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I think artificial intelligence, at least in my lifetime, is the most transformative thing that I've seen.
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There's really two lifetimes of AI. One is the AI that I was trained on as a PhD
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student, which was how do you encode and deal with really large [music] data sets. That's been around for 60 to 70
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years. Now, of course, in the last three to four years, it's [music] been taken over by a version of artificial
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intelligence, which we all know as generative AI. All of artificial intelligence is based
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[music] on the premise of handling really large data sets, and also making predictions. So, whether it's predicting
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motion for vehicles, or whether it's predicting text from data, they're all large-scale prediction problems. And the
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nice thing about [music] large language models is lots of people can go type in prompts that specify business problems.
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[music] Anything that democratizes what I call empirically driven decision-making is good for business.
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[music] It's going to change, I hope, to make more equitable and fair decisions. We as humans are going to
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benefit a lot from artificial intelligence well before businesses make the type of transformations that
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everybody's predicting. Do I think we're going to have automated robots in a lot less than 50 years?
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Absolutely. I think it's going to unlock a huge amount of wealth and potential for society. So, what's going to happen
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is there's going to have to be a redistribution. What I mean by [music] that is the smart
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companies won't get rid of people. The smart companies will redistribute [music] talent. I do understand why
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there's nervousness about what those jobs of the future are going to be, and I think all of us are just trying to
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guess what's going to happen to business [music] and society as a result of the widespread use of artificial
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intelligence. People ask me all the time, "Professor Bradlow, do you regret all the math,
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computer science, and stat classes you took?" I don't regret one of them. The reason people study is to learn
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about how to take large unstructured problems and put structure on them. It taught me how to take large amorphous
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problems [music] and break it down into little pieces. It taught me what I call empirical [music] common sense. I look
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at results all the time and like, "That makes no sense whatsoever." And last time I checked, I'm going to be able to
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do this better now that I have a really strong [music] decision-making support tool called artificial intelligence. So,
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for all of us that learn how to think in a structured way, this is our golden age. It's not going
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to replace us. It's going [music] to support us.