Caltech AI Professor: The One Skill | AI Can't Replace | Anima Anandkumar

Summary

In a world increasingly dominated by AI, Anima Anandkumar, an AI professor at Caltech, asserts that curiosity remains a uniquely human trait that AI cannot replicate. She encourages young people to embrace curiosity and hard problem-solving rather than fear displacement by AI. Anandkumar highlights her journey in AI, from academia to significant roles in industry, advocating for the application of AI in solving complex scientific challenges. She emphasizes the importance of critical thinking and intuitive approaches in education, allowing students to explore and pursue their passions. Anandkumar also shares groundbreaking work on AI models for weather prediction, showcasing AI's potential to revolutionize traditional fields. However, she stresses that human agency is vital in directing AI's tasks and ensuring the technology complements rather than stifles human curiosity.

Highlights

• Curiosity is a uniquely human trait that AI can't replicate. 🔍
• Embrace curiosity and hard problem-solving to thrive alongside AI. 🚀
• Anima's journey in AI spans academia and industry, driving innovation. 🌟
• Critical thinking is vital; students should question and explore their passions. 🎓
• Breakthrough AI models for weather prediction can save lives and reduce costs. 🌪️
• Human agency is crucial in directing AI's role and ensuring its efficacy. 🙌

Key Takeaways

• AI can't replace human curiosity! 🔍
• Stay curious and solve hard problems—AI can't do that for you! 🤯
• Critical thinking and questioning are essential in education. 🧠
• AI has transformed complex tasks like weather prediction! ⛈️
• Humans must guide AI and provide feedback—it can't do everything on its own! 🤖

Overview

In the ever-evolving landscape of AI, Anima Anandkumar, a distinguished professor at Caltech, underscores the enduring importance of human curiosity. She believes that curiosity and the pursuit of challenging problems are innate human characteristics that AI cannot replicate. Her advice to young learners is to embrace these traits, encouraging them to remain inquisitive and fearless in the face of AI advancements.

Anandkumar's illustrious career spans both academia and industry, with notable roles such as Principal Scientist at Amazon Web Services and AI Research Lead at Nvidia. At Caltech, she leads the AI and Science Lab, focusing on developing AI methods to tackle scientific challenges. Introducing AI models for complex tasks, such as weather prediction, illustrates her commitment to transforming traditional fields through AI.

Despite AI's capabilities, Anandkumar emphasizes the necessity of human guidance and critical thinking. She advocates for a shift in educational focus, empowering students to pursue their interests passionately. While AI can greatly enhance data-driven tasks, humans must maintain the ultimate oversight and direction, ensuring AI serves as a tool that aids in exploration and innovation.

Chapters

• 00:00 - 00:30: Introduction In the 'Introduction' chapter, the focus is on the advancements in AI tools. It emphasizes that although AI technologies are improving, they are limited to performing predefined instructions and rely heavily on human input for direction. The chapter stresses the ongoing importance of human abilities to articulate tasks and determine the programming of AI systems. It concludes with a perspective that inquisitiveness and problem-solving skills remain irreplaceable by AI, offering young people the advice to cultivate these attributes.
• 00:30 - 01:00: Curiosity and Career The chapter 'Curiosity and Career' emphasizes the importance of cultivating curiosity rather than fearing AI or worrying about which skills might be replaced by AI. Animant Kumar, a professor at Caltech with extensive experience in both academia and industry, encourages this perspective. Kumar, who has been at Caltech for 8 years and has held influential positions at Amazon Web Services and Nvidia, leads the AI and science lab at the institute.
• 01:00 - 02:00: AI in Science The chapter "AI in Science" discusses the application of AI in addressing complex challenges within scientific and engineering fields. It highlights not only the use of current AI technologies to tackle these issues but also the development of new methodologies. A key insight is that the innate human trait of curiosity and the pursuit of difficult problems is irreplaceable by AI.
• 02:00 - 03:00: Questioning and Intuition In the chapter 'Questioning and Intuition', the focus is on encouraging students to approach learning with a questioning mindset and to use intuition alongside critical thinking. It begins by emphasizing the importance of questioning everything and thinking critically. The educator shares their method of starting classes with questions rather than jumping straight into technical details or math equations. They emphasize using intuition based on previous experiences and understanding. For instance, when discussing the design of a fire alarm, the educator prompts students to think and question when it should go off, leading to intuitive and critical thinking exercises.
• 05:30 - 08:00: Hurricane Prediction and Neural Operators The chapter discusses the challenges and strategies involved in setting thresholds for sensor data, specifically in the context of fire alarms. The speaker highlights the importance of balancing sensitivity to avoid false alarms while ensuring responsiveness to actual fires. The discussion touches on practical considerations, such as sensor noise and its impact on decision-making, particularly in an educational setting where students are involved in modeling and analysis.
• 10:00 - 11:00: AI as a Tool for Learning The chapter 'AI as a Tool for Learning' discusses the role of artificial intelligence in educational settings. It explores how individuals who may lack formal mathematical training still possess practical and intuitive skills that are valuable in learning scenarios. The chapter highlights the use of real-world measures, such as assessing noise levels or using candles of different sizes, to teach concepts. It acknowledges that while intuitive ideas are a strong starting point, they are not always correct. The text emphasizes the importance of using intuition as a component of the learning process, but not as an absolute answer.

Caltech AI Professor: The One Skill | AI Can't Replace | Anima Anandkumar Transcription

• 00:00 - 00:30 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
• 00:30 - 01:00 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'm an animant Kumar. I'm brand 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, led AI research at Nvidia until recently. So at Caltech I lead the AI and science lab
• 01:00 - 01:30 which means uh 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. For
• 01:30 - 02:00 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, right? Not going into the details but just intuitively based on everything that you've seen you've done what do you think I asked them simple cases for instance if you were to design for a fire alarm when should it
• 02:00 - 02:30 say that there is fire or not that's something you can change right you can put a threshold you know 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. And sometimes I see with students who may
• 02:30 - 03:00 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. that and you know many times people already maybe 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 okay because intuitions are not always the only answer right so I think a lot of it
• 03:00 - 03:30 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's interested in music they can delve deeper into that if somebody is interested in art so it just has to that spark has to come from within and I think giving students more the freedom to pursue where they are passionate where they have a spark I think is going
• 03:30 - 04:00 to be the future and that's the right thing rather than forcing everybody to learn everything I'm always motivated by the hardest challenge 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 my sore as a kid, I loved just solving math problems, you know, going to my parents' factory. I was reading up their
• 04:00 - 04:30 program manuals. I was, you know, 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 and I'm like oh but how does it go into the computer and how does the computer tell the machine what to do 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
• 04:30 - 05:00 an immediate answer I would remember that there is a gap and then later when I was introduce use 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 when I was growing up, AI was considered science fiction. Naturally, there were lots of science
• 05:00 - 05:30 fiction movies where I saw and was fascinated. But that's not something people thought were practical. Uh 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
• 05:30 - 06:00 considered practical. So after I joined Caltech and wanted to explore problems at the intersection of AI and science and I was talking to everybody on campus, I was like okay 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
• 06:00 - 06:30 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 what is currently being done with traditional simulations. And to do that we developed neural operators. We've invented an AI technology called neural operators that is trained to understand physical behaviors not just highle reasoning with
• 06:30 - 07:00 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 skill to predict where hurricanes are going to go. And for to do that, we need finecale information and finecale modeling. So this cannot be just a core scale image like the image of a cat where even if it's gets blurry, you know it's a cat. The same techniques don't work for phenomena like hurricanes. Once we developed tools and
• 07:00 - 07:30 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
• 07:30 - 08:00 challenge. In fact, just a few months before we released our model, there were a group of very wellrespected weather scientists who published in the Royal Society Journal thinking they're 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
• 08:00 - 08:30 thousands of times faster. So what would take a big supercomput for traditional weather models can now be run on a local gaming PC with just a consumer GPU. So that's the beauty of uh machine learning as a field. We are not always stopped by what others think as difficult. As long as we can get the data and we can design the methods, we can just go and try it. So my mission is to constantly be curious and learning and not assume that
• 08:30 - 09:00 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
• 09:00 - 09:30 and beyond and understand how the universe is put together. There are still lots of open challenges. Many other teams such as Google Deepine 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
• 09:30 - 10:00 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. To me, human
• 10:00 - 10:30 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 better. AI is a tool. It can both help curiosity but also kill it depending on
• 10:30 - 11:00 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
• 11:00 - 11:30 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 highlevel 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,
• 11:30 - 12:00 ensure those programs are written well will be in more demand than ever.