Machine learning and big data will have a tremendous influence on our society. The machine minds are coming online, and you had better learn to adapt if you want to succeed. Computers have certain capacities. They have a memory to store information. They have computational power, with some machines performing more “flops” per second than others. What happens when these machines face such a large amount of data that traditional techniques of analysis start to fail? That’s the world of big data, and it’s a world we’re living in now.
You might be a smart person, but we know in our modern world that you have to be a specialist to survive. No one is a jack-of-all-trades anymore. The world has become too complex for humans to even try to know everything. That’s what big data is: It’s the realization that there exist piles of data where traditional human methods of analysis fail. The world is too complicated for the human mind to make sense of every part of it. This presents a serious challenge and a tremendous opportunity for enterprising people who can apply automate techniques to help solve problems.
A simple example is the stock market. You can look at all sorts of information about the stock market, and once in a while you might come up with a good trade, but you can’t possibly keep up with computerized trading platforms that execute thousands of trades per second. It’s just too much information for your mind to process. You need a computerized solution to take in all the information that is relevant to the problem.
That’s where machine learning comes in. Machine learning is a field that combines artificial intelligence, computer programming, business and philosophy all in one. Machine learning is a way for humans to solve problems without actually knowing how to solve them or why a particular approach works. Machines can try out every possible choice and do it very fast. If there are a million options but only one of them is correct, computers can simulate all million events and systematically determine the correct outcome.
This article originally appeared on Forbes.com. To read the full article, click here.
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