(Clockwise from top left) Kate Crawford, senior principal researcher at Microsoft Research; Fei-Fei Li, founder of World Labs and professor at Stanford; Daphne Koller, co-founder and CEO of Insitro; Shafi Goldwasser, Turing Award winner, pioneering cryptographer. Image: Getty Images
“Nature has created this virtuous cycle of seeing and doing, powered by spatial intelligence … and if we want to advance AI beyond its current capabilities, we want AI that can do more than see and talk. We want AI that can do.”
Fei-Fei Li, 48, was explaining “spatial intelligence” in a TED talk last year, from which the comment is cited. It’s intelligence that’s innate to humans and animals, and Li and her colleagues are trying to teach it to AI.
Li is a pioneer in computer vision and often referred to as a “godmother of AI”. She is the co-director of the Stanford Human-Centered AI Institute, which focuses on ensuring AI technologies benefit humanity. She is known particularly for her contributions to deep learning and the creation of the ImageNet project.
Li co-founded the ImageNet project in 2009, which revolutionised the way machines understand and classify visual data. She is a professor of computer science at Stanford University and has also served as the director of the Stanford Artificial Intelligence Laboratory.
Her work focuses on advancing AI technologies that can understand and interact with the world in a human-like way. She is a passionate advocate for diversity in tech and has worked to ensure that AI is developed in a way that is inclusive and benefits all of humanity.
Li was named to Time Magazine’s 100 Most Influential People in AI in 2023. She was chief scientist of AI/ML at Google Cloud from 2017 to 2018.
More recently, along with three other co-founders, Li has been in the headlines as the founder of World Labs Technologies, an AI startup that came out of stealth in September 2024 and announced a $230 million funding from investors including a16z, Radical Ventures, Marc Benioff, Eric Schmidt and Geoffrey Hinton.
Daphne Koller, co-founder and CEO of Insitro
“Drug discovery in the past 50 years is a tale of glass half full and glass half empty. On the half-full side, we have transformative medicines that have made a very big difference to patients. On the half-empty side is the so-called Eroom’s Law, the reverse of Moore’s Law, where the cost of drug discovery has grown exponentially year on year without an increase in new drug output.”
That was Daphne Koller, 56, in an interview with McKinsey & Co. in 2022. Time magazine counted her among its 100 most influential names in AI in 2024. Daphne Koller is the co-founder and CEO of Insitro, a biotechnology company that uses machine learning and AI to accelerate drug discovery. San Francisco-based Insitro, founded in 2018, integrates data science, bioengineering, and automation to develop new therapies more efficiently than traditional pharmaceutical approaches.
Koller’s work at Insitro is seen as a pathbreaking shift in drug discovery, using AI to analyse biological data, identify disease targets, and optimise drug development. The company has partnered with major pharmaceutical firms like Gilead and Bristol-Myers Squibb.
Insitro, backed by investors including a16z, Google Ventures, Temasek and SoftBank Vision Fund, has raised $643 million, according to private markets intelligence provider Tracxn. Most recently, Insitro and Koller made the headlines for finding a drug target that could lead to a medicine to treat ALS or Amyotrophic Lateral Sclerosis, a disease that causes loss of muscle control.
Previously, Koller co-founded Coursera, one of the world’s largest online learning platforms, and she’s also known for her work as a professor of computer science at Stanford University. Before Insitro, she was the chief computing officer at Calico Labs, a Google-backed company focused on aging and longevity research.
Manuela Veloso, head of AI research, JPMorgan Chase
“People are not yet fully aware… that there are tons of data and that there is no way to benefit from it without AI and machine learning. Education has to move to the concept of data thinking—it has to be pervasive,” Manuela M Veloso, 67, said in a study conducted by The Economist Intelligence Unit, commissioned by Google on the impact of machine learning. That was in 2017.
Veloso is Head of JP Morgan Chase AI Research and Herbert A Simon University Professor Emerita at Carnegie Mellon University, where she was previously faculty in the Computer Science Department and head of the Machine Learning Department, according to her bio on the US banking giant’s website.
Veloso is a prominent researcher and expert in the field of artificial intelligence (AI) and robotics. She is known for her work on machine learning, particularly in areas such as robotic perception, multi-agent systems, and AI planning. Veloso’s research has contributed to the development of AI systems that enable robots to interact intelligently with their environment, collaborate with other agents, and make decisions based on complex data inputs.
She’s also known for her work with robot soccer—as founder of the RoboCup competition—where teams of robots play soccer against each other.
Kate Crawford, senior principal researcher at Microsoft Research
“There is no quick technical fix to bias. It’s really tempting to want to think that there’s going to be some type of silver bullet solution, that we can just tweak our algorithms or use different sorts of training datasets or try to boost signal in particular ways. The problem of this is that it really doesn’t look to the deep social and historical issues that human data is made from.”
Kate Crawford, 49, who said that in 2018 in a Microsoft podcast interview, is one of the leading scholars on the social implications of AI, including issues such as ethics, bias, and fairness. She has written extensively on how AI impacts society, and her work emphasises the importance of ensuring these technologies are developed and deployed responsibly.
Currently, she is senior principal researcher at Microsoft Research New York, and research professor at USC Annenberg School for Communication and Journalism. Crawford is known for her work studying the societal and political effects of AI systems. Her research explores how AI technologies shape human experiences and influence global systems, with a focus on bias, fairness, and accountability.
She is also a co-founder of the AI Now Institute, which advocates for a human-centered approach to AI development and policy. Crawford wrote an influential book, Atlas of AI: Mapping the Dark Side of Artificial Intelligence, which delves into the hidden environmental and human costs of AI.
Shafi Goldwasser, Turing Award winner, pioneering cryptographer
“If society is going to be controlled by these big data-driven automated systems, then making sure that it’s done properly will have direct societal implications.”
Shafrira (Shafi) Goldwasser, 65, said that in an interview with the Indian unit of Association of Computing Machinery, during a visit to India. Goldwasser, RSA Professor of Electrical Engineering and Computer Science at MIT, is a pioneering cryptographer who has made groundbreaking contributions to the fields of cryptography, data security and AI.
Goldwasser is the winner of the Turing Award (2012)—often called the Nobel Prize for computer science. She is known for her work on zero-knowledge proofs (ZKPs), a concept that is integral to the security of AI systems. Along with Silvio Micali, she co-invented ZKPs, a cryptographic technique that allows one to prove that something is true without revealing any additional information. ZKPs are seen as crucial in secure data verification and authentication.
“Goldwasser’s work is the foundation upon which our most sensitive data, bank transactions, encrypted communications, digital identities, rests,” Colin WP Lewis, a professor of AI, data science and behavioural economics at University of Warsaw, writes in his newsletter, The One Percent Rule. “The encryption methods she helped pioneer ensure that online banking transactions remain secure from eavesdroppers, that journalists can communicate safely in oppressive regimes, and that everyday users can browse the internet with confidence that their personal information is protected.”
Goldwasser also co-invented a technique called probabilistic encryption, often considered the gold standard for data encryption security. This uses randomness to ensure that encrypted data remains secure even if an attacker has vast amounts of compute power.