To many, AI is just a horrible Steven Spielberg movie. To others, it’s the next generation of learning computers. But what is artificial intelligence, exactly? The answer depends on who you ask. Broadly, artificial intelligence (AI) is the combination of computer science and robust datasets, deployed to solve some kind of problem.
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Many definitions of artificial intelligence include a comparison to the human mind or brain, whether in form or function. Alan Turing wrote in 1950 about “thinking machines” that could respond to a problem using human-like reasoning. His eponymous Turing test is still a benchmark for natural language processing. Later, Stuart Russell and John Norvig observed that humans are intelligent, but we’re not always rational. Russell and Norvig saw two classes of artificial intelligence: systems that think and act like a human being, versus those that think and act rationally. Today, we’ve got all kinds of programs we call AI.
‘AI does not have to confine itself to methods that are biologically observable’
Many AIs employ “neural nets,” whose code is written to emulate some aspect of the architecture of neurons or the brain. However, not all intelligence is human-like. Nor is it necessarily the best idea to emulate neurobiological information processing. That’s why engineers limit how far they carry the brain metaphor. It’s more about how phenomenally parallel the brain is, and its distributed memory handling. As defined by John McCarthy in 2004, artificial intelligence is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
Moreover, the distinction between a neural net and an AI is often a matter of philosophy, more than capabilities or design. Many “AI-powered” systems are neural nets, under the hood. We also call some neural nets AIs. For example, OpenAI’s powerful GPT-3 AI is a type of neural net called a transformer (more on these below). A robust neural net’s performance can equal or outclass a narrow AI. There is much overlap between neural nets and artificial intelligence, but the capacity for machine learning can be the dividing line.
What is an Artificial Intelligence Made Of?
Conceptually: In the sense of its logical structure, to be an AI, you need three fundamental parts. First, there’s the decision process — usually an equation, a model, or just some code. AIs often perform classification or apply transformations. To do that, the AI must be able to decide on patterns in the data. Second, there’s an error function — some way for the AI to check its work. And third, if the AI is going to learn from experience, it needs some way to optimize its model. Many neural networks do this with a system of weighted nodes, where each node has both a value and a relationship to its network neighbors. Values change over time; stronger relationships have a higher weight in the error function.
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