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If I want to know what robotics will look like and the future of deep reinforcement learning, I'd rather ask Covariant.ai than a guy whose expertise is little v

by gwern 7y ago

If I want to know what robotics will look like and the future of deep reinforcement learning, I'd rather ask Covariant.ai than a guy whose expertise is little vacuums whose AI is "while true, spin in a circle and go forward at random".

a_t48 7y ago
Current Roombas are a little more fancy than that.
- gwern 7y ago
  But not much. You can hardly compare anything Roomba has ever done to even, say, Boston Dynamics.
  - Digit-Al 7y ago
    The difference is that Roomba's are used by millions of people around the world whilst Boston Dynamics robots, whilst impressive, are currently used by... nobody? Anyway, it's an apples to oranges comparison. Boston Dynamics are building incredibly expensive and sophisticated AI systems that will be first of interest to the military, who have very deep pockets and can afford to spend millions if it is deemed a good use of the money. Roomba are building stuff that can be bought by the average person on the street.
magusdei 7y ago
Yeesh, gwern. Usually I enjoy your comments, but this one seems a bit unfair. Of course if you want to know the long-term future of robotics, you should look to sci-fi authors and research prototypes. But if you want to know the short-term future (<10 years), you should listen to people with actual experience building actual products, even if you don't see those products as impressive from a research perspective. I think the article provides an illustrative example of the different factors and complications that make it so difficult to go from a research prototype to an actual consumer product (a process which seems to take about 20 years on average). As usual, the lackadaisical improvement of battery technology is one of the central bottlenecks. Of course, if your main interest is industrial robotics, where DRL approaches are more likely to become mainstream in the near-term, insights related to consumer products might not be as relevant. But note that industrial improvements, while important, will be mostly invisible to consumers except in terms of prices, unemployment and increased customization options for products. Even then, the article can provide some perspective. Our processes (whether assembly lines or household vacuuming) are already highly optimized for a particular way of doing things, and researchers in particular tend to underestimate the amount of effort it takes to change systems to accommodate a slightly different method. So any new system not only has to be competitive with the existing one in just about every parameter, it has to justify the often huge costs of small alterations to the process. Thus, even in industry, getting to mainstream adoption of RL will likely take quite a while.

by gwern 7y ago

a_t48 7y ago

Current Roombas are a little more fancy than that.

gwern 7y ago
But not much. You can hardly compare anything Roomba has ever done to even, say, Boston Dynamics.
- Digit-Al 7y ago
  The difference is that Roomba's are used by millions of people around the world whilst Boston Dynamics robots, whilst impressive, are currently used by... nobody? Anyway, it's an apples to oranges comparison. Boston Dynamics are building incredibly expensive and sophisticated AI systems that will be first of interest to the military, who have very deep pockets and can afford to spend millions if it is deemed a good use of the money. Roomba are building stuff that can be bought by the average person on the street.

magusdei 7y ago

Yeesh, gwern. Usually I enjoy your comments, but this one seems a bit unfair. Of course if you want to know the long-term future of robotics, you should look to sci-fi authors and research prototypes. But if you want to know the short-term future (<10 years), you should listen to people with actual experience building actual products, even if you don't see those products as impressive from a research perspective. I think the article provides an illustrative example of the different factors and complications that make it so difficult to go from a research prototype to an actual consumer product (a process which seems to take about 20 years on average). As usual, the lackadaisical improvement of battery technology is one of the central bottlenecks. Of course, if your main interest is industrial robotics, where DRL approaches are more likely to become mainstream in the near-term, insights related to consumer products might not be as relevant. But note that industrial improvements, while important, will be mostly invisible to consumers except in terms of prices, unemployment and increased customization options for products. Even then, the article can provide some perspective. Our processes (whether assembly lines or household vacuuming) are already highly optimized for a particular way of doing things, and researchers in particular tend to underestimate the amount of effort it takes to change systems to accommodate a slightly different method. So any new system not only has to be competitive with the existing one in just about every parameter, it has to justify the often huge costs of small alterations to the process. Thus, even in industry, getting to mainstream adoption of RL will likely take quite a while.