Dr. Alex Best, of Harmonic discusses AI's struggles with technical and ethical limitations like uninterpretable methods and a lack of diverse datasets make facial recognition and medical software dangerously biased. If algorithms output answers without little explanation, how can one trust those results? Interpretability is a prerequisite to replace the work of human researchers. But using AI for formalizing mathematics offers some mitigation, Alex Best says. "Instead of asking for an answer, you ask for an answer and a machine-checkable proof that that answer is correct."

Dr. Kristen Lauter, director of FAIR (Fundamental AI Research) Labs, Meta, North America, discusses how cryptographers are using AI.

Online, private information is secured using difficult-to-solve math problems. Researchers must test those problems thoroughly to be sure they are truly difficult - if hackers can solve the problem, they'll have access to your information.

Dr. Po-Shen Loh of Carnegie Mellon University, discusses how AI is changing the landscape of mathematics and looks ahead at what might come.

As algorithms have matured, mathematicians have embraced computation as means to accelerate math discoveries. Researchers integrate AI with "proof checking" tools, like the software Lean, to avoid errors more quickly. According to Loh, this makes it easier for large teams to collaborate and for peers to check each other's discoveries for accuracy. According to a study in the journal Nature, mathematicians anticipate that AI will guide human intuition, and help to prove unsolved conjectures.

Dr. Abiy Tasissa of Tufts University, discusses the mathematics he and colleagues used to study particle collider data, including optimal transport and optimization.

Collider physics often result in distributions referred to as jets. Dr. Tasissa and his team used "Earth Mover's Distance" and other mathematical tools to study the shape of jets. "It is interesting for me to see how mathematics can be applied to study these fundamental problems answering fundamental equations in physics, not only at the level of formulating new ideas, which is, in this particular case, a notion of distance, but also how the importance of designing fast optimization algorithms to be able to actually compute these distances," says Dr. Tasissa.

Dr. Outi Tervo of Greenland Institute for Natural Resources, shares how mathematics helps recommend speed limits for marine vessels, which benefits narwhals and Inuit culture.

Narwhals "can only be found in the Arctic," said Outi Tervo, a senior scientist at GINR. "These species are going to be threatened by climate change more than other species that can live in a bigger geographical area." The collaboration has already lobbied on behalf of the narwhals to reduce the level of sea traffic in their habitat, after using mathematical analysis to identify how noise from passing boats changes the narwhals' foraging behavior.

Dr. Valentina Wheeler of University of Wollongong, Australia, shares how her work influences efforts to understand wildfires and red blood cells.

In Australia, where bushfires are a concern year-round, researchers have long tried to model these wildfires, hoping to learn information that can help with firefighting policy. Mathematician Valentina Wheeler and colleagues began studying a particularly dangerous phenomenon: When two wildfires meet, they create a new, V-shaped fire whose pointed tip races along to catch up with the two branches of the V, moving faster than either of the fires alone. This is exactly what happens in a mathematical process known as mean curvature flow. Mean curvature flow is a process in which a shape smooths out its boundaries over time. Just as with wildfires, pointed corners and sharp bumps will change the fastest.

Dr. Stan Wagon of Macalester College discusses the mathematics behind rolling a square smoothly.

In 1997, inspired by a square wheel exhibit at The Exploratorium museum in San Francsico, Dr. Stan Wagon enlisted his neighbor Loren Kellen in building a square-wheeled tricycle and accompanying catenary track. For years, you could ride the tricycle at Macalester College in St. Paul, Minnesota. The National Museum of Mathematics in New York now also has square-wheeled tricycles that can be ridden around a circular track. And more recently, the impressive Cody Dock Rolling Bridge was built using rolling square mathematics by Thomas Randall-Page in London.

Dr. Rekha Thomas from the University of Washington discusses three-dimensional image reconstructions from two-dimensional photos.

The mathematics of image reconstruction is both simpler and more abstract than it seems. To reconstruct a 3D model based on photographic data, researchers and algorithms must solve a set of polynomial equations. Some solutions to these equations work mathematically, but correspond to an unrealistic scenario — for instance, a camera that took a photo backwards. Additional constraints help ensure this doesn't happen. Researchers are now investigating the mathematical structures underlying image reconstruction, and stumbling over unexpected links with geometry and algebra.

Imelda Flores Vazquez from Econometrica, Inc. explains how economists use mathematics to evaluate the efficacy of health care policies.

When a hospital or government wants to adjust their health policies — for instance, by encouraging more frequent screenings for certain diseases — how do they know whether their program will work or not? If the service has already been implemented elsewhere, researchers can use that data to estimate its effects. But if the idea is brand-new, or has only been used in very different settings, then it's harder to predict how well the new program will work. Luckily, a tool called a microsimulation can help researchers make an educated guess.

Stacey Finley from University of Southern California discusses how mathematical models support the research of cancer biology.

Cancer research is a crucial job, but a difficult one. Tumors growing inside the human body are affected by all kinds of factors. These conditions are difficult (if not impossible) to recreate in the lab, and using real patients as subjects can be painful and invasive. Mathematical models give cancer researchers the ability to run experiments virtually, testing the effects of any number of factors on tumor growth and other processes — all with far less money and time than an experiment on human subjects or in the lab would use.