Thanks to the advent of some pretty amazing technology, our devices are starting to get a lot smarter. Depending on where you live, you may have seen self-driving vehicles making test runs around your town, and if you have used an online help feature when placing an order, you may have interacted with a chatbot. Smartphones are getting wiser and a robot has been programmed to solve a Rubik’s cube. Sophisticated platforms like Qualcomm’s Artificial Intelligence platform — on top of giving users improved connectivity, reliability and security — enable this increase in intelligence, which can be labelled as machine learning, smart learning or artificial intelligence.
To get a better sense of what these terms mean and how they are connected and different, check out the following:
The best way to think of these three terms is to think of concentric circles with artificial intelligence— the concept that came first — as the largest circle, with machine learning, which came next, in the middle circle and then deep learning in the center. Artificial intelligence, or AI, first got its start in 1956 when a group of scientists came up with the term at the Dartmouth Conferences. The researchers dreamed of a world where computers would have the same characteristics as human intelligence and think like we do. While we are not quite there yet, we do have a number of technologies that certainly do a decent job doing one specific task as well as, or better than, we can. Great examples are face recognition on Facebook, which will allow you back into your account if you are locked out, virtual personal assistants like Siri and websites that suggest items for you to buy, based on your past purchases.
Machine learning takes the concept of AI and expands on it a bit more. While AI relies on computer programming, machine learning involves uses complex algorithms to analyze a huge amount of data, glean patterns and then make a prediction — all without having a person program the device ahead of time. A great example of machine learning is when it is used to identify certain items. If a device that is capable of machine learning incorrectly says a tomato is a pomegranate, machine learning will allow it to recognize patterns to improve over time, learn from past errors and eventually identify the fruit correctly, just like a human would. Additionally, machine learning can be found in wearable devices that track health; this can enable the creation of realistic fitness goals specific to the user.
Just as machine learning is a subset of AI, deep learning is a subset of machine learning. Deep learning is a specific class of machine learning algorithms that use complex neural networks to take the idea of computer intelligence to a whole new level. Deep learning involves taking an enormous amount of data and computation to allow the computer or other device to mimic the deep neural networks that we have in our brains; these allow us to classify data and find connections between them. The more data a device has, the more accurate it will be able to predict what things are. Going back to our tomato/pomegranate example, while machine learning can eventually tell the difference between the two kinds of fruit, deep learning will examine the huge amount of data like shape, size, color and more, to determine if the tomato is a cherry tomato, heirloom variety or beefsteak.
While artificial intelligence, machine learning and deep learning do have definite differences, they also share a common trait: helping machines to work smarter and learn more about their users. Thanks to this technology, machines are sure to get smarter as time goes on.