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MIT AGI: Building machines that see, learn, and think like people (Josh Tenenbaum)

MIT AGI: Building machines that see, learn, and think like people (Josh Tenenbaum)

53 segments available

This is a talk by Josh Tenenbaum for course 6.S099: Artificial General Intelligence. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world. INFO: Course website: https://agi.mit.edu Contact: agi@mit.edu Playlist: https://goo.gl/tC9bHs CONNECT: - If you enjoyed this video, please subscribe to this channel. - AI Podcast: https://lexfridman.com/ai/ - Show your support: https://www.patreon.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Twitter: https://twitter.com/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Slack: https://deep-mit-slack.herokuapp.com

Segments Timeline

1
0:00 - 0:24
0:24 duration73 words

Introduction to Josh Tenenbaum

Josh Tenenbaum, a professor at MIT, introduces himself and his work in computational cognitive science. He discusses his fascination with how humans learn efficiently from minimal data and how these insights can inform the development of AI systems that learn more effectively.

"today we have Josh Tenenbaum he's a professor here at MIT leading the computational cognitive science group among many other topics and cognition and intelligence he is fascinated with the question of..."

2
0:24 - 1:01
0:37 duration105 words

The Quest for Artificial Intelligence

Tenenbaum emphasizes the importance of understanding artificial intelligence from both scientific and engineering perspectives. He highlights the collaborative efforts at MIT's Center for Brains, Minds, and Machines, aiming to bridge the gap between cognitive science and AI engineering.

"welcome all right thank you very much thanks for having me decided to be part of what looks like really quite a very impressive lineup especially starting after today and it's I think quite a great op..."

3
1:01 - 2:01
0:59 duration208 words

The State of AI Today

Tenenbaum discusses the current state of AI, noting that while we have advanced technologies that perform tasks previously thought to require human intelligence, these systems lack true general intelligence and common sense. He contrasts current AI capabilities with human flexibility and adaptability.

"who are affiliated with the Center for brains minds and machines so you can see up there on my affiliation academically I'm part of brain and cognitive science or course nine I'm also part of csail bu..."

4
2:01 - 3:04
1:03 duration243 words

Understanding Human Intelligence

In this segment, Tenenbaum explores what differentiates human intelligence from current AI technologies. He reflects on the innate abilities humans possess to learn and adapt across various tasks without the need for extensive engineering or resources.

"the reasons that you know brought you all here I don't have to tell you this we have all these ways in which AI is kind of finally here we finally live in the era of something like real practical AI o..."

5
3:04 - 4:01
0:57 duration214 words

The Role of Pattern Recognition

Tenenbaum explains the significance of pattern recognition in AI and intelligence. He acknowledges the advancements made through deep learning but stresses that true intelligence encompasses more than just recognizing patterns; it involves modeling the world and understanding complex concepts.

"one thing so alphago might beat the worlds best but it can't drive to the match or even tell you that go it what go is it can't even tell you the go is a game because it doesn't even know what a game ..."

6
4:01 - 5:30
1:29 duration285 words

Modeling the World

This segment focuses on the necessity of modeling the world as a core component of intelligence. Tenenbaum discusses how humans not only recognize patterns but also explain, understand, and imagine scenarios, which are critical for problem-solving and planning.

"again in CBMM is summarized here um what what drives the success is right now in AI especially in industry okay and all these AI technologies is many many things many things but what's what where the ..."

7
5:30 - 6:44
1:14 duration258 words

Reverse Engineering Intelligence

Tenenbaum introduces the concept of reverse engineering human intelligence to inform AI development. He advocates for a scientific approach that mirrors engineering principles, aiming to translate cognitive science insights into practical AI applications.

"weights in a neural net that are used for those purposes but many activities of learning are about building out new models right either refining reusing improving old models or actually building funda..."

8
6:44 - 8:01
1:16 duration278 words

Historical Context of AI Techniques

In this segment, Tenenbaum provides a historical overview of deep learning and reinforcement learning techniques. He highlights the foundational research in psychology and cognitive science that has shaped current AI methodologies, emphasizing the importance of understanding their origins.

"think what what I want to talk to you about here is one route for trying to get there and this is the route that CBMM stands for the idea that by reverse engineering how intelligence works in the huma..."

9
8:01 - 9:55
1:53 duration373 words

Future Directions in AI Research

Tenenbaum outlines his vision for the future of AI research, focusing on fundamental questions about consciousness, meaning, and learning. He discusses the potential for machines to emulate human-like intelligence and the engineering challenges that lie ahead.

"you know maybe some of you have read these original papers here's here's the original paper by rumelhart Hinton and colleagues in which they introduced the back propagation algorithm for training mult..."

10
9:55 - 12:01
2:06 duration498 words

Visual Intelligence as a Focus Area

This segment highlights the emphasis on visual intelligence in AI research. Tenenbaum explains the significance of understanding the human visual system and how it can inspire advancements in machine vision, aiming to replicate the rich representation of the world that humans naturally achieve.

"software systems right but this is where the basic the basic math came from and it came from doing science like an engineer so what I want to talk about in our vision is what is the future of this loo..."

11
12:01 - 16:22
4:21 duration940 words

The Complexity of Visual Perception

Tenenbaum discusses the complexities of visual perception, illustrating how humans perceive a rich world despite receiving limited visual information. He emphasizes the brain's ability to stitch together fragmented data into coherent representations, a challenge that AI systems must overcome.

"focus is around visual intelligence and there's many reasons for that again we can build on the successes of deep networks and a lot of pattern recognition and machine vision it's a good way to put th..."

12
15:39 - 17:13
1:34 duration345 words

Understanding Visual Intelligence

In this segment, Tenenbaum delves into the concept of visual intelligence, explaining how our brains not only perceive objects but also interpret the intentions and thoughts of others. He discusses the cognitive processes involved in understanding our environment and the significance of symbols in describing our experiences. This exploration sets the stage for discussing the architecture of visual intelligence.

"haven't turned around in a while right but some part of your brain is tracking the whole world around you right and how many people are behind you yeah like a few hundred right I mean I don't know if ..."

13
17:13 - 19:42
2:28 duration529 words

Building a Cognitive Architecture

Tenenbaum outlines the development of a cognitive architecture for visual intelligence, inspired by how the human brain operates. He introduces the concept of a 'brain OS' that integrates perceptual inputs with prior knowledge to create a coherent understanding of the world. This segment emphasizes the need for a scientific approach to reverse-engineering human-like intelligence in machines.

"what we've been doing in CBMM is trying to develop an architecture for visual intelligence and I'm not going to go into any of the details of how this works and this is just notional this is just a pi..."

14
19:42 - 21:15
1:32 duration307 words

The Limitations of Current AI

In this segment, Tenenbaum critiques the current state of AI, particularly in visual intelligence, highlighting the successes and failures of industry-driven approaches like image captioning. He discusses the overfitting issues in AI systems and how they often fail to understand context, leading to superficial interpretations of images. This critique underscores the gap between current AI capabilities and true human-like understanding.

"industry incentives especially optimized for it's not even really trying to take us to these things so think about for example a case study of visual intelligence that is in some ways as pattern recog..."

15
21:15 - 22:56
1:41 duration406 words

The Role of Data in AI Understanding

Tenenbaum presents a case study involving a Twitter bot that uses AI to caption images, illustrating the limitations of current AI systems in accurately interpreting visual content. He shares examples of both successes and failures in the bot's captions, emphasizing the importance of quality data over quantity. This segment highlights the challenges AI faces in achieving a deeper understanding of visual information.

"what you can see when you really dig into these things is there's often a lot of what I would call data set overfitting it's not overfitting to the training set but it's overfitting to whatever are th..."

16
22:56 - 30:01
7:04 duration1623 words

The Future of AI and Human Intelligence

In this concluding segment, Tenenbaum reflects on the future of AI in relation to human intelligence. He references insights from leading AI researchers, discussing the slow progress in achieving true understanding in AI systems. Tenenbaum argues for a more science-based approach to AI development, emphasizing the need for systems that can grasp complex human-like cognition and understanding.

"musical instrument right so that's a mixed success or failure here's some pretty good one a group of people on a on a field playing football that's I would call that a you know a result maybe even A+ ..."

17
30:01 - 31:01
1:00 duration235 words

The Tic-Tac-Toe Analogy

Tenenbaum uses the example of tic-tac-toe to illustrate the disparity between machine capabilities and human cognition. He points out that while machines can excel at complex games, they struggle with simple tasks that require contextual understanding, emphasizing the need for a deeper cognitive architecture in AI.

"what we're talking about or here's another I'll just give you one other example of a couple of photographs from my recent vacation and a nice warm tropical look how which I think illustrates ways in w..."

18
31:01 - 32:01
0:59 duration208 words

Perception to Cognition: The Cognitive Core

This segment explores the transition from perception to cognition in AI, as Tenenbaum discusses the importance of understanding objects, goals, and plans. He argues that current AI systems lack the ability to integrate these elements effectively, which is crucial for achieving human-like intelligence.

"we put the X and completed and now they've got three in a row right that's that's literally child's play okay you showed this sort of thing though to one of these you know image understanding caption ..."

19
32:01 - 33:02
1:01 duration219 words

Robotics and Human-Like Cognition

Tenenbaum highlights the gap between robotic capabilities and human-like cognition, referencing Boston Dynamics and Google's robotics efforts. He discusses the limitations of current robotic systems in mimicking human learning and manipulation, emphasizing the need for a more sophisticated understanding of intelligence.

"one that also motivates I think a lot of really good work on the engineering side and a lot of our interest in the science side is think about robotics and think about what do you have to do to you kn..."

20
33:02 - 34:02
0:59 duration229 words

Learning from Children: A Case Study

In this segment, Tenenbaum shares videos of children engaging in play to illustrate their cognitive abilities. He contrasts their intuitive understanding of tasks with the limitations of current AI systems, emphasizing the need for AI to replicate the flexible learning exhibited by young children.

"very much on the same page we both want to know how do you build the kind of intelligence that can control these bodies like the way a human does alright um here's another example of an industry robot..."

21
34:02 - 35:52
1:49 duration414 words

Symbolic Cognition in Early Childhood

Tenenbaum discusses the concept of symbolic cognition as demonstrated by children stacking cups. He explains how children can plan and adapt their actions based on their understanding of objects and goals, showcasing the cognitive processes that current AI systems struggle to replicate.

"and a half year old and the other ones a one year old so just watch this one and a half year old here doing a popular activity for many kids as a playing hmm you see video up there I'd okay there we g..."

22
35:52 - 37:10
1:18 duration295 words

Object Permanence and Cognitive Development

This segment focuses on the concept of object permanence as demonstrated by a baby in a video. Tenenbaum explains how this cognitive ability reflects a deeper understanding of the world, which AI systems currently lack, and discusses the implications for developing more advanced AI.

"again Boston Dynamics now has robots that could pick themselves up after that that's really impressive again but all the other stuff to get to that point we don't really know how to do in a robotic se..."

23
37:10 - 39:03
1:52 duration434 words

Animal Intelligence: Crows and Orangutans

Tenenbaum shares examples of animal intelligence, particularly focusing on crows and orangutans. He discusses their ability to manipulate objects and plan actions, drawing parallels to the challenges faced in developing AI that can achieve similar cognitive feats.

"was able to incorporate it in his plan right there's a moment before that when he's about to reach for it but then he sees this other one right and it's only when he's now exhausted all the other obje..."

24
39:03 - 40:02
0:58 duration218 words

The Mouse vs. Cracker Experiment

In this segment, Tenenbaum describes a humorous yet insightful experiment involving a mouse trying to retrieve a cracker. He highlights the cognitive processes involved in the mouse's determination and problem-solving, emphasizing the complexity of even simple tasks that AI struggles to replicate.

"here you've got this this famous Mouse this you can find on the internet under the mouse versus cracker video and what you'll see here over the course of this video is a mouse valiantly and mostly hop..."

25
40:02 - 44:10
4:08 duration886 words

The Future of AI: Learning from Human Behavior

Tenenbaum concludes by discussing the potential for AI to learn from human behavior, particularly in terms of helping and understanding goals. He emphasizes the importance of developing AI that can intuitively assist humans, drawing on insights from cognitive science to inform future advancements.

"one more video that is really more about science these other ones are you know some of them actually were from scientific experiments but this is one that motivates a lot of the science that I do and ..."

26
43:40 - 44:50
1:09 duration251 words

The Future of Assistive Robots

Tenenbaum explores the potential of creating robots that can intuitively assist humans in daily activities without explicit programming. He highlights the flexibility of human understanding in action and the goal of engineering technology that can replicate this capability, making robots more reliable and trustworthy companions.

"payoffs in particular suppose we could build a robot that could do what this kid and many other kids and these experiments do just say help you out around the house without having to be programmed or ..."

27
44:50 - 46:10
1:20 duration287 words

Probabilistic Programming Explained

In this segment, Tenenbaum introduces the concept of probabilistic programming as a computational abstraction for capturing common-sense knowledge. He explains how this approach generalizes Bayesian networks, allowing for more expressive knowledge representation and the ability to perform probabilistic and causal inference.

"intuitive understanding of physical objects in people's goals how do I build a model of that model you have in the head probabilistic programs a little bit more technically our one way to understand t..."

28
46:10 - 47:32
1:21 duration253 words

Combining AI Paradigms

Tenenbaum discusses the integration of various AI paradigms, including symbolic representation, probabilistic inference, and neural networks. He emphasizes the strengths and weaknesses of each approach and how combining them can lead to more robust AI systems capable of learning and transferring knowledge across tasks.

"symbolic representation or symbolic languages for knowledge representation probabilistic inference in generative models to capture uncertainty ambiguity learning from sparse data and in their hierarch..."

29
47:32 - 48:59
1:27 duration292 words

Game Engines as Cognitive Models

Tenenbaum draws parallels between modern video game engines and cognitive processes in humans. He explains how game engines can simulate physical interactions and intelligent behaviors, suggesting that these tools can serve as a foundation for understanding common-sense knowledge representations in AI.

"something which is a very mature technology in computer systems and programming languages probabilistic programs I'll just sort of advertise mostly are a way to combine the strengths of all of these a..."

30
48:59 - 50:57
1:58 duration424 words

Intuitive Physics Engine

In this segment, Tenenbaum presents the concept of an intuitive physics engine developed in his lab. He describes experiments demonstrating how this model uses probabilistic reasoning to predict physical interactions, showcasing its potential to understand common-sense reasoning in both children and AI.

"convergence of a number of different AI tools are happening and when and this will be absolutely necessary for making the kind of architecture that I'm talking about work another key idea which we've ..."

31
50:57 - 53:01
2:03 duration438 words

Infants and Common-Sense Reasoning

Tenenbaum discusses research involving infants and their understanding of physical scenes. He explains how looking time measures can reveal infants' expectations and surprises, linking these observations to probabilistic models that capture their intuitive grasp of physical interactions.

"scratch so what are called game physics engines and in some sense are a set of principles but also hacks from Newtonian mechanics and other areas of physics that allow you to simulate plausible lookin..."

32
53:01 - 54:44
1:43 duration340 words

The Red-Yellow Task Experiment

Tenenbaum introduces the red-yellow task experiment designed to assess infants' common-sense reasoning. He explains how the experiment measures looking time to determine whether infants find certain outcomes surprising, providing insights into their cognitive development and understanding of probability.

"the model we built does it basically a little bit of probabilistic inference in a game style physics engine it perceives the physical state and imagines a few different possible ways the world could g..."

33
54:44 - 58:39
3:54 duration873 words

Connecting Probability and Surprise

In this concluding segment, Tenenbaum highlights the relationship between probabilistic reasoning and surprise in infants. He discusses how the model developed in his research correlates with infants' expectations, illustrating the significance of probabilistic inference in understanding cognitive processes from a young age.

"it's like to be an objective one of these experience we just did the experiment here the data is all captured on video sort of right okay you could see that sometimes people were very quick other time..."

34
58:06 - 59:39
1:33 duration322 words

Understanding Goals in Infants

In this segment, Tenenbaum presents research on infants' understanding of goals through animated scenes. He describes experiments that reveal how infants assess the effort an agent exerts to achieve a goal, suggesting that even young children possess a basic understanding of utility calculus, which relates to their perception of intentional actions.

"right people have there are literally hundreds of studies if not more using looking time measures to study what infants know but only with this paper that we published a few years ago did we have a qu..."

35
59:39 - 1:01:01
1:22 duration286 words

Modeling Human Goal Prediction

Tenenbaum explains how his team developed a machine learning model that predicts human goal-directed actions based on physical constraints and planning. He illustrates this with an interactive example, demonstrating how the model can infer goals before actions are completed, showcasing the potential for machines to understand human intentions.

"the naive utility calculus so the idea that there's a basic calculus of cost and benefit you know we take actions which are a little bit costly to achieve goal states which give us some reward that's ..."

36
1:01:01 - 1:02:48
1:47 duration425 words

Inverse Planning in Robotics

This segment delves into the concept of inverse planning in robotics, where Tenenbaum discusses how a physics engine can be used to model human-like decision-making. He emphasizes the importance of understanding the physical context of actions and how this can enhance the development of intelligent systems capable of interpreting complex scenes.

"they're up by now alright and notice I was looking at your hands not here but went but what happened is most of the hands were up at the about the time when that gray or the one that - line shot up ok..."

37
1:02:48 - 1:04:13
1:24 duration328 words

Helping and Hindering: Infants' Social Understanding

Tenenbaum shares insights into how infants understand social interactions, specifically the concepts of helping and hindering. He describes models that illustrate how infants can discern the intentions of agents in various scenarios, highlighting the cognitive abilities that emerge in early childhood related to social dynamics.

"apply to much more interesting scenes that you haven't really seen much of before so take the scene on the left right where again you see somebody reaching for one of a four by four array of objects b..."

38
1:04:13 - 1:06:29
2:15 duration521 words

The Hard Problem of Learning

In this segment, Tenenbaum addresses the complexities of learning in artificial intelligence, contrasting it with human learning processes. He emphasizes the need for AI systems to develop a deeper understanding of learning as programming, akin to how children adapt and refine their cognitive models through experience.

"in a range of scenes I'll just say one last word about learning because everybody wants to know about learning and and the the key thing here and it's definitely part of any picture of AGI but the tho..."

39
1:06:29 - 1:08:03
1:33 duration349 words

Children as Hackers: Learning Through Experimentation

Tenenbaum introduces the metaphor of children as 'hackers' in the context of learning, suggesting that children's play and experimentation are akin to coding and refining programs. He discusses the implications of this perspective for understanding how children learn and adapt their cognitive frameworks.

"take now this is what you could call the hard problem of learning if you come to learning from say neural networks or other tools and machine learning right so what makes machine makes most of machine..."

40
1:08:03 - 1:12:14
4:11 duration931 words

Advancements in AI: Human-Level Concept Learning

In the final segment, Tenenbaum highlights significant advancements in AI, particularly in human-level concept learning. He discusses a notable project that achieved impressive results in visual concept learning, showcasing the potential for AI systems to mimic human-like understanding and creativity in generating new concepts.

"awesome that more awesome can mean more accurate but it can also mean faster more elegant more transportable to other applications or their tasks more explainable to others maybe just more entertainin..."

41
1:12:01 - 1:12:58
0:57 duration199 words

The Future of Human-like AI Systems

Tenenbaum shares insights on the future of artificial general intelligence (AGI), focusing on the potential of machines to learn like humans. He discusses the importance of understanding human cognitive processes and the role of programming languages in developing smarter AI systems that can interact with the human world.

"a current PhD student who works partly with me but also with armando salar Lezama and cecil this is kevin Ellis it's an example of what's now I think again a urging exciting area and AI well beyond an..."

42
1:12:58 - 1:14:43
1:44 duration386 words

Bridging Academia and Industry for AI Development

In this segment, Tenenbaum addresses the relationship between academia and industry in advancing AI research. He discusses the challenges of brain drain from academia to industry and emphasizes the need for collaboration to achieve breakthroughs in human-like AI systems.

"human-like machines so just to end then what I've tried to tell you here is taught first of all identify the ways in which human intelligence goes beyond pattern recognition to really all these activi..."

43
1:14:43 - 1:15:28
0:45 duration170 words

The Role of Emotions in AI Understanding

Tenenbaum responds to questions about the significance of emotions in AI. He acknowledges the importance of understanding emotions and mental models in developing AI systems that can replicate human cognitive processes, highlighting ongoing research in this area.

"maybe even with us or if any one of these other activities of human intelligence excite you I think taking the kind of science-based reverse engineering approach that we're doing and then trying to pu..."

44
1:15:28 - 1:16:19
0:50 duration150 words

Neuroscience Insights for AI Development

In this segment, Tenenbaum discusses the relevance of neuroscience in understanding human cognition and its implications for AI. He emphasizes the need for insights into brain circuitry to inform the development of intelligent systems that mimic human behavior.

"[Applause] hi there so early in the talk you expressed some skepticism about whether or not industry would get us to understanding human level intelligence it seems that there's a couple of trends tha..."

45
1:16:19 - 1:17:04
0:45 duration186 words

Industry's Focus on Short-term AI Solutions

Tenenbaum critiques the industry's focus on short-term AI solutions, arguing that this approach may not lead to significant advancements in human-like intelligence. He advocates for a balance between immediate practical applications and long-term research goals.

"think well that's a really good question and it's got several good questions packed into one there right I didn't mean to say I didn't this wasn't meant to say go academia bad industry right what I wa..."

46
1:17:04 - 1:18:49
1:44 duration387 words

Combining Academic and Industrial Strengths

In this segment, Tenenbaum emphasizes the necessity of combining the strengths of academia and industry to foster innovation in AI. He discusses the potential for collaborative efforts to address the challenges facing the AI community and to push the boundaries of what is possible.

"okay so what when we say what I'm talking about is the technologies which right now industry sees as meeting that specification and what I'm saying is right now I think those that's that's not where t..."

47
1:18:49 - 1:19:34
0:44 duration174 words

The Importance of Understanding Human Models

Tenenbaum concludes by reiterating the importance of understanding human cognitive models in AI development. He stresses that insights from cognitive science can guide the creation of AI systems that not only perform tasks but also understand and interact with the world like humans.

"Google I mean we just spent a few days talking to Google about exactly this issue that this was a talk I prepared partly for that purpose so we wanted to raise those issues and and it's just I mean re..."

48
1:24:42 - 1:25:39
0:57 duration201 words

The Mystery of Brain Computation

Josh Tenenbaum discusses the remarkable properties of the brain's neural circuits, emphasizing the contrast between the slow nature of neurons and the rapid intelligence they produce. He explores the mystery of how these slow elements can lead to quick intelligent behavior, highlighting the importance of understanding brain circuits for advancements in AI and engineering.

"look for and to know when you've found even viable answers so I think that's you know that's the standard kind of reductionist program but it's not that's it's not I also think it's it's not one that ..."

49
1:25:39 - 1:26:46
1:07 duration221 words

Energy Efficiency of the Brain

In this segment, Tenenbaum addresses the brain's astonishing energy efficiency compared to conventional hardware. He illustrates this with a personal anecdote about his daughter, who accomplished significant coding tasks on minimal energy intake, raising questions about how to replicate such efficiency in AI systems while addressing power consumption challenges.

"embedded circuits okay but also maybe most important is the power consumption and again many people have-have have noted this right if you look at the power consumption the power that the brain consum..."

50
1:26:46 - 1:28:07
1:20 duration290 words

Innovations in Low-Power Computing

Tenenbaum introduces Joe Bates and his startup, Singular Computing, which aims to develop brain-inspired low-power computing technologies. He discusses the potential for creating powerful computing systems that mimic the brain's efficiency, emphasizing the need for innovations in hardware to achieve human-level AI capabilities.

"problem for basically every area of engineering right if you want to if you want to have any kind of robot the power consumption is a key bottleneck same for self-driving cars if we want to build AI w..."

51
1:28:07 - 1:29:10
1:02 duration232 words

The Role of Video Games in AI Development

Tenenbaum highlights the influence of the video game industry on advancements in AI hardware and software. He suggests that the demand for complex simulations in gaming could drive innovations that benefit AI research, advocating for engagement with the gaming industry to enhance computational systems.

"don't didn't think you were interested in the brain if you want to build the kind of AI were talking about and run it on physical Hardware of any sort and understanding how the brain circuits compute ..."

52
1:29:10 - 1:30:23
1:12 duration262 words

Understanding Neural Circuits for AI

In this segment, Tenenbaum emphasizes the importance of studying neural circuits to inform AI development. He contrasts the complexity of biological neurons with current AI models, arguing that insights from neuroscience are crucial for creating more sophisticated AI systems that can operate efficiently.

"I don't know the answer to that question I but I think the I think what we can say is this um individual neurons I mean again this goes back to another reason to study neural circuits um if you look a..."

53
1:30:23 - 1:31:24
1:01 duration215 words

Bridging Software and Hardware in AI

Tenenbaum discusses the need to connect software and hardware perspectives in AI research. He argues that understanding the brain's computational strategies can inform engineering practices, leading to more effective AI systems that leverage insights from both neural circuits and algorithmic approaches.

"innovations in that support current a I was mostly not AI it was the video game industry I'm when I point to the video game engine in your head that's a similar thing that was driven by the video game..."