There are many kinds of neural networks, but in general they consist of systems of nodes with weighted interconnections among them. Nodes, also known as "neurons," are arranged in multiple layers, including an input layer where the data is fed into the system; an output layer where the answer is given; and one or more hidden layers, which is where the learning takes place. Typically, neural networks learn by updating the weights of their interconnections, Parker said.
Deep learning refers to what's sometimes called a "deep neural network," or one that includes a large system of neurons arranged in several hidden layers. A "shallow" neural network, by contrast, will typically have just one or two hidden layers.
"The idea behind deep learning is not new, but it has been popularized more recently because we now have lots of data and fast processors that can achieve successful results on hard problems," Parker said.
5. Cognitive computing: It's complicated
Cognitive computing is another subfield under the AI umbrella, but it's not as easily defined. In fact, it's a bit controversial.
Essentially, cognitive computing "refers to computing that is focused on reasoning and understanding at a higher level, often in a manner that is analogous to human cognition -- or at least inspired by human cognition," Parker said. Typically, it deals with symbolic and conceptual information rather than just pure data or sensor streams, with the aim of making high-level decisions in complex situations.
Cognitive systems often make use of a variety of machine-learning techniques, but cognitive computing is not a machine-learning method per se. Instead, "it is often a complete architecture of multiple AI subsystems that work together," Parker said.
"This is a subset of AI that deals with cognitive behaviors we associate with 'thinking' as opposed to perception and motor control," Dietterich said.
Whether cognitive computing is a true category of AI or simply a popular buzzword isn't entirely clear, however.
"'Cognitive' is marketing malarkey," said Tom Austin, a vice president and fellow at Gartner, in an email. "It implies machines think. Nonsense. Bad assumptions lead to bad conclusions."
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