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Machine Learning for Dummies

Machine Learning for Dummies

I write a lot about data-driven algorithms, in particular those informed by Machine Learning. I thought it would be nice to give the low-down on machine learning for the uninitiated. Below, I discuss four essential questions. The answers are based, in part, from a recent discussion with Pedro Domingos, author of The Master Algorithm.

1. Should we care about artificial intelligence and machine learning?
Machine learning and AI touch your life every minute of every day, from applications you use at work to how you choose products to buy (Amazon recommendations). Even who you marry or date (Match.com, Tinder recommendations). A third of all marriages start on the internet and the matches are created by algorithms. As a citizen, consumer, and professional, you don’t need the the gory details of how machine learning works, but you do need the big picture.

2. What’s the distinction here between AI and Machine Learning?
AI means getting computers to do all the things that it takes human intelligence to do like reasoning, understanding language and the visual world, navigating, and manipulating objects. Machine learning is a sub-field of AI that deals with the ability to learn. Learning is the one thing that underlies all the others. If you had a robot that was as good as humans at everything, but couldn’t learn, five minutes later it would have fallen behind.

3. Let’s look at the history of AI – how did we get here, and what was the most important turning point?

If we rewind back to the early days of the field, one of the interesting aspects was that the field got named artificial intelligence. The runner up was complex information processing, which of course sounds incredibly boring. Calling it AI made it extremely ambitious which has been partly responsible for a lot of the progress. At the same time, it also created these very inflated expectations which were premature. Intelligence seems like an easier problem than it really is because we are intelligent, and we take it for granted. But evolution spent 500 Million years making us intelligent.

People believed that the way to really build intelligent systems was to have them learn. Initially both the understanding of the problem and the computing resources were not up to the test. Your brain is the best supercomputer on earth and people were trying to do this through the computers that they had back then. They ran a little bit ahead of themselves. In the eighties, there was a shift towards so-called knowledge systems. In these systems, you provide the system as much knowledge as possible. This allowed systems to do seemingly intelligent things like play chess and diagnose diseases. But the moment the system encountered a situation outside the knowledge base, the system failed. In the end, the systems were too brittle. They didn’t learn.

Then, what happened led to the present explosion of AI. People went back and said that learning is actually essential — we’re never going to be able to have intelligence without learning. Within learning, the most recent success is based on algorithms that emulate the brain on tasks like vision and speech recognition (techniques known as neural networks and deep learning).

4. What does a machine learning algorithm “look” like?
There isn’t just one algorithm. We have many algorithms and approaches today – based on statistical approaches (e.g. Bayesian learning), evolutionary techniques (genetic algorithms), logical induction (association rules) and approaches that mimic the brain (neural nets). No one algorithm is good at everything. In practice, one chooses from these. The question is whether there is a master algorithm that can learn an infinite variety of things. That’s a separate topic I’ll discuss in the future and that Pedro discusses in detail in his book.

Kartik Hosanagar is a Professor of Technology & Digital Business at The Wharton School.


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