Introduction to Machine Learning (ML)
The rise of AI has been largely driven by one tool in AI called Machine Learning (ML). So what is Machine Learning? As per Wikipedia, Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.
The most commonly used type of machine learning is a type of AI that learns A to B, or input to output mappings. This is called supervised learning.
Let's see some examples. If the input A is an email and the output B is whether email is spam or not (0 or 1), then this is the core piece of AI used to build a spam filter. Another example is if you want to input English and have it output a different language, Chinese, Spanish, or something else, then this is machine translation. The most lucrative form of supervised learning, of this type of machine learning, maybe be online advertising, where all the large online ad platforms have a piece of AI that inputs some information about an ad, and some information about you, and tries to figure out, will you click on this ad or not? By showing you the ads you're most likely to click on, this turns out to be very lucrative. Maybe not the most inspiring application, but certainly having a huge economic impact today.
A larger example could be if you want to build a self-driving car, one of the key pieces of AI is in the AI that takes as input an image, and some information from their radar, or from other sensors, and output the position of other cars, so your self-driving car can avoid the other cars.
In manufacturing, you take as input a picture of something you've just manufactured, such as a picture of a cell phone coming off the assembly line and you want to output, is there a scratch, or is there a dent, or some other defects on this thing you've just manufactured? This is visual inspection which is helping manufacturers to reduce or prevent defects in the things that they're making.
This set of AI called supervised learning, just learns input to output, or A to B mappings. On one hand, input to output, A to B it seems quite limiting. But when you find a right application scenario, this can be incredibly valuable.
The most important idea in AI has been machine learning, has basically supervised learning, which means A to B, or input to output mappings. What enables it to work really well is data. The more data you have the more accurate predictions can be done and the performance will be better. In other words, AI will be able to predict more accurate results if we use a larger set of “training” data to make the system act on any scenario. The more scenarios are added, the better the performance. When you add neural networks and deep learning to machine learning, the results will be even better.
AI is currently being used in a lot of day to day applications like speech recognition, online advertising, building self-driving car, where having a high-performance, highly accurate is important. The researchers are working on making this technology fool proof and it is not far away when we will see self driven cars moving around with us on the roads and perhaps driving better than humans!
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