Artificial Intelligence and Deep Learning Explained for My Grandpa
My grandpa recently discovered that I’ve been writing medium articles. After reading a few that I wrote about AI, he sent me a text asking for me to write an article explaining it to “seniors” like him. This is my best attempt at that.
What is Artificial Intelligence?
Artificial Intelligence (AI) is a phrase used by computer scientists to describe programs that can do things that require applying knowledge. But isn’t any computer program ‘applying’ the knowledge given to it in its own programming? For example, a program that counts to ten is just applying its knowledge of the ordering of the numbers. In practice, we reserve the term AI for programs that do things that require not just intelligence, but specifically human-like intelligence. This includes harder problems that are often too complicated for simple machines to solve.
What’s the Point?
I think an important thing to know about to fully understand the spirit of AI is something called the Turing Test (also known as the Imitation Game). Developed by Alan Turing in 1950, near the dawn of computer science, it tests a machine’s ability to exhibit human-like intelligence. The idea is that you put a human and a machine in separate rooms and digitally communicate with each one. If you are unable to reliably tell which room has the machine and which has the person, the machine can be said to have ‘passed’ the Turing Test. Much of Artificial Intelligence research since then has been aimed toward eventually developing machines that can ‘pass’ this test.
Still, there are a whole category of other tasks that do not involve language but that still require applying some knowledge at a human level. The entire field of AI is rooted in a deep fascination with the power of the human mind to do all sorts of things. We can identify all the things around us, imagine how they interact, and communicate our thoughts about all of these things in any number of ways. Tackling the whole problem of machines that can replicate humans along all of these different abilities is what computer scientists call Artificial General Intelligence (AGI). To eventually be able to build these AGI machines, we will first need to be able to solve all of these challenges on their own. That’s why most research today is specific to different ‘tasks.’
There are AI researchers studying a whole host of tasks that require intelligence. The biggest two categories, however, are Natural Language Processing (NLP) and Computer Vision. NLP focuses on building AI that can read, understand, and write human languages. This includes AI that can summarize articles, translate between languages, or automatically detect misinformation on facebook. Computer vision is focused on solving problems relating to digital images or videos. The problem that gets the most attention in this space is that of self-driving cars, which take pictures of the world to try to make decisions about how to drive.
How do we Make Artificially Intelligent Machines?
From the time when Turing proposed his test all the way until the 2010s, the way we made machines ‘intelligent’ was pretty similar to the way you might go about learning a new skill yourself: asking the experts to explain it. You may have heard of the famous 1997 chess matches of IBMs Deep Blue vs. world champion Garry Kasparov. IBM’s computer worked in part by rating each possible move based on rules provided by chess grandmasters. Similarly, Google Translate before 2016 worked by reading a bunch of translated text and coming up with patterns to feed the computer. Rule-based and statistical programs (like early Google Translate) were everywhere, and they were humanity’s best attempt at recreating intelligence. But what changed?
In about 2010, a way to create AI programs called Machine Learning suddenly started blowing all these programs out of the water. Machine learning is a group of computer programs that can use data to get smarter. These programs basically take in and study lots of information in order to learn patterns, and then take in new data and apply the patterns they learn in order to make predictions.
To make it a little more clear what machine learning really entails, imagine you own a lemonade stand. You sell a cup of lemonade for $3, and you sell three cups for $9. If someone gave this data to a machine learning algorithm, and then asked it how much you should sell two cups of lemonade for, it should say $6. This example isn’t too complex, but you can imagine how these problems can get pretty hard pretty fast. Just imagine making a computer predict how much to sell a house for (which Zillow actually did!). There are a lot more factors to consider now: location, square footage, age of the construction, etc. But good machine learning models, with enough data to study from, can learn the patterns and make the predictions.
Deep Learning
Specifically, the type of program that has powered most of the recent eruption in Machine Learning progress is known as Deep Learning. This type of program has gotten so good that it has basically taken over the fields of AI and Machine Learning completely (all three of these terms are almost used interchangeably because of this). The way deep learning works is fairly complex, and getting into the math behind it is enough to give anyone a major headache. But what makes Deep Learning so powerful is the ability to guess and check very well.
At a high level, Deep learning programs take in numbers, then add, subtract, and multiply them with each other, and with other numbers called parameters. They then do some more addition and multiplication to get a single number that represents some belief that the program has. For example, the Zillow house-pricing model might take in everything that is known about a house, and then add and multiply this information with parameters to receive a final number that represents how much the house is predicted to cost. And I know what you’re thinking… how could we get anything that means anything by just doing a bunch of random math? The secret comes in the parameters and how we choose them; that’s where the learning comes in.
We start with random numbers for our parameters and just start making fairly random guesses. We then do some math to find out how wrong we were, and how we could change our parameters to get closer to the right answer. Continuing with our house analogy, say our first guess tells us that a million dollar home is worth only $5. We would then go back and update some of the parameters we are multiplying to be bigger, to try to make our answer closer to what is correct. By repeating this process over and over again with a lot of examples, the parameters eventually get shockingly good at making accurate guesses.
Why is All of This Such a Big Deal?
I wrote all about why I believe this is such a big deal today in a different article. But basically, Deep Learning has gotten really, really good. Most of this is because we have access to faster computers and more data every year. We can now make algorithms that can do all sorts of things from writing books to driving cars. Even though it’s still not clear if we can eventually use these programs to pass the Turing Test or to achieve Artificial General Intelligence, its still really exciting to think about how many ways programs like these can help us live better lives and get more done.
There is also the idea that she who can replicate something must understand it. Scientists aren’t yet sure if the way that Deep Learning works is actually similar to the human brain, but it’s enticing to think that getting closer to reproducing human-like intelligence might also give us the answers to mysteries about how our own minds function. We may have to look outwards first to look inwards.
Clearly, I think this stuff is a big deal. That’s why I hope that I’ve helped my grandpa — and anyone else who decides to read this — understand AI and its many implications for the world.
