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Lex Fridman Podcast
#74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI
Feb 24, 2020
01:46:17
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Quick takeaways
- Artificial intelligence should be focused on understanding and empowering human beings at all levels of abstraction.
- The field of AI should aim to go beyond human intelligence and explore different forms of intelligence.
- Creating direct markets between producers and consumers can enhance human happiness and create new economic opportunities.
- AI researchers value collaboration and knowledge-sharing, recognizing the importance of learning from others.
- AI should be seen as a branch of engineering that focuses on creating human-centric systems and problem-solving.
Deep dives
The Importance of Broadening the Scope of Artificial Intelligence
Michael I. Jordan argues for broadening the scope of artificial intelligence beyond just engineering algorithms and robots to understand and empower human beings at all levels of abstraction.
The Emergence of Chemical Engineering and Electrical Engineering
Jordan compares the development of AI to the emergence of chemical engineering and electrical engineering, emphasizing the need for a proto field of engineering based on statistical and computational ideas to create systems that bring value to human beings at scale.
The Complexity of Understanding the Human Brain
Jordan discusses the limited understanding of the human brain and the complexity of neural processes, emphasizing that we are far from comprehending the brain's computational capabilities and how thought emerges from electrochemical processes.
The Importance of Connecting Producers and Consumers Directly
Jordan highlights the value of creating direct markets between producers and consumers, where economic value is liberated by connecting them without relying solely on advertising models. He suggests that companies should prioritize this connection to enhance human happiness and create new economic opportunities.
The Role of Intelligence in AI
Intelligence, the concept of what it truly means to be intelligent, is a difficult question to answer. We can study human intelligence, but it is just one facet of a broader range of intelligences. The field of AI is not just about replicating human intelligence, but about exploring different forms of intelligence, such as the intelligence seen in markets or other decentralized systems. AI should aim to go beyond human intelligence and focus on a more comprehensive understanding of intelligence as a whole.
The Cooperative Nature of AI
The field of AI is highly cooperative, with collaboration and knowledge-sharing being key components. Researchers in AI are always open to learning from others and exchanging ideas. It is a community where expertise is valued, but it is also a field where everyone recognizes their own limitations and the need to learn from others. The cooperative nature of AI extends beyond individual researchers and encompasses the global AI community, which is diverse and international in its scope.
Advice for Aspiring AI Researchers
For aspiring AI researchers, the key is to be open-minded and dedicated to the journey of learning. Apprenticeship is vital in this field, as you learn from experienced advisors and immerse yourself in a community of experts. Embrace the breadth of AI by exploring different disciplines, such as mathematics, psychology, and economics. Engage in collaborations and seek out opportunities to broaden your knowledge and skills. Remember that success in AI is not solely based on brilliance, but on hard work, persistence, and an eagerness to continuously learn and grow.
The Future of AI and Language
One area of AI that holds great promise is natural language understanding. Truly deep and comprehensive understanding of language is a significant scientific challenge. While achieving this goal is a long way off, it presents an exciting opportunity for researchers to make breakthroughs in natural language processing. Mastery of language opens up new possibilities for human communication and contributes to a deeper understanding of the human mind and semantics. Though this remains a distinct discipline within AI, it should be explored and invested in given its potential impact.
Expanding the Scope of AI
The term AI, as it is commonly used today, does not reflect the full breadth of the field. AI should be seen as a branch of engineering, one that focuses on creating human-centric systems. This emerging engineering discipline has the potential to conceive historically new ways of thinking and problem-solving. It is crucial to broaden the scope of AI, move beyond the narrow definitions, and tackle the serious challenges ahead with a human-centric approach.
9.
The Difference Between Pattern Recognition and Decision Making in the Real World
4min
Michael I. Jordan is a professor at Berkeley, and one of the most influential people in the history of machine learning, statistics, and artificial intelligence. He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio.
EPISODE LINKS:
(Blog post) Artificial Intelligence—The Revolution Hasn’t Happened Yet
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.
This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
OUTLINE:
00:00 – Introduction
03:02 – How far are we in development of AI?
08:25 – Neuralink and brain-computer interfaces
14:49 – The term “artificial intelligence”
19:00 – Does science progress by ideas or personalities?
19:55 – Disagreement with Yann LeCun
23:53 – Recommender systems and distributed decision-making at scale
43:34 – Facebook, privacy, and trust
1:01:11 – Are human beings fundamentally good?
1:02:32 – Can a human life and society be modeled as an optimization problem?
1:04:27 – Is the world deterministic?
1:04:59 – Role of optimization in multi-agent systems
1:09:52 – Optimization of neural networks
1:16:08 – Beautiful idea in optimization: Nesterov acceleration
1:19:02 – What is statistics?
1:29:21 – What is intelligence?
1:37:01 – Advice for students
1:39:57 – Which language is more beautiful: English or French?
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