Summary of the Below: I think there are classes of tasks where machine intelligence will have extremely high quality information to use as a target to regress towards, leading to super intelligent performance. The reason robotics tasks provide high quality information is that each robotic manipulation is a natural experiment, where the robot can compare the direct consequences of taking action [A] vs [B] (no actions are an action) and determine the causal relationships of the world.
There’s a couple aspects of this problem I feel are completely unexamined.
There are a large number of real life problems today that fall in the following class:
They involve manipulations in the physical world, where all the major elements including the direct consequences of a robotic system’s actions can be simulated and scored, for immediate feedback.
Problems that fall in this class:
[all autonomous vehicles, all autonomous logistics, all manufacturing, all mining, all agriculture and similar resource gathering, most cleaning tasks, most construction tasks]
This means the above problems are all solvable with sufficient financial investment. This also means platforming and general agents can be developed.
For many of these tasks, there are common subtasks including object manipulation, vehicle dynamics, working memory, behavior prediction, and so on can all be modeled.
And for many of tasks, there are many common framework elements that can be shared between robotic systems.
What does this mean? It means that one route to a kind of transformative AI is as follows:
a. Autonomous cars eventually become reliable enough to deploy in large numbers. Several companies succeed.
b. Several of these companies begin porting and generalizing their vehicle autonomy stacks to automate more things such as generic robotics
c. Revenue from autonomous cars and general robotics floods into these companies. It trivially will hit hundreds of billions and then exceed trillions per year in licensing fees. Just cars alone, if you can garner 0.25 per mile covered, and half the 3 trillion miles driven in the USA are autonomous, is 375 billion annually in revenue. Double that if you include Europe.
d. The money stimulates development of ever larger silicon arrays, more sophisticated algorithms, less labor intensive to deploy frameworks, and common shared subcomponents that learn collectively from every robotic system in the world using the subcomponent.
e. Now that robotic systems are becoming easy to define—at a certain stage there will be probably cloud-hosted editors, some high level scripting language, and ready-to-use premade realtime software/hardware autonomy stacks. All you would need to do to define a new system is import some data showing a human completing a task successfully, and license a few hundred readymade submodules, with automated tools holding your hand as you do so.
Eventually it would reach the point that it’s a matter of hours to automate a task. (and yes, unemploy every human on earth doing the task now). This would lead to self replicating machinery.
This point is hit when there is an automated system to manufacture every part used in every part of state of the art robotics, including mining the materials, trucking them, building the infrastructure, and so on. This might be transformative.
f. If e isn’t transformative, these “dumb agents” should be able to scale to designing other robotic systems, including ones for environments and scales unexplored by humans. (the lunar environment, the nanoscale). This would be done using techniques similar to the hide and seek openAI paper.
g. If f isn’t transformative, the infrastructure needed to support “dumb*” agents as described will provide the pieces for future work to develop true AGI.
*dumb agents: cast the world into a series of state spaces, using pretrained neural networks for most transforms, with the final state space used to make control decisions. Example:
[input camera space] → [object identity space] → [collision risk space] → [ potential path space] → MAX([dollar value of each path space]) → [control system output]
Summary of the Below: I think there are classes of tasks where machine intelligence will have extremely high quality information to use as a target to regress towards, leading to super intelligent performance. The reason robotics tasks provide high quality information is that each robotic manipulation is a natural experiment, where the robot can compare the direct consequences of taking action [A] vs [B] (no actions are an action) and determine the causal relationships of the world.
There’s a couple aspects of this problem I feel are completely unexamined.
There are a large number of real life problems today that fall in the following class:
They involve manipulations in the physical world, where all the major elements including the direct consequences of a robotic system’s actions can be simulated and scored, for immediate feedback.
Problems that fall in this class:
[all autonomous vehicles, all autonomous logistics, all manufacturing, all mining, all agriculture and similar resource gathering, most cleaning tasks, most construction tasks]
This means the above problems are all solvable with sufficient financial investment. This also means platforming and general agents can be developed.
For many of these tasks, there are common subtasks including object manipulation, vehicle dynamics, working memory, behavior prediction, and so on can all be modeled.
And for many of tasks, there are many common framework elements that can be shared between robotic systems.
What does this mean? It means that one route to a kind of transformative AI is as follows:
a. Autonomous cars eventually become reliable enough to deploy in large numbers. Several companies succeed.
b. Several of these companies begin porting and generalizing their vehicle autonomy stacks to automate more things such as generic robotics
c. Revenue from autonomous cars and general robotics floods into these companies. It trivially will hit hundreds of billions and then exceed trillions per year in licensing fees. Just cars alone, if you can garner 0.25 per mile covered, and half the 3 trillion miles driven in the USA are autonomous, is 375 billion annually in revenue. Double that if you include Europe.
d. The money stimulates development of ever larger silicon arrays, more sophisticated algorithms, less labor intensive to deploy frameworks, and common shared subcomponents that learn collectively from every robotic system in the world using the subcomponent.
e. Now that robotic systems are becoming easy to define—at a certain stage there will be probably cloud-hosted editors, some high level scripting language, and ready-to-use premade realtime software/hardware autonomy stacks. All you would need to do to define a new system is import some data showing a human completing a task successfully, and license a few hundred readymade submodules, with automated tools holding your hand as you do so.
Eventually it would reach the point that it’s a matter of hours to automate a task. (and yes, unemploy every human on earth doing the task now). This would lead to self replicating machinery.
This point is hit when there is an automated system to manufacture every part used in every part of state of the art robotics, including mining the materials, trucking them, building the infrastructure, and so on. This might be transformative.
f. If e isn’t transformative, these “dumb agents” should be able to scale to designing other robotic systems, including ones for environments and scales unexplored by humans. (the lunar environment, the nanoscale). This would be done using techniques similar to the hide and seek openAI paper.
g. If f isn’t transformative, the infrastructure needed to support “dumb*” agents as described will provide the pieces for future work to develop true AGI.
*dumb agents: cast the world into a series of state spaces, using pretrained neural networks for most transforms, with the final state space used to make control decisions. Example:
[input camera space] → [object identity space] → [collision risk space] → [ potential path space] → MAX([dollar value of each path space]) → [control system output]