Chapter 1: What is the main topic discussed in this episode?
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Chapter 2: What is the main topic discussed in this episode?
Today, how AI is transforming scientific research, whether it's weather forecasting or drug discovery. Professor and AI innovator, Anima Anandkumar, sheds light on the way scientists can use AI to capture a whole range of physical phenomena and why this could be crucial in the fight against climate change. After a short break. And now, our TED Talk of the day.
I grew up with parents who were engineers. They were among the first to bring computerized manufacturing to my hometown in India. Growing up as a young girl, I remember being fascinated how these computer programs didn't just reside within a computer, but touched the physical world and produced these beautiful and precise metal parts.
Over the last two decades, as I pursued AI research, this memory continued to inspire me to connect the physical and digital worlds together. I am working on AI that transforms the way we do science and engineering. Scientific research and engineering design currently involves a lot of trial and error. Many long hours are spent in the lab doing experiments.
So it's not just the great ideas that propel science forward. You need these experiments to validate findings and spark new ideas. How can language models help here? What if I ask Chad Chippity to come up with a better design of an aircraft wing or a drone that flies under turbulent winds?
Chapter 3: How can AI bridge the gap between digital and physical worlds?
It may suggest something. It may even draw something. But how do we know this is any good? We don't. Language models hallucinate because they have no physical grounding. While language models may help generate new ideas, they cannot attack the hard part of science, which is simulating the necessary physics to replace the NAB experiments.
In order to model scientific and physical phenomena, text alone is not sufficient. To get to AI with universal physical understanding, we need to train it on the data of the world we observe, and not just that, also its hidden details.
from the intricacies of quantum chemistry that happen at the smallest level to molecules and proteins that influence how all biological processes work to ocean currents and clouds that happen at planetary scales and beyond. We need AI that can capture this whole range of physical phenomena. we need AI that can really zoom into the fine details in order to simulate these phenomena accurately.
To capture the cloud movements and predict how clouds move and change in our atmosphere, we need to be able to zoom into the fine details of the turbulent fluid flow. Standard deep learning uses a fixed number of pixels. So if you zoom in, it gets blurry, and not all the details are captured.
We invented an AI technology called neural operators that represents the data as continuous functions or shapes and allows us to zoom in indefinitely to any resolution or scale. Neural operators allow us to trade on data at multiple scales or resolutions.
and also allows us to incorporate the knowledge of mathematical equations to fill in the finer details when only limited resolution data is available. Such learning at multiple scales is essential for scientific understanding, and neural operators enable this.
With neural operators, we can simulate physical phenomena such as fluid dynamics as much as a million times faster than traditional simulations. Last year, we used neural operators to invent a better medical catheter. A medical catheter is a tube that draws fluids out of the human body. Unfortunately, the bacteria tend to swim upstream against the fluid flow and infect the human.
In fact, annually, there's more than half a million cases of such healthcare-related infections, and this is one of the leading causes. Last year, we used neural operators to change the inside of the catheter from smooth to ridged. With ridges, now we have vortices created as the fluid flows, and we can hope to stop the bacteria from swimming upstream because of these vortices.
But to get this correct, we need the shape of the ridges to be exactly right. In the past, this would have been done by trial and error. Design a version of the catheter, build it out, take it to the lab, observe a hypothesis if something went wrong, rinse and repeat and redesign again. But instead, we taught AI the behavior of the fluid flow inside the tube.
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