AI Sees Our World Through a Child’s Eyes

Our adult brains are littered with shortcomings and biases. How can quantum computing and AI help us transcend them to solve problems?

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When it comes to chemistry, the processes we use at the bench are still very similar to the techniques that were used in the 18th century. Quantum mechanic Alán Aspuru-Guzik thinks that innovation could be exponentially faster if we used quantum computers and artificial intelligence (AI) to help overcome human biases.

“It turns out that humans, because they have preconceptions, are usually more biased than AI,” says Aspuru-Guzik, professor of chemistry and computer science at the University of Toronto and principal investigator of the Matter Lab.

“AI is more like a child that is discovering things on its own. And what is really cool about that is if you ask your child, for example, to find something in the room, they will find you a pen under the table or stuff like that that you would not usually do as an adult, saying, ‘no I must have left my pen on this table here’, and not look under it.”

Beyond looking in unexpected places, computers can objectively think about complex multi-dimensional data sets and find interconnected patterns in ways that humans can’t.

“Humans are good at imagining things in three dimensions,” says University of Toronto undergraduate student Akshat Nigam. “Anything beyond that, let’s say four dimensions, and we completely collapse there.”

Considering the vast data sets that are now common, and the number of different compounds that can be present in chemical reactions, it can be nearly impossible for the human mind to interpret so many relationships. Computers don’t share these limitations.

“Machines have an ability to compress really high-dimensional spaces into interpretable models for us,” adds Nigam. “So imagine a hundred-dimensional space being compressed to three dimensions. Some might lead us to pretty interesting answers.”

Quantum computers could disrupt how things have been done in chemistry labs since the 18th century, says postdoctoral researcher Jenya Vestfrid. These tools will accelerate bigger discoveries.

“If we can be helped by smart machines, the timing will be much faster, the output will be bigger,” says Vestfrid. “I do believe that it will promote chemistry to the next evolution step.”

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Alán Aspuru-Guzik’s research lies at the interface of computer science with chemistry and physics. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of materials acceleration platforms.

These “self-driving laboratories” promise to accelerate the rate of scientific discovery, with applications to clean energy and optoelectronic materials. Aspuru-Guzik also develops quantum computer algorithms for quantum machine learning and has pioneered quantum algorithms for the simulation of matter.

He is jointly appointed as a Professor of Chemistry and Computer Science at the University of Toronto. He is a faculty member of the Vector Institute for Artificial Intelligence. Previously, he was a full professor at Harvard University where he started his career in 2006.

Aspuru-Guzik is currently the Canada 150 Research Chair in Quantum Chemistry as well as a CIFAR AI Chair at the Vector Institute. Amongst other awards, he is a recipient of the Google Focused Award in Quantum Computing, the MIT Technology Review 35 under 35, and the Sloan and Camille and Henry Dreyfus Fellowships. He is also a fellow of the American Association of the Advancement of Science and the American Physical Society. He is a co-founder of Zapata Computing and Kebotix, two early-stage ventures in quantum computing and self-driving laboratories respectively.

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