Jitendra Malik, Professor of EECS at UC Berkeley discusses building AI from the ground-up, focusing on sensorimotor capabilities before language. Topics include limitations of current AI technology, the importance of physical interaction in language models, the role of vision in robotic locomotion, progress and challenges in computer vision, the potential impact of AI in medicine and healthcare, and advice for PhD students pursuing a career in AI research.
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Quick takeaways
Acquiring sensory motor competence is crucial for the development of intelligent robots.
AI systems need to prioritize the development of foundational sensory motor skills to achieve comprehensive intelligence.
Simulation, when used in combination with learning approaches, can greatly contribute to advancing robotics.
Deep dives
The Importance of Skill Acquisition in Robotics
The podcast episode explores the significance of skill acquisition in robotics. The guest, Chitendra Molik, emphasizes the need for robots to acquire human-like intelligence through the development of sensory motor competence. He highlights the importance of skills such as walking, object manipulation, and sensory perception in the overall development of intelligent robots. The guest discusses the concept of rapid motor adaptation, a technique that enables robots to quickly adapt their motor actions to different terrains or environments. This approach involves inferring the terrain's characteristics through sensory inputs and adjusting the robot's behavior accordingly. The guest also expresses excitement about the potential applications of robotics in various domains, including medicine, elder care, and healthcare, where robots can assist humans and improve quality of life.
The Evolution of Intelligence and the Role of Language Models
The podcast delves into the evolution of intelligence and the limitations of current AI systems compared to human intelligence. The guest explains the Moravec's paradox, which posits that certain basic skills, such as sensory motor competence, were developed early in the evolution of intelligence. The guest discusses how early cognitive development in humans involves acquiring skills grounded in sensory motor experiences. They emphasize the importance of these foundational skills, as well as their connection to language acquisition and understanding. The guest suggests that AI systems need to prioritize the development of these basic skills, along with language models, to achieve more comprehensive intelligence.
The Significance of Benchmarking and Image Recognition
The podcast highlights the importance of benchmarking in computer vision and the impact of deep learning on image recognition. The guest shares their experience in promoting benchmarking in the field, which led to the development of datasets and challenges such as ImageNet. They discuss the transformative power of deep learning in image recognition tasks, surpassing traditional computer vision techniques. The guest also touches upon the concept of adversarial examples and the need for deep learning models to have a better understanding of images through top-down and bottom-up processing. They emphasize the potential applications of AI in various fields, including medicine, healthcare, and elder care, and address concerns about job displacement, suggesting that AI will likely work in conjunction with humans rather than replacing them.
The Power of Simulation in Advancing Robotics
Simulation, facilitated by the rapid increase in computing power, is a powerful tool that can revolutionize robotics. Unlike in the past, when physicists relied on analytical solutions and simplifying assumptions, simulation allows for accurate and comprehensive modeling of complex systems without compromising the realism of the environment. While simulation benefits from Moore's law and technological advancements, it still requires an underlying physical model to accurately represent the real world. However, efforts are being made to capture real-world diversity and improve the simulation environment. Simulation, when used artfully and in combination with learning approaches, can greatly contribute to advancing the state of the art in robotics.
The Synergy between Computer Vision and Robotics
Computer vision plays a critical role in enabling robots to navigate and interact with the world. While significant progress has been made in core computer vision tasks such as object recognition, 3D reconstruction, and scene segmentation, challenges still exist, particularly in recovering 3D structures from a single image. Moreover, the interaction between computer vision and language comprehension remains an open area for exploration. The field of computer vision intersects with robotics, where vision guides action. Understanding the synergy between vision and cognition, including language, remains an active research area. The dynamism of computer vision research lies in its connection to other disciplines, fostering breadth of knowledge and adaptability among researchers.