True, but AlphaGo is specialized on a very specific task where planning and deep thinking is a basic requirement for high level play.
We don't need to think 10 "turns" ahead when trying to walk through a door, we just try to push or pull on it. And if the door is locked or if there's another person coming from the opposite side we'll handle that situation when we come across it.
That’s not true, human beings plan ahead when opening doors more than many things — should I try to open this bathroom door or will that make it awkward if it’s locked and I have to explain that to my coworker afterwards? Should I keep this door open for a while so the guy behind me gets through as well? Not to mention that people typically route plan at doorways.
Doors are basically planning triggers more than many things.
Horses don't plan though, and they are much better than computers at a lot of tasks. If we can make a computer as smart as a horse, then we can likely also make it as smart as a human by bolting some planning logic on top of that.
Can you expand on this statement? While I have no way to “debug” a horse’s brain in real-time, my experiences suggest they absolutely conduct complex decision-making while engaging in activities.
Two examples which immediately come to mind where I believe I see evidence of “if this, then that” planning behavior:
1. Equestrian jumping events; horses often balk before a hurdle
2. Herds of wild horses reacting to perceived threats and then using topographic and geographic features to escape the situation.
> intelligence is mostly about getting through the next 10-30 seconds of life without screwing up
In this context horses don't plan or have much capacity for shared learning, at least not as far as I know.
Quote: “This study indicates that horses do not learn from seeing another horse performing a particular spatial task, which is in line with most other findings from social learning experiments,”
> intelligence is mostly about getting through the next 10-30 seconds of life without screwing up
This is probably a variant of Andrew Ng's affirmation that ML can solve anything a human could solve in one second, with enough training data.
But intelligence actually has a different role. It's not for those repeating situations that we could solve by mere reflex. It's for those rare situations where we have no cached response, where we need to think logically. Reflex is model-free reinforcement learning, and thinking is model-based RL. Both of them are necessary tools for taking decisions, but they are optimised for different situations.
It took us millions/billions of years of evolution and a couple of years of training in real life to be able to walk through a door. It's not a simple task even for humans. It requires maintaining a dynamic equilibrium which is basically solving a differential equation just to keep from falling.
We don't need to think 10 "turns" ahead when trying to walk through a door, we just try to push or pull on it. And if the door is locked or if there's another person coming from the opposite side we'll handle that situation when we come across it.