2 ms·

> Simply put because AI is way too stupid for that kind of stuff. When we talk about AI today most of what we talk about is actually just processing sensory inp

> Simply put because AI is way too stupid for that kind of stuff. When we talk about AI today most of what we talk about is actually just processing sensory input at a low level. This is a very narrow view of AI. In fact, the limitations you are talking about apply only to deep learning (which is frequently and mistakenly confused for AI nowadays). Other well-studied branches of artificial intelligence include planning [1] and reasoning [2]. By far the biggest problem is to formalize the real world and build a realistic simulation which the AI system can play with. After that, a decent system would blow most humans out of the water. > Even Deepmind's starcaft bot was stupid in this sense. It didn't understand starcraft strategy at all (which was visible in it's nonsensical unit combinations it was building) but simply out-microed its opponent, it just had better mechanics. Maybe, what about go and chess? The feeling there was that humans did not understand the game very well. [1] https://en.wikipedia.org/wiki/Automated_planning_and_scheduling https://en.wikipedia.org/wiki/Automated_planning_and_schedul... [2] https://en.wikipedia.org/wiki/Automated_reasoning https://en.wikipedia.org/wiki/Automated_reasoning

pbronez 6y ago
> limitations you are talking about apply only to deep learning (which is frequently and mistakenly confused for AI nowadays) Deep learning is also where all the money and attention is going, and where the revolutionary gains have been seen in the last few years. They’re pretty good at compressing huge datasets into a queriable structure and generating features for hierarchical data, but are just one component of a generalized system that could handle something like war. > By far the biggest problem is to formalize the real world and build a realistic simulation which the AI system can play with Strong agree! The fundamental challenge is to create digital model of the world with enough detail & accuracy that you can run optimizations on it and trust the results. This is as true for robotic aircraft as it is for autonomous cars or an e-commerce data warehouse. People need to decide what subset of the world to observe, deploy & maintain sensors to collect those observations, build information systems to organize & serve the observations, and maintain the whole thing. Then maybe you can start running AI/ML on it.

pbronez 6y ago

> limitations you are talking about apply only to deep learning (which is frequently and mistakenly confused for AI nowadays) Deep learning is also where all the money and attention is going, and where the revolutionary gains have been seen in the last few years. They’re pretty good at compressing huge datasets into a queriable structure and generating features for hierarchical data, but are just one component of a generalized system that could handle something like war. > By far the biggest problem is to formalize the real world and build a realistic simulation which the AI system can play with Strong agree! The fundamental challenge is to create digital model of the world with enough detail & accuracy that you can run optimizations on it and trust the results. This is as true for robotic aircraft as it is for autonomous cars or an e-commerce data warehouse. People need to decide what subset of the world to observe, deploy & maintain sensors to collect those observations, build information systems to organize & serve the observations, and maintain the whole thing. Then maybe you can start running AI/ML on it.