When a weakness is also a strength

The weakness of machine learning and computing power is that it does not consider the circumstances. This is also a strength.

The hammer does not care what you use it to build, it simply pounds the nail. If your idea is good or bad, it does not care. It does its work.

This is a weakness of machine learning and AI because it limits the help it can give us. However, our ability to understand the context of a situation weakens us as much as it helps us.

Context tells the bomb expert that he could die and makes him jittery. The bomb robot just defuses the bomb and doesn’t even know it could be destroyed.

A machine is not affected by its mistakes because it doesn’t even remember the last thing it did and it doesn’t know what it will do until the moment it does it.

For the human, the worst thing we can do after a loss is let it affect us and diminish our abilities and bring on more losses.

We enter the Kübler-Ross model of denial, anger, bargaining, depression, and acceptance. The computer is focused on the one thing it’s doing right now. Success or failure doesn’t exist and it moves to the next thing and does it just as well.

We get demoralized, but computers do not. Computers don’t have pride, ego, or ambition and they don’t get embarrassed after a mistake. They get right back to work with no detrimental effects.

It would be interesting if we could use our humanity to become a little less human when the circumstances call for it. Some humans can do that, the greatest baseball closers don’t care about losing the game, they make the best pitch. Special Forces operators are cold and calculated under some of the most intense pressure a human can face.

What if we could do the same in business? Learn from a loss and immediately forget about it as if it never happened. Even if a position seems completely lost, we would still work as if we were guaranteed a win.

That’s a focus I would love to have, and maybe someday I will.