Paths of Curiosity in an AI World

Listening to Black Holes Collide: How Machines and Volunteers Tell a Signal From Noise
Paths of Curiosity in an AI World

The most sensitive instruments humans have ever built can feel two black holes collide a billion light-years away. They can also feel a gust of wind, a passing car, and an earthquake on the other side of the world.

Telling the difference is the job. Dr. Michael Zevin helps teach machines and volunteers to do it.

In this episode, we sit down with Dr. Michael Zevin, an astronomer at the Adler Planetarium in Chicago, where he studies how black holes form and what makes two of them collide. He is one of the lead researchers of Gravity Spy, a citizen science project where machine learning and volunteers work together to sort real signals from noise in LIGO’s data. He earned his PhD in astronomy and physics from Northwestern and is among the contributors who shared the 2016 Special Breakthrough Prize in Fundamental Physics for LIGO’s detection of gravitational waves.

Together, we explore:

• What LIGO actually is, what a gravitational wave is, and how the 2015 collision stretched the entire Earth by about the width of a proton
• What a single signal reveals about two black holes: how far away they were, how heavy, how fast they were spinning, and which way
• How two black holes in a mostly empty universe end up close enough to merge, from stars born in pairs to a “black hole mosh pit” inside a dense cluster
• What a glitch is, and why understanding every kind of noise is essential to seeing anything real in the universe
• How volunteers on Gravity Spy catch glitch types a machine was never trained to look for
• Whether machines are getting better than people at catching the unexpected in this data, and why Dr. Zevin says pairing them is the most powerful move

Dr. Zevin also shares a path that started with a kid in love with dinosaurs and became what he calls stellar paleontology: studying the fossils that stars leave behind. It took him through years as a science educator and into a PhD, where he chose to try gravitational waves knowing little about them, and the first-ever detection arrived within a year. His advice for students: learn to code and learn to use AI tools to code, because most of modern astrophysics now happens on a computer.

This conversation will change how you think about every signal you assume is real, and every machine you assume can tell.

Because the biggest questions in the universe are still open, and AI can help us process the data. But the questions worth asking still need a human.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Making Atoms That Don’t Exist on Earth to Understand Where Gold Comes From
Paths of Curiosity in an AI World

Most of the elements heavier than helium were made inside stars and stellar explosions. Dr. Artemis Spyrou makes the short-lived nuclei from those explosions here on Earth so we can study them.

In this episode, we sit down with Dr. Artemis Spyrou, Professor of Physics at Michigan State University and the Facility for Rare Isotope Beams, where she studies the nuclear reactions that drive stars and stellar explosions using accelerator facilities around the world. She is a fellow of the American Physical Society and has led education and outreach for the lab for several years.

Together, we explore:

• What the Facility for Rare Isotope Beams actually is — and how you build a nucleus that disappears in a fraction of a second
• Why more than half the elements on the periodic table were made in processes we still don’t fully understand
• What the 2017 neutron star merger confirmed about where gold comes from — and how many new questions it opened up at the same time
• How you measure a nucleus that exists for only milliseconds, and how you trust a measurement when you only get a handful of atoms in an entire week
• How to estimate the properties of a nucleus you can never directly observe — and why a football and a soft wall is the right analogy
• Whether machines are getting better than physicists at predicting nuclear properties — and what Dr. Spyrou thinks AI still fundamentally cannot replace

Dr. Spyrou also shares a path that started in Greece with plans to become a high school math teacher, took a sharp turn through a hospital field trip that almost made her faint, and landed at one of the most advanced nuclear physics facilities in the world. Her advice for students: the curiosity has to come from you, because that is the one thing a machine cannot give you.

This conversation will change how you think about every element in your body, every piece of gold in the world, and what it means to study something that only exists for a moment.

Because somewhere in a star that exploded billions of years ago, the atoms you are made of were born. And we are still figuring out exactly how.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Most of the matter in our universe is something humans are unable to see, touch, or fully explain. Daniel Whiteson has spent his career hunting for it — then wrote a book called We Have No Idea.

In this episode, we sit down with Dr. Daniel Whiteson, Professor of Physics and Astronomy at UC Irvine and member of the ATLAS Collaboration at the Large Hadron Collider at CERN, where he searches for exotic new particles. He is also the author of We Have No Idea and Do Aliens Speak Physics?, and co-host of the podcast Daniel and Kelly’s Extraordinary Universe.

Together, we explore:

• What dark matter actually is — and why eighty percent of the matter in the universe is made of something we have never directly detected
• How you can know something exists without ever seeing it, and why gravity is the only force dark matter uses to announce itself
• What happened when a student walked into Dr. Whiteson’s office with data that looked exactly like a new particle — and what six months of cross-checks revealed
• Why a machine learning model finding a pattern in collider data would still need humans to decide whether it means anything
• Whether a machine could learn physics in a way that looks nothing like ours — and what that says about understanding versus pattern recognition
• How AI is already changing the pace of particle physics, and what job title it is quietly making obsolete

Dr. Whiteson also shares a path that took him through plasma physics he hated, condensed matter physics where he spent a whole summer fixing a broken laser, and finally particle physics — where five minutes of actual research told him he had found his thing. His advice for students still searching is the most honest in the series: pay attention to the voice that tells you whether you are having fun, because you cannot rely on the big discoveries to keep you going.

This conversation will change how you think about what it means to search for something you may never find — and why that might still be the best job in the world.

Because most of the universe is still out there waiting. And the bigger fraction of human knowledge is yet to be discovered.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Who Really Makes Your Choices? Six Researchers on the Hidden Systems Shaping Your Life
Paths of Curiosity in an AI World

You get to pick. Make the predictions yourself, or hand the job to an algorithm. You get paid for how accurate they are. And before you choose, you watch the algorithm practice — and it's wrong a lot. But it's still better than you. It would earn you more money than you would yourself.

Most people still didn't pick it.

The people who never saw it work trusted it. The people who watched it make mistakes fired it, bet on themselves, and made less money doing it. They picked being wrong their own way over being less wrong the algorithm's way.

That is the question this whole quarter. When you make a choice, how much of it is really yours?

In this recap episode, Alora and I revisit all six conversations from our Data, Behavior, and Human Systems miniseries — and trace the single thread running through all of them.

Together, we work through:

• How a text message and a wristband made tuberculosis patients in Kenya two-thirds more likely to finish treatment — and what that tells you about every choice environment you are inside right now
• Why your credit score is a black box that punishes you for being new, and how your email address, your phone type, and what time you shop predict your financial future better than most people realize
• How algorithmic management gives companies total control over gig workers without ever calling them employers — and why the drivers who value the flexibility most are also the ones most exposed when something goes wrong
• Why the original Luddites were not afraid of technology — and what it means that the question they were asking two hundred years ago is the same one we need to ask about every app on your phone
• What the law of amplification says about who actually benefits when AI gets more powerful
• Why seeing an algorithm fail makes you trust it less even when it is still better than you — and the one fix that works

We also trace the career paths of all six guests, because not one of them started where they ended up. A would-be diplomat who became a field experimentalist. A teenage organizer who ended up writing financial policy. A historian who spent seven years riding with gig workers and then went inside the company she had been studying. A chemist who changed his major on a single elective. A physicist who spent a decade building technology and came home with a law about what it cannot do. A finance major who realized he did not want to make predictions — he wanted to understand how people make them.

None of them planned it. All of them found the crossing point between two things they cared about and followed it.

This conversation is for anyone who has spent this quarter wondering how much of their life is a choice they made — and how much was designed for them to make.

Because the illusion is not that the choice is fake. It was never fully free and was never fully taken. There is a real decision buried in there somewhere.

Finding it is the work. And that work is wide open.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Why Seeing an Algorithm Fail Makes You Trust It Less — Even When It's Still Better Than You
Paths of Curiosity in an AI World

What if the algorithm was actually right and you still refused to trust it?

Dr. Berkeley Dietvorst discovered that people abandon algorithms the moment they see them make a mistake — even when those algorithms consistently outperform humans. And then he found something even more surprising: giving people just a tiny amount of control over the output is often enough to fix it.

In this episode, we sit down with Dr. Dietvorst, Professor of Marketing at the University of Chicago Booth School of Business and the researcher who coined the term algorithm aversion.

Together, we explore:

• What algorithm aversion actually is — and why seeing an algorithm perform makes people less likely to use it, not more
• Why a small ability to tweak an algorithm's output changes everything psychologically
• The critical difference between a prediction and a decision — and why algorithms should only ever be responsible for one of them
• What randomness in a domain has to do with whether you should trust an algorithm at all
• Why hiring, parole, and medical diagnosis feel different — and what your resistance to algorithms there might actually be telling you
• What a student sorted by algorithms their entire life should understand about the systems shaping their choices

Dr. Dietvorst also shares his own unexpected path — from a finance major fascinated by investing to a behavioral scientist studying why humans make the decisions they make — and why following what genuinely interested him at each step was the only strategy that worked.

This conversation will change how you think about every algorithm nudging your decisions, from college applications to career choices to what your feed decides you should see next.

Because in a world where algorithms are often better than us at prediction, the real question was never whether to trust them. It's understanding exactly what they can and cannot do — and who gets to make the final call.

LinkedIn Post:

What if the algorithm was right and you still refused to use it?

Dr. Berkeley Dietvorst discovered that people abandon algorithms the moment they see them make a mistake — even when those algorithms consistently outperform humans. He coined a term for it: algorithm aversion. And then he found something even more surprising: giving people just a tiny amount of control over the output is often enough to fix it entirely.

In our latest episode of Paths of Curiosity in an AI World, we sat down with Dr. Dietvorst, Professor of Marketing at the University of Chicago Booth School of Business.

Here's the framing that changed how I think about every AI tool I use:

Algorithms are for predictions. Humans are for decisions.

A prediction tells you there's a 20% chance of rain. The decision — whether to go to the outdoor concert anyway — depends on how much you hate getting wet, how much you love the band, and how you'd feel if you stayed home and it didn't rain. No algorithm can know that. Only you can.

The same logic applies to college admissions, hiring, parole, medical diagnosis. The resistance people feel toward algorithms in those domains isn't always aversion. Sometimes it's the right instinct — because those aren't just prediction problems. They're value problems. And value problems belong to humans.

What else we got into:

• Why seeing an algorithm perform makes people trust it less, not more
• How a tiny tweak to the output changes everything psychologically
• Why some domains are too random for any algorithm to ever look good — even when it's still beating humans
• What growing up sorted by algorithms does to how young people make decisions

Dr. Dietvorst started as a finance major drawn to investing, took one class on human decision-making, and never looked back. His advice: take the econ class, take the psychology class, take the statistics class — and then go read the actual academic papers, not the summaries. That's where the real skill starts to build.

When was the last time you trusted an algorithm's prediction but made a different decision anyway — and were you right to?

#AI #BehavioralScience #AlgorithmAversion #DecisionMaking #FutureOfWork #PathsOfCuriosity

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Technology Doesn't Fix Broken Systems. It Makes Them Worse. A Warning From Inside Microsoft.
Paths of Curiosity in an AI World

What if the most honest thing anyone in tech has ever said is that technology almost never does what we think it will?

Dr. Kentaro Toyama spent a decade at Microsoft building technology for some of the world's most underserved communities. What he found changed everything he thought he knew — and led him to write a book called Geek Heresy.

In this episode, we sit down with Dr. Toyama, W.K. Kellogg Professor of Community Information at the University of Michigan and author of Geek Heresy: Rescuing Social Change from the Cult of Technology. He co-founded Microsoft Research India, ran over fifty humanitarian technology projects, and came to a conclusion that Silicon Valley still doesn't want to hear.

Together, we explore:

• The law of amplification: why technology makes already positive situations better and broken systems even worse
• What happened when Microsoft built a brilliant solution for rural schools in India — and why it failed anyway
• Why the people who build educational technology often send their own children to schools with almost no electronics
• What it means that AI is the first technology that can actually change your mind — and why that breaks the rules
• Who really has power over AI right now, and what their actual behavior tells us about what gets amplified
• Why if the AI guidance is free, your brain might be the product

Dr. Toyama also reflects on a career that took him from physics at Harvard to computer science at Yale to Microsoft to a research lab in Ghana to one of the most cited critics of technology utopianism in the world — and why none of it was planned.

This conversation will change how you think about every promise you have ever heard about what AI will do for education, healthcare, and hiring.

Because in a world where technology amplifies everything underneath it, the most important question was never what the tool can do. It's who controls it — and what they actually want.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Sorted, Scored, and Steered: The Political Economy of AI With a Real Luddite
Paths of Curiosity in an AI World

You've been sorted, scored, and steered by systems you never agreed to. Someone built those systems. Someone profits from them. And the word we've been taught to use as an insult is actually the most precise tool we have to fight back.

In this episode, we continue our AI, Data, Behavior, and Human Systems miniseries with Dr. Jathan Sadowski, Associate Professor and ARC Future Fellow at Monash University's Emerging Technologies Research Lab and author of Too Smart: How Digital Capitalism is Extracting Data, Controlling Our Lives, and Taking Over the World, and The Mechanic and the Luddite: A Ruthless Criticism of Technology and Capitalism.

Together, we explore:

• What the original Luddites actually believed — and why recovering that history changes how you think about AI resistance today
• How smart technologies from school laptops to John Deere tractors are built around two imperatives: extracting data and expanding control
• What it means that your data isn't just collected but accumulated like capital — and what that means for someone who has been datafied since childhood
• The landlord model of tech platforms, and how AI deepens a system where you rent access to the infrastructure your life depends on
• Why "the algorithm did it" is always a lie — and who is actually making decisions about your life behind it
• What the Marie Kondo test for technology actually looks like in practice

Dr. Sadowski also shares his own sliding-door moment: a single philosophy elective at a technology university that pulled him out of a polymer chemistry degree and onto a path he never could have planned — studying the systems that shape all of our lives before most people knew those systems existed.

This conversation will change how you think about every platform you use, every form you sign, and every choice a company tells you that you're making freely.

Because in a world designed to prevent you from thinking critically about it, the most radical thing you can do is ask: does this thing actually make my life better?

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Inside the Algorithm: What 5,000 Miles With Gig Workers Taught One Researcher About Power, Control, and Design
Paths of Curiosity in an AI World

What if the app that decides your Uber driver's next ride was specifically designed to make them feel free while controlling almost everything they do?

Dr. Alex Rosenblat spent seven years finding out.

In this episode, we continue our AI, Data, Behavior, and Human Systems miniseries with Dr. Alex Rosenblat, Director of Sociotechnical Research at The Markup and Cal Matters, and author of Uberland: How Algorithms Are Rewriting the Rules at Work. She rode over five thousand miles with gig workers across twenty-five cities, then did something almost no outside researcher has ever done — she went inside Uber to try to change it.

Together, we explore:

• How algorithmic management gives companies total control over workers without ever having to call them employees
• Why drivers crowdsource the rules in online forums because the platform never actually tells them what the rules are
• What dark patterns look like in the real world — from gig apps to hospital registration tablets — and how they steer choices you didn't know were being steered
• What happened when Dr. Rosenblat tried to get a copy of her own child's hospital consent forms after surgery
• Why surveillance pricing — getting charged a different amount than the person standing next to you — may be the most underreported story in tech right now
• What ethnography gives researchers that no dataset ever could

Dr. Rosenblat also reflects on what it means to study a system from the outside, work inside it, and come back out to investigate again — and why understanding how every stakeholder thinks is the skill most researchers never develop.

This conversation will change how you read every form you're handed, every app you open, and every choice a platform tells you that you're making freely.

Because in a world where design is policy, the most important question isn't what the system can do — it's what it's been built to make you do without noticing.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Paths of Curiosity in an AI World

For decades, banks discriminated against borrowers in person. Then algorithms inherited those same biases and made them invisible.

Aaron Klein says AI could finally fix that — or make it far worse.

In this episode, we continue our AI and Human Decision-Making: Choices, Bias, and Behavior miniseries with Aaron Klein, Miriam K. Carliner Chair and Senior Fellow in Economic Studies at the Brookings Institution, former Deputy Assistant Secretary for Economic Policy at the U.S. Treasury Department, and former Chief Economist of the Senate Banking Committee — a person who helped write the laws that govern the financial system you're already living inside.

Together, we explore:

• What actually happens in the first seconds after you apply for a credit card — and why a mysterious black box number you've never heard of may already be shaping your financial life
• How banks once decided loans in person, face to face, and why that created the discrimination that federal law was written to stop
• What your device, your email address, your shopping hours, and even your name might already be telling a lender about you — before you've said a word
• Why the same characteristic — gender — is perfectly legal in car insurance but completely illegal in credit cards, and what that contradiction reveals about how financial rules actually get made
• How AI could finally extend credit to people the current system has locked out for generations — and what's standing in the way
• Why the entire financial system is tuned to an out-of-tune oboe, and how hard it is to get everyone to retune at once

Klein also shares his own path from organizing his rundown high school in Silver Spring, Maryland to the Senate Banking Committee to the Treasury Department during the 2008 financial crisis — and why the only two professions people describe as a calling are the priesthood and public service.

This conversation will change how you think about every financial decision you make — and every decision already being made about you.

Because in a world where an algorithm decides whether you get a loan before a human ever looks at your name, the question isn't just whether the system is fair. It's whether anyone with the power to change it actually will.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/

Paths of Curiosity in an AI World

Every cafeteria, every app, and every website you use today was designed to push you towards a decision before you even knew you were making one.

And the people who built it? They're not thinking about you.

In this episode, we kick off our AI and Human Decision-Making: Choices, Bias, and Behavior miniseries with Dr. Syon Bhanot, Associate Professor of Economics at Swarthmore College and Senior Researcher at MIT's Applied Cooperation Team, whose field experiments with governments, cities, and international organizations — including the World Bank and the U.S. Federal Government — have changed how millions of people save, conserve, and make decisions about their own lives. He is the recipient of the 2024 Exeter Prize for his research on attention and decision-making.

Together, we explore:

• How a text message and a wristband made people two-thirds more likely to finish tuberculosis treatment in Kenya — and what that tells us about why we fail to do even the things that matter most
• What TikTok and Instagram actually know about your psychology — and why calling you a "user" is no accident
• Why telling people they're losing a water conservation competition caused them to use more water, not less
• The moment money ruins everything: why adding $11 to helping a friend move makes you less likely to show up
• Why the happiest people aren't optimizers — and what that means for every major decision you're about to make
• What AI nudging looks like when the system doing the nudging answers to an advertiser, not to you

Dr. Bhanot also shares his own winding path from a year at a refugee camp in Kenya to stumbling into microeconomics in grad school to running experiments that shape federal policy — and why the best metaphor for a career is surfing, not planning.

This conversation will change how you think about every choice you make — and every choice that's already being made for you.

Because in a world where algorithms, platforms, and institutions are all nudging you somewhere, the question isn't just whether you're being influenced. It's whether anyone nudging you actually has your interests in mind.

Paths of Curiosity in an AI World
Exploring tomorrow’s careers through the lens of AI
Follow us: @pathsofcuriosity

Website: https://pathsofcuriosity.com/
Youtube: youtube.com/@PathsofCuriosity
X: https://x.com/PathsCuriosity
LinkedIn: https://www.linkedin.com/in/paths-of-curiosity-in-an-ai-world-2117803aa/