Finshots Daily
What's up with AI and the 2024 Physics Nobel Prize?
In today’s episode for 11th October 2024, we explore why two pioneers of AI—John Hopfield and Geoffrey Hinton—were awarded the 2024 Nobel Prize in Physics.
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- Broadcast on:
- 11 Oct 2024
- Audio Format:
- other
Hello and welcome to Finshots Daily. In today's episode we talk about AI and the 2024 physics Nobel Prize. Before we start today's episode, here's a quick message from Team Ditto. Did you know that 83% of Indian households are put in grave financial danger if the breadwinner passes away? This is why it's important to be financially prudent and buy a term insurance. If you're young, you can secure your family with a cover of over 1 crore rupees at nominal premiums of just 10 to 15k a year. At Ditto Insurance, a product of Finshots, we've helped thousands of people select a term plan with the help of certified advisors. We promise a spam-free experience, no pressure for you to buy and a claim support team that's always by your side. Head over to the link in the description below or google Ditto Insurance to speak to an expert now. Now on to today's episode. In a surprising turn of events, two pioneers of AI, John Hopfield and Jeffrey Hinton were awarded the 2024 Nobel Prize in Physics. Yes, AI and physics. Not something that you would expect, right? But before we dive into the AI physics connection, let's take a little detour to a tech giant you've probably heard of, NVIDIA. NVIDIA recently overtook Microsoft to become the world's second largest company by market capitalization right behind Apple. And if you've guessed that AI had a role to play in this, well, you're spot on. And NVIDIA's rise? Well, it's all thanks to their GPUs or graphics processing units. Simply put, GPU is a chip or an electronic circuit capable of rendering graphics for display on an electronics device. It was initially designed for gamers craving super smooth high-quality graphics. But here's the twist. Those same GPUs are perfect for AI. How you ask? Imagine you're teaching an AI model to recognize dog breeds. It needs to process thousands of online images in a blink. And that's where NVIDIA's GPUs come in. They handle complex calculations at lightning speed, making them the go-to hardware for AI developers. But it's not just about hardware. NVIDIA has also built software tools like CUDA to optimize AI applications for their GPUs. And guess how all of this ties to the Nobel Prize? Well, one of the winners this year, Jeffrey Hinton, figured all of this early on. Even before NVIDIA, back in 2009, Hinton tried to get a free GPU from NVIDIA for his experiments. They turned him down, but Hinton used their GPU anyway. In 2012, he and his students developed an artificial neural network that could teach itself to recognize images thanks to NVIDIA's CUDA platform. This was a watershed moment. Hinton proved that GPUs could dramatically accelerate AI training something NVIDIA hadn't realized until then. Hinton's breakthrough work showed NVIDIA that their GPUs had massive potential beyond gaming, which was the moment NVIDIA pivoted to AI. Before this, NVIDIA's CUDA was mainly used for high performance computing such as CT scans, financial modeling and animation. So in a way, Hinton's experiments didn't just revolutionize AI, they also helped NVIDIA understand the full power of its own technology and the rest as they say is history. Now back to our main story. Why did this AI breakthrough win a physics Nobel Prize? You see, two Nobel Prize winners, Hopfield and Hinton laid the groundwork for something called artificial neural networks. They are crucial to the story and basically the building blocks of modern AI. Think of a neural network as a system inspired by how the human brain works. Just like your brain learns to recognize faces or words, a neural network does the same with data. It takes in information, processes it, and makes decisions learning from experience. Also, the idea of neural networks isn't new. It dates back to the 1940s when scientists like modern McCulloch and Walter Pitts first proposed simple models of neural activity. But yes, practical applications were limited until the 80s until John Hopfield developed a Hopfield network which uses principles of physics to model how these neural networks can learn from incomplete data. For instance, if a picture of a cat is blurry, his model helped the computer guess what it should look like. This was a huge step forward but still had its limitations. It was great at recognizing patterns but wasn't enough to build AI systems that could predict or generate new information. That's where Jeffrey Hinton came in. Hinton took Hopfield's ideas to the next level by introducing something called the Boltzmann machine. He added hidden layers, neural networks, allowing machines to analyze data in more sophisticated ways. These hidden layers act like a subconscious, helping computers not just recognize things but also make predictions. For example, instead of just identifying how the cat would look like in a photo, a computer could now guess what the cat might look like in a completely different scene. And this idea of hidden layers became the foundation of today's AI, whether it's chat GPT generating coherent text or Dolly creating original artwork. So why is all of this worthy of a physics Nobel Prize? Well, these neural networks we just spoke about are based on three principles from physics, how the brain works, which is biophysics, how data is processed, which is statistical physics, and how computers solve complex problems, which is computational physics. Hopfield's neural networks were inspired by biophysics, which models how the brain works using math. Then there's statistical physics, which helps AI process huge amounts of data and find patterns. And finally, there's computational physics, which stood as a driving force behind the complex AI models we use today. So without the work of Hopfield and Hinton, AI wouldn't be what it is today. Their contributions lay the groundwork for neural networks. The brains behind Siri, Alexa, and even medical imaging systems that detect cancer faster than doctors. Take alpha fold, for instance, an AI that predicts protein structures. It's revolutionizing drug discovery and biochemistry. And it's all built on Hinton's breakthroughs. AI is solving problems in fields like astronomy, particle physics, and climate science. Working with data at a speed and scale, we never thought possible. Even self driving cars. Yes, they rely on this tech too. And according to a 2023 McKinsey report, generator AI could inject up to 4.4 trillion dollars into the global economy every year. That's staggering. Of course, there's a flipside to this, as the IMF warns that nearly 40% of global jobs could be impacted by AI. Yet, all this reminds us of a timeless truth. Breakthroughs in one field often spark revolutions across industries, and they could reshape how the world works. It's a ripple effect we're seeing unfold right now in front of us. And as Steve Jobs once said, you can't connect the dots looking forward. You can only connect them looking backward. The next time you chat with a virtual assistant or CAI in action, remember that those dots all trace back to physics. Of course, some argue that Hopfields and Hinton's work is more about mathematics and computer science. Maybe they're right. Maybe not. In the end, only time will tell which dots will connect next. Thank you for listening to today's episode. FinShots Daily is available on a bunch of streaming platforms such as Spotify, Apple Podcasts and Google Podcasts. So make sure you follow us on your favorite podcast streaming platform. Until next time.