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Michael I. Jordan: Machine Learning, Recommender Systems, and Future of AI | Lex Fridman Podcast #74

Michael I. Jordan: Machine Learning, Recommender Systems, and Future of AI | Lex Fridman Podcast #74

56 segments available

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. This episode is presented by Cash App. Download it & use code "LexPodcast": Cash App (App Store): https://apple.co/2sPrUHe Cash App (Google Play): https://bit.ly/2MlvP5w PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 EPISODE LINKS: (Blog post) Artificial Intelligence -- The Revolution Hasn’t Happened Yet: https://hdsr.mitpress.mit.edu/pub/wot7mkc1 OUTLINE: 0:00 - Introduction 3:02 - How far are we in development of AI? 8:25 - 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? CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman

Segments Timeline

1
0:00 - 2:59
2:59 duration478 words

The Michael Jordan of Machine Learning

In this introduction, Lex Fridman sets the stage for his conversation with Michael I. Jordan, a leading figure in machine learning and artificial intelligence. He draws a parallel between Michael I. Jordan's achievements in AI and the legendary basketball player Michael Jordan, emphasizing the significance of Jordan's contributions to the field and his influence on future generations of researchers.

"the following is a conversation with michael i jordan a professor at berkeley and one of the most influential people in the history of machine learning statistics and artificial intelligence he has be..."

2
3:02 - 5:52
2:50 duration515 words

A Historical Perspective on AI Development

Michael I. Jordan shares his insights on the evolution of artificial intelligence, comparing it to the development of chemical and electrical engineering. He discusses the current state of AI as a proto-field, highlighting the need for a deeper understanding of the principles behind machine learning and its applications in creating value for humanity.

"given that you're one of the greats in the field of ai machine learning computer science and so on you're trivially called the michael jordan of machine learning although as you know you were born fir..."

3
5:52 - 8:43
2:50 duration573 words

Understanding the Human Brain

In this segment, Jordan reflects on the challenges of understanding the human brain and its computational processes. He emphasizes the complexity of neural interactions and the limitations of current scientific knowledge, arguing that we are still far from grasping how the brain functions at a fundamental level.

"and computational ideas of previous generations but do you think there's something deeper about ai in his dreams and aspirations as compared to chemical engineering and electrical engineering well the..."

4
8:43 - 10:26
1:42 duration340 words

The Hopes for Brain-Computer Interfaces

Jordan discusses the potential of brain-computer interfaces, such as Neuralink, while expressing skepticism about the current understanding of brain mechanisms. He highlights the importance of scientific rigor in neuroscience and the challenges of translating brain signals into meaningful applications.

"send electrical signals just as you said even the basic mechanism of communication in the brain is not something we understand but do you hope without understanding the fundamental principles of how t..."

5
10:26 - 12:34
2:08 duration437 words

Engineering vs. Scientific Understanding

In this thought-provoking segment, Jordan distinguishes between engineering achievements in AI and the deeper scientific understanding of intelligence. He critiques the current hype around AI demonstrations and emphasizes the need for a solid theoretical foundation to truly advance the field.

"so just uh in hopes of getting you to dream uh you've mentioned kolmogorov and touring might pop up do you think that there might be breakthroughs they'll get you to sit back in five ten years and say..."

6
12:34 - 14:30
1:55 duration448 words

Future Breakthroughs in AI and Engineering

Jordan reflects on the future of AI and engineering breakthroughs, suggesting that while significant advancements will occur, they may not align with current expectations. He emphasizes the importance of understanding the principles behind these technologies to create lasting value for society.

"revolutionize the fundamentals of engineering of manufacturing of of saying here's a problem we know how to do a demo of and actually taking it to scale yeah so so there's going to be all kinds of bre..."

7
14:30 - 16:41
2:10 duration458 words

The Distinction Between AI and Machine Learning

In this segment, Jordan clarifies the distinction between artificial intelligence and machine learning. He discusses the limitations of current AI systems and the need for a deeper understanding of intelligence, reasoning, and decision-making processes in both humans and machines.

"we're in the very early days of really doing interesting stuff there and we'll get to that but it's just taking a quick step back can you give uh we kind of threw off the historian hat i mean you brie..."

8
17:00 - 18:03
1:02 duration248 words

Reclaiming AI: A Call for New Terminology

Jordan critiques the historical naming of AI, suggesting that the term has led to unrealistic expectations. He advocates for a new terminology that better reflects the current state of the field, emphasizing the importance of creating effective systems rather than pursuing the elusive goal of true intelligence.

"and aspects of this will look intelligent it will look computers were so dumb before they will seem more intelligent we will use that buzz word of intelligence so we can use it in that sense but you k..."

9
18:03 - 19:12
1:09 duration243 words

Science: Personalities vs. Principles

In this thought-provoking discussion, Jordan explores whether scientific progress is driven by personalities or fundamental principles. He acknowledges the role of vibrant personalities in attracting newcomers to the field but warns against the misconceptions that can arise from overly exuberant promises.

"know i if you read one of my little things um the history was basically that uh mccarthy needed a new name because cybernetics already existed and he didn't like you know no one really liked norbert w..."

10
19:12 - 20:40
1:27 duration296 words

Disagreement with Yann LeCun

Jordan shares insights into his relationship with Yann LeCun, highlighting their shared mentality of building and testing ideas. He discusses their differing emphases on pattern recognition and prediction, arguing that decision-making is equally crucial in the context of AI and machine learning.

"research that's outside of personality both and i wouldn't say there should be one kind of personality i have mine and i have my preferences and i have a kind of network around me that feeds me and an..."

11
20:40 - 22:47
2:07 duration501 words

The Importance of Decision-Making in AI

Jordan emphasizes the significance of decision-making in AI systems, arguing that prediction alone is insufficient. He illustrates the complexities of real-world decision-making, including risk evaluation and the need for human judgment, which current AI systems often overlook.

"all right so you know um and it's interesting to try to take that as far as you can if you could do perfect prediction what would that give you kind of as a thought experiment um and um i think that's..."

12
22:47 - 24:28
1:41 duration369 words

Creating Markets for Music Creators

In this segment, Jordan presents a vision for a new market for music creators, contrasting it with the current record label system. He advocates for a transparent platform that connects creators directly with consumers, enabling artists to earn a living from their work and fostering a more equitable music industry.

"blah i'm not the only one working on those but we're a smaller tribe and right now we're not the the one that people talk about the most um but you know if you go out in the real world in industry um ..."

13
24:28 - 28:40
4:12 duration939 words

AI's Role in Job Creation

Jordan discusses how AI can create new job opportunities by establishing markets that connect producers and consumers. He envisions a future where AI facilitates personal connections between artists and their audiences, ultimately enhancing human happiness and economic viability in the creative sector.

"which i will be beyond my expertise and and there are lots of things that are going to be very non-obvious to think about just like just again i like to think about history a little bit but think abou..."

14
27:59 - 30:01
2:02 duration409 words

Creating Markets for Musicians

Michael I. Jordan discusses the potential of the internet to create direct markets for musicians, allowing fans to easily purchase merchandise and support artists. He emphasizes the importance of personal connections in music and how technology can facilitate these transactions without the need for traditional advertising, ultimately enhancing human happiness and creating new economic opportunities.

"a millionaire all right and and now even think about really the value of music is in these personal connections even so much so that um a young kid wants to wear a t-shirt with their favorite musician..."

15
30:01 - 32:00
1:59 duration426 words

The Role of Recommender Systems

In this segment, Jordan explores the significance of recommender systems in connecting consumers with creators. He reflects on the evolution of these systems and their impact on industries like Amazon, highlighting how effective recommendations can enhance user experience and drive sales, while also acknowledging the challenges of scaling these systems across different types of products.

"to create such markets is it an ai problem is it an interface problem so interface design is it uh some other kind of it was an economics problem who should they hire to solve these problems well part..."

16
32:00 - 34:00
1:59 duration420 words

Challenges of AI in Content Recommendation

Jordan addresses the complexities of using AI for content recommendation, particularly in sensitive areas like politics and news. He critiques the advertising-driven monetization models of platforms like Facebook and Google, arguing that these models hinder genuine connections between producers and consumers, and suggests that a shift towards direct economic relationships could improve the situation.

"think pretty high recommend you know um there's no magic in the algorithms but a good recommender system is way better than a bad recommender system and uh recommender systems was a billion dollar ind..."

17
34:00 - 36:40
2:40 duration599 words

Rethinking Advertising Models

In this insightful discussion, Jordan critiques the current advertising models that dominate platforms like Facebook and Google. He argues that these models often prioritize clicks over meaningful connections, leading to a degradation of content quality. He advocates for a new approach that balances advertising with direct consumer-producer relationships, which could ultimately enhance user experience and satisfaction.

"i think facebook just launched facebook news so they're having recommend the kind of news that are most likely for you to be interesting you think this is this ai solvable again whatever term want to ..."

18
36:40 - 39:00
2:20 duration584 words

The Future of Consumer-Producer Relationships

Jordan envisions a future where companies like Amazon can foster direct relationships between consumers and producers, reducing reliance on traditional advertising. He discusses the potential for micro-payments and direct value exchanges to create a more sustainable economic model, emphasizing the need for companies to innovate beyond current practices to meet consumer needs effectively.

"and the business model is that really what are are there some human producers and consumers out there is there some economic value to be liberated by connecting them directly is it such that it's so v..."

19
41:19 - 43:02
1:42 duration417 words

The Role of Advertising in AI Markets

Michael I. Jordan discusses the importance of advertising as a signal of product value in AI markets. He argues that effective advertising can inform consumers about quality products, but current trends in advertising often annoy users rather than provide useful information. He emphasizes the need for companies to rethink their advertising strategies to connect producers and consumers more effectively.

"will not find it and ai will not fix that okay at all right how do you allow that vacuum cleaner to start to get in front of people be sold well advertising and here what advertising is it's a signal ..."

20
43:03 - 44:25
1:22 duration235 words

Consumer-Producer Connections

In this segment, Jordan explores the ideal relationship between consumers and producers, suggesting that direct connections are the most effective form of advertising. He critiques the current state of advertising, particularly on platforms like Facebook, and advocates for a model that prioritizes genuine connections and transparency over intrusive data collection.

"um and they're com you know facebook you know again god bless them they they bring you know grandmother's uh you know uh they bring children's pictures into grandmother's lives it's fantastic um but t..."

21
44:26 - 46:14
1:47 duration396 words

Trust Issues with Facebook

Jordan expresses skepticism about Facebook's ability to foster trust with users, citing the company's history of exploiting personal data. He contrasts Facebook's approach with that of Microsoft, which he believes is working to build trust with its users. This segment highlights the broader implications of data privacy and the need for companies to prioritize user relationships.

"component all that push first of all i i what we just talked about i was defending advertising okay so i was defending it as a way to get signals into a market that don't come any other way especially..."

22
46:15 - 48:25
2:09 duration485 words

The Future of Recommender Systems

Jordan discusses the potential for recommender systems to enhance user experience while emphasizing the importance of human connection. He argues against algorithms that merely track user behavior and instead advocates for systems that facilitate genuine interactions and creativity. This segment raises questions about the ethical implications of AI in personal recommendations.

"it's not for them it's not it's not that it's facebook that's not doing it because they care about them right in any real sense and they shouldn't they should not be a big brother caring about us that..."

23
48:26 - 50:43
2:17 duration473 words

Optimism for AI and Human Interaction

In this segment, Jordan shares his optimistic view on the future of AI, suggesting that technology can enhance human happiness if designed with transparency and user control in mind. He contrasts this with the current state of AI, which often feels intrusive. Jordan emphasizes the need for companies to prioritize user agency and meaningful interactions.

"has the kind of personal information about us that could create a beautiful thing so i i'm really optimistic of what facebook could do uh it's not what it's doing but what it could do so i don't see t..."

24
50:44 - 52:00
1:15 duration297 words

Creating Frictionless Systems

Jordan reflects on the challenges of creating frictionless systems similar to Uber for various services. He discusses the importance of user experience and the need for systems that facilitate easy transactions without overwhelming users with advertisements. This segment highlights the balance between convenience and user autonomy in technology.

"getting at is that little transaction how difficult is it to create a frictionless system like uber has for example for other things what's your intuition there uh so i first of all i pay little bits ..."

25
52:01 - 54:32
2:30 duration577 words

The Complexity of Human Preferences

In this thought-provoking segment, Jordan delves into the complexity of human preferences and the limitations of current recommender systems. He argues that while AI can provide recommendations, it should not attempt to predict or control human behavior. Instead, he advocates for systems that allow users to explore and discover new interests organically.

"really interesting question we may disagree maybe not i think that i love recommender systems and i want to give them everything about me in a way that i trust yeah but you but you don't because so fo..."

26
54:32 - 56:45
2:13 duration527 words

The Importance of Control and Transparency

Jordan concludes by emphasizing the need for control and transparency in AI systems. He argues that users should have agency over their interactions with technology and that companies must prioritize ethical practices. This segment encapsulates the overarching theme of balancing innovation with user trust and well-being.

"not going to do those things you're going to make things worse okay so right now just give this a chance uh right now the recommender systems are the creepy people in the shadow watching your every mo..."

27
57:00 - 57:52
0:52 duration203 words

The Evolution of Privacy

Jordan compares historical privacy norms in small communities to modern urban living, questioning whether the loss of privacy has led to a better society. He suggests that technology should empower individuals to manage their privacy and foster community support.

"what does control over privacy look like do you think you should be able to just view all the data that no it's much more than that i mean first of all it should be an individual decision some people ..."

28
57:52 - 58:58
1:05 duration250 words

The Complexity of Privacy Engineering

Jordan discusses the emerging field of privacy engineering, likening it to the early days of electrical engineering. He highlights the need for new structures and frameworks to address the multifaceted challenges of privacy in technology.

"want certain people who i trust and there should be relationships i should kind of manage all those but who knows what about me i should have some agency there it shouldn't i shouldn't be a drift in a..."

29
58:58 - 1:00:00
1:02 duration231 words

The Need for Thoughtful Discourse

Jordan emphasizes the importance of nuanced discussions about technology's role in society, advocating for forums that facilitate meaningful dialogue rather than polarized opinions. He notes the resurgence of podcasts as a response to the public's desire for deeper conversations.

"a lot of new structures that we don't have right now so it's kind of hard to talk about it and you're saying there's a lot of money to be made if you get it right so absolutely a lot of money to be ma..."

30
1:00:00 - 1:01:11
1:11 duration266 words

Human Nature and Technology

In this philosophical exploration, Jordan reflects on whether humans are fundamentally good or influenced by their environment. He suggests that technology has the potential to broaden perspectives and reduce ignorance, but acknowledges that it has not yet fulfilled this promise.

"hurting i'm not hurting yeah and you think technically speaking it's possible to help i don't know the answers but it's it's a it's a less anonymity a little more locality um you know worlds that you ..."

31
1:01:11 - 1:02:30
1:18 duration271 words

Modeling Life as an Optimization Problem

Jordan discusses the complexities of modeling human life and society as optimization problems. He argues that while optimization is a useful mathematical tool, it is insufficient to capture the intricacies of human behavior and societal dynamics.

"so sorry for the big philosophical question but on that topic do you think human beings because you've also out of all things had a foot in psychology too the do you think human beings are fundamental..."

32
1:02:30 - 1:03:02
0:32 duration98 words

The Nature of Optimization

In this segment, Jordan delves into the properties of optimization problems, discussing concepts like convexity and non-convexity. He highlights the limitations of optimization in addressing the complexities of real-world scenarios.

"you actually are well but do you think individual human life or society could be modeled as an optimization problem um not the way i think typically i mean that's your time one of the most complex phe..."

33
1:03:02 - 1:04:00
0:57 duration183 words

Determinism vs. Stochasticity

Jordan shares his perspective as a statistician on the nature of the world, favoring a stochastic view over a deterministic one. He discusses the implications of uncertainty and probability in decision-making processes.

"complexity of such things what properties of optimization problems do you think so do you think most interesting problems that could be solved through optimization uh what kind of properties does that..."

34
1:04:00 - 1:05:11
1:11 duration226 words

Multi-Agent Systems and Optimization

Jordan explores the challenges of optimization in multi-agent systems, emphasizing the role of game theory. He discusses how individual agents interact within a system and the complexities that arise from decentralized decision-making.

"and the sampling paradigm and i want the um you know the the single point that's the best point in the par in the sample in the uh optimization paradigm now if you were optimizing in the space of prob..."

35
1:05:11 - 1:06:01
0:50 duration169 words

Saddle Points in Game Theory

In this segment, Jordan explains the concept of saddle points in game theory, particularly in relation to Nash equilibria. He discusses the limitations of traditional game theory and the need for broader frameworks to understand complex interactions.

"sense sort of uh at least from from a robotics perspective for a single robot for a single agent trying to optimize some objective function when you start to enter the real world this game theoretic c..."

36
1:06:01 - 1:07:10
1:09 duration248 words

Equilibria in Economic Systems

Jordan elaborates on different types of equilibria in economic systems, such as Nash and Stackelberg equilibria. He highlights the strategic considerations involved in decision-making and the implications for understanding human behavior.

"we talk about you know mixtures of humans and computers and markets and so on so forth there'll be certain principles that allow us to understand what's happening and whether or not the actual algorit..."

37
1:07:10 - 1:08:00
0:49 duration167 words

The Future of Optimization and Game Theory

Jordan discusses the future of optimization and game theory, emphasizing the importance of addressing open problems in the field. He highlights the need for innovative approaches to understand complex interactions in decentralized systems.

"look for saddle points that's a good thing oh not optimization that's just game theory that that's so uh there's all kinds of different equilibria in game theory and some of them are highly explanator..."

38
1:08:00 - 1:10:15
2:15 duration489 words

Deep Learning and Loss Functions

In this concluding segment, Jordan reflects on deep learning as a minimization problem of complex loss functions. He discusses the characteristics of these functions and their implications for optimization in real-world applications.

"nash equilibria have their limitations they are definitely not that explanatory in many situations they're not what you really want um there's other kind of equilibria and there's names associated wit..."

39
1:09:41 - 1:10:54
1:13 duration221 words

Deep Learning and the Nature of Loss Functions

In this segment, Jordan delves into the optimization of deep learning models, focusing on the minimization of complex loss functions. He explains the characteristics of these loss surfaces, noting their smoothness and the existence of multiple paths to reasonable optima. Jordan also raises questions about the nature of these surfaces and how they may evolve in the future, emphasizing the importance of understanding optimization theory.

"you know data analysis context is that yeah even the game of poker is fascinating space whenever there's any uncertainty your lack of information is it's a super exciting space yeah uh just uh lingard..."

40
1:10:54 - 1:12:43
1:49 duration385 words

The Evolution of Optimization Algorithms

Michael I. Jordan explores the current state of optimization algorithms, particularly stochastic gradient descent. He discusses the potential for new algorithms to emerge that could better navigate optimization spaces, emphasizing the co-evolution of algorithm design and architectural choices. This segment highlights the ongoing research in optimization and the mathematical properties of gradients.

"this those will change in 10 years it will not be exactly those surfaces there'll be some others that are and optimization theory will help contribute to why other surfaces are why other algorithms la..."

41
1:12:43 - 1:14:02
1:18 duration277 words

The Surprising Power of Stochasticity

Jordan explains the role of stochasticity in optimization, illustrating how randomness can help navigate complex surfaces and avoid pitfalls associated with deterministic approaches. He discusses the mathematical implications of stochastic gradient methods and their effectiveness in high-dimensional spaces, emphasizing the need for a deeper understanding of these concepts in the field of AI.

"um so uh that that i can't really anticipate that co-evolution process but you know gradients are amazing uh mathematical objects um they uh have a lot of people who uh start to study them more deeply..."

42
1:14:02 - 1:15:51
1:49 duration374 words

Nesterov Acceleration: A Breakthrough in Optimization

In this segment, Jordan highlights the significance of Nesterov's work on acceleration in optimization. He explains how this method utilizes previous gradients to improve convergence rates compared to traditional gradient descent. Jordan discusses the mathematical complexity of this approach and its implications for future research in optimization algorithms.

"still very very important um now we know that just providing descent has got all kinds of problems it gets stuck in many ways and it hadn't have you know good dimension dependence and so on so um my o..."

43
1:15:51 - 1:17:40
1:49 duration363 words

Understanding Statistics: A Bridge Between Math and Science

Michael I. Jordan provides an insightful overview of statistics, describing it as a discipline that straddles the line between mathematics and science. He discusses the principles of making inferences and decisions based on data, emphasizing the importance of understanding errors and probabilities. This segment offers a historical perspective on the evolution of statistics and its relevance in modern decision-making.

"to work in the field is that you get to try to understand this mathematics and um but long story short you know partly empirically it was discovered stochastic gradient is very effective and theory ki..."

44
1:17:40 - 1:19:00
1:19 duration280 words

The Duality of Bayesian and Frequentist Approaches

Jordan explores the philosophical and practical differences between Bayesian and frequentist statistics. He discusses how both approaches can yield different results and the importance of understanding their implications in decision theory. This segment highlights the complexities of statistical interpretation and the ongoing debates within the field.

"not clear yet because what one of our case score is a lower bound that's that's probably the best you can do what gradient is one over k but is there something better and so i think as a surprise to m..."

45
1:22:35 - 1:23:35
1:00 duration217 words

Understanding Bayesian vs. Frequentist Approaches

Michael I. Jordan explains the differences between Bayesian and Frequentist approaches in statistics, likening it to wave-particle duality in physics. He discusses how both perspectives can yield different answers and emphasizes the importance of understanding these frameworks in decision theory.

"around can you try to maybe i think i don't even want to try um let me just say a colleague at steve steven stickler at university of chicago wrote a really beautiful paper on james stein estimation w..."

46
1:23:35 - 1:24:55
1:20 duration294 words

The Role of Loss Functions in Decision Theory

In this segment, Jordan delves into decision theory, focusing on loss functions that depend on unknown data and parameters. He contrasts Bayesian and Frequentist methods in how they handle uncertainty and make predictions, highlighting the practical implications of each approach.

"field can you define beijing and frequencies yeah in decision theory you can make i have a like i have a video that people could see it's called are you a bayesian or a frequentist and kind of help tr..."

47
1:24:55 - 1:26:01
1:06 duration238 words

Empirical Bayes: Bridging Two Worlds

Jordan introduces the concept of Empirical Bayes, which combines Bayesian and Frequentist approaches. He discusses how this framework allows for the integration of human expertise and real-world uncertainties, leading to more robust statistical methods.

"out there and people are using all kinds of data sets you want to have a stamp a guarantee on it that as people run it on many many data sets that you never even thought about that 95 of the time it w..."

48
1:26:01 - 1:27:19
1:18 duration299 words

False Discovery Rate: A New Criterion

Michael I. Jordan explains the False Discovery Rate (FDR) as a criterion for evaluating multiple hypothesis tests. He contrasts it with traditional metrics like accuracy and precision, emphasizing its Bayesian perspective and its relevance in statistical decision-making.

"all right and i do agree with that especially in situations where you're working with a scientist you can learn a lot about the domain and you really only focus on certain kinds of data and you've gat..."

49
1:27:19 - 1:28:55
1:35 duration323 words

The Complexity of Human Intelligence

In this philosophical discussion, Jordan reflects on the challenges of defining human intelligence. He considers the limitations of psychological studies and the complexity of human reasoning, suggesting that understanding intelligence requires a broader perspective beyond just human capabilities.

"approval under certain assumptions this thing will work so perhaps you asked question what's my favorite you know or what's the most surprising nice idea so one that is more accessible is something ca..."

50
1:28:55 - 1:30:01
1:06 duration190 words

Intelligence Beyond Humans: Markets and Systems

Jordan argues that intelligence is not limited to humans but can also be observed in markets and other systems. He discusses how decentralized decision-making in economic systems can exhibit intelligent behavior, drawing parallels between human and market intelligence.

"argument has come out it's not certainly mine but it it sort of came out from robin's around 1960. uh brad ephron has written beautifully about this in various papers and books and uh and the fdr is y..."

51
1:30:01 - 1:31:36
1:34 duration312 words

The Future of AI and Human-Level Intelligence

In this speculative segment, Jordan discusses the potential for creating human-level or superhuman intelligence. He emphasizes the importance of focusing on present challenges rather than getting lost in science fiction, while acknowledging the intriguing possibilities of future intelligent systems.

"at least an expert at this point you know a psychologist aims to understand human intelligence right and i think many psychologists i know are fairly humble about this they they might try and understa..."

52
1:31:36 - 1:33:00
1:24 duration296 words

Advice for Aspiring AI Researchers

Michael I. Jordan shares valuable advice for undergraduates interested in machine learning and AI. He emphasizes the importance of hard work, humility, and building connections within the field, likening the journey to that of musicians and artists honing their craft.

"of decisions looking at it from far enough away it's just like a collection of neurons everyone every neuron is making its own little decisions presumably in some way and if you step back enough every..."

53
1:36:24 - 1:37:01
0:37 duration112 words

The Dangers of Science Fiction in AI

Michael I. Jordan discusses the potential pitfalls of focusing too much on science fiction narratives in public discussions about AI. He emphasizes the importance of addressing real challenges and dangers in the field rather than getting lost in speculative ideas. This segment highlights the need for grounded conversations about the future of AI.

"first challenges and dangers and real things to build and all that um such that uh you know uh spending too much time on science fiction at least in public fora like this i think is is not what we sho..."

54
1:37:01 - 1:39:57
2:56 duration616 words

Advice for Aspiring AI Researchers

In this segment, Jordan shares valuable advice for undergraduates interested in machine learning and AI. He stresses the importance of apprenticeship, hard work, and building human connections within the research community. He likens the journey in AI to that of musicians and artists, emphasizing the need for practice, humility, and collaboration.

"some of the brightest sort of some of the seminal figures in the field can you uh give advice to people who undergraduates today what does it take to take you know advice on their journey if they're i..."

55
1:39:57 - 1:42:55
2:58 duration655 words

The Beauty of Language Learning

Michael I. Jordan reflects on his experiences learning languages, particularly French and Italian. He discusses how language learning enriches personal experiences and fosters empathy. Jordan encourages students to pursue language studies alongside technical subjects, highlighting the cultural and creative aspects of language that contribute to a well-rounded education.

"where and how and why did you learn french and which language is more beautiful english or french um great question so um first of all i think italian's actually more beautiful than french and english..."

56
1:42:55 - 1:44:39
1:43 duration370 words

The Future of AI: A Human-Centric Approach

In closing, Jordan shares insights from his blog post on the future of AI, advocating for a human-centric engineering discipline. He calls for a broader understanding of AI beyond its current hype, urging the community to recognize the serious challenges ahead. This segment encapsulates his vision for a more responsible and inclusive approach to AI development.

"that i think i've said in some interview at some point that if i had you know millions of dollars on the infinite time whatever what would you really work on if you really wanted to do ai and for me t..."