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The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. I can understand academia & researcher hyping it up, th

The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. I can understand academia & researcher hyping it up, they need to chase the funding. But code boot-camp "ML" engineers being all over-hyping is really, really annoying and contribute to the problem far worse. These people are easy to spot: go to an ML/AI meetup or community that also shares latest tech. Since those does not understand the underlying math, they cant follow the paper, the talk or the discussion, so they quickly try to shift the discussion to "AI ethics" or "AI will take over the world" hypothetical useless discussions. These seems to also be the first to run out to promise big things with "AI".

dvfjsdhgfv 6y ago
> The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. Of course, for example when you use the phrase "ML/AI".
kerng 6y ago
You might underestimate the speed and velocity of advancement in the field. A few years ago creating artificial data or images that are indistinguishable from real data was impossible. Now machines generate information, like entire videos that are real but fake. This can, and has already (look at facebook and fake profiles), led to big problems and in a few years the world will change quite drastically because of further advances and automation. This can go in good and bad directions... I'm just worried that downplaying it will cause pivoting towards undesirable outcomes.
- NalNezumi 6y ago
  I don't think I underestimate the speed nor velocity of the advancement, just being realistic about the scope of the advancements. I follow conferences and read ML papers (DL & DL/ML application in Robotics) regularly so I think I have a some grasp on the advancement in the field. GAN sure have been a big step, and have found a great usage. (deepfake etc) but I'd say it is still highly overblown. Like GPT3 it will see wide adaptation enough to make visible improvement (and problems) in certain industries, just as better classifiers changed a lot. I also don't like the blanket term of "automation" because it is like AI: too vague and often used by someone who don't fully grasp HOW hard automation tasks are (and how the complexity scale and differs from the task ). Just as optimism in Self-driving car is now more on low-fire. The over-promise of ML is something akin to people expecting "it to just get better" similar to Moore's law. If you take a look at what direction the algorithms are improving, what incremental advancements are being made the scope of the speed and velocity become much more clear; and just because cars get faster, doesn't mean it will one day fly to space like a rocket. If we one day in short term future see great improvement, it will probably not be the ML improvements, but due external factors (hardware, network speed, etc)
  - kerng 6y ago
    I don't think ML over promises anything, its just a new way to help create software to solve problems that can't really be solved with traditional programming.
- dvfjsdhgfv 6y ago
  I appreciate your enthusiasm but we're still not there. The videos look almost perfect, with "almost" still oscillating around the uncanny valley. Self-driving cars are almost totally self-driving and are almost not killing people. GPT3 uses fluent English and can be used to generate almost meaningful content in most contexts. Speech recognition progressed enormously and can be used to transcribe most well-pronouncing native English speakers in favorable circumstances. It seems like we're slowly getting there, but the last mile is the hardest to finish. For many companies this is not a problem because they can afford not to care. When you operate at the scale of Google, you can not afford not to use some form of ML to detect abuse, and you don't care about false positives because they're meaningless at this scale - you just get a couple of grumpy folks complaining they lost their accounts. But when you do something more meaningful, for example translation, you can't depend on machine translation - you can very well use it for the first draft, but if you delivered it to your client in that form, you'd be laughed at and you'd quickly be out of business. So yes, great advancements, still far from being there.
  - Veedrac 6y ago
    This is what I've started thinking of as the ‘pretty dumb for a human’ take on AI. If your requirement for believing something is on the path to AGI is for it to practically already be AGI, it's no longer predictively meaningful. This sort of argument suffices to dismiss, for example, the possibility that today's humans evolved from apes, or even that today's humans could have come from pre-language, pre-civilization humans.
    - username90 6y ago
      Problem is that the AI we have is nothing like biological intelligence. Our super computers can't even compete with the intelligence of an ant, how am I to believe that replicating human intelligence is around the corner or that we are even moving the right direction to get there?
      - Veedrac 6y ago
        > Our super computers can't even compete with the intelligence of an ant I don't see what might spur anyone to claim this. It's obviously ridiculous.
blackrock 6y ago
The so called “AI” as they are practicing it now, is not intelligent at all. Sure, they found a new trick to recognize cat pictures. But this technique was discovered decades ago. It just took this long for computers to speed up, for it to be usable. Instead, they should change the moniker from Artificial Intelligence, to Artificial Insight. Because all it is doing is to comb through all that random data, to look for interesting signals. But, the algorithm doesn’t even know if what it found, is interesting. That’s up to the human to decide. Hence, the algorithm can provide Insight. But, it is the human in the loop, that will provide the intelligence.
- sanxiyn 6y ago
  > But this technique was discovered decades ago. It just took this long for computers to speed up, for it to be usable. This isn't really true. Old techniques don't work well even with modern compute. Now, try replacing sigmoid with ReLU in old networks and see it improve.
  - Retric 6y ago
    Many 90’s era attempts that failed do work vastly better on modern hardware. There are many tweaks that showed up once people could fine tune using vast amounts of processing power, but I would put ~80% of the advancements from HW and 20% from algorithms. Having a million or more times the processing power really makes or breaks modern ML.
- tauchunfall 6y ago
  >Instead, they should change the moniker from Artificial Intelligence, to Artificial Insight. Several times when I spoke to people about this point I got empty faces. Some seem to like the idea of automating things more than augmenting human thought with better tools. Related article about Moldable Development: >We can trace both models back to the early years of the first electronic computers. However, the struggle between these two worlds is still present today. For instance, Intelligence Augmentation (IA), is all about empowering humans with tools that make them more capable and more intelligent, while Artificial Intelligence (AI) has been about removing humans fully from the loop. You can read an excellent essay (Shan Carter and Michael Nielsen) about this topic. via: https://osoco.es/thoughts/2019/05/designing-media-for-thought-with-moldable-development/ https://osoco.es/thoughts/2019/05/designing-media-for-though...

dvfjsdhgfv 6y ago

> The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. Of course, for example when you use the phrase "ML/AI".

kerng 6y ago

You might underestimate the speed and velocity of advancement in the field. A few years ago creating artificial data or images that are indistinguishable from real data was impossible. Now machines generate information, like entire videos that are real but fake. This can, and has already (look at facebook and fake profiles), led to big problems and in a few years the world will change quite drastically because of further advances and automation. This can go in good and bad directions... I'm just worried that downplaying it will cause pivoting towards undesirable outcomes.

NalNezumi 6y ago
I don't think I underestimate the speed nor velocity of the advancement, just being realistic about the scope of the advancements. I follow conferences and read ML papers (DL & DL/ML application in Robotics) regularly so I think I have a some grasp on the advancement in the field. GAN sure have been a big step, and have found a great usage. (deepfake etc) but I'd say it is still highly overblown. Like GPT3 it will see wide adaptation enough to make visible improvement (and problems) in certain industries, just as better classifiers changed a lot. I also don't like the blanket term of "automation" because it is like AI: too vague and often used by someone who don't fully grasp HOW hard automation tasks are (and how the complexity scale and differs from the task ). Just as optimism in Self-driving car is now more on low-fire. The over-promise of ML is something akin to people expecting "it to just get better" similar to Moore's law. If you take a look at what direction the algorithms are improving, what incremental advancements are being made the scope of the speed and velocity become much more clear; and just because cars get faster, doesn't mean it will one day fly to space like a rocket. If we one day in short term future see great improvement, it will probably not be the ML improvements, but due external factors (hardware, network speed, etc)
- kerng 6y ago
  I don't think ML over promises anything, its just a new way to help create software to solve problems that can't really be solved with traditional programming.
dvfjsdhgfv 6y ago
I appreciate your enthusiasm but we're still not there. The videos look almost perfect, with "almost" still oscillating around the uncanny valley. Self-driving cars are almost totally self-driving and are almost not killing people. GPT3 uses fluent English and can be used to generate almost meaningful content in most contexts. Speech recognition progressed enormously and can be used to transcribe most well-pronouncing native English speakers in favorable circumstances. It seems like we're slowly getting there, but the last mile is the hardest to finish. For many companies this is not a problem because they can afford not to care. When you operate at the scale of Google, you can not afford not to use some form of ML to detect abuse, and you don't care about false positives because they're meaningless at this scale - you just get a couple of grumpy folks complaining they lost their accounts. But when you do something more meaningful, for example translation, you can't depend on machine translation - you can very well use it for the first draft, but if you delivered it to your client in that form, you'd be laughed at and you'd quickly be out of business. So yes, great advancements, still far from being there.
- Veedrac 6y ago
  This is what I've started thinking of as the ‘pretty dumb for a human’ take on AI. If your requirement for believing something is on the path to AGI is for it to practically already be AGI, it's no longer predictively meaningful. This sort of argument suffices to dismiss, for example, the possibility that today's humans evolved from apes, or even that today's humans could have come from pre-language, pre-civilization humans.
  - username90 6y ago
    Problem is that the AI we have is nothing like biological intelligence. Our super computers can't even compete with the intelligence of an ant, how am I to believe that replicating human intelligence is around the corner or that we are even moving the right direction to get there?
    - Veedrac 6y ago
      > Our super computers can't even compete with the intelligence of an ant I don't see what might spur anyone to claim this. It's obviously ridiculous.

blackrock 6y ago

The so called “AI” as they are practicing it now, is not intelligent at all. Sure, they found a new trick to recognize cat pictures. But this technique was discovered decades ago. It just took this long for computers to speed up, for it to be usable. Instead, they should change the moniker from Artificial Intelligence, to Artificial Insight. Because all it is doing is to comb through all that random data, to look for interesting signals. But, the algorithm doesn’t even know if what it found, is interesting. That’s up to the human to decide. Hence, the algorithm can provide Insight. But, it is the human in the loop, that will provide the intelligence.

sanxiyn 6y ago
> But this technique was discovered decades ago. It just took this long for computers to speed up, for it to be usable. This isn't really true. Old techniques don't work well even with modern compute. Now, try replacing sigmoid with ReLU in old networks and see it improve.
- Retric 6y ago
  Many 90’s era attempts that failed do work vastly better on modern hardware. There are many tweaks that showed up once people could fine tune using vast amounts of processing power, but I would put ~80% of the advancements from HW and 20% from algorithms. Having a million or more times the processing power really makes or breaks modern ML.
tauchunfall 6y ago
>Instead, they should change the moniker from Artificial Intelligence, to Artificial Insight. Several times when I spoke to people about this point I got empty faces. Some seem to like the idea of automating things more than augmenting human thought with better tools. Related article about Moldable Development: >We can trace both models back to the early years of the first electronic computers. However, the struggle between these two worlds is still present today. For instance, Intelligence Augmentation (IA), is all about empowering humans with tools that make them more capable and more intelligent, while Artificial Intelligence (AI) has been about removing humans fully from the loop. You can read an excellent essay (Shan Carter and Michael Nielsen) about this topic. via: https://osoco.es/thoughts/2019/05/designing-media-for-thought-with-moldable-development/ https://osoco.es/thoughts/2019/05/designing-media-for-though...