May 04, 2025
Weather Forecasting 10,000x Faster—Without the Supercomputer
An interview with Anima Anandkumar, Founder of Caltech
Founder Focused
Just months before Caltech professor Anima Anandkumar released her revolutionary weather prediction model, respected meteorologists published a paper declaring that AI wouldn't be ready to replace traditional forecasting for at least a decade.
They were spectacularly wrong. Anandkumar's neural operators didn't just match traditional weather models—they delivered the same accuracy while running tens of thousands of times faster, turning what required massive supercomputers into something that could run on a gaming PC.
As the Bren Professor at Caltech leading the AI and Science Lab, Anandkumar has spent her career tackling problems others deemed impossible. In this interview, she reveals why curiosity—not technical skills—will be the ultimate differentiator in an AI-dominated world, and how students can develop the mindset to thrive alongside artificial intelligence rather than compete against it.
Watch the full interview now on EO's YouTube channel! Below is the complete transcription of the interview. Minor edits have been made for clarity and readability.
Key Highlights:
"A lot of these AI tools are getting better, but that only means they can do a certain set of instructions which are seen in data. You still need to provide AI what to do, right? You still need to be able to describe it, and that ability to describe what are the tasks AI should do, what are the programs to be written, is still important."
"I think one job that will not be replaced by AI is the ability to be curious and go after hard problems."
"So for young people, my advice is not to be afraid of AI or worry what skills to learn that AI may replace them with, but really be in that path of curiosity."
"I think one job that will not be replaced by AI is the ability to be curious and go after hard problems."
"So for young people, my advice is not to be afraid of AI or worry what skills to learn that AI may replace them with, but really be in that path of curiosity."
The Curiosity Revolution
Tell us about yourself and your work at Caltech.
Anima Anandkumar: I'm Anima Anandkumar. I'm Bren Professor at Caltech. I've been a professor at Caltech for about 8 years now, and during that time I also had stints in industry. I was principal scientist at Amazon Web Services.
So at Caltech I lead the AI and science lab, which means working on some of the hardest challenges we see in science and engineering, and how we can not only use existing AI methods to solve them, but really develop new ones.
I think one job that will not be replaced by AI is the ability to be curious and go after hard problems.
So at Caltech I lead the AI and science lab, which means working on some of the hardest challenges we see in science and engineering, and how we can not only use existing AI methods to solve them, but really develop new ones.
I think one job that will not be replaced by AI is the ability to be curious and go after hard problems.
How do you cultivate curiosity in your students?
Anima Anandkumar: For a lot of students, there is a strong motivation to just conform and go ahead, right? The number one thing I would ask is to question everything, think critically. I always begin the classes by asking questions, not writing down math equations, not going into the details, but just intuitively based on everything that you've seen, you've done, what do you think?
I ask them simple cases, for instance, if you were to design for a fire alarm, when should it say that there is fire or not? That's something you can change, right? You can put a threshold in what level of smoke or when does it think it's smoky, and that's a very practical question.
So if you just had fire alarms every day, we'd be just out and it would be useless. We would not have a functional office space, but on the other hand, if we never set fire, that would be bad too. So how to balance this and how to model how noisy this sensor could be.
I ask them simple cases, for instance, if you were to design for a fire alarm, when should it say that there is fire or not? That's something you can change, right? You can put a threshold in what level of smoke or when does it think it's smoky, and that's a very practical question.
So if you just had fire alarms every day, we'd be just out and it would be useless. We would not have a functional office space, but on the other hand, if we never set fire, that would be bad too. So how to balance this and how to model how noisy this sensor could be.
What do you see when students approach these problems?
Anima Anandkumar: Sometimes I see with students who may not have the mathematical training, but they're very intuitive and practical. They may be like, 'Oh, I would go and measure how noisy it is, or I would show different levels of smoke, have like candles of different sizes, and go and test it.'
Many times people already may be somewhat aware of this, so they have some intuitive ideas, so that already is a good starting point, and sometimes they are maybe really wrong. They have an intuition, but that's a wrong intuition, which is still OK because intuitions are not always the only answer, right?
So I think a lot of it comes by asking questions and now you can use these AI tools to get answers very quickly and also verify them. A lot of it comes from being just like curious or interested, and that could be one specific topic and if somebody is interested in music, they can delve deeper into that if somebody is interested in art, so it just has to, the spark has to come from within.
Many times people already may be somewhat aware of this, so they have some intuitive ideas, so that already is a good starting point, and sometimes they are maybe really wrong. They have an intuition, but that's a wrong intuition, which is still OK because intuitions are not always the only answer, right?
So I think a lot of it comes by asking questions and now you can use these AI tools to get answers very quickly and also verify them. A lot of it comes from being just like curious or interested, and that could be one specific topic and if somebody is interested in music, they can delve deeper into that if somebody is interested in art, so it just has to, the spark has to come from within.
Building Mental Maps from Childhood
What drives your motivation to tackle the hardest challenges?
Anima Anandkumar: I'm always motivated by the hardest challenges, you know, I want to know what is difficult, but also why it's difficult, right? And even though I may not be able to solve it today, how do we build up the foundations to get there?
Growing up in Mysore as a kid, I loved just solving math problems, you know, going to my parents' factory. I was reading up their program manuals. I was learning how they could be programmed, and unlike in other computer programs here, if something was wrong, that would lead to like physical failure. Parts being like not manufactured correctly, then I'm like, oh, but how does it go into the computer and how does the computer tell the machine what to do?
Growing up in Mysore as a kid, I loved just solving math problems, you know, going to my parents' factory. I was reading up their program manuals. I was learning how they could be programmed, and unlike in other computer programs here, if something was wrong, that would lead to like physical failure. Parts being like not manufactured correctly, then I'm like, oh, but how does it go into the computer and how does the computer tell the machine what to do?
How did those early experiences shape your approach to learning?
Anima Anandkumar: So there were always gaps because as a kid you don't know everything, but to me it was like observing and then understanding what the gap is, and even if I didn't get an immediate answer, I would remember that there is a gap, and then later when I was introduced to those topics, I was, in my mind, I was like, oh, that's what it relates to.
So somehow I had built up that mental map and I had put places of where things I knew and things I didn't know, and I kept kind of growing that in my mind as I progressed through the years.
So somehow I had built up that mental map and I had put places of where things I knew and things I didn't know, and I kept kind of growing that in my mind as I progressed through the years.
When Science Fiction Became Reality
How has AI evolved since you were growing up?
Anima Anandkumar: So when I was growing up, AI was considered science fiction. Naturally, there were lots of science fiction movies where I saw and was fascinated, but that's not something people thought were practical. Now since I was in middle and high school, and now it's almost 30 years, right? It is a long time, but the amount of progress that has happened is also so astounding in so many ways.
So since I joined Caltech in 2017, the timing just felt right to use AI as a tool and a framework to solve some of the hardest problems which until that point was not considered practical.
So since I joined Caltech in 2017, the timing just felt right to use AI as a tool and a framework to solve some of the hardest problems which until that point was not considered practical.
How did you identify which problems to tackle with AI?
Anima Anandkumar: So after I joined Caltech and wanted to explore problems of the intersection of AI and science, and I was talking to everybody on campus. I was like, OK, do you need compute? What do you need it for? Let me understand the problems that you're tackling.
And you know, I can't possibly go and solve each one of them problems myself, right? My question then was, are there general tools we can develop that could impact so many different areas? And that again put me back to mathematical foundations.
So because a lot of these different real world phenomena are modeled by partial differential equations. So now can we design AI that can solve this and do it much faster, do it much better than and what is currently being done with traditional simulations, and to do that, we develop neural operators.
And you know, I can't possibly go and solve each one of them problems myself, right? My question then was, are there general tools we can develop that could impact so many different areas? And that again put me back to mathematical foundations.
So because a lot of these different real world phenomena are modeled by partial differential equations. So now can we design AI that can solve this and do it much faster, do it much better than and what is currently being done with traditional simulations, and to do that, we develop neural operators.
Breaking Weather Prediction Barriers
Tell us about neural operators and how they work.
Anima Anandkumar: We've invented an AI technology called neural operators that is trained to understand physical behaviors, not just high level reasoning with text. So think of a hurricane. The hurricane, if you will just eyeball it, can you tell where it's going to go? You know, most humans cannot, right? It's a superhuman scale to predict where hurricanes are going to go and to do that we need fine scale information and fine scale modeling.
So this cannot be just a coarse scale image like the image of a cat where even if it gets blurry, you know it's a cat. The same techniques don't work for phenomena like hurricanes.
So this cannot be just a coarse scale image like the image of a cat where even if it gets blurry, you know it's a cat. The same techniques don't work for phenomena like hurricanes.
Why did you focus on weather modeling as a practical application?
Anima Anandkumar: Once we develop tools, and then the next natural question is what were practical use cases that involved. And the weather models was a natural one because it's widely used, it has huge implications on our lives, especially if extreme weather events like hurricanes, if we get them right, that has the potential to save human lives and also bring down economic costs.
So I was motivated by how it can be helpful to people, but I was also motivated by that being considered a very hard technical challenge.
So I was motivated by how it can be helpful to people, but I was also motivated by that being considered a very hard technical challenge.
What was the reaction when you released your weather prediction model?
Anima Anandkumar: In fact, just a few months before we released our model, there were a group of very well respected weather scientists who published in the Royal Society Journal thinking they were under the impression that AI would take more than a decade or even longer to replace traditional ways to forecast weather, and they felt AI was just not ready. This problem is way too difficult, and we released this, and it just took everybody by surprise.
It was not only accurate. It was tens of thousands of times faster. So what would take a big supercomputer for traditional weather models can now be run on a local gaming PC with just a consumer GPU.
So that's the beauty of machine learning as a field. We are not always stopped by what others think is difficult. As long as we can get the data and we can design the methods, we can just go and try it.
It was not only accurate. It was tens of thousands of times faster. So what would take a big supercomputer for traditional weather models can now be run on a local gaming PC with just a consumer GPU.
So that's the beauty of machine learning as a field. We are not always stopped by what others think is difficult. As long as we can get the data and we can design the methods, we can just go and try it.
The Future of Human Agency in AI
What's your mission going forward?
Anima Anandkumar: So my mission is to constantly be curious and learning and not assume that any problem is easy. I can't imagine a world where scientists will be out of jobs, because the definition of a scientist is somebody who tackles open problems, right? So there are harder and harder problems to tackle.
You know, if you want to look at the deep secrets of our universe, go down to the smallest scale and understand at the atomic and subatomic level how matter is constructed to, of course, level of galaxy and beyond and understand how the universe is put together. There are still lots of open challenges.
You know, if you want to look at the deep secrets of our universe, go down to the smallest scale and understand at the atomic and subatomic level how matter is constructed to, of course, level of galaxy and beyond and understand how the universe is put together. There are still lots of open challenges.
How do you see AI's role in scientific discovery?
Anima Anandkumar: Many other teams such as Google DeepMind focus on what is known as an AI scientist, meaning AI that comes up with new ideas, but so much of scientific progress is not limited by the lack of new ideas, right? Lots of people have lots of ideas, but the bottleneck is going to the lab or going to the real world and testing them. That is slow, that is expensive.
So my focus is how we can replace those lab experiments. Can we come up with AI that inherently understands the physics better so we can completely avoid the lab experiments or maybe only do it to do the final testing. And so with that focus and with that physical knowledge, we can come up with AI designed answers that we can go directly to the real world and minimize this need for testing.
So my focus is how we can replace those lab experiments. Can we come up with AI that inherently understands the physics better so we can completely avoid the lab experiments or maybe only do it to do the final testing. And so with that focus and with that physical knowledge, we can come up with AI designed answers that we can go directly to the real world and minimize this need for testing.
What does human agency mean to you in the context of AI?
Anima Anandkumar: To me, human agency is driving AI to do something that you want it to be done. You have the agency as a human to decide what tasks AI does, and then you're evaluating and you're in charge, right? So you go and verify whether what AI is saying is true or not, and then over time provide that feedback to AI and make it get better.
What's your advice for young people navigating an AI world?
Anima Anandkumar: AI is a tool, it can both help curiosity but also kill it depending on how it's used, right? So for young people, my advice is not to be afraid of AI or worry what skills to learn that AI may replace them with, but really be in that path of curiosity, right? Use AI as a tool to drive that curiosity, learn new skills.
New knowledge and you can do that in a much more interactive way, even when it comes to writing computer programs, you know, a lot of these AI tools are getting better, but that only means they can do a certain set of instructions which are seen in data. You still need to provide AI what to do, right? You still need to be able to describe it and that ability to describe what are the tasks AI should do, what are kind of high level understanding of what AI is doing when it writes these computer programs is still important because a bad programmer who is not better than AI will be replaced. But a great programmer who can assess what AI is doing, make fixes, ensure those programs are written well, will be in more demand than ever.
New knowledge and you can do that in a much more interactive way, even when it comes to writing computer programs, you know, a lot of these AI tools are getting better, but that only means they can do a certain set of instructions which are seen in data. You still need to provide AI what to do, right? You still need to be able to describe it and that ability to describe what are the tasks AI should do, what are kind of high level understanding of what AI is doing when it writes these computer programs is still important because a bad programmer who is not better than AI will be replaced. But a great programmer who can assess what AI is doing, make fixes, ensure those programs are written well, will be in more demand than ever.
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