A Common Misunderstanding of AI

June 14, 2022

In this age of exponentially increasing information, humanity has lost its patience for complexity. The impulse to simplify means the truth can easily get twisted. Perhaps ironically, this is what has happened to our understanding of artificial intelligence. ZDNet attempts to correct a prevailing perception in, “AI: The Pattern Is Not in the Data, It’s in the Machine.”

Writer Tiernan Ray explains machine learning models “learn” by evaluating changes in weights (aka parameters) as they are fed data examples and the labels that accompany them. What the AI then “knows” is actually the value of these weights, and any patterns it discerns are patterns of how these weights change. Founders of machine learning, like James McClelland, David Rumelhart, and Geoffrey Hinton, emphasized this fact to an audience that still accepted nuance. It may seem like a fine distinction, but comprehending it can mean the difference between thinking algorithms have some special insight into reality and understanding that they certainly do not. Ray writes:

“Today’s conception of AI has obscured what McClelland, Rumelhart, and Hinton focused on, namely, the machine, and how it ‘creates’ patterns, as they put it. They were very intimately familiar with the mechanics of weights constructing a pattern as a response to what was, in the input, merely data. Why does all that matter? If the machine is the creator of patterns, then the conclusions people draw about AI are probably mostly wrong. Most people assume a computer program is perceiving a pattern in the world, which can lead to people deferring judgment to the machine. If it produces results, the thinking goes, the computer must be seeing something humans don’t. Except that a machine that constructs patterns isn’t explicitly seeing anything. It’s constructing a pattern. That means what is ‘seen’ or ‘known’ is not the same as the colloquial, everyday sense in which humans speak of themselves as knowing things. Instead of starting from the anthropocentric question, What does the machine know? it’s best to start from a more precise question, What is this program representing in the connections of its weights? Depending on the task, the answer to that question takes many forms.”

The article examines those task-related forms in the areas of image recognition, games like chess and poker, and human language. Navigate there for those explorations. Yes, humans and algorithms have one thing in common—we both tend to impose patterns on the world around us. And the patterns neural networks construct can be quite useful. However, we must make no mistake: such patterns do not reveal the nature of the world so much as illustrate the perspective of the observer, be it human or AI.

Cynthia Murrell, June 14, 2022


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