Technology outlet The Decoder (Sept. 18) published a blog post reporting thatNvidiaSenior Scientist Jim Fan predicts thatthe coming yearsrobotThe "GPT-3 moment" is approaching in the field.
Biography of Jim Fan
Jim Fan received his PhD from the Vision Lab at Stanford University under the supervision of Prof. Feifei Li. He has worked in a wide range of research areas, including multimodal fundamental models, reinforcement learning, and computer vision, and has interned at leading organizations such as Google Cloud AI, OpenAI, and Baidu's Silicon Valley Artificial Intelligence Lab.
Jim Fan currently leads AI-related research at NVIDIA, where his team is working on Project Groot, the company's effort to create a basic model of a humanoid robot.
Research breakthroughs in the next two to three years
Jim Fan predicts that in the next 2-3 yearsSignificant breakthroughs will occur in research related to basic robotics modeling, though he also admits that it will take longer for robots to make their way into everyday life.
In an interview with Sequoia Capital.Fan says he's looking forward to a "GPT-3 moment" in robotics.-- i.e., a breakthrough in basic robotics modeling, with an impact comparable to that of GPT-3 in the field of language processing.
Translate their views below:
- Getting robots into people's daily lives is not just about technology. Robots need to be affordable and mass-producible, and we also need hardware security as well as privacy and regulatory safeguards.
- The world is built around the human form, right? Our restaurants, our factories, our hospitals and all the equipment and tools -- they are all designed for the human form and hands.
He argues that a capable humanoid robot could theoretically perform any task a human could perform, and predicts that the ecosystem for humanoid robot hardware will be ready in two to three years.
NVIDIA's robotics-related research
NVIDIA uses a combination of three data types in developing robotic AI: Internet data, simulated data, and real-world robotics data.Dr. Fan highlights the strengths and weaknesses of each approach and argues that their combination is the key to success.
NVIDIA is developing technologies such as "Eureka," which uses language models to generate reward functions for robot training and automate the process.
In addition to the real world, Pham's team is also working on AI agents for virtual environments such as video games. He has found similarities between these domains and is working to develop a unified model that can control both virtual and physical agents in the long term.