과학 및 엔지니어링 과제 해결을 위한 AI의 역할

신경 연산자를 통해 AI는 물리적 현상을 시뮬레이션하여 과학 연구와 엔지니어링 설계에 혁신을 가져올 수 있습니다.

### 과학적 발견과 엔지니어링 설계를 위한 AI의 발전
* 🔬 AI 연구자인 일리야 수츠케버 박사는 **물리적 세계와 디지털 세계를 연결하여 과학 연구와 엔지니어링 설계를 혁신하는 AI**를 연구하고 있습니다.
* 🧠 ChatGPT와 같은 언어 모델은 **아이디어를 생성**할 수는 있지만 물리적 근거가 부족하고 **복잡한 물리적 현상을 시뮬레이션**할 수 없습니다.
* 🌐 과학적, 물리적 현상을 정확하게 모델링하려면 **AI를 실제 데이터로 학습**시키고 **숨겨진 세부 사항을 통합**해야 합니다.
* 🌊 서츠케버 박사팀이 개발한 AI 기술인 **뉴럴 연산자는 데이터를 연속 함수로 표현**하여 다양한 규모로 무한 확대 및 시뮬레이션을 가능하게 합니다.
* 💉 신경 수술은 보다 효과적인 의료용 카테터를 설계하는 데 사용되어 **박테리아 오염을 100배 이상 줄였습니다.**
* 🌪 신경 연산자 기반의 날씨 모델인 **FourCastNet은 기존 모델보다 수만 배 빠르며 경우에 따라 더 정확**합니다.
* 🔥 신경 연산자는 핵융합로에서 플라즈마를 시뮬레이션하는 데도 적용되어 **잠재적으로 핵융합을 실행 가능한 청정 에너지원**으로 만들 수 있습니다.
* 🤖 수츠케버 박사는 모든 물리적 현상을 시뮬레이션하고 다양한 영역에서 새로운 솔루션을 설계할 수 있는 **보편적인 물리적 이해를 갖춘 범용 AI 모델**을 구축하는 것을 구상하고 있습니다.

I grew up with parents who are 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 ChatGPT to come up with a better design of an aircraft wing, or a drone that flies on a turbulent wind?

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 these 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 train 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 is 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, and with it, our neural operator model
was able to directly propose

an optimized design. We 3D-printed the design
only once to verify that it worked. In the video, you're seeing our catheter
being tested in the lab. The bacteria are not able
to swim upstream, are instead being pushed out
with the fluid flow. In fact, we measured the reduction
in bacterial contamination by more than 100-fold. So in this case, the neural operators
were specialized to understand fluid flow in a tube. What other applications can AI tackle and help us solve such pressing problems? Can deep learning beat
numerical weather models? A group of leading weather scientists
asked this question in February 2021, in a Royal Society publication. They felt that AI
was still in its infancy, and that a number of fundamental
breakthroughs would be needed for AI to become competitive
with traditional weather models, and that would take years or even decades. Exactly a year later, we released FourCastNet.

Using neural operators, we built the first fully AI-based weather model that is high resolution and is tens of thousands of times faster than traditional weather models. What used to take a big supercomputer can now run on a gaming PC that you may have at home. This model is also running at the European Centre for Medium-Range Weather Forecasting, one of the premier weather agencies of the world. And our AI model is not just tens of thousands of times faster than traditional models. It's also more accurate in many cases. On September 16 last year, Hurricane Lee hit the coast of Nova Scotia, Canada. A full ten days earlier, our FourCastNet model correctly predicted that the hurricane would make landfall, but the traditional weather model predicted the hurricane would skip the coast. Only five days later, on September 11, did the traditional weather model correct its forecast to predict landfall. Extreme weather events such as Hurricane Lee will only increase further unless we take action.

on climate change, such as finding new,
clean sources of energy. Nuclear fusion is one of them. But unfortunately, there are
still big challenges with it. The fusion reactor heats up the plasma to extremely high temperatures
to get fusion started. And sometimes this hot plasma
can escape confinement and can damage the reactor. We train neural operators
to simulate and predict the evolution of plasma
inside the reactor. And with it, we can use this to predict
disruptions before they occur and take corrective action
in the real world. We are enabling the possibility
of nuclear fusion becoming a reality. So neural operators and AI broadly are enabling us to tackle
hard scientific challenges such as climate change
and nuclear fusion. To me, this is just the beginning. So far, these AI models are limited
to the narrow domains they're trained on. What if you had an AI model that could solve all
and any scientific problem? From designing better
drones, aircraft, rockets,

and even better drugs
and medical devices? Such an AI model would greatly
benefit humanity. This is what we are working on. We are building a generalist AI model
with emergent capabilities that can simulate any physical phenomena and generate novel designs
that were previously out of reach. This is how we scale up neural operators to enable general intelligence
with universal physical understanding. Thank you. (Applause)

AI That Connects the Digital and Physical Worlds | Anima Anandkumar | TED

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AI가 물리적 세계와 디지털 세계를 연결하는 데 미치는 영향

AI 연구는 물리적 세계와 디지털 세계 사이의 연결에서 영감을 받아 광범위한 실험의 필요성을 줄여 과학과 공학을 혁신하는 것을 목표로 하고 있습니다.

언어 모델은 아이디어를 생성할 수는 있지만, 현재 물리적 기반과 필요한 물리학을 시뮬레이션할 수 있는 기능이 부족하여 과학의 어려운 부분을 다루기에는 불충분합니다.

광범위한 물리적 현상을 진정으로 포착하려면 양자 화학의 가장 작은 규모부터 해류와 구름의 움직임과 같은 행성 수준의 과정에 이르기까지 관찰된 세계의 숨겨진 세부 사항을 AI에 학습시키고 통합해야 합니다.

유체 역학 및 의료 발명을 위한 AI 기술의 발전

신경 연산자라고 하는 AI 기술을 사용하면 데이터를 연속 함수로 표현하여 난류 유체 흐름의 세부 사항을 확대하여 다양한 규모로 훈련하고 기존 방법보다 훨씬 빠르게 물리적 현상을 시뮬레이션할 수 있습니다.

신경 연산자는 더 나은 의료용 카테터의 디자인을 매끄러운 형태에서 융기된 형태로 변경하여 유체 흐름에 소용돌이를 일으켜 박테리아가 상류로 헤엄치는 것을 방지하고, AI 모델이 직접 융기 모양을 제안할 수 있도록 하여 더 나은 카테터를 개발하는 데 사용되었습니다.

이 기술은 기존의 시행착오 방식을 대체하여 유체 흐름의 거동을 학습하는 AI가 직접 설계 제안을 하는 방식으로 카테터 설계 프로세스에 혁신을 가져왔습니다.

신경 연산자를 사용하여 튜브의 박테리아 오염을 100배 이상 줄임으로써 유체 흐름을 이해하는 데 있어 AI의 잠재력을 보여주었습니다.

AI 기반 기상 모델인 포캐스트넷은 기존 모델보다 수천 배 더 빠르고 정확해져 허리케인 리의 상륙을 훨씬 더 일찍 예측하는 데 성공했습니다.

AI는 일기 예보에 혁신을 일으킬 잠재력을 가지고 있으며, 높은 해상도와 향상된 정확도를 제공하여 기상이변으로 인해 점점 더 어려워지는 문제를 해결할 수 있습니다.

핵융합 발전과 그 너머를 위한 신경 연산자의 역할

핵융합은 유출된 플라즈마로 인한 잠재적 손상 등 중대한 도전에 직면해 있지만, 신경 연산자는 중단을 방지하기 위해 플라즈마 진화를 시뮬레이션하고 예측하도록 훈련받고 있습니다.

이러한 연산자는 AI와 함께 다양한 과학적 문제를 해결하고 새로운 솔루션을 설계하기 위해 개발되고 있으며, 보편적인 물리적 이해를 위한 일반 지능을 달성하는 것을 목표로 하고 있습니다.