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Comment by JustAnotherPat | Hacker News Reader

>Second, the possible moves are several orders of magnitude fewer than the steps needed to build a working car out of parts.

Aren't the number of chess moves, for practical purposes, near infinite? A car can be made only in so many ways, but a chess game can be won under scenarios that outnumber the atoms in the universe.

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Chess has 16 pieces each in one of 65 positions, so the state space is roughly 65^16. A car has more than 16 parts each in more than 65 possible positions.

There are around 100 legal moves for any chess position, and only a handful of rules. The possible actions and rules for building cars is literally all of physics and engineering.

> Chess has 16 pieces each in one of 65 positions, so the state space is roughly 65^16.

This is a correct upper bound, but the state space is much smaller than that, as the only position that two pieces can share is "captured".

Edit: it's not a correct upper bound (or at least, it may be larger than the state space, but only by coincidence), in that while there are at most 16 pieces, the pawns may be in an officer state rather than a pawn state.

I didn't read it that way. You're essentially reading it as the monkeys will eventually write Shakespeare scenario.

The way I read it was, you have a robot and it:

1. knows it has to build a ferrari (implying it knows what it looks like at least)

2. knows nothing about building, parts, etc

3. can engage in some sort of learning behavior

Once a robot knows it can only connect two parts by screwing them together, it will only try to screw them together, and it might even transfer that behavior over to other parts.

The possible scenarios become much more limited as the robot gets smarter.

You're probably imagining an assembly line, where one step follows another in a logical procession, and there doesn't seem to be much of a branching factor. However, each step along that line is the process of many deep optimizations. It is more complicated than you think; thousands of people work on it. Consider just the case of choosing a screw, probably one of the most simple steps in building a car. These 24 pages only scratch the surface of the choices available: http://www.fastecindustrial.com/images/MediaCatFastener.pdf

> Once a robot knows it can only connect two parts by screwing them together, it will only try to screw them together, and it might even transfer that behavior over to other parts.

Why would learning to screw invalidate other means of connecting things? Why not use an adhesive? Or weld the parts together? Or shape the materials so they can be joined without a separate fastener, like a dovetail or something. Even if it learns those, how does it know there isn't some way of connecting two parts by a chemical means that humans haven't discovered?

This wouldn't work for a human being who knows nothing about making automobiles, so why would it work for a robot?

In the same way that the AI’s series of chess moves don’t have to lead anywhere near victory, the system doesn’t have to build anything that resembles a car.

With this consideration, there are many more ways to go wrong in the car scenario.

Chess moves are limited by the chess piece selection and board.

Building something IRL isn’t really limited by anything; For example, you could combine just two pieces in hundreds of ways (tape, staple, weld, etc), in unlimited number of positions (off by 1mm, off by 2mm, off by 3mm, etc).

Wasn't chess solved early because the number of possible moves within the next x moves is limited? I think that's the main difference to Go where a bruteforce approach doesn't work.

Chess hasn't been solved; that would mean knowing the perfect strategy, and whether the first or second player can always force a win or a draw.

Examples of games that have been solved include reversi, gomoku, connect four and draughts.

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