I think the biggest thing missing is how to explore and incorporate new environments/tasks. There are teams that can solve any *one set* of tasks (especially in controlled settings), but the generalization and data-efficiency is a big step behind. Simulators can help us pre-solve many generic environments, but people want them in homes and we are no where near there.
"General-Purpose Robots will likely be bigger than cars...." The whole economic analysis hinges on this statement, but there's no evidence to support it.
In contrast, consider that Google, which has the AI expertise and compute hardware to make this a reality, sold Boston Dynamics in June 2017 because they didn't see a commercial opportunity. I'm sure that you, Sergey Alexashenko, are a smart guy, but my money is on Sergey Brin, Larry Page, and Astro Teller. They had all the ingredients in their hands but made the decision to sell Boston Dynamics. (https://techcrunch.com/2017/06/08/softbank-is-buying-robotics-firm-boston-dynamics-and-schaft-from-alphabet/)
why can't ai that controls a robot predict or learn properties of objects by interacting with pieces of the object and a 3d model of it in virtual reality?
by having the ai controlled robotic arm interact with pieces of the object it could improve the simulation which improves the ai.
I think the biggest thing missing is how to explore and incorporate new environments/tasks. There are teams that can solve any *one set* of tasks (especially in controlled settings), but the generalization and data-efficiency is a big step behind. Simulators can help us pre-solve many generic environments, but people want them in homes and we are no where near there.
I suspect I will write about this soon. The exploration and where do we get the data question is a good way to think of it. You may be interested in poking around my content https://democraticrobots.substack.com/p/the-uncanny-world-of-at-home-robots or here is a post on exploration https://lilianweng.github.io/lil-log/2020/06/07/exploration-strategies-in-deep-reinforcement-learning.html.
"General-Purpose Robots will likely be bigger than cars...." The whole economic analysis hinges on this statement, but there's no evidence to support it.
In contrast, consider that Google, which has the AI expertise and compute hardware to make this a reality, sold Boston Dynamics in June 2017 because they didn't see a commercial opportunity. I'm sure that you, Sergey Alexashenko, are a smart guy, but my money is on Sergey Brin, Larry Page, and Astro Teller. They had all the ingredients in their hands but made the decision to sell Boston Dynamics. (https://techcrunch.com/2017/06/08/softbank-is-buying-robotics-firm-boston-dynamics-and-schaft-from-alphabet/)
Polanyi's Paradox says no.
Moravec's Pardox says no.
why can't ai that controls a robot predict or learn properties of objects by interacting with pieces of the object and a 3d model of it in virtual reality?
by having the ai controlled robotic arm interact with pieces of the object it could improve the simulation which improves the ai.