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�鶹��Ѱ�����Boulder applied mathematician Brian Zaharatos asks, ‘What becomes of thinking when machines think for you?’

When you ask an AI model to solve a math problem, it will. Ask it to draft a legal brief, summarize a medical study or grade a stack of essays, and increasingly, it can do that too.��

Whether the resulting output is accurate is less assured.��

Even so, artificial intelligence technology is advancing faster than most people can reckon with what it means for them. From its impact in the workplace to decaying trust in information, even the researchers building today’s leading AI models don’t have the answers.��

Brian Zaharatos, a teaching professor in the �鶹��Ѱ����� Department of Applied Mathematics, has been searching for one through a philosopher’s lens.��

Brian Zaharatos is a teaching professor in the �鶹��Ѱ�����Boulder Department of Applied Mathematics.

His new book, isn’t about artificial intelligence inherently, but the ponderings within its pages have much overlap.��

“I don’t want to oversell any AI angle. The book is primarily about the inescapably philosophical nature of statistics, and disagreements about the nature of probability and inference,” Zaharatos says.��

Yet, Zaharatos raises a question many people are asking themselves right now. What’s left for the human mind once machines can think for us?��

‘Computation can be automated. Judgment cannot’

The book’s real focus is a century-old argument over what justifies drawing a conclusion from gathered evidence. Rather than settling the debate, the proliferation of AI into nearly every facet of daily life has raised the stakes.��

“Inference is the process of drawing conclusions based on evidence. It’s about moving from data to theories. As the great computer scientist and philosopher Judea Pearl says, ‘How we go from �ɳ󲹳���to �ɳ��,’” Zaharatos explains.��

As an undergraduate, Zaharatos took much inspiration from the work of philosopher Hubert Dreyfus, whose interpretations of Heidegger and his existentialist theories, shaped how he thought about meaning.��

“At times, I have been quite convinced by Dreyfus’ main theses about what computers can’t do. His arguments are nuanced, and the fact that AI has come such a long way in the last several years is not by itself strong evidence that he was wrong,” Zaharatos says.��

He goes on to explain that he was right about some things, including that AI doesn’t “understand” or “care” in the same way humans do.��

“But it seems like he was wrong about others. For example, he famously claimed (during the era of first-gen AI) that AI would never have the ability to drive a car. Enter, Waymo,” Zaharatos says.��

What’s more important than deciding what AI can do, however, is deciding when it can be trusted. This, Zaharatos says, is one of the defining questions of our current time.��

“Who judges whether we take the proposed solution to be correct with little or no critical thinking on our part, or whether we put it to a rigorous test? Clearly, we do,” Zaharatos says.��

The dangers of an agreeable machine

Zaharatos didn’t write a word of Patterns and Static with AI, but he did lean on various AI tools to pressure test his own arguments while drafting it. He came away wary.��

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“The danger is that we surrender our critical thinking abilities to machines… so I think figuring out how to resist that in some ways and to further hone your skills is difficult but important,” says �鶹��Ѱ�����Boulder mathematician Brian Zaharatos.

“I often found it too agreeable. Or I’d find myself questioning what it was saying to me because I know it can be sycophantic. Do you really think my presentation of this argument is strong or are you doing what you’ve been nudged to do—flatter me and keep me engaged?” he asks.��

This agreeableness, Zaharatos warns, is a real danger with AI tools. Not so much that AI gets things wrong (which it will), but that it makes believing the output easy, without requiring one to take a moment to step back and examine it.��

“Critical thinking is a skill to be cultivated and fine-tuned, similar to physical skills like playing a sport. If we do not practice critical thinking, we will lose it. AI and technology allow us to think less,” he explains.��

Zaharatos has seen this playing out both in his own life and in the classroom, where he’s consciously trying to find ways to work against it.��

“It’s important to put yourself in situations that seem synthetic or not quite real world that force you to do critical thinking. It actually is very good training for realistic situations when you’re responsible for making a judgment,” he says.��

Zaharatos employs a variety of puzzles and scenarios to train his own reasoning skills and those of his students. These problems come with guardrails, forcing one to work through them with nothing but a pencil and paper.��

“The danger is that we surrender our critical thinking abilities to machines… so I think figuring out how to resist that in some ways and to further hone your skills is difficult but important,” he says.��

Why bother doing _______, if AI can do it?��

Across academic circles, corporate workplaces, and even family living rooms, this question has been asked ad nauseum over the past few years. Zaharatos hears it often.��

His answer returns to the idea that a machine can’t do the real thinking for you.��

“We’re often so focused on the output. But the output that you get is going to be a function of what you feed into the system. I think you have to know something about your problem and about inference to begin with in order to give AI the proper direction,” he says.��

This is part of the reason why he wrote Patterns and Static��in the first place. It’s a love letter to inference and to what it really means to decide something based on what you know.��

“The fact that there are fundamental disagreements among the top minds in statistics and science about how to properly conduct inference is interesting in its own right,” he says.

Statistics isn’t going to be everyone’s passion, and Zaharatos knows it. But in a world where AI makes it easy to hand off your thinking, his advice has less to do with math than it does with finding whatever it is that makes you want to sit around and ponder.��

“If these topics are not inherently interesting to you, my advice is to find something that is. Curiosity is a wonderful state to be in,” he says.��

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Patterns and Static: Philosophy and the Question Concerning Statistics, and a free sample of the first two chapters, is available now at .

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