I've been down this road. I used to be interested in "embodiment" as a path to AI, but in the sense of things that could move around in the physical world, not fall down, and not bump into stuff. Low-end mammal level AI was the goal - mouse level. I made some progress in that area [1], hit the limits of available simulators, spent several years on improving physics engine technology, and eventually sold that off to a game middleware company.
Boston Dynamics took that much further. But it's the same approach I used - analysis of the dynamics, not AI. It's a complicated problem in dynamics, but it barely needs AI at all. Boston Dynamics, unfortunately, demonstrated that even if you spend $120 million, you're not at a minimum viable product that sells yet. Really cool legged robot prototypes, though.
This was all before machine learning took off. For a while I was looking at adaptive model-based control, which is a lot like machine learning.
Machine learning seems to be getting good at what the front end sections of the visual and auditory cortexes do. This is real progress. But a whole organism is still out of reach.
There's a business case for focusing on language skills, but in a way, it's a distraction. The mammals all have close DNA compatibility, but only humans do language much. If we can get into the entry-level mammal range of AI, we should be getting close. I once said something like that to Rod Brooks when he was promoting Cog (a robot humanoid head with a lot of compute power), and he said "I don't want to go down in history as the man who developed the world's greatest robot mouse."
Reverse engineering biology is going very slowly. See "openworm.org", which is an effort to develop a good computerized model of the C. elegans nematode that runs in simulation. C. elegans has 302 neurons, the wiring diagram is known, and it still doesn't work. This shows how little we really know about nervous systems.
Boston Dynamics took that much further. But it's the same approach I used - analysis of the dynamics, not AI. It's a complicated problem in dynamics, but it barely needs AI at all. Boston Dynamics, unfortunately, demonstrated that even if you spend $120 million, you're not at a minimum viable product that sells yet. Really cool legged robot prototypes, though.
This was all before machine learning took off. For a while I was looking at adaptive model-based control, which is a lot like machine learning. Machine learning seems to be getting good at what the front end sections of the visual and auditory cortexes do. This is real progress. But a whole organism is still out of reach.
There's a business case for focusing on language skills, but in a way, it's a distraction. The mammals all have close DNA compatibility, but only humans do language much. If we can get into the entry-level mammal range of AI, we should be getting close. I once said something like that to Rod Brooks when he was promoting Cog (a robot humanoid head with a lot of compute power), and he said "I don't want to go down in history as the man who developed the world's greatest robot mouse."
Reverse engineering biology is going very slowly. See "openworm.org", which is an effort to develop a good computerized model of the C. elegans nematode that runs in simulation. C. elegans has 302 neurons, the wiring diagram is known, and it still doesn't work. This shows how little we really know about nervous systems.
[1] https://www.youtube.com/watch?v=kc5n0iTw-NU