5 ms·

Imagine if you went back 50 years and told Terry Winograd or Marvin Minsky that in 50 years we'd still be trying to figure out how to get robots to stack blocks

by briga 9y ago

Imagine if you went back 50 years and told Terry Winograd or Marvin Minsky that in 50 years we'd still be trying to figure out how to get robots to stack blocks on top of each other. They'd think you were nuts.

asperous 9y ago
More accurately it's "Robots learning unprogrammed tasks by observing behavior" they'd be pretty impressed. Programming robots to stack blocks directly is pretty easy.
- erikpukinskis 9y ago
  Why is that impressive?
  - ArchReaper 9y ago
    Are you asking why AI is impressive?
    - erikpukinskis 9y ago
      I'm asking why it's so impressive that it learns by watching a person. Specifically, why we think Terry Winograd would be impressed that 50 years of AI research led to this. Didn't Deep Blue do the same thing as part of its chess training in the 1990s? Social/observational/modeling learning has been studied in psychological research for over 50 years: https://en.wikipedia.org/wiki/Social_learning_theory https://en.wikipedia.org/wiki/Social_learning_theory Isn't basically every recommender system, all of the vision systems, and the machine translation we all use... isn't all of that just observational learning? I'm not saying it's not interesting. It's a cool toy. But Elon Musk gets an idea for a neat machine learning toy, using an idea that predates AI as a field, and Terry Winograd is supposed to be impressed by this? The point is, AI has only been narrowly successful. Narrow success is rad. I'm not hating. But none of the promises of broad intelligence have really progressed. Siri is not a particularly meaningful advance beyond SHRDLU. Instead of just stacking blocks, she stacks text on Messages.app and pushes buttons in Weather.app.
      - Houshalter 9y ago
        What's interesting about this particular paper, is that it can learn from a single demonstration. Instead of taking thousands of examples before it can do anything. Even cooler, it learns how to learn. They didn't hand code how to do this, it figured it out through training. On top of that it uses machine vision to learn to see all on it's own, without having to be hand coded with information about where things are. More generally, it uses a deep neural network. Which is very different than the older approaches you mention, and can learn much more sophisticated functions. And has enabled a lot of results that would have been unimaginable previously. As for the early AI researchers? They were insanely overly optimistic about how difficult AI would be. It didn't seem like a hard problem. They thought they could just assign a bunch of graduate students to solve machine vision over the summer. It seems pretty simple. We can see great without even thinking about it, how hard can it be? But after biting their teeth into it a bit, I'm sure they would appreciate our modern methods. They might not be as elegant as they hoped. They wanted to find "top down" solutions to AI. Simple algorithms based on symbolic manipulation and logic and reasoning. Such an algorithm probably doesn't exist, and an enormous amount of the history of AI was wasted searching for it. And even if they did discover our modern methods much earlier, they wouldn't have been able to use them. It's only recently that computers have gotten fast enough to do anything interesting. It's like they were trying to go to the moon with nothing but hand tools. Sometimes you just need to wait for the tech tree to unlock the prerequisite branches first.
romaniv 9y ago
My thought exactly. Here is a 1971 video of a robot arm stacking cubes: https://www.youtube.com/watch?v=O1oJzUSlTeY https://www.youtube.com/watch?v=O1oJzUSlTeY Obviously, it's not entirely equivalent to what's described in the article, but the point still stands.
- hacker_9 9y ago
  Pretty interesting. I suppose the point here though is that the machine learned the movement through general observation, opposed to someone writing in a very specific complex algorithm by hand.
ThomPete 9y ago
On the other hand we have done things they wouldn't even imagine.
- romaniv 9y ago
  I think you would be surprised by how many things they imagined and even prototyped in some form. The Society of Mind videos have some very interesting historic tidbits (and more): https://www.youtube.com/watch?v=-pb3z2w9gDg&list=PLUl4u3cNGP61E-vNcDV0w5xpsIBYNJDkU https://www.youtube.com/watch?v=-pb3z2w9gDg&list=PLUl4u3cNGP...
- crush-n-spread 9y ago
  I don't think so, I'm sure they imagined the internet in some abstract way, and smartphones really are quite a step backward if you want to quote that. Search is also quite imaginable. We have had hundreds of thousands of software engineers working in advertising companies or making hardware that gets used to sell products and keep the attention of the youth perpetually captured, hindering their growth as humans. I say we've regressed in a big way and they would think the same. Check out Alan Kay's talks from this week if you still feel good about today's software industry.
  - ThomPete 9y ago
    Well the Parent talked about specifics thats what I was referring to. Of course they imagined abstracts just like we do. Yeah I saw it great videos.
  - romaniv 9y ago
    You mean this one? https://www.youtube.com/watch?v=ZDM33CMJvp8 https://www.youtube.com/watch?v=ZDM33CMJvp8 https://www.youtube.com/watch?v=DIR6Rmhm3To https://www.youtube.com/watch?v=DIR6Rmhm3To (I'm trying to watch all of his recent talks. Despite [or because of?] being critical of the industry, they are some of the most inspiring videos I've seen in years.)
- giardini 9y ago
  Winograd's still working in AI and Minsky died last year, so they (are, were) very up-to-date. https://hci.stanford.edu/winograd/ https://hci.stanford.edu/winograd/ http://web.media.mit.edu/~minsky/ http://web.media.mit.edu/~minsky/ Since both cooperated with DARPA at a high level they were aware of defense-related efforts which your typical AI grunt wouldn't necessarily see. Also both are extraordinarily imaginative and bright, so it is more likely they could show you things they've done that you would not imagine.
rmc 9y ago
It's called Moravec's paradox. Basically the AI things that we thought would be easy ("computer vision, pfft, give it a few months") turned out to be really really hard. And the AI/computer things that we thought would be hard were actually not that hard (Some scenes of Star Trek: The Next Generation have data pausing for a moment to return the result of a query. Google has faster response times today) https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox
- Razengan 9y ago
  Maybe the pause is for adding human-like "flavor" and not really necessary? To take a Steve Jobs'ism, once tech advances sufficiently you have to introduce qualities that may have nothing to do with tech, to make things more pleasing to us humans.

by briga 9y ago

asperous 9y ago

More accurately it's "Robots learning unprogrammed tasks by observing behavior" they'd be pretty impressed. Programming robots to stack blocks directly is pretty easy.

erikpukinskis 9y ago
Why is that impressive?
- ArchReaper 9y ago
  Are you asking why AI is impressive?
  - erikpukinskis 9y ago
    I'm asking why it's so impressive that it learns by watching a person. Specifically, why we think Terry Winograd would be impressed that 50 years of AI research led to this. Didn't Deep Blue do the same thing as part of its chess training in the 1990s? Social/observational/modeling learning has been studied in psychological research for over 50 years: https://en.wikipedia.org/wiki/Social_learning_theory https://en.wikipedia.org/wiki/Social_learning_theory Isn't basically every recommender system, all of the vision systems, and the machine translation we all use... isn't all of that just observational learning? I'm not saying it's not interesting. It's a cool toy. But Elon Musk gets an idea for a neat machine learning toy, using an idea that predates AI as a field, and Terry Winograd is supposed to be impressed by this? The point is, AI has only been narrowly successful. Narrow success is rad. I'm not hating. But none of the promises of broad intelligence have really progressed. Siri is not a particularly meaningful advance beyond SHRDLU. Instead of just stacking blocks, she stacks text on Messages.app and pushes buttons in Weather.app.
    - Houshalter 9y ago
      What's interesting about this particular paper, is that it can learn from a single demonstration. Instead of taking thousands of examples before it can do anything. Even cooler, it learns how to learn. They didn't hand code how to do this, it figured it out through training. On top of that it uses machine vision to learn to see all on it's own, without having to be hand coded with information about where things are. More generally, it uses a deep neural network. Which is very different than the older approaches you mention, and can learn much more sophisticated functions. And has enabled a lot of results that would have been unimaginable previously. As for the early AI researchers? They were insanely overly optimistic about how difficult AI would be. It didn't seem like a hard problem. They thought they could just assign a bunch of graduate students to solve machine vision over the summer. It seems pretty simple. We can see great without even thinking about it, how hard can it be? But after biting their teeth into it a bit, I'm sure they would appreciate our modern methods. They might not be as elegant as they hoped. They wanted to find "top down" solutions to AI. Simple algorithms based on symbolic manipulation and logic and reasoning. Such an algorithm probably doesn't exist, and an enormous amount of the history of AI was wasted searching for it. And even if they did discover our modern methods much earlier, they wouldn't have been able to use them. It's only recently that computers have gotten fast enough to do anything interesting. It's like they were trying to go to the moon with nothing but hand tools. Sometimes you just need to wait for the tech tree to unlock the prerequisite branches first.

romaniv 9y ago

My thought exactly. Here is a 1971 video of a robot arm stacking cubes: https://www.youtube.com/watch?v=O1oJzUSlTeY https://www.youtube.com/watch?v=O1oJzUSlTeY Obviously, it's not entirely equivalent to what's described in the article, but the point still stands.

hacker_9 9y ago
Pretty interesting. I suppose the point here though is that the machine learned the movement through general observation, opposed to someone writing in a very specific complex algorithm by hand.

ThomPete 9y ago

On the other hand we have done things they wouldn't even imagine.

romaniv 9y ago
I think you would be surprised by how many things they imagined and even prototyped in some form. The Society of Mind videos have some very interesting historic tidbits (and more): https://www.youtube.com/watch?v=-pb3z2w9gDg&list=PLUl4u3cNGP61E-vNcDV0w5xpsIBYNJDkU https://www.youtube.com/watch?v=-pb3z2w9gDg&list=PLUl4u3cNGP...
crush-n-spread 9y ago
I don't think so, I'm sure they imagined the internet in some abstract way, and smartphones really are quite a step backward if you want to quote that. Search is also quite imaginable. We have had hundreds of thousands of software engineers working in advertising companies or making hardware that gets used to sell products and keep the attention of the youth perpetually captured, hindering their growth as humans. I say we've regressed in a big way and they would think the same. Check out Alan Kay's talks from this week if you still feel good about today's software industry.
- ThomPete 9y ago
  Well the Parent talked about specifics thats what I was referring to. Of course they imagined abstracts just like we do. Yeah I saw it great videos.
- romaniv 9y ago
  You mean this one? https://www.youtube.com/watch?v=ZDM33CMJvp8 https://www.youtube.com/watch?v=ZDM33CMJvp8 https://www.youtube.com/watch?v=DIR6Rmhm3To https://www.youtube.com/watch?v=DIR6Rmhm3To (I'm trying to watch all of his recent talks. Despite [or because of?] being critical of the industry, they are some of the most inspiring videos I've seen in years.)
giardini 9y ago
Winograd's still working in AI and Minsky died last year, so they (are, were) very up-to-date. https://hci.stanford.edu/winograd/ https://hci.stanford.edu/winograd/ http://web.media.mit.edu/~minsky/ http://web.media.mit.edu/~minsky/ Since both cooperated with DARPA at a high level they were aware of defense-related efforts which your typical AI grunt wouldn't necessarily see. Also both are extraordinarily imaginative and bright, so it is more likely they could show you things they've done that you would not imagine.

rmc 9y ago

It's called Moravec's paradox. Basically the AI things that we thought would be easy ("computer vision, pfft, give it a few months") turned out to be really really hard. And the AI/computer things that we thought would be hard were actually not that hard (Some scenes of Star Trek: The Next Generation have data pausing for a moment to return the result of a query. Google has faster response times today) https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox

Razengan 9y ago
Maybe the pause is for adding human-like "flavor" and not really necessary? To take a Steve Jobs'ism, once tech advances sufficiently you have to introduce qualities that may have nothing to do with tech, to make things more pleasing to us humans.