Geoffrey Hinton: The 2024 Nobel Prize in Physics: When Jensen Huang’s Nvidia ‘Godfather of AI’ was denied |

It is appropriate that 2024 nobel prize given in physics to gentlemen working in Aye area because, since chatgpt Launched in 2022, almost every conversation has been about Skynet’s dangerous descendants. Managers became extremely excited about being able to replace an entire workforce with one big language model. Big eating model named George RR Martin sued OpenAICreator of ChatGPT, for plagiarism. As did Scarlett Johansson to imitate his voice, which led to this becoming one of the best husband jokes. Colin Jost But Saturday night Live.

Over the past year, you’ve all seen Jensen Huang, resplendent in his leather jacket, pretending to be the coolest rich man on Earth, after it was revealed in 2023 that the omnipotent, omniscient, and ominous (if you deny A.I. are) ChatGPT was trained on a NVIDIA Supercomputer Later, Huang revealed that Nvidia had sold similar supercomputers to 50 other companies. Overnight, the company’s value increased by two billion dollars, and today it is the second largest company in the world by market cap (behind only Apple and slightly ahead of Google).
In the war for AI supremacy, Huang held all the keys. Interestingly, Huang’s Nvidia might not have become an AI company if it had not been this year’s winner, also known as the ‘Godfather of AI’. The Physics Nobel of 2024 will be awarded to Professor John J. Hopfield and Geoffrey E. Hinton for their fundamental discoveries and inventions that have enabled this. machine learning using the artificial neural networkprofessor Geoffrey HintonThe ‘Godfather of AI’, who unknowingly helped Nvidia transform from a chip maker to an AI company. And 15 years ago, when he asked for a free gift from Jensen Huang’s company, he was refused.

While Sabine Hofstadter, a German physicist who runs a hugely popular YouTube channel, joked that this year’s Nobel had nothing to do with physics, the Nobel Committee argued that the laureates used physics to develop those methods. Applied principles that contribute to the development of today’s advanced machine. Learning systems. John Hopfield designed structures capable of storing and reconstructing information, while Geoffrey Hinton developed a method that allows machines to independently find patterns in data – an approach now widely used in large artificial neural networks. is used.
In 2009, Hinton used Nvidia’s CUDA platform to train a neural network to recognize human speech. He also wrote an email to Nvidia, saying, “Look, I just told a thousand machine-learning researchers that they should go and buy Nvidia cards. Can you send it to me for free?” Nvidia didn’t. But Hinton will continue to urge researchers to use CUDA. Hinton’s research group used CUDA to train neural networks for speech recognition. The results were so impressive that he began urging his students to further explore the possibilities of CUDA. In 2012, Hinton, together with two of his students in Toronto, Ilya Sutskever and Alex Krizhevsky, developed a neural network capable of analyzing thousands of images and recognizing everyday objects such as flowers, dogs, and cars.
The success was significant not just for Hinton’s team but for the entire field of AI. The use of GPUs in machine learning was suddenly validated, and in the years that followed, almost every competitor in the competition used neural networks powered by GPUs. When Huang asked what else they could train neural networks for, the answer seemed to be: everything.
Overnight, Huang pivoted the company from gaming to AI. The gamble paid off. The ability of neural networks to solve complex, unstructured problems like image recognition opens the door to AI applications in all industries, from journalism to autonomous driving. Nvidia’s CUDA and GPUs became the backbone of AI development, accelerating machine learning models and allowing them to process large amounts of data at unprecedented speeds.

Hinton’s insistence on exploring the potential of GPUs and Huang’s foresight in adopting AI turned Nvidia into a trillion-dollar company. Today, its technology powers AI systems that are reshaping industries, demonstrating how a once ignored technology has become central to the future of computing.
However, in the past year, unlike Oppenheimer, Hinton has become increasingly wary of an uncontrolled AI arms race. Hinton recently left his Google role and is deeply concerned about the impact of rapid AI development, including misinformation, job displacement, and the risk of AI systems evolving beyond human control. As the AI ​​race heats up between tech giants like Google and Microsoft, Hinton’s warnings are a reminder of the dual nature of technological progress. Hinton remains particularly nervous about AI on the battlefield, or as he calls them, “robot soldiers.”
he told the new York Times In 2023: “The idea that this thing could actually be smarter than people – few people believed it. But most people thought it was too far. And I thought it was too far. I thought it was 30 to 50 years or more away. Obviously, I don’t think so anymore.
Huang, on the other hand, does not believe that AI poses any significant risks. When one user compared AI racing to the Industrial Revolution (which led to the global decline of horses), Huang pointed out that horses have “limited career options.”
However for most regular people – those lacking Huang or Hinton’s deep understanding of AI – it sounds a bit like the end of The Matrix when Neo says to the deus ex machina: “Where do we go from here, we you “Depend on.”