@maxdancona,
maxdancona wrote:
I am a Software Engineer working in AI (specifically Speech Recognition and Natural Language Processing). My career includes design and programming. You can insult me all you want. But I do know what I am talking about, and I am trying to bepatient to explain it to you. If what I am saying is not interesting to you... then there is no need for me to be wasting my time or yours.
The programming example you give is interesting, but underneath are a bunch of switches. It boils down to a machine that does exactly what it was designed to do. I don't know how the human mind works.... but the human mind does not work like a computer program.
AI is not magic. The current technology involves creating a mathematical module that is designed by human engineers. The computer comes up with a model (basically a set of parameters) that is complex ... but it is using a technique that is carefully crafted by engineers. That is what people like me do for a living. You may not believe this, but we aren't magicians. We are engineers using a specific set of processes to analyze data and create models.
I don't know what you are getting upset about. I am only here because you said that you were interested in discussing how AI (as currently used) works. If you are already set in your ideas about what is going on... then there really point in continuing this discussion.
You are free to believe whatever you want. And, you are free to swear at anyone who tries to explain to you why your beliefs might not be correct.
I have made the point that AI is just a mathematical process. It is not magic, and in its current form it is nowhere near creating a sentient machine that can have its own will any more than a desk lamp or a transistor radio. So unless there is some intelligent discussion (rather than ad hominems) I will leave it at that.
I feel like you were trying to insult my intellect by asking if I thought a lamp has motivation. I thought you would understand why I was using the word motivation. I know a machine doesn't have feelings nor emotions. However the machine would have "needs" all machines have needs.
An AI unit would need power. If it can produce it's own power internally then it's needs for power are taken care of. No need to keep track of it.
However; maybe there are other needs.
The aspect is very very simple. All it needs is to know when it's need has occurred. Such as a sensor. Maybe it's lubrication or something.
If there is a need for lubrication. Such that the sensor turns a switch to on.
Which in programming is a booling, true false. That is a switch, on or off.
If it is triggered to true, then the AI knows to run the functions associated with obtaining lubrication.
We are really no different. We have sensors, pain sensors, hunger pain sensors, temperature sensors, ect ect ect.
When we feel cold, we run through a very quick series of solutions.
PROBLEM:
You are cold.
SOLUTION:
Deal with it. (problem solved??)
Find warmer spot.
Find more clothing.
Get out of the cooler.
Turn up the heat.
Steal your boyfriends jacket.
Ect ect ect.
It's a process that we do that is incredibly fast, we don't even really recognize it. The same for AI would be very similar.
The AI would have a list of needs. power, repair parts, task functions, ect
And it would have algorithms for each contingency.
But this takes out learning. Learning is a different process. Learning is being able to adapt one bit of information, a solution to a new problem.
Humans are extremely good at this. We have past experience, solutions that worked in the past, but as soon as we are given a new problem to solve we can utilize the past solutions to see if they solve our new problem. We do this very well.
The problem is, machines are terrible at this. Because the solution has a series of steps. But some times those steps are not relevant to the new problem. For us it's easy to omit a step, we are really good at leaving steps out that we find are unnecessary. But computer code is terrible at this. It takes a lot of redundant checks to see if we need this information or not. Or if a piece of information is useful to this new problem.
Learning is the ability to do this even when the two problems are drastically different.