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Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416
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Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the most influential researchers in the history of AI. Please support this podcast by checking out our sponsors: - HiddenLayer: https://hiddenlayer.com/lex - LMNT: https://drinkLMNT.com/lex to get free sample pack - Shopify: https://shopify.com/lex to get $1 per month trial - AG1: https://drinkag1.com/lex to get 1 month supply of fish oil TRANSCRIPT: https://lexfridman.com/yann-lecun-3-transcript EPISODE LINKS: Yann's Twitter: https://twitter.com/ylecun Yann's Facebook: https://facebook.com/yann.lecun Meta AI: https://ai.meta.com/ 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 OUTLINE: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future SOCIAL: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Reddit: https://reddit.com/r/lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Segments Timeline
The Dangers of Proprietary AI
Yann LeCun discusses the risks associated with the concentration of power in proprietary AI systems. He argues that keeping AI under lock and key for security reasons could lead to a future where information is controlled by a few companies. LeCun believes in the goodness of people and advocates for open-source AI as a means to empower humanity.
"I see the danger of this concentration of power to to proprietary AI systems as a much bigger danger than everything else what works against this is people who think that for reasons of security we sh..."
Limitations of Auto-Regressive LLMs
LeCun critiques auto-regressive large language models (LLMs) like GPT-4 and Llama 2, stating they lack essential characteristics of intelligent behavior such as understanding the physical world, reasoning, and planning. He emphasizes that while LLMs can be useful, they are not the path to achieving superhuman intelligence.
"friends here's Yan laon you've had some strong statements technical statements about the future of artificial intelligence recently throughout your career actually but recently as well uh you've said ..."
Data Quantity vs. Human Learning
LeCun compares the vast amounts of data LLMs are trained on with the sensory experiences of a four-year-old child. He highlights that while LLMs process enormous text data, human learning is enriched by sensory input, which provides a deeper understanding of the world beyond language.
"mistake that is not to say that auto regressive LS are not useful they're certainly useful um that they're not interesting that we can't build a whole ecosystem of applications around them of course w..."
The Role of Language in Intelligence
In this segment, LeCun debates whether intelligence needs to be grounded in reality. He argues that true intelligence requires an understanding of the physical world, which LLMs lack. He emphasizes that much of human knowledge comes from interaction with the real world rather than language alone.
"that uh through sensory input we see a lot more information than we than we do through language and that despite our intuition most of what we learn and most of our knowledge is through our observatio..."
Mental Models and Planning
LeCun discusses the importance of mental models in human cognition, explaining how we plan actions based on our understanding of the world. He contrasts this with LLMs, which generate responses without the ability to plan or think abstractly, highlighting a significant gap in their capabilities.
"reality doesn't need to be you know physical reality could be simulated but um but the environment is just much richer than what you can express in language language is a very approximate representati..."
The Challenge of Learning Physical Tasks
LeCun reflects on the difficulty LLMs face in learning physical tasks compared to humans. He points out that while LLMs can excel in language-based tasks, they struggle with practical skills like driving or household chores, which require a deeper understanding of the physical world.
"even imagine require intelligence right this is the old marac Paradox from the pioneer of Robotics and SMC we said you know how is it that with computers it seems to be easy to do high Lev complex tas..."
World Models and Predictive Learning
LeCun elaborates on the concept of world models, explaining that they are essential for predicting future states based on actions. He argues that current generative models, including LLMs, are inadequate for building comprehensive world models due to their reliance on language rather than sensory data.
"language model construct a world model that does know how to drive and does know how to fill a dishwasher but just doesn't know how to deal with visual data at this time so it it can operate in space ..."
The Complexity of Video Prediction
In this segment, LeCun discusses the challenges of predicting video content using generative models. He explains that while LLMs can predict text sequences, the complexity of video data makes it difficult to represent and predict future frames accurately, highlighting a significant limitation in current AI capabilities.
"able to do this with the type of llms that we are uh working with today and there's a number of reasons for this but uh the main reason is the way llm LMS are trained is that you you take a piece of t..."
The Challenge of Video Prediction
Yann LeCun discusses the complexities of predicting video frames compared to text. He highlights the difficulty in representing distributions over high-dimensional continuous spaces, emphasizing that video contains far richer information than text, making accurate predictions challenging.
"at at Fair uh some colleagues and I have been trying to do this for about 10 years um and you can't you can't really do the same trick as with LM because uh you know llms as I said you can't predict e..."
Latent Variables in Video Modeling
LeCun explains the concept of latent variables in video prediction models. He describes the failure of current methods to accurately predict detailed aspects of video content, such as textures and specific objects, and the need for models that can represent unseen information effectively.
"this is because the world is incredibly more complicated and richer in terms of information than than text text is discret video is high dimensional and continuous a lot of details in this um so if I ..."
Failures in Image and Video Reconstruction
LeCun shares insights on the shortcomings of various techniques aimed at reconstructing images and videos from corrupted versions. He notes that while these methods work well for text, they have not been successful in visual domains, highlighting the challenges in learning effective representations.
"I can predict all those details so the the way to handle this is one way possibly to handle this which we've been working for a long time is to have a model that has what's called a latent variable an..."
The Ineffectiveness of Self-Supervised Learning
LeCun critiques self-supervised learning approaches that focus on reconstructing images from corrupted inputs. He argues that these methods fail to produce good representations for image recognition tasks compared to supervised learning with labeled data.
"Nets we tried Gans we tried uh you know Vees all kinds of regularized Auto encoders we tried um many things we also tried those kind of methods to learn uh good representations of images or video um t..."
Introducing Joint Embedding Predictive Architecture
LeCun introduces the concept of Joint Embedding Predictive Architecture (JEPA) as a solution to the limitations of traditional models. He explains how JEPA uses both full and corrupted images to train a predictor, aiming to improve representation learning in visual data.
"failure and it works really well for text that's the principle that is used for LMS right so where is the failure exactly is that that it's very difficult to form a good representation of an image a g..."
Contrastive Learning and Its Limitations
LeCun discusses contrastive learning methods that have been used to train models effectively. He explains the importance of showing both similar and different image pairs to prevent representation collapse, while also acknowledging the limitations of these methods.
"like this where you train the system by corrupting text except you corrupt images you remove Patches from it and you train a gigantic neet to reconstruct the features you get are not good and you know..."
Advancements in Non-Contrastive Learning
LeCun highlights recent advancements in non-contrastive learning techniques that do not rely on negative samples. He discusses how these methods can improve representation learning by focusing on different views of the same object.
"by reconstruction okay so what's the alternative the alternative is joint embedding what is joint embedding what are what are these architectures that you're so excited about okay so now instead of tr..."
JEPA's Role in Achieving AGI
LeCun explores the potential of JEPA in advancing towards Artificial General Intelligence (AGI). He contrasts it with generative architectures, emphasizing that JEPA focuses on extracting abstract representations rather than predicting every detail of the input.
"JEA so that means joint embedding predictive architecture because it's joint embedding and there is this predictor that predicts the representation of the good guy from from the bad guy um and the big..."
The Importance of Abstraction in AI
LeCun discusses the necessity of abstraction in AI systems, explaining how higher levels of abstraction can simplify the learning process. He draws parallels between human understanding and AI's need to model the world at various abstraction levels.
"appeared over the last six seven years um that can revive this this type of method um some of them from Far some of them from from Google and other places um but there are limitations to those contras..."
Combining Visual and Language Data
LeCun addresses the potential of integrating visual and language data for improved AI learning. He warns against premature combinations that could lead to superficial understanding, emphasizing the need for robust visual learning before merging with language.
"French yes and Ami stands for advanced machine intelligence right um but either way can japa take us to that towards that advanced machine intelligence well so it's a it's a first step okay so first o..."
Learning Common Sense Through Joint Embedding
LeCun articulates the hope that JEPA can help AI systems learn common sense reasoning. He discusses how the architecture aims to capture essential information about the world, enabling machines to understand and predict actions effectively.
"predictable okay so there's a lot of things in the world that we cannot predict like for example if you have a s driving car driving down the street or road uh there may be uh trees around the around ..."
The Role of Redundancy in Learning
LeCun explains how redundancy in sensory input enhances the learning capabilities of AI systems. He contrasts the redundancy in visual data with the more compressed nature of language, suggesting that this difference impacts the effectiveness of self-supervised learning.
"think about this this is something we do absolutely all the time whenever we describe a phenomenon we describe it at a particular level of abstraction and we don't always describe every natural phenom..."
Building World Models for Autonomous Systems
LeCun discusses the potential for AI systems to develop world models capable of understanding and interacting with their environment. He emphasizes the importance of learning physical constraints and common sense reasoning for tasks like driving.
"by directly predicting words so joint embedding it's still generative but it's generative in this abstract representation space yeah and you're saying language we were lazy with language cuz we alread..."
The Future of Joint Embedding Techniques
LeCun concludes by discussing the future of joint embedding techniques in AI. He highlights ongoing research and the potential for these methods to significantly enhance AI's understanding of complex environments and tasks.
"you're right about that but that means it's also less redundant and so self supervision will not work as well is it possible to join the self-supervised training on visual data and self-supervised tra..."
Introducing V-JEPA: Video Representation Learning
Yann LeCun discusses V-JEPA, a system designed for video representation learning. He explains how it masks segments of video frames to predict the full representation, achieving significant accuracy in identifying actions within videos. This segment highlights the advancements in video understanding and the importance of representation learning in AI.
"know geometri transformation and blurring and things like that that are really image specific uh a more recent version of of this that we have is called V JEA so is basically the same idea as I except..."
Building a World Model for AI
LeCun elaborates on the concept of a world model that allows AI to understand and predict physical interactions in videos. He describes how this model can help in planning actions, such as driving a car, by predicting future states based on current actions. This segment emphasizes the potential of AI to develop a deeper understanding of the physical world.
"that seem to indicate that the representation allows us allow our system to tell whether the video is physically possible or completely impossible because some object disappeared or an object you know..."
Hierarchical Planning in AI
In this segment, LeCun explains the necessity of hierarchical planning for complex actions, using the example of traveling from New York to Paris. He discusses how AI needs to break down high-level goals into manageable sub-goals and the challenges of training systems to perform such planning effectively.
"have uh a video and the a complete video and what you're doing to this video is that you're either translating it in time towards the future so you only see the beginning of the video but you don't se..."
The Challenge of Action Planning
LeCun addresses the limitations of current AI systems in planning actions at a granular level. He contrasts the need for detailed physical understanding with the capabilities of language models, emphasizing that while they can generate plans, they lack the experiential knowledge necessary for precise physical actions.
"can predict that uh if I have uh an object like this right and I open my hand it's going to fall right and uh and if if I push it with a particular force on the table it's going to move if I push the ..."
The Role of Experience in AI Learning
This segment focuses on the importance of experience in AI learning, particularly in understanding physical actions. LeCun argues that while language models can provide high-level plans, they cannot replicate the nuanced understanding gained from real-world experience, which is crucial for effective action execution.
"been planned since computers have been around so since the early 60s essentially so yes for model predictive control but you also often talk about hierarchical planning can hierarchical planning emerg..."
Limitations of Language Models
LeCun critiques the capabilities of language models in executing detailed plans. He explains that while they can generate responses based on training data, they struggle with tasks requiring physical interaction and real-world experience, highlighting the gap between language understanding and physical action.
"muscle control first that would be incredibly expensive but it will also be completely impossible because you don't know all the conditions of what's going to happen uh you know how long it's going to..."
Self-Supervised Learning in AI
LeCun discusses the significance of self-supervised learning in AI development. He highlights how this approach has led to advancements in language understanding and representation learning, showcasing its potential to improve AI systems' performance across various tasks.
"individual muscles uh maybe not whatever you can actually act upon using your mind right so there's a lot of questions that are sort implied by this right so the first thing is llms will be able to an..."
Advancements in Multilingual AI Systems
In this segment, LeCun shares insights into the progress made in multilingual AI systems, particularly in translation and speech recognition. He emphasizes the role of self-supervised learning in enabling these systems to operate effectively across multiple languages, including those without written forms.
"words like you you can't you can't do it um you you need that's one of the reasons you need experience of the physical world which is much higher bandwidth than what you can express in words in human ..."
The Evolution of AI Representation Learning
LeCun reflects on the evolution of representation learning in AI, detailing the challenges faced in developing effective models for images and videos. He discusses the breakthroughs achieved through self-supervised learning and the implications for future AI research.
"but if I apply everything you said today and in general to like I don't know 10 years ago maybe a little bit less no let's say three years ago I wouldn't be able to predict the uh success of llms so d..."
Scaling Language Models: An Unexpected Success
LeCun examines the surprising success of scaling language models, particularly in understanding language. He discusses how larger models trained on extensive datasets have demonstrated emergent capabilities, challenging previous assumptions about the limitations of AI.
"the system to not elaborate a representation of the text from looking at the enti text but only predicting a word from the words that are come before right and you do this by the constraining the arch..."
The Turing Test and AI Intelligence
In this segment, LeCun critiques the Turing Test as a measure of intelligence, arguing that fluency in language does not equate to true understanding. He emphasizes the need to recognize the limitations of AI systems while acknowledging their impressive capabilities.
"mean there were there were work from from various places but uh uh if if you want to kind of you know place it in the in the GPT uh timeline that would be around gpt2 yeah well I just cuz you said it ..."
The Power of Self-Supervised Learning
Yann LeCun discusses the advancements in self-supervised learning since 2014, highlighting its success in multilingual translation systems and speech recognition. He emphasizes how these systems can operate with mostly unlabeled data, showcasing the potential of self-supervised methods in various domains including text, images, and audio.
"then discovered that supervisor leing actually works pretty well if you can collect enough data and so the whole idea of you know unsupervised supervisor kind I took a a backseat for for a bit and the..."
Common Sense vs. Language Models
LeCun explores the limitations of large language models (LLMs) in understanding common sense reasoning. He argues that LLMs lack the foundational low-level experiences that humans acquire through observation, which are crucial for high-level reasoning. This segment delves into the differences between human reasoning and the capabilities of current AI systems.
"from Reading text the entirety of the Public Public avilable text in the world that I cannot get from New York to Paris by snapping my fingers that's not going to work right yes uh but there's you kno..."
The Role of Experience in Learning
In this segment, LeCun elaborates on how humans learn through sensory experiences rather than just language. He contrasts the rich data acquired from observation in early life with the limitations of text-based learning in AI, emphasizing the need for AI systems to incorporate experiential learning to achieve human-like understanding.
"that's not there in llms llms are purely trained from Tex so so then the other statement you made um I would not I would not agree with the fact that implicit in all languages in the world is the unde..."
Understanding AI Hallucinations
LeCun explains the phenomenon of hallucinations in large language models, detailing how errors accumulate exponentially with each token generated. He discusses the implications of this drift and the challenges it poses for ensuring the reliability of AI-generated responses, highlighting the need for improved training methods.
"systems I think in one of your slides you have this nice plot that is one of the ways you show that llms are limited I wonder if you could talk about hallucinations from your perspectives the why hall..."
The Limitations of Autoregressive Models
In this segment, LeCun critiques autoregressive models, explaining their inability to allocate computational resources effectively for complex problems. He contrasts this with human reasoning, which adapts based on the complexity of the task, and discusses the need for future AI systems to develop more sophisticated planning and reasoning capabilities.
"you mean that exact prompt or do you mean a prompt that's like in many parts very different than like is that easy to ask a question or to say a thing that hasn't been said before on the internet I me..."
Blueprint for Future AI Dialog Systems
LeCun outlines a vision for future dialog systems that integrate reasoning and planning capabilities. He proposes a model that optimizes responses based on abstract representations rather than relying solely on token prediction, suggesting a shift towards more intelligent and adaptable AI interactions.
"there's a prediction element there's a iterative element where you're like uh adjusting your understanding of a thing by going over over and over and over there's a hierarchical element so on does thi..."
Energy-Based Models Explained
In this segment, LeCun delves into energy-based models and their role in AI. He explains how these models can assess the compatibility of answers to prompts and the optimization process involved. By minimizing a cost function, these models can refine answers in a more efficient manner compared to traditional LLMs, which often generate multiple hypotheses.
"dialog systems um they will think about their answer plan their answer by optimization before turning it into text uh and that is tur complete can you explain exactly what the optimization problem the..."
Training Energy-Based Models
LeCun elaborates on the training process for energy-based models, discussing the importance of defining good representations for inputs and outputs. He highlights the challenges of ensuring that the model accurately reflects compatibility between questions and answers, and the necessity of using contrastive and non-contrastive methods to achieve this.
"much more efficient you can only do this in continuous spaces with differentiable functions you're talking about the reasoning like ability to think deeply or to reason deeply how do you know what is ..."
The Role of Abstract Representations
This segment focuses on the significance of abstract representations in AI systems. LeCun argues that effective AI must operate in a space of concepts rather than just concrete sensory information. He discusses how this approach can lead to better reasoning capabilities and the potential for more nuanced understanding in AI.
"if you push down the energy function for particular regions in the XY space it will automatically go up in other places because there's only a limited volume of space that can take low energy okay by ..."
Reinforcement Learning vs. Model Predictive Control
LeCun critiques reinforcement learning (RL) and advocates for model predictive control as a more efficient alternative. He explains how RL can be used to adjust world models when predictions fail, emphasizing the importance of learning good representations before applying RL techniques.
"an image or a video here's a corrupted shifted or transformed version of that image or video or masked okay and then uh the energy of the system is the prediction error of the representation uh the th..."
Human Feedback in AI Training
In this segment, LeCun discusses the transformative impact of reinforcement learning with human feedback (RLHF) on large language models. He explains how human ratings can be used to train models to produce better answers and the potential for using these techniques for planning rather than just fine-tuning.
"objective you don't need a unless the way you measure whether you succeed might be inexact your idea of you know whether you're going to fall from your bike might be wrong or whether the person you're..."
The Necessity of Open Source AI
LeCun argues for the importance of open-source AI to combat biases inherent in AI systems. He draws parallels between the need for diverse information sources in democracy and the necessity for diverse training data in AI, emphasizing that no AI can be completely unbiased and that open-source solutions can help mitigate these issues.
"very critical of the recently released Google's Gemini 1.5 for essentially in my words I could say super woke woke in the negative connotation of that word uh there is some almost hilariously absurd t..."
AI's Future: Mediated by Diverse Systems
LeCun envisions a future where AI systems mediate our interactions with the digital world, from smart glasses providing real-time translations to dialog systems replacing traditional search engines. He stresses the importance of not allowing a few companies to control this technology, advocating for open source solutions that enable a diverse range of AI assistants tailored to different cultural and linguistic needs.
"the what's the answer to this and the the answer is the same answer that we found in Liberal democracy about the press the Press to be free and uh diverse we have free speech for a good reason is beca..."
Diversity in AI: A Path to Inclusivity
In this segment, LeCun highlights the necessity of diversity in AI systems to cater to various cultural and linguistic contexts. He shares examples of initiatives in France and India aimed at fine-tuning AI models to support local languages and cultures. LeCun argues that open source platforms are essential for fostering this diversity, allowing different groups to create specialized AI systems that reflect their unique needs.
"if some of those Tob systems are open source anybody can use them anybody can fine-tune them um if we put in place some systems that allows any group of people whether they are um individual citizens ..."
Business Models for Open Source AI
LeCun explores potential business models for companies like Meta to profit from open source AI. He discusses how providing foundational models in an open source format can lead to a thriving ecosystem where businesses can build applications on top of these models. This approach not only fosters innovation but also allows companies to maintain a competitive edge while contributing to the broader AI community.
"systems political opinions um technical abilities in various domains and you can have an industry an ecosystem of companies that fine-tune those open source systems for vertical applications in Indust..."
Challenges of Big Tech in AI Development
LeCun addresses the challenges faced by big tech companies in developing AI products amidst political pressures and public scrutiny. He acknowledges the difficulty of creating unbiased systems that satisfy diverse audiences and emphasizes the need for open source solutions to mitigate these issues. LeCun argues that diversity in AI development is crucial for avoiding the pitfalls of ideological bias.
"business models right the business model that uh MAA is built around is um youer a service and the the financing of that service is uh either through ads or through business customers so for example i..."
The Role of Open Source in Ethical AI
In this segment, LeCun discusses the ethical implications of AI systems and the importance of open source in fostering responsible development. He argues that open source allows for the implementation of guardrails to ensure safety and non-toxicity in AI outputs. LeCun believes that a diverse set of contributors can help navigate the complex ethical landscape of AI, leading to more balanced and fair systems.
"progress to make the system available to a sort of a a wide community of people and and there is literally thousands of businesses who are building applications with it so um so our ability to meta's ..."
The Complexity of AI and Human Ethics
LeCun reflects on the inherent complexities of aligning AI systems with human ethics and values. He discusses the challenges of creating AI that can navigate moral and cultural disagreements, emphasizing that open source can facilitate a more nuanced approach. This segment highlights the ongoing dialogue about the responsibilities of AI developers in shaping technology that respects diverse human perspectives.
"going to see it as biased and then you push it the other way and another set of people is going to see it that's biased and then in addition to this there's the issue of if you push the system perhaps..."
Guardrails and the Future of AI
In the concluding segment, LeCun discusses the potential for implementing guardrails in AI systems to ensure they operate safely and ethically. He acknowledges the need for limits on what AI can produce while advocating for open source as a means to achieve this. LeCun envisions a future where AI technology empowers diverse communities while maintaining a commitment to ethical standards.
"Source essentially the problem there is then trying to minimize the number of people who are going to be unhappy y um and you're saying like the only that that almost impossible to do right and it's t..."
The Limitations of LLMs in Dangerous Knowledge
Yann LeCun discusses the limitations of large language models (LLMs) in providing actionable knowledge for creating dangerous technologies like bioweapons. He emphasizes that while LLMs can provide information, they do not simplify the complexities involved in actual implementation, which requires extensive expertise and is often restricted by international treaties.
"something you've commented on or at least there's a paper where a collection of researchers is trying to understand the social impacts of these llms and I guess one threshold is nice is like does the ..."
The Complexity of Building Weapons
LeCun elaborates on the challenges of constructing weapons, highlighting that having instructions is vastly different from the practical skills needed to execute them. He points out that even with a team of experts, the process is fraught with difficulties, making the actual creation of such weapons much harder than it appears.
"um that's the first thing the second thing is it's one thing to have a list of instructions of how to make a chemical weapon for example or bioweapon it's another thing to actually build it and it's m..."
Excitement for LLaMA's Future Developments
Yann LeCun expresses his enthusiasm for the upcoming LLaMA 3 and the potential advancements in open-source AI. He discusses the improvements expected in future versions, including better multimodal capabilities and systems that can understand and plan based on real-world knowledge.
"works um and you know it's the same with uh you know chemical weapons or biow weapons or things like this it requires expertise you know in the in the real world that n is not going to help you with a..."
The Path to Human-Level Intelligence
LeCun shares his excitement about the future of AI, particularly the potential for systems that can reason, plan, and learn from video. He reflects on the progress made in AI research and the collaborative efforts across institutions to develop models that could lead to human-level intelligence.
"source under meta well a number of things so uh there's going to be like various versions of of Lama that are uh you know improvements of previous llamas bigger better multimodal things like that and ..."
The Gradual Journey to AGI
LeCun discusses the misconception that achieving artificial general intelligence (AGI) will be a sudden event. He explains that it will be a gradual process involving incremental advancements in AI capabilities, emphasizing the need for systems that can learn and adapt over time.
"tasks by reinforcement running um and a lot of work at brookley by Peter iil S leine bunch of other people of that type uh I'm collaborating with actually in the context of some grants with my NYU hat..."
The Misconceptions of AI Doomsayers
LeCun critiques the fears surrounding AI doom scenarios, arguing that the emergence of superintelligence will not be a singular event. He explains that AI development will involve multiple systems, each with safety measures, and that the idea of a rogue AI taking over is largely unfounded.
"really excited about this what I like is that you know it uh somewhere we we get onto like a good direction and perhaps succeed before my brain turns to white sauce or or before I need to retire yeah ..."
Designing Safe AI Systems
LeCun emphasizes the importance of designing AI systems with safety in mind. He draws parallels to the development of reliable turbojet engines, explaining that safety will improve as AI systems become more advanced and that iterative design will be key to ensuring their proper functioning.
"aspect of it the meaning like the software well I used to be a hardware guy many years ago yes yes that's decades ago Hardware has improved a little bit changed a little bit yeah I mean certainly scal..."
The Future of AI Interaction
LeCun envisions a future where AI systems mediate all interactions with the digital world. He discusses the implications of this shift, suggesting that personal AI assistants will play a crucial role in discerning truth from deception in communications, thereby enhancing user safety.
"basically combination of Transformers and cets right and uh so you know there's still some ways to go until uh we're going to saturate we're going to have to come up with like new new principles new f..."
The Safety of AI Systems
Yann LeCun discusses the importance of designing better AI systems to ensure safety, drawing parallels to the evolution of Turbo Jet technology. He emphasizes that improvements in AI will inherently lead to safer systems, as better design leads to more reliable and controllable AI.
"and did we do this because we invented like a general principle of how to make Turbo Jet safe no we it took decades to kind of fine-tune the design of those systems so that they they were safe is ther..."
AI as a Weapon of Influence
LeCun explores the potential for AI systems to manipulate public opinion, likening them to weapons that could control minds. He discusses the implications of governments using AI to influence voters and the challenges of AI systems interacting with each other in this context.
"designed to be more useful uh and more controllable so let's imagine a system AI system that's able to be incredibly convincing and can convince you of anything I I can at least imagine such a system ..."
The Race of AI Development
In this segment, LeCun argues that the development of AI will be gradual and iterative, contrasting it with the rapid advancements seen in nuclear weapons. He explains how the competitive nature of AI development will prevent any one system from gaining a significant advantage over others.
"system designed by Vladimir Putin or whatever or his uh minions uh you know is going to be uh like talking to trying to talk to every American to uh convince them to vote for you know whoever whoever ..."
The Role of Industry in AI Progress
LeCun highlights the role of the tech industry in AI advancements, noting that breakthroughs will be disseminated quickly. He discusses how the rapid sharing of information within the industry fosters innovation and prevents secrecy, leading to faster progress in AI technology.
"like it there's always going to be this kind of race where nobody's way ahead that's the history of the world history of the world is you know whenever there is a prog at some someplace there is a cou..."
Human Psychology and Technology Fear
LeCun addresses the natural fear humans have towards new technologies, drawing parallels to historical reactions to innovations. He discusses how societal fears often lead to calls for regulation and control, reflecting a deep-seated anxiety about change.
"quickly we've seen this over the last few years right where you have a new like you know even take alphao this was reproduced within three months even without like particularly detailed information ri..."
The Ballpen Analogy
Using the analogy of a ballpen, LeCun illustrates how new technologies often face backlash due to fears of misuse. He humorously critiques the tendency to overreact to innovations, comparing it to the fears surrounding AI and its potential societal impacts.
"information so maybe I just to linger on the psychology of AI doomers you give uh in the classic Yan laon way a pretty good example of just when a a new technology comes to be you say uh engineer says..."
Embracing Change vs. Resisting It
LeCun discusses the dichotomy between embracing technological change and resisting it. He emphasizes the importance of understanding the real dangers of technology versus imagined fears, advocating for a balanced perspective on AI's role in society.
"know being threatened by Major Transformations um that are either cultural phenomena or technological um revolutions and they fear for their culture they feel for their job they feel for they fear for..."
The Need for Open Source AI
In this segment, LeCun argues for the necessity of open source AI platforms to prevent the concentration of power in the hands of a few. He stresses that diverse AI systems are crucial for preserving democracy and ensuring a variety of perspectives in AI interactions.
"Innovation or uh a cultural phenomenon um you know the this is wonderful examples of uh uh you know jazz or comic books being blamed for uh unemployment or or you know young people not wanting to work..."
The Dangers of Proprietary AI
LeCun warns about the risks associated with proprietary AI systems, which could lead to a future where information is controlled by a small number of companies. He advocates for open access to AI technology to promote a more equitable and democratic society.
"platforms yeah I just wanted to nail the point home more and more yes um so let me ask you on your like I said you do get a little bit uh um you know flavorful on the internet uh yos shabbach tweeted ..."
Trusting Humans with AI
LeCun reflects on the importance of trusting humans to use AI technology for good. He discusses the balance between security concerns and the need for innovation, emphasizing that good AI will ultimately prevail over bad AI in a competitive landscape.
"and the problem I'm seeing is um is that um which is why I've been so vocal and sometimes a little sardonic about it never stop never stop Yan we love it is because I see the danger of this concentrat..."
The Future of Humanoid Robots
LeCun shares his vision for the future of humanoid robots, discussing the potential for AI to enhance their capabilities. He highlights the challenges of creating robots that can effectively collaborate with humans and the importance of advancements in AI for this goal.
"that what democracy and free speech is all about I think so do you trust institutions to do the right thing do you trust people to do the right thing and and yeah there's bad people who are going to d..."
Challenges in Robotics Development
In this segment, LeCun addresses the current limitations in robotics, particularly in achieving fully autonomous systems. He discusses the need for advanced AI to enable robots to understand and navigate complex environments effectively.
"to be more effective collaborators with us humans so um since uh Tesla's Optimus uh team has been showing off some progress on humanoid robots I think it really reinvigorated the whole industry that's..."
The Complexity of Everyday Tasks for Robots
LeCun elaborates on the intricacies of programming robots to perform everyday tasks, such as cleaning and cooking. He emphasizes the sophistication required for robots to navigate and interact with human environments effectively.
"mhm uh and we're certainly very far away from having uh you know level five autonomous driving bi A system that can train Itself by driving 20 hours like any 17y old uh so until we have uh again World..."
The Future of AI and Human Interaction
LeCun concludes by discussing the exciting potential for AI to enhance human-robot interactions. He expresses hope for advancements in AI that will allow for meaningful collaboration between humans and robots in various aspects of life.
"be you know necessarily I mean we have demos actually because you know there is a So-Cal embodied AI group at at fair and uh you know they've been not building their own robots but using commercial ro..."
The Challenge of Hierarchical Planning in AI
Yann LeCun discusses the complexities of hierarchical planning in artificial intelligence. He emphasizes the need for AI systems to learn how to plan actions in various contexts, not just physical ones, and highlights the current limitations in achieving effective hierarchical planning. This segment explores the intricacies of developing AI that can autonomously plan and execute tasks.
"without necessarily scaling up then there is how you do planning with a learn World model if the world the system evolves in is not the physical world but it's the world of let's say the internet or y..."
AI's Role in Everyday Tasks
In this segment, LeCun reflects on the potential of AI to assist in everyday tasks, such as cooking or navigating complex environments. He underscores the challenges AI faces in executing seemingly simple household tasks and the implications of these challenges for future AI development. This discussion highlights the intricacies of AI's integration into daily life.
"demonstration of hierarchical planning uh in AI where the various levels of representations that are necessary have been learned we can do like two level hierarchy hierarchical planning when we design..."
Hope for Humanity Through AI
Yann LeCun shares his optimistic vision for the future of humanity with the advancement of AI. He believes that AI can enhance human intelligence and empower individuals, likening its potential impact to that of the printing press. This segment delves into the transformative power of AI and its ability to foster knowledge and understanding in society.
"future of humanity we're talking about so many exciting Technologies so many exciting possibilities what gives you hope when you look out over the next 10 20 50 100 years if you look at social media m..."
The Printing Press Analogy
LeCun draws a compelling analogy between the invention of the printing press and the rise of AI. He discusses how the printing press democratized knowledge and led to significant societal changes, while also acknowledging the challenges it posed. This segment explores the historical context of technological advancements and their impact on human progress.
"to make Humanity smarter and the analogy I've been using is the fact that perhaps an equivalent event in a history of humanity to what might be provided by journalized is the invention of the printing..."
AI Regulation and Job Market Transformation
In this segment, LeCun addresses the concerns surrounding AI regulation and its potential effects on the job market. He argues that while AI will transform professions, it will not lead to mass unemployment. This discussion emphasizes the need for adaptability in the workforce as AI continues to evolve and reshape various industries.
"uh and it didn't ban it uh for all languages only for Arabic you could actually print books in Latin or Hebrew or whatever in the Ottoman Empire just not in Arabic and uh I thought it was because the ..."
Closing Thoughts on AI and Humanity
LeCun concludes with a reflection on the importance of open-source AI and its role in empowering humanity. He expresses gratitude for the opportunity to share his insights and emphasizes the positive potential of AI in enhancing human capabilities. This segment encapsulates the overarching themes of hope and progress in the realm of artificial intelligence.
"properly well I think both you and I believe in humanity and I think I speak for a lot lot of people in saying thank you for pushing the open source movement pushing to making both research in AI open..."