The Rise of Artificial Intelligence through Deep Learnng

Our world is altering in many ways and one of the effects, which is going to have a huge influence on our future, is artificial intelligence, AI bringing another Industrial Revolution. Previous industrial revolutions depleted humans mechanical power, this new revolution, this second Machine Age, is going to expand our cognitive abilities. Our mental power computers are not just going to replace manual labor, but also mental labor.

So where do we stand today? A machine learning system called Alphago, used deep learning to beat the world champion at the game of Go. Go is an ancient Chinese game, which had been much more difficult for computers to master than the game of chess. How did it succeed now, after decades of AI research, Alphago was trained to play? Go first by watching over tens of millions of moves made by very strong human players, then, by playing against itself millions of games. Machine learning allows computers to learn from examples to learn from data. Machine learning has turned out to be a key to cram knowledge into computers, and this is important because knowledge is what enables intelligence. Putting knowledge into computers had been a challenge for previous approaches to AI. Why? There are many things, which we know intuitively, so we cannot communicate them verbally. We do not have conscious access to that intuitive knowledge.

How can we program computers with that knowledge? What is the solution, the solution is for machines to learn that knowledge by themselves. Just as we do, this is important because knowledge is what enables intelligence. There are a few key principles, just like the laws of physics, simple principles that could explain our own intelligence and help us build intelligent machines, for example. Think about the laws of aerodynamics, which are general enough to explain the flight of both birds and planes. Would not it be amazing to discover such simple but powerful principles that would explain intelligence itself. Computers can now do a good job of recognizing the content of images, in fact approaching human performance on some benchmarks.

Over the last five years, a computer can now get an intuitive understanding of the visual appearance of a go board that is comparable to that of the best human players. More recently, following some discoveries made in lab, deep learning has been used to translate from one language to another, and you know start seeing this in Google Translate. This is expanding the computer's ability to understand and generate natural language. We are still very far from a machine that would be as able as humans to learn to master many aspects of our world. So, let's take an example: even a two-year-old child is able to learn things in a way that computers are not able to do right now, a two-year-old child actually masters intuitive physics. She knows when she drops a ball that it is going to fall down when she spilled some liquids. She expects the resulting mess her parents do not need to teach her about Newton's laws or differential equations. She discovers all these things by herself in an unsupervised way. Own supervised learning actually remains one of the key challenges for AI and it may take several more decades of fundamental research to crack that not unsupervised learning is actually trying to discover representations of the data. Consider a page on the screen that you are seeing with your eyes, or that the computer is seeing as an image a bunch of pixels. In order to answer a question about the content of the image, you need to understand its high-level meaning and this high-level meaning corresponds to the highest level of representation in your brain, lower down.

You have the individual meaning of words and even lower down. You have characters, which make up the words, those characters could be rendered in different ways with different strokes that make up the characters, those strokes are made up of edges, and those edges are made up of pixels.

Immense positives may come along with negatives such as military use or rapid disruptive changes in the job market. To make sure the collective choices that will be made about AI the next few years will be, for the benefit of all. Every citizen should take an active role in defining how AI will shape our future.

Source: HOB

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