case-studies/alphago-the-story-so-far https://deepmind.com/research/case-studies/alphago-the-story... [3] https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go https://deepmind.com/blog/article/alphazero-shedding-new-lig... [4] https://openai.com/projects/five https://openai.com/projects/five [5] https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery https://deepmind.com/blog/article/AlphaFold-Using-AI-for-sci... [6] https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii https://deepmind.com/blog/article/alphastar-mastering-real-t...
- username90 6y ago
  AI playing games is cool, but applying those techniques to real world scenarios would require a huge breakthrough. If a huge breakthrough happens then sure, but my point was based on us continuing using techniques similar to what we currently use. Huge breakthroughs happens very rarely so I wouldn't count on it.
  - PartiallyTyped 6y ago
    We can learn a lot by observing what we learned from games. With starcraft in particular, we learned that RL agents can achieve godlike micro play but they are weaker at macro. Dota Five showed that it is possible to coordinate multiple agents at the same time with little information shared between them. This suggests that human theory crafting and ML accuracy should be able to achieve great things. One step at a time.
    - 6y ago