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I can't speak for other people, but the reason that I think we are in an AI boom has nothing to do with online advertising. It is that computers can now recogni

I can't speak for other people, but the reason that I think we are in an AI boom has nothing to do with online advertising. It is that computers can now recognize images, translate language, have conversations, generate articles, make realistic looking pictures, play the game of go, solve protein folding, generate realistic text to speech, recognize voices. All these things were not possible 5 years ago. Every year there is significant progress. I believe that in a couple of years we will see many more applications. This will be as much of a paradigm shift as the invention of the personal computer, the internet and smart phones.

nopriorarrests 6y ago
>recognize voices. All these things were not possible 5 years ago. FTR: https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking Dragon Systems released NaturallySpeaking 1.0 as their first continuous dictation product in 1997. <...> As of 2012 LG Smart TVs include voice recognition feature powered by the same speech engine as Dragon NaturallySpeaking.
- fossuser 6y ago
  Yeah I played with dragon in 97 and it was awful - it didn’t work at all, completely unusable. Today voice transcription is a solved problem and while their engine might be the same in name - I’d be surprised if the approach isn’t totally different than what they were doing in 97, either that or the LG tv voice transcription probably doesn’t work as well as everyone else’s. The deep learning revolution and the applications we’ve seen since 2015 are a major step forward and something truly different. People pretending otherwise are just acting cynical in some attempt to project intelligence or seem wise, it doesn’t work.
  - nopriorarrests 6y ago
    Of course it was awful. It was 1997. But you can't claim that something "wasn't possible 5 years" ago, if 7 years ago said feature was included in inexpensive consumer product (LG TV). I'm not acting cynical, but it's tiresome for me to see people who claim that 20-30 years ago we all were living in a caves and catching bugs with wooden sticks, and now boom, ML! Regarding "something truly different", well, my personal computing / mobile experience not changed that much from 2015. Honestly speaking, progress from 1995 to 2000 felt much more impressive and 'truly different'. I mean, think of it, during this timeframe we went from DOOM via V.34 modems to amazon.com and ordering pizza online.
    - ma2rten 6y ago
      It matters if it actually works and if it works broadly or just for limited use cases. As I said in the original comment, I think that you will see many applications in the near future. The last 5 years have been focused on research (comparable to 1990-1995), only now are we getting ready for commercial applications.
      - fossuser 6y ago
        Yeah - entire classes of problems went from unsolvable to solved. Some of that is in the consumer space and some of it is not. I feel like an AGI could accidentally wipe out half of humanity and there would still be people commenting on HN about how the exact same technology already existed in a roomba seven years ago.
        nopriorarrests 6y ago
        Honest question, no snark --- which consumer space problems were solved, if I don't play Go and don't have FB account to recognize me on a group photos (both of these two statements are true)?
        fossuser 6y ago
        A couple quick things I can think of: - Voice transcription - Tesla Autopilot - Facial recognition (photo sorting on iphones, better photos) - Better graphical performance on Nvidia cards (https://developer.nvidia.com/dlss https://developer.nvidia.com/dlss), also better compression for streaming. - Much better translation - Colorizing and repairing old photos - Visual recognition allowing better search of images I’m sure there are some I left out. I think we’ll see a lot more interesting applications (particularly around tooling) in the next few years. https://medium.com/@karpathy/software-2-0-a64152b37c35 https://medium.com/@karpathy/software-2-0-a64152b37c35 Outside of the consumer space, there are also things that hint at more generalizable intelligence. Check out GPT-3’s performance on arithmetic tasks in the original paper (https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165) Pages: 21-23, 63 Which shows some generality, the best way to accurately predict an arithmetic answer is to deduce how the mathematical rules work. That paper shows some evidence of that and that’s just from a relatively dumb predict what comes next model. It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Nobody would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules https://deepmind.com/blog/article/muzero-mastering-go-chess-...
  - literallycancer 6y ago
    Live captions for English video or audio are nice, but it still doesn't work for music (not even rap), and it doesn't work for other languages. It might work in a lab setting, but doesn't in currently available phones.
- deleted 6y ago
  [deleted]
- Nbox9 6y ago
  Along the same vain, Siri was launched in 2011.
tracer4201 6y ago
I was at a non tech company 7 years ago where we were generating article summaries and performing image recognition. We had some at least one org applying speech to text for a business use case. I’m not saying there hasn’t been innovation in the past 5 years — absolutely there has. In that industry, 2013-2016 were peak “ML” and “Cloud” where the CIOs at my company and competitors were fully bought into the ML and cloud hype, how it was going to solve all kinds of problems, without understanding what those problems were, and without realizing the complexity of getting meaningful, applicable data for those problems. In the past few years, it feels like more people realized that ML is less mathematical magic and more of different “kinds” of curve fitting.
Barrin92 6y ago
you can make a list like that every five years. When deep blue beat kasparov people said the same thing, because on the surface it looks exceptional, but really when it comes down to it all those applications are extremely limited, closed systems. We will have more of them in five years, but as the saying goes, you don't get to the moon by climbing up trees. In five years we will without a doubt have systems that play more games, solve more puzzles, fold more proteins and so on, but we'd have more progress on intelligence if we could build a machine that could reliably plumb a toilet or catch a mouse as well as a cat.
oakfr 6y ago
mjburgess has a point. There is a relationship with online advertising for the following reason: online advertising has been a perfect testbed for the latest AI research for years because (1) it leverages massive amounts of data (2) is not mission-critical (the human cost of failure is low) (3) has little accountability regarding fairness and explainability. For these reasons, online advertising has been surfing the latest AI hype, well in front of other domains (healthcare, defense, education, etc.) where either (1), (2) or (3) is not met.
tsimionescu 6y ago
Out of your list, translating language and solving protein folding are the only 2 that are likely to have any kind of major impact on the world in the somewhat near future, in my opinion. Image recognition may be a distant third, as it could prove very useful as a tool in many domains that have lots of visual data to sift through. Voice recognition is neat, and it is extremely useful in certain niches, but it is generally far inferior to mouse+keyboard or touch or dedicated buttons in all cases where those are practical. It is almost unusable in public spaces. Text-to-speech is mostly in the same boat - extremely useful in some niches, not a game-changer in any way I expect. Text generation is only "realistic" in shape, but for now remains a novelty. Picture generation is the same. They are at best at the level where it already costs pennies to obtain similar texts through human labor. Having conversations in a meaningful way is even farther. Playing Go better than humans is a fun gimmick - an awesome achievement in some sense, but with 0 practical applications outside of Go. Overall, there has been good progress in AI/ML, but I don't expect we'll see any major changes too soon from this. Especially since the idea of actually seeing commercially available self-driving cars has been pushed back a decade or three compared to the initial optimism. I'd also add that most of the progress in ML/AI in the last few years has essentially been of a technological/engineering kind. There haven't really been that many deep scientific insights, we don't necessarily understand any of these problem spaces better. We've just been able to match certain neural net architectures with certain problem spaces, and massively advanced in realistically-available computing power to actually train these nets on enough data. But we haven't gotten any new insights into stuff like language from GPT-3, for example. We haven't even gotten any new insights into Go from AlphaGo!
- ummonk 6y ago
  Computer vision is used for many driver assistance technologies, which are a big deal for ease of life.
  - amelius 6y ago
    People obsess too much over minor conveniences. I don't care about driving. It still takes the same amount of time. What I care more about is cooking. I'd rather have that one solved, as it would actually save me time. (No, I'm not looking for restaurants, meal delivery services or microwave meals).
Zelphyr 6y ago
I don’t disagree but OP’s argument is, to me, why AI isn’t going to put programmers out of a job anytime soon.

nopriorarrests 6y ago

>recognize voices. All these things were not possible 5 years ago. FTR: https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking Dragon Systems released NaturallySpeaking 1.0 as their first continuous dictation product in 1997. <...> As of 2012 LG Smart TVs include voice recognition feature powered by the same speech engine as Dragon NaturallySpeaking.

fossuser 6y ago
Yeah I played with dragon in 97 and it was awful - it didn’t work at all, completely unusable. Today voice transcription is a solved problem and while their engine might be the same in name - I’d be surprised if the approach isn’t totally different than what they were doing in 97, either that or the LG tv voice transcription probably doesn’t work as well as everyone else’s. The deep learning revolution and the applications we’ve seen since 2015 are a major step forward and something truly different. People pretending otherwise are just acting cynical in some attempt to project intelligence or seem wise, it doesn’t work.
- nopriorarrests 6y ago
  Of course it was awful. It was 1997. But you can't claim that something "wasn't possible 5 years" ago, if 7 years ago said feature was included in inexpensive consumer product (LG TV). I'm not acting cynical, but it's tiresome for me to see people who claim that 20-30 years ago we all were living in a caves and catching bugs with wooden sticks, and now boom, ML! Regarding "something truly different", well, my personal computing / mobile experience not changed that much from 2015. Honestly speaking, progress from 1995 to 2000 felt much more impressive and 'truly different'. I mean, think of it, during this timeframe we went from DOOM via V.34 modems to amazon.com and ordering pizza online.
  - ma2rten 6y ago
    It matters if it actually works and if it works broadly or just for limited use cases. As I said in the original comment, I think that you will see many applications in the near future. The last 5 years have been focused on research (comparable to 1990-1995), only now are we getting ready for commercial applications.
    - fossuser 6y ago
      Yeah - entire classes of problems went from unsolvable to solved. Some of that is in the consumer space and some of it is not. I feel like an AGI could accidentally wipe out half of humanity and there would still be people commenting on HN about how the exact same technology already existed in a roomba seven years ago.
      - nopriorarrests 6y ago
        Honest question, no snark --- which consumer space problems were solved, if I don't play Go and don't have FB account to recognize me on a group photos (both of these two statements are true)?
        fossuser 6y ago
        A couple quick things I can think of: - Voice transcription - Tesla Autopilot - Facial recognition (photo sorting on iphones, better photos) - Better graphical performance on Nvidia cards (https://developer.nvidia.com/dlss https://developer.nvidia.com/dlss), also better compression for streaming. - Much better translation - Colorizing and repairing old photos - Visual recognition allowing better search of images I’m sure there are some I left out. I think we’ll see a lot more interesting applications (particularly around tooling) in the next few years. https://medium.com/@karpathy/software-2-0-a64152b37c35 https://medium.com/@karpathy/software-2-0-a64152b37c35 Outside of the consumer space, there are also things that hint at more generalizable intelligence. Check out GPT-3’s performance on arithmetic tasks in the original paper (https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165) Pages: 21-23, 63 Which shows some generality, the best way to accurately predict an arithmetic answer is to deduce how the mathematical rules work. That paper shows some evidence of that and that’s just from a relatively dumb predict what comes next model. It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Nobody would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules https://deepmind.com/blog/article/muzero-mastering-go-chess-...
- literallycancer 6y ago
  Live captions for English video or audio are nice, but it still doesn't work for music (not even rap), and it doesn't work for other languages. It might work in a lab setting, but doesn't in currently available phones.
deleted 6y ago
[deleted]
Nbox9 6y ago
Along the same vain, Siri was launched in 2011.

tracer4201 6y ago

I was at a non tech company 7 years ago where we were generating article summaries and performing image recognition. We had some at least one org applying speech to text for a business use case. I’m not saying there hasn’t been innovation in the past 5 years — absolutely there has. In that industry, 2013-2016 were peak “ML” and “Cloud” where the CIOs at my company and competitors were fully bought into the ML and cloud hype, how it was going to solve all kinds of problems, without understanding what those problems were, and without realizing the complexity of getting meaningful, applicable data for those problems. In the past few years, it feels like more people realized that ML is less mathematical magic and more of different “kinds” of curve fitting.

Barrin92 6y ago

you can make a list like that every five years. When deep blue beat kasparov people said the same thing, because on the surface it looks exceptional, but really when it comes down to it all those applications are extremely limited, closed systems. We will have more of them in five years, but as the saying goes, you don't get to the moon by climbing up trees. In five years we will without a doubt have systems that play more games, solve more puzzles, fold more proteins and so on, but we'd have more progress on intelligence if we could build a machine that could reliably plumb a toilet or catch a mouse as well as a cat.

oakfr 6y ago

mjburgess has a point. There is a relationship with online advertising for the following reason: online advertising has been a perfect testbed for the latest AI research for years because (1) it leverages massive amounts of data (2) is not mission-critical (the human cost of failure is low) (3) has little accountability regarding fairness and explainability. For these reasons, online advertising has been surfing the latest AI hype, well in front of other domains (healthcare, defense, education, etc.) where either (1), (2) or (3) is not met.

tsimionescu 6y ago

Out of your list, translating language and solving protein folding are the only 2 that are likely to have any kind of major impact on the world in the somewhat near future, in my opinion. Image recognition may be a distant third, as it could prove very useful as a tool in many domains that have lots of visual data to sift through. Voice recognition is neat, and it is extremely useful in certain niches, but it is generally far inferior to mouse+keyboard or touch or dedicated buttons in all cases where those are practical. It is almost unusable in public spaces. Text-to-speech is mostly in the same boat - extremely useful in some niches, not a game-changer in any way I expect. Text generation is only "realistic" in shape, but for now remains a novelty. Picture generation is the same. They are at best at the level where it already costs pennies to obtain similar texts through human labor. Having conversations in a meaningful way is even farther. Playing Go better than humans is a fun gimmick - an awesome achievement in some sense, but with 0 practical applications outside of Go. Overall, there has been good progress in AI/ML, but I don't expect we'll see any major changes too soon from this. Especially since the idea of actually seeing commercially available self-driving cars has been pushed back a decade or three compared to the initial optimism. I'd also add that most of the progress in ML/AI in the last few years has essentially been of a technological/engineering kind. There haven't really been that many deep scientific insights, we don't necessarily understand any of these problem spaces better. We've just been able to match certain neural net architectures with certain problem spaces, and massively advanced in realistically-available computing power to actually train these nets on enough data. But we haven't gotten any new insights into stuff like language from GPT-3, for example. We haven't even gotten any new insights into Go from AlphaGo!

ummonk 6y ago
Computer vision is used for many driver assistance technologies, which are a big deal for ease of life.
- amelius 6y ago
  People obsess too much over minor conveniences. I don't care about driving. It still takes the same amount of time. What I care more about is cooking. I'd rather have that one solved, as it would actually save me time. (No, I'm not looking for restaurants, meal delivery services or microwave meals).

Zelphyr 6y ago

I don’t disagree but OP’s argument is, to me, why AI isn’t going to put programmers out of a job anytime soon.