News of a startup's funding round in Paris last month captured the attention of the global information technology industry. AMI Labs, a startup founded by Yann LeCun, the New York University professor known as the godfather of artificial intelligence, raised $1.03 billion (approximately 1.55 trillion won) in initial investment. Various venture capital firms as well as Nvidia and Samsung Electronics (005930.KS) participated in the funding. The list of individual investors included IT giants such as Amazon Chairman Jeff Bezos and former Google Chairman Eric Schmidt. With this massive capital, Professor LeCun's AMI Labs is now pursuing world model development.
World models are next-generation AI technology that global companies are eyeing. As the name suggests, they refer to AI models that understand and simulate how the real world operates. Nvidia defines world models as "neural networks that understand the dynamics of the real world, including physics and spatial properties." Simply put, they are models that understand the principles governing the real world and can replicate them inside a computer.
Physics Understanding Enables Simulation Predictions
Understanding dynamics is a crucial factor that differentiates world models from existing AI models. When you ask ChatGPT to draw a picture or use Nanobanana to create a video, AI produces images that resemble reality. It depicts scenes like an apple falling from the sky or a boat moving forward as oars paddle through water. However, generative AI does not actually know that gravity causes the apple to fall to the ground. It merely describes phenomena based on countless images and videos it has learned and user commands. World models, in contrast, understand the principles that cause phenomena to occur.
Even before world models, digital twin technology existed to convert real-world spaces and object placements into data and transfer them into three-dimensional computer environments. World models resemble digital twins in that they create a virtual replica that closely mirrors reality. However, world models include capabilities that transcend the inherent limitations of digital twins: prediction. While digital twins focus on replicating the real world in virtual space, world models go a step further by learning spatial configurations and physical dynamics of reality to predict future outcomes.
