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Artificial intelligence’s progress is staggering. Over the past few decades efforts to advance AI has resulted in some truly amazing innovations. Big data analytics, medical research, and automated transportation are only a handful of the incredible applications emerging from AI.

What makes data mining, natural language processing, and driving software possible? Here are 3 key concepts of AI to help you understand how these applications work.

Machine Learning

It’s likely that you’ve interacted with some form of artificial intelligence in your day-to-day activities. If you use Gmail, for example, you may enjoy the automatic email filtering feature. If you own a smartphone, you may enjoy filling out your planner with the help of Siri, Cortana, or Bixby. If you own a newer vehicle, perhaps you’ve benefitted from a driver assist feature while driving.

As helpful and as ingenious as these software products can be, they lack the ability to learn independently. They cannot think outside their code. Machine learning is a branch of AI that endeavors to give machines the ability to learn a task without pre-existing code.

How does machine learning work? In the simplest terms, machines are given a large amount of trial examples of a certain task. As they go through these trials, machines learn and adapt their strategy to achieve their goals. So, if it’s image recognition software, a machine may be given millions of pictures to analyze. After going through endless permutations, the machine acquires the ability to recognize patterns, shapes, faces, and so on.

Deep Learning

How do we get machines to learn more than just a specific task? What if we want a machine to be able to take what it’s learned from analyzing photographs, and use that knowledge to analyze different data sets? This requires computer scientists to formulate general-purpose learning algorithms that help machines learn more than just about one task.

One famous example of deep learning in action is Google’s AlphaGo project written in Lua, C++, and Python code. The AlphaGo AI was able to beat professional Go players, a feat that was thought impossible given the game’s incredible complexity and reliance on focused practice and human intuition to master.

How was a program able to master a game that calls for human intuition? Practice, practice, practice — and a little help from an artificial neural network.

Neural Network

Deep learning is often made possible by artificial neural networks, which imitate neurons, or brain cells. Artificial neural networks were inspired by things we find in our own biology. The neural net models use math and computer science principles to mimic the processes of the human brain, allowing for more general learning. An artificial neural network tries to simulate the processes of densely interconnected brain cells, but instead of being built from biology, these neurons, or nodes, are built from code.

Neural networks are comprised of three layers, the input layer, the hidden layer and the output layer, which are comprised of thousands, sometimes millions, of nodes. Information is fed into the input layer. Inputs are given a certain weight, and interconnected nodes multiply the weight of the connection as they travel. Essentially if the unit of information reaches a certain threshold, then it is able to pass to the next layer. Then, in order to learn from experience, machines compare outputs from a neural network, then modify connections, weights, and thresholds based on the differences between them.

Conclusion

Machine learning, deep learning, and neural networks are extraordinarily interesting subjects within AI, and you don’t need to be a computer scientist to understand why. Machine learning, deep learning, and neural networks make it so machines can think and act dynamically, outside the confines of code.

Artificial intelligence is an exciting topic, and hopefully, with this brief overview of some key concepts, you’ll feel more able to understand the basics of artificial intelligence. With the basics understood, you’ll be able to enjoy more advanced AI topics, such as artificial general intelligence, superintelligence and AI, as well as ethics in AI.