At a popular level, “AI” is the marketing label du jour for virtually every innovation in software, hardware, and technology that is up for sale or open to investment. Though the term is in ubiquitous use with vague semantic content, surely a more technical definition is on hand. Consider the literal definition of artificial: something made or produced by humans rather than occurring naturally. Everything about generative artificial intelligence is human in origin. Intelligence can also be difficult to define adequately. One fundamental definition is the ability to differentiate between this and that. Surely we can clarify this ubiquitous term.
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Many are anxious about the rise of generative software marketed as "artificial intelligence": software such as ChatGPT for text, Midjourney for images, Sora for video, and Suno for music. Some have raised concerns about a loss of truth and creativity, or about whole categories of gainful employment being decimated. To be sure, we've all seen our feeds populated by deceptive images and videos and lazy AI slop. There are bound to be many other harmful uses of generative software. Some jobs will be lost, others gained. Nevertheless, I’d like to make a case, from the perspective of a creator and graphic designer, that the anxiety about generative AI is largely unwarranted.
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The reason I wrote the book is not to settle old scores or give my version of the match, but to say that we should not be paralyzed by a dystopian vision of the future – worrying about killer AI and super-intelligent robots, which is like worrying about overcrowding on Mars.
Computers are actually pretty simple. We’re sitting here on a bench in this café. Let’s assume that you understood only the most rudimentary of directions and you asked how to find the rest room. I would have to describe it to you in very specific and precise instructions. I might say, “Scoot sideways two meters off the bench. Stand erect. Lift left foot. Bend left knee until it is horizontal. Extend left foot and shift weight 300 centimeters forward…” and on and on. If you could interpret all those instructions 100 times faster than any other person in this café, you would appear to be a magician: You could run over and grab a milk shake and bring it back and set it on the table and snap your fingers, and I’d think you made the milk shake appear, because it was so fast relative to my perception. That’s exactly what a computer does. It takes these very, very simple-minded instructions — “Go fetch a number, add it to this number, put the result there, perceive if it’s greater than this other number” — but executes them at a rate of, let’s say, 1,000,000 per second. At 1,000,000 per second, the results appear to be magic.