Mediaboss Marketing

Anima Anandkumar, Bren Professor of computing at the California Institute of Technology and senior director of machine learning research at Nvidia, has a bone to pick with the matrix. Her misgivings are not about the sci-fi movies, but about mathematical matrices grids of numbers or variables used throughout computer science. While researchers typically use matrices to study the relationships and patterns hiding within large sets of data, these tools are best suited for two-way relationships. Complicated processes like social dynamics, on the other hand, involve higher-order interactions.

Luckily, Anandkumar has long savored such challenges. When she recalls Ugadi, a new years festival she celebrated as a child in Mysore (now Mysuru), India, two flavors stand out: jaggery, an unrefined sugar representing lifes sweetness, and neem, bitter blossoms representing lifes setbacks and difficulties. Its one of the most bitter things you can think about, she said.

Shed typically load up on the neem, she said. I want challenges.

This appetite for effort propelled her to study electrical engineering at the Indian Institute of Technology in Madras. She earned her doctorate at Cornell University and was a postdoc at the Massachusetts Institute of Technology. She then started her own group as an assistant professor at the University of California, Irvine, focusing on machine learning, a subset of artificial intelligence in which a computer can gain knowledge without explicit programming. At Irvine, Anandkumar dived into the world of topic modeling, a type of machine learning where a computer tries to glean important topics from data; one example would be an algorithm on Twitter that identifies hidden trends. But the connection between words is one of those higher-order interactions too subtle for matrix relationships: Words can have multiple meanings, multiple words can refer to the same topic, and language evolves so quickly that nothing stays settled for long.

This led Anandkumar to challenge AIs reliance on matrix methods. She deduced that to keep an algorithm observant enough to learn amid such chaos, researchers must design it to grasp the algebra of higher dimensions. So she turned to what had long been an underutilized tool in algebra called the tensor. Tensors are like matrices, but they can extend to any dimension, going beyond a matrixs two dimensions of rows and columns. As a result, tensors are more general tools, making them less susceptible to overfitting when models match training data closely but cant accommodate new data. For example, if you enjoy many music genres but only stream jazz songs, your streaming platforms AI could learn to predict which jazz songs youd enjoy, but its R&B predictions would be baseless. Anandkumar believes tensors make machine learning more adaptable.

Its not the only challenge shes embraced. Anandkumar is a mentor and an advocate for changes to the systems that push marginalized groups out of the field. In 2018, she organized a petition to change the name of her fields annual Neural Information Processing Systems conference from a direct acronym to NeurIPS. The conference board rejected the petition that October. But Anandkumar and her peers refused to let up, and weeks later the board reversed course.

Quanta spoke with Anandkumar at her office in Pasadena about her upbringing, tensors and the ethical challenges facing AI. The interview has been condensed and edited for clarity.

In the early 1990s they were among the first to bring programmable manufacturing machines into Mysore. At that time it was seen as something odd: We can hire human operators to do this, so what is the need for automation? My parents saw that there can be huge efficiencies, and they can do it a lot faster compared to human-operated machines.

Yeah. And programming. I would see the green screen where my dad would write the program, and that would move the turret and the tools. It was just really fascinating to see understanding geometry, understanding how the tool should move. You see the engineering side of how such a massive machine can do this.

My mom was a pioneer in a sense. She was one of the first in her community and family background to take up engineering. Many other relatives advised my grandfather not to send her, saying she may not get married easily. My grandfather hesitated. Thats when my mom went on a hunger strike for three days.

As a result, I never saw it as something weird for women to be interested in engineering. My mother inculcated in us that appreciation of math and sciences early on. Having that be just a natural part of who I am from early childhood went a long way. If my mom ever saw sexism, she would point it out and say, No, dont accept this. That really helped.

The rest is here:
The AI Researcher Giving Her Field Its Bitter Medicine - Quanta Magazine