What is Machine Learning?

If you’re new to Machine Learning, let’s start at the top. The whole field of artificial intelligence is broken up into two categories – Strong AI and Narrow AI.

Strong AI is coming up with a robot that looks and behaves like a person. Narrow AI, or “neural networks” attempt to duplicate the brain’s neurological processes that have been perfected over millions of years of biological development.

Machine Learning is a subset of Narrow AI that does pattern classification. It’s an engine – an algorithm that learns without explicit programming. It learns from the data. What does that mean? Given one set of data, it’s going to come up with an answer. But given a different set of data, it will come up with something different.

Self-Organizing Map is a type of neural network that adjusts to training data. However, it makes no assumptions about the characteristics of the data. So, if you look at the whole field of artificial intelligence, and then we look at machine learning as a subset of that, there’s two parts: supervised neural networks and unsupervised neural networks. Unsupervised is where you feed it the data and say “you go figure it out.” In supervised neural networks, you give it both the data and the right answer. Some examples of supervised neural networks would be convolutional neural networks and deep learning algorithms. Convolutional is a more classical type of a supervised neural network, where for every data sample, we know the answer.

Here’s a classical example of a supervised neural network: Your uncle just passed away and gave you the canning operations in Cordova, Alaska. You go there and observe the employees taking the fish off the conveyor and manually sorting them by type – buckets for eels and buckets for flounder and so forth. Can you use AI (machine learning) to do something more efficient? Perhaps have those employees do something more productive? Absolutely! As the eels come along, you weigh them, you take a picture of them, you see what the scales are, general texture, you get some idea about the general shape of them. There’s three properties already. You continue running eels through and maybe get up to four or five properties, including measurements, etc. The neural network is then trained on eels. Then, you do the same thing with all the flounder. There are going to be variations, of course, but in attribute space, of those four or five properties that we made for each one, they’re going to wind up in a different cluster in attribute space. And that’s how we tell the difference between eels and flounder. Everything else that you can’t classify very well, you don’t know. All of that goes into the algorithm. That’s the difference between supervised neural networks and unsupervised neural networks.

At Geophysical Insights, we believe we should be able to query our seismic data for information with learning machines just as effortlessly and with as much reliability as we query the web for the nearest gas station.

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