On the one hand I am an experienced software engineer with a computer science degree. On the other hand I was never interested in AI and I basically know "nothing" about AI, computer vision and artificial neural networks. AI always seemed too far off and bothered my engineering spidey sense for intractable problems.
So I may embarrass myself with the following...
The more software engineering experience I get the more neural networks bother me. I have not studied them in detail... and yet, they seem like a crappy solution.
Throw a lot resources at something, add a bit of math and push a pile of data through.
Does that ever lead to result that can't be bested though simpler more traditional methods?
For example, the math behind how the network is organized and learns, that same math can be used to write algorithms which reveal patterns in data. Patterns which can then be studied further.
Then there is the doing of things. What ever pattern is responsible for doing something in a network, the same function can be described much more simply by an algorithm.
It just seems like neural networks would be interesting only if an alien spaceship with stupefying computing resources fell on us. And it fell on us in such a way that we could fully utilize the power of this new super computer while at the same time we could not learn anything new about algorithms or anything else. Only then does it seem like "train the neural network" is a smart strategy.
In every other scenario is just seems like a decent if somewhat lazy start to an investigation. Really just a way to kick the tires of a problem.
So did I just completely embarrass myself with my lack understanding of ANNs?
So I may embarrass myself with the following...
The more software engineering experience I get the more neural networks bother me. I have not studied them in detail... and yet, they seem like a crappy solution.
Throw a lot resources at something, add a bit of math and push a pile of data through.
Does that ever lead to result that can't be bested though simpler more traditional methods?
For example, the math behind how the network is organized and learns, that same math can be used to write algorithms which reveal patterns in data. Patterns which can then be studied further.
Then there is the doing of things. What ever pattern is responsible for doing something in a network, the same function can be described much more simply by an algorithm.
It just seems like neural networks would be interesting only if an alien spaceship with stupefying computing resources fell on us. And it fell on us in such a way that we could fully utilize the power of this new super computer while at the same time we could not learn anything new about algorithms or anything else. Only then does it seem like "train the neural network" is a smart strategy.
In every other scenario is just seems like a decent if somewhat lazy start to an investigation. Really just a way to kick the tires of a problem.
So did I just completely embarrass myself with my lack understanding of ANNs?