MIT AGI: Building machines that see, learn, and think like people (Josh Tenenbaum) | Summary and Q&A

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February 8, 2018
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Lex Fridman
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MIT AGI: Building machines that see, learn, and think like people (Josh Tenenbaum)

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Summary

In this video, Josh Tenenbaum, a professor at MIT, discusses the work of his group and their focus on understanding how humans learn and how this knowledge can be used to build more efficient AI systems. He emphasizes the importance of modeling the world and understanding beyond just pattern recognition. He discusses the need for a broader set of tools and the limitations of current AI technologies. He presents a vision of an architecture for visual intelligence and the challenges in bridging the gap between perception and cognition. He also raises questions about the current trajectory of AI in industry and the potential for more science-based approaches.

Questions & Answers

Q: What is the focus of Josh Tenenbaum's work?

Josh Tenenbaum is focused on understanding how humans learn and using these insights to build more efficient AI systems.

Q: What is the vision of the Center for Brains Minds and Machines?

The Center for Brains Minds and Machines aims to jointly pursue the science of intelligence in the human mind and brain, as well as the engineering of human-like intelligence in machines.

Q: How does Josh Tenenbaum describe current AI technologies?

Josh Tenenbaum describes current AI technologies as "AI technologies" rather than true AI, as they can perform certain tasks but lack the general-purpose intelligence and common sense of humans.

Q: What makes human intelligence different from current AI technologies?

Human intelligence possesses common sense, flexibility, and the ability to learn a wide range of skills or tasks without being explicitly engineered. Current AI technologies are limited in their capabilities and do not possess common sense or flexible intelligence.

Q: What are some limitations of deep learning in AI?

Deep learning has made great strides in pattern recognition, but intelligence goes beyond just pattern recognition. Deep learning systems lack the ability to explain, understand, imagine, plan, and solve problems, which are crucial aspects of human intelligence.

Q: How does Josh Tenenbaum define intelligence?

Josh Tenenbaum defines intelligence as the ability to model the world, explain and understand what is observed, imagine unseen things, set goals, make plans, and solve problems. These cognitive abilities, along with learning and communication, are integral to human intelligence.

Q: What is the approach taken by the Center for Brains Minds and Machines?

The Center for Brains Minds and Machines takes a reverse engineering approach to understand how intelligence works in the human mind and brain. They believe that by understanding the science in engineering terms, they can gain deeper insights and translate them into engineering applications.

Q: How does Josh Tenenbaum describe the history of deep learning?

Josh Tenenbaum describes the history of deep learning as a story of scientists thinking like engineers. The basic mathematical principles behind deep learning were developed by scientists in psychology and cognitive science departments and published in journals focused on theoretical and mathematical psychology.

Q: What are the research goals of the Center for Brains Minds and Machines?

The research goals of the Center for Brains Minds and Machines include addressing basic questions about consciousness, meaning in language, real learning, culture, and creativity. They aim to study how humans become aware of the world, grasp the meaning of words, learn from previous generations, and generate new ideas and solutions.

Q: What is the short-term focus of the Center for Brains Minds and Machines?

In the short term, the Center for Brains Minds and Machines is focused on visual intelligence. They aim to study and develop an architecture for visual intelligence that goes beyond pattern recognition and includes cognitive core, modeling of the world, and symbol-based tasks.

Q: How does Josh Tenenbaum illustrate the limitations of current AI technologies?

Josh Tenenbaum illustrates the limitations of current AI technologies by comparing their performance on tasks like image captioning and recognizing objects. He shows how current systems tend to overfit to specific data sets and struggle to understand context, humor, and abstract concepts.

Takeaways

The current state of AI technologies, while impressive in certain areas such as pattern recognition, lacks the breadth and depth of human intelligence. True intelligence involves more than just pattern recognition and requires the ability to model the world, explain and understand what is observed, imagine new scenarios, plan and solve problems, and communicate ideas. The current trajectory of AI in industry, focused on deep learning and pattern recognition, may not address these higher-level cognitive abilities. Therefore, there is a need for a more science-based and engineering approach, like the one taken by the Center for Brains Minds and Machines, to bridge the gap between perception and cognition. The goal is to develop an architecture for visual intelligence and ultimately build AI systems that possess human-like intelligence.

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