a. So fundamental algorithms are cool and critical contributions to CS. But where did we get the stuff we have now? Well, arguably the things we have now are (1) absurdly powerful silicon devices (2) a large amount of open source and proprietary software. Most of all of this was developed by industry or non-academic open source contributors.
b. What will it take to make AGI a reality? The thing is, I think it is similar to comparing Werner Von Braun’s work before he had a nation backing him, and after. I think the most probable route to AGI is as follows:
1. Today we are trying to build practical systems that work as [sensor inputs] → [intermediate state abstractions: example, collidable objects on a grid around the agent] → [goal state abstractions: example, predicted $ for each path the agent takes, with negative dollars for risks ]. They are fairly simple.
2. I think AGI will essentially be “more meta”. There will be many more feeder subsystems that supply more complex intermediate states. And more layers of meta states that ultimately result in high level abstractions like ‘awareness’ and ‘self desires’ and so on.
To me all this looks like immense scale. You need a gigantic software infrastructure that gets reused thousands of times over. Modern example: wordpress. You need a massive hardware infrastructure to host it and giga-dollar budgets.
Also I think that many of the fundamental R&D steps—finding better activation functions, better neural network architectures, finding optimal configurations for a given problem, alternative algorithms—can be found better by massive autonomous systems that explore the possibility space. I don’t think human researchers will be able to contribute much directly. Here’s one paper as an example.
If you want to be a researcher who can exploit this you need to be a damn good programmer, and you need a big budget for cloud runs.
c. Exponential progress is going to come not from throwing more humans at the problem, but by building clever software that bootstraps early progress in AI to make further progress. Example, a neural network to generate potential functions for regression—which may not even be neural networks—to solve general regression problems.
My personal thought is:
a. So fundamental algorithms are cool and critical contributions to CS. But where did we get the stuff we have now? Well, arguably the things we have now are (1) absurdly powerful silicon devices (2) a large amount of open source and proprietary software. Most of all of this was developed by industry or non-academic open source contributors.
b. What will it take to make AGI a reality? The thing is, I think it is similar to comparing Werner Von Braun’s work before he had a nation backing him, and after. I think the most probable route to AGI is as follows:
1. Today we are trying to build practical systems that work as [sensor inputs] → [intermediate state abstractions: example, collidable objects on a grid around the agent] → [goal state abstractions: example, predicted $ for each path the agent takes, with negative dollars for risks ]. They are fairly simple.
2. I think AGI will essentially be “more meta”. There will be many more feeder subsystems that supply more complex intermediate states. And more layers of meta states that ultimately result in high level abstractions like ‘awareness’ and ‘self desires’ and so on.
To me all this looks like immense scale. You need a gigantic software infrastructure that gets reused thousands of times over. Modern example: wordpress. You need a massive hardware infrastructure to host it and giga-dollar budgets.
Also I think that many of the fundamental R&D steps—finding better activation functions, better neural network architectures, finding optimal configurations for a given problem, alternative algorithms—can be found better by massive autonomous systems that explore the possibility space. I don’t think human researchers will be able to contribute much directly. Here’s one paper as an example.
If you want to be a researcher who can exploit this you need to be a damn good programmer, and you need a big budget for cloud runs.
c. Exponential progress is going to come not from throwing more humans at the problem, but by building clever software that bootstraps early progress in AI to make further progress. Example, a neural network to generate potential functions for regression—which may not even be neural networks—to solve general regression problems.