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I think people here are underestimating how big a thing this actually is and I think the headline is kind of to blame for that. This is much less about fooling

I think people here are underestimating how big a thing this actually is and I think the headline is kind of to blame for that. This is much less about fooling humans than about what this actually means. Humans spend a huge part of our early live learning to listen and to connect the dots between what we see and what we hear. The fact that Deep Learning algos now can simulate audio based on what they see thats the big thing here. Not the production of the sound that fools humans. You can almost sense how imagination and inspiration is inside the reach of machine learning (yes there are some way to go yet) We are now not only seeing individual senses being simulated but also the relationship between them. And as a bonus what one machine learns one place can be instantly added to the knowledge of the other. Thats IMO the big deal here.

YeGoblynQueenne 10y ago
>> And as a bonus what one machine learns one place can be instantly added to the knowledge of the other. That's actually one big problem with machine learning algorithms: it's not at all clear how to integrate their knowledge with that of other algorithms (and that includes different instances of the same algorithm). Such algorithms build a single model of one domain at a time, and we're talking about very strict domains. What we're seeing lately is many teams announcing that they trained an algorithm to do this or that pretty damn amazing thing, but watch closely: how many of those announcements describe a system that can integrate its learning into a wider cognitive architecture? There's teams that trained models to recognise images, to combine images, or to map images to strings, but all these things are simple tasks, that are only useful in a very limited range of circumstances. Machine learning algorithms unfortunately are one trick ponies. They do one thing well- and that's it. >> You can almost sense how imagination and inspiration is inside the reach of machine learning (yes there are some way to go yet). That's an understatement- the bit about having some way to go. We're not even close, really. To train a machine learning algorithm the first thing you need is a lot of examples of the thing you want it to learn. It's really hard to see how one would compile a set of examples of imagination, not least because it's inside peoples' heads. Not to mention we don't even know what human imagination is in the first place.
- ThomPete 10y ago
  I think its very counterfactual to look at this from a linear perspective. Make it exponential and everything changes around it.
- visarga 10y ago
  > Machine learning algorithms unfortunately are one trick ponies. Most humans only know a few of the skills that other people know. We are specialized, too.
  - YeGoblynQueenne 10y ago
    You're talking about specialisation in a restricted field, like maths or a scientific discipline. That's part of education. I'm talking about how all (healthy) humans learn about the world they inhabit, by building a broad context of the entities and concepts in it. We all learn to speak a language for instance, in fact I believe most people actually learn a couple. We learn to interpret facial expressions, who is our friend and who is not, how to find sustenance and so on. We learn a whole bunch of things outside of formal education and specialised technical knowledge. We specialise even in the kind of knowledge I describe, sure, but we can also change specialisation without too much hassle. I myself have been making my living as a programmer for the past several years coming from a completely non-technical background for most of my life. It was hard going to learn a new thing from scratch, but I was perfectly able to do so. We lose this flexibility as we grow older but for most of our lives we have nothing like the limitations of machine learning.
    - visarga 10y ago
      > we have nothing like the limitations of machine learning Well, that works both ways. Humans are quite limited, we haven't doubled our IQ in the last 1000 years. But machines have even more potential to grow than we do, in the next 50-100 years they will surely have matched our ability to adapt. Today, a company releases a translation software for a couple of languages, next year they release translation between 100 languages. A human can't keep up with that. Yes, they still need tuning and architecture design, but that might change soon, maybe in a few years. Also, the education of humans needs to be human supervised, but machine learning can be unsupervised (like, AlphaGo playing millions of self games to fine tune its value function), thus, cheaper. I am sure Lee Sedol needed much more energy to train to get up to that level of play. He's 33 years old, and in order to get to his level he required resources, teachers, food, etc. AlphaGo played a few million self play games and only consumed a bunch of cheap electricity, while doing it a hundred times faster and surpassing the man.

YeGoblynQueenne 10y ago

>> And as a bonus what one machine learns one place can be instantly added to the knowledge of the other. That's actually one big problem with machine learning algorithms: it's not at all clear how to integrate their knowledge with that of other algorithms (and that includes different instances of the same algorithm). Such algorithms build a single model of one domain at a time, and we're talking about very strict domains. What we're seeing lately is many teams announcing that they trained an algorithm to do this or that pretty damn amazing thing, but watch closely: how many of those announcements describe a system that can integrate its learning into a wider cognitive architecture? There's teams that trained models to recognise images, to combine images, or to map images to strings, but all these things are simple tasks, that are only useful in a very limited range of circumstances. Machine learning algorithms unfortunately are one trick ponies. They do one thing well- and that's it. >> You can almost sense how imagination and inspiration is inside the reach of machine learning (yes there are some way to go yet). That's an understatement- the bit about having some way to go. We're not even close, really. To train a machine learning algorithm the first thing you need is a lot of examples of the thing you want it to learn. It's really hard to see how one would compile a set of examples of imagination, not least because it's inside peoples' heads. Not to mention we don't even know what human imagination is in the first place.

ThomPete 10y ago
I think its very counterfactual to look at this from a linear perspective. Make it exponential and everything changes around it.
visarga 10y ago
> Machine learning algorithms unfortunately are one trick ponies. Most humans only know a few of the skills that other people know. We are specialized, too.
- YeGoblynQueenne 10y ago
  You're talking about specialisation in a restricted field, like maths or a scientific discipline. That's part of education. I'm talking about how all (healthy) humans learn about the world they inhabit, by building a broad context of the entities and concepts in it. We all learn to speak a language for instance, in fact I believe most people actually learn a couple. We learn to interpret facial expressions, who is our friend and who is not, how to find sustenance and so on. We learn a whole bunch of things outside of formal education and specialised technical knowledge. We specialise even in the kind of knowledge I describe, sure, but we can also change specialisation without too much hassle. I myself have been making my living as a programmer for the past several years coming from a completely non-technical background for most of my life. It was hard going to learn a new thing from scratch, but I was perfectly able to do so. We lose this flexibility as we grow older but for most of our lives we have nothing like the limitations of machine learning.
  - visarga 10y ago
    > we have nothing like the limitations of machine learning Well, that works both ways. Humans are quite limited, we haven't doubled our IQ in the last 1000 years. But machines have even more potential to grow than we do, in the next 50-100 years they will surely have matched our ability to adapt. Today, a company releases a translation software for a couple of languages, next year they release translation between 100 languages. A human can't keep up with that. Yes, they still need tuning and architecture design, but that might change soon, maybe in a few years. Also, the education of humans needs to be human supervised, but machine learning can be unsupervised (like, AlphaGo playing millions of self games to fine tune its value function), thus, cheaper. I am sure Lee Sedol needed much more energy to train to get up to that level of play. He's 33 years old, and in order to get to his level he required resources, teachers, food, etc. AlphaGo played a few million self play games and only consumed a bunch of cheap electricity, while doing it a hundred times faster and surpassing the man.