Yann LeCun: An Architect of Modern Artificial Intelligence

Yann Lecun AI Executive Summary

In the rapidly evolving domain of Artificial Intelligence (AI), certain individuals emerge whose contributions are so foundational and transformative that they earn the designation of “legend.” Yann LeCun is unequivocally such a figure. His work over several decades has not only pushed the boundaries of machine learning and computer perception but has also laid the very groundwork upon which much of modern AI is built. This recognition is not mere hyperbole; it reflects a career characterized by pioneering research, influential ideas, and tangible, world-altering impact. LeCun is frequently cited as one of the “three musketeers” or “godfathers” of deep learning, a distinction he shares with Geoffrey Hinton and Yoshua Bengio.¹ This informal title, widely acknowledged in both academic and industry circles ⁴, underscores his seminal role in conceptualizing and popularizing the techniques that now power a vast array of AI applications.

Defining a Legend in Artificial Intelligence

The consistent application of the “godfather” label to LeCun, Hinton, and Bengio signifies more than individual brilliance or the invention of isolated techniques. It points to their collective role in shaping the entire paradigm of AI research and fostering the community that drives it. These individuals, LeCun prominent among them, championed neural networks and deep learning approaches during periods when these ideas were met with considerable skepticism, sometimes referred to as “AI winters”.⁶ Their persistence and intellectual stewardship were instrumental in keeping these concepts alive and developing them to the point where they could ignite the current AI revolution. This foundational influence, which involves setting research agendas, mentoring new generations of scientists, and constructing the intellectual scaffolding of a field, is a hallmark of a true scientific legend.

Thesis Statement

This report will establish Yann LeCun’s legendary status by delineating his groundbreaking scientific contributions, particularly in convolutional neural networks and self-supervised learning; tracing his influential career path through academia and industry leadership at Bell Labs and Meta; analyzing his distinct and often prescient vision for the future of AI, including his perspectives on Artificial General Intelligence (AGI) and AI safety; and assessing the profound and lasting impact of his work on technology and society. LeCun’s prominence is uniquely characterized by a potent combination of profound theoretical breakthroughs, critical engineering contributions that enabled widespread practical application, and a vocal, often constructively contrarian, public intellectual role in shaping the discourse on AI’s trajectory and its societal implications.

Overview of Report Structure

The subsequent sections of this report will navigate the arc of Yann LeCun’s career and influence. It will begin by exploring his formative years and early career, highlighting the experiences and intellectual currents that set him on his path. It will then delve into his foundational contributions to deep learning, focusing on Convolutional Neural Networks, advancements in network training, and Self-Supervised Learning. Following this, the report will examine his current pivotal role at Meta, steering the company’s AI research and articulating a distinct vision for its future. The report will also contextualize his work within the broader AI community, particularly his collaborations and shared recognition with other leading figures, and analyze his philosophical stance on AI’s trajectory, including AGI and safety. Finally, it will assess his enduring legacy and the future outlook for his work, underscoring his indelible mark on the field of intelligence itself.

The Formative Years and Early Career: Seeds of a Revolution

Early Life and Education

Yann LeCun was born in Soisy-sous-Montmorency, France, in 1960 and spent his formative years in the outskirts of Paris.¹ His fascination with the potential of artificial intelligence was ignited at the remarkably young age of nine, after seeing Stanley Kubrick’s iconic film “2001: A Space Odyssey”.¹ This early exposure to the imaginative possibilities of intelligent machines planted a seed that would shape his lifelong career trajectory. This deep-seated interest was not merely a passing fancy but a sustained intellectual curiosity about the fundamental nature of intelligence.

His formal education provided the technical grounding for his future endeavors. He received an engineering diploma (Diplôme d’Ingénieur) from the École Supérieure d’Ingénieurs en Électrotechnique et Électronique (ESIEE) Paris in 1983.² He then pursued doctoral studies, earning a PhD in Computer Science from the Sorbonne Université (then Université Pierre et Marie Curie) in 1987.² His doctoral thesis, titled “Modèles connexionnistes de l’apprentissage” (Connectionist Learning Models) ³, clearly indicates his early focus on neural network-based approaches to learning, a field that was, at the time, far from the mainstream of AI research. Further illustrating his early interdisciplinary curiosity, LeCun was inspired to delve into neural networks during his undergraduate studies after reading about Rosenblatt’s perceptron in a book discussing the Piaget versus Chomsky debate on language acquisition.⁹ This demonstrates an early inclination to bridge concepts from cognitive science, learning theory, and computational methods.

Postdoctoral Research and Early Influences

Following his PhD, LeCun undertook postdoctoral research from 1987 to 1988 in Geoffrey Hinton’s group at the University of Toronto.² This period was profoundly influential, immersing him in one of the few research environments actively exploring the potential of deep learning. His decision to work with Hinton was deliberate; LeCun had actively sought out Hinton after being impressed by his papers, recognizing him as a key mind in the field.⁶ This collaboration not only deepened his expertise but also forged a connection with another future “godfather” of AI, laying the groundwork for decades of shared intellectual pursuit.

AT&T Bell Laboratories: Where Theory Met Practice (1988-1996, and later AT&T Labs until 2002)

In 1988, LeCun transitioned from academia to the renowned industrial research environment of AT&T Bell Laboratories in Holmdel, New Jersey.² This move marked the beginning of a highly productive period where his theoretical insights would find fertile ground for practical application. He eventually became head of the Image Processing Research Department in 1996 ², a testament to his growing leadership and impact within the organization.

It was at Bell Labs that LeCun conducted his pioneering work on Convolutional Neural Networks (CNNs). He was instrumental in developing early forms of CNNs and refining the application of the backpropagation algorithm for training them. A landmark 1989 paper, co-authored with colleagues at Bell Labs, detailed the application of backpropagation to a CNN for recognizing handwritten zip codes.¹ This work was pivotal, demonstrating not only the architectural innovations of CNNs but also their practical utility in solving complex, real-world pattern recognition problems.

Building on this foundation, the LeNet-5 architecture, developed throughout the 1990s and formally published in a seminal 1998 paper ³, became an archetypal CNN. LeNet-5 was famously deployed for optical character recognition, particularly for reading handwritten characters on bank checks. This system achieved remarkable success, processing a significant portion—reportedly over 10 percent—of all checks in the United States during the late 1990s and early 2000s.¹ This deployment was a powerful demonstration of the commercial viability and scalability of LeCun’s research, proving that neural networks could deliver robust solutions for large-scale industrial challenges. The success of the check-reading system at Bell Labs established a defining characteristic of LeCun’s career: the tight integration of fundamental research with high-impact, practical applications. This was not merely an academic exercise but a solution to a major industrial problem, foreshadowing his later role at Meta in driving research that directly informs product development. This early success in deploying advanced AI into tangible systems likely solidified his pragmatic approach to AI and his focus on building systems that function effectively in the real world, a theme that persists in his current work on architectures like JEPA for robotics.¹¹

Beyond CNNs, LeCun’s tenure at Bell Labs was marked by other notable contributions, showcasing the breadth of his technical expertise. He collaborated with Léon Bottou and Patrick Haffner on the DjVu image compression technology ³, which became a widely used standard. With Bottou, he also co-developed the Lush programming language ³, designed for researchers working on large-scale numerical and machine learning applications.

The pattern of LeCun’s early academic choices and inspirations—from Kubrick’s “2001” to the Piaget vs. Chomsky debate, and his proactive pursuit of Geoffrey Hinton ¹—reveals a persistent and profound intellectual curiosity about the nature of intelligence itself, extending beyond mere engineering solutions. This philosophical underpinning is a crucial element of his scientific persona. It suggests a drive motivated by fundamental questions about learning, perception, and cognition. This intrinsic motivation likely fuels his long-term vision for AI, his willingness to challenge prevailing paradigms, and his current ambitious pursuit of AGI through novel architectures.¹³ It is this combination of deep intellectual curiosity and practical engineering prowess that set the stage for his subsequent groundbreaking contributions.

Table 1: Timeline of Yann LeCun’s Key Milestones and Contributions

Year	Event/Contribution	Significance	Key Sources
1960	Born in Soisy-sous-Montmorency, France	–	1
~1969	Saw Kubrick’s “2001: A Space Odyssey”	Early inspiration for AI	1
1983	Engineering Diploma, ESIEE Paris	Formal education in engineering	2
1987	PhD, Sorbonne Université (Pierre et Marie Curie)	Thesis: “Modèles connexionnistes de l’apprentissage” (Connectionist Learning Models)	2
1987-1988	Postdoc with Geoffrey Hinton, University of Toronto	Collaboration with a key figure in deep learning	2
1988	Joined AT&T Bell Labs	Start of influential industrial research career	2
1989	Paper on backpropagation for handwritten zip code recognition (early CNN)	Introduced foundational CNN concepts, practical backpropagation	1
1990s	Co-developed DjVu image compression (with Bottou & Haffner)	Widely used image compression technology	3
1990s	Co-developed Lush programming language (with Bottou)	A language for prototyping and numerical computing	3
1998	LeNet-5 paper published (“Gradient-based learning applied to document recognition”)	Landmark CNN architecture, deployed for check reading	1
2003	Joined New York University (NYU) as Professor	Began influential academic and mentorship role; founding director of NYU Center for Data Science	1
2013	Joined Meta (then Facebook) as first Director of Facebook AI Research (FAIR)	Established and led a major industrial AI research lab	1
~2010s	Coined/Popularized “Self-Supervised Learning” (SSL)	Articulated and championed a key paradigm for AI to learn from unlabeled data	18
2018	Awarded ACM A.M. Turing Award (with Hinton & Bengio)	Highest recognition in computer science for deep learning work	8
2022	Received Princess of Asturias Award (with Hinton, Bengio & Hassabis)	Major international award for scientific research	2
2022-Present	Spearheading Joint Embedding Predictive Architectures (JEPA/V-JEPA) at Meta	Meta’s strategic direction for next-gen AI, focusing on world models	1
2024	Named a “Great Immigrant” by Carnegie Corporation of New York	Recognition of contributions as an immigrant to the US	8
2025	Awarded Queen Elizabeth Prize for Engineering (jointly)	Prestigious engineering award for modern machine learning contributions	3

Foundational Contributions to Deep Learning: Building the Pillars of Modern AI

Yann LeCun’s contributions to deep learning are not merely incremental improvements but foundational pillars upon which much of the field now stands. His work on Convolutional Neural Networks revolutionized how machines perceive the world, while his advancements in neural network training and his conceptualization of Self-Supervised Learning have profoundly shaped the trajectory of AI research.

Convolutional Neural Networks (CNNs): A Paradigm Shift in Perception

The introduction of Convolutional Neural Networks by LeCun and his team at Bell Labs in 1989 marked a paradigm shift in how computers approach tasks involving grid-like data, most notably images.⁹ Their 1989 paper, which detailed the training of non-linear CNNs using backpropagation for handwritten digit recognition, was a seminal moment.¹⁰ Prior to CNNs, prevailing methods often involved flattening images into one-dimensional vectors, a process that discarded crucial two-dimensional spatial information inherent in visual data. CNNs, by contrast, were designed to preserve and exploit this structure, enabling the learning of spatial hierarchies of features.¹⁰ This was not just an algorithmic improvement but a fundamental representational breakthrough. By encoding principles like spatial hierarchies and translation invariance, CNNs provided a mechanism for machines to process and “understand” visual data in a manner that bears resemblance to biological vision, unlocking capabilities that were previously the domain of science fiction. The sheer breadth of current CNN applications, from medical imaging to autonomous vehicles ¹, stands as a testament to the power of this representational shift.

The key architectural innovations of these early CNNs were revolutionary:

• Local Receptive Fields: Inspired by biological vision, neurons in a convolutional layer respond only to a small, localized region of the input (their receptive field).⁹ This allows the network to learn elementary features like edges or corners.
• Shared Weights (Parameter Sharing): This is perhaps the most crucial innovation. The same set of weights, forming a filter or kernel, is convolved across the entire input image. This means the network can detect a specific feature (e.g., a horizontal edge) regardless of its position in the image. This drastically reduces the number of learnable parameters compared to fully connected networks, making CNNs more efficient and less prone to overfitting, while also introducing a degree of translation invariance.¹⁰
• Pooling (Subsampling): Typically following convolutional layers, pooling layers (like average or max pooling) reduce the spatial dimensions of the feature maps. This makes the learned representations more robust to small translations and distortions in the input and helps control overfitting.¹⁰ LeNet-5, for instance, introduced Max Pooling.¹⁰

The LeNet-5 architecture, detailed in 1998 ⁹, became the archetypal CNN. This 7-layer network elegantly integrated convolutional layers for feature extraction, pooling layers for dimensionality reduction and robustness, and fully connected layers for classification. It set a blueprint that influenced countless subsequent CNN designs.⁹ LeNet-5 was famously and successfully applied to practical tasks such as reading handwritten digits on bank checks, achieving a very low error rate (0.95% on the MNIST dataset) and demonstrating significant real-world utility.¹

A core reason for the success of CNNs lies in their incorporation of inductive bias. These are assumptions about the nature of the data (e.g., the local structure of images, the fact that features are often translation-invariant) that are built into the architecture itself.¹⁰ This makes CNNs more data-efficient and inherently better suited for visual tasks than more generic models, as they don’t have to learn these properties from scratch. This design philosophy reflects a more human-like approach to image processing.

The transformative impact of CNNs cannot be overstated. They form the cornerstone of modern computer vision ¹ and have enabled a vast array of applications that permeate daily life and specialized industries. These include:

• Image and video recognition for tasks ranging from photo tagging to content analysis.¹
• Facial recognition systems used in security and social media platforms.¹
• Medical image analysis, aiding in the detection of tumors in MRI scans, analysis of X-rays, and other diagnostic tasks.¹
• Perception systems for autonomous vehicles, enabling lane detection, obstacle avoidance, and traffic sign recognition.¹
• Automated content moderation on online platforms, helping to identify and filter inappropriate content such as hate speech.⁵
• Document analysis, including the recognition of handwritten text.¹⁷

Advancements in Neural Network Training: The Power of Backpropagation

While the concept of backpropagation pre-dates LeCun’s main work, he made crucial contributions to its development and practical application, particularly for training deep neural networks, starting in the mid-1980s.¹ The backpropagation algorithm allows neural networks to learn efficiently by iteratively adjusting their internal weights based on the error (the difference between the network’s output and the desired output) that is propagated backward through the network’s layers.

LeCun’s contribution to backpropagation extends significantly beyond the algorithm itself to its engineering and dissemination as a scalable, modular component. This practical engineering focus, particularly his collaboration with Léon Bottou at Bell Labs, led to the development of a “building-block principle” for backpropagation.⁶ This principle has become foundational and now underpins virtually all modern deep learning software platforms, including popular frameworks like PyTorch and TensorFlow.⁶ LeCun himself has cited this work as one of his proudest accomplishments.⁶ This engineering feat was absolutely essential for the entire deep learning field to take off. It provided the robust and efficient tools necessary for researchers and developers worldwide to design, build, and train increasingly complex neural network models. This is a profound second-order impact: by creating the tools, LeCun enabled countless first-order innovations by others across the globe, effectively democratizing the ability to conduct deep learning research and development.

Self-Supervised Learning (SSL): Enabling Machines to Learn from the World

Yann LeCun is widely credited with coining or at least popularizing the term “self-supervised learning” (SSL) and articulating its conceptual framework, carefully distinguishing it from purely unsupervised learning, which he considered a “loaded and confusing term”.¹⁸ He characterized SSL as a learning paradigm where the system effectively generates its own supervisory signals from the input data itself. In his words, the model is tasked to “pretend there is a part of the input you don’t know and predict that”.¹⁸ This allows the machine to learn rich and useful representations from vast quantities of unlabeled data.

The motivation behind SSL is to overcome a critical bottleneck inherent in supervised learning: the dependence on massive, manually labeled datasets. Creating such datasets is an expensive, time-consuming, and often error-prone process, requiring significant human effort.¹⁸ SSL aims to enable machines to learn more like humans and animals do—by observing the world, making predictions about it, and learning from the discrepancies between those predictions and subsequent observations, without explicit labels for every piece of data.¹³

Technically, SSL involves designing “pretext tasks.” These are tasks where parts of the input data are intentionally hidden or corrupted, and the model is trained to predict or reconstruct these missing parts. Examples include predicting masked words in a sentence, predicting the relative position of image patches, colorizing grayscale images, or predicting future frames in a video.¹⁸ The model learns by minimizing a loss function based on these self-generated supervisory signals, effectively learning underlying structure and dependencies within the data.¹⁸

LeCun views SSL as a cornerstone for the future development of AI, particularly for building systems that possess a deeper understanding of the world, common sense, and the ability to reason and plan effectively.¹³ It is a core component of his vision for achieving Artificial General Intelligence and underpins advanced architectures like the Joint Embedding Predictive Architecture (JEPA) that he is currently spearheading.¹³ Theoretical work in the field is also beginning to formalize the intuitions behind SSL, with some research suggesting that SSL implicitly defines labels that optimize for robust performance on a wide range of potential downstream tasks.²⁰

LeCun’s advocacy for Self-Supervised Learning represents more than just the introduction of a new set of techniques; it embodies a strategic vision for AI to escape the “tyranny of labels.” This approach aims for AI to learn with an efficiency and adaptability that begins to mirror that of biological systems. This is not merely an incremental improvement but a philosophical shift towards creating AI that actively makes sense of the world through observation and prediction. This shift is crucial for his long-term ambitions for AGI, as it provides a pathway for machines to acquire the vast amounts of background knowledge and understanding about the world that humans and animals learn implicitly.

Yann LeCun at Meta: Steering the Future of AI

Yann LeCun’s role at Meta (formerly Facebook) has positioned him at the forefront of industrial AI research, where he not only leads significant scientific endeavors but also articulates a distinct and often contrarian vision for the future of artificial intelligence.

Leadership at Facebook AI Research (FAIR) and Meta AI

In 2013, Mark Zuckerberg recruited Yann LeCun to become the founding Director of Facebook AI Research (FAIR).¹ This was a pivotal moment, signaling Facebook’s serious commitment to fundamental AI research. LeCun’s current role is Vice President and Chief AI Scientist at Meta.¹ In this capacity, he steers the company’s strategic AI initiatives, a role that has evolved from his initial directorship of FAIR.¹

A significant organizational development occurred when LeCun’s team was integrated more directly into Meta’s “product” organization (reported around early 2025, though this date may be illustrative of a recent shift).⁵ This move was designed to elevate the importance of AI research as an essential ingredient for the long-term success of Meta’s products and its ambitious goal of creating Artificial General Intelligence (AGI).⁵ This integration underscores Meta’s intent to translate cutting-edge research into tangible product innovations more rapidly.

A Contrarian Vision: Beyond Large Language Models (LLMs)

Yann LeCun has become one of the most prominent and articulate skeptics regarding the capabilities of current Large Language Models (LLMs)—such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude—to serve as the primary pathway to true AGI.1

He has controversially stated his belief that today’s LLMs will be “largely obsolete within five years”.1 His critique centers on the argument that LLMs, despite their impressive fluency, lack a meaningful understanding of the physical world, common sense, and the abilities to reason, plan complex sequences of actions, or remember information effectively over long periods.1 He characterizes them as operating predominantly in the “simple, discrete space—language” 1 and functioning as “System 1” reactive systems, good at intuitive, fast pattern matching but poor at deliberate, “System 2” reasoning.1

Furthermore, LeCun contends that training AI models solely on text, however vast the corpus, is fundamentally insufficient for achieving human-level intelligence. Humans, he points out, learn and interact with the world primarily through rich sensory data, processing vastly more information through vision and other senses than through language alone.¹ He has unequivocally stated, “We’re never going to get to human-level intelligence by just training on text. It’s never going to happen”.¹ This conviction has led him to advise young developers to look beyond current LLMs: “Don’t work on LLMs… You should work on next-gen AI systems that lift the limitations of LLMs”.¹

LeCun’s vocal skepticism towards LLMs as the sole or primary path to AGI is more than a mere technical disagreement; it represents a fundamental challenge to the dominant narrative and the massive investment trends currently shaping the AI industry. His advocacy for world models learned primarily from sensory data, embodied in architectures like JEPA ¹¹, constitutes a significant, and potentially disruptive, alternative research direction. This direction is not just an academic proposition but is backed by the substantial resources of Meta. If this alternative approach proves more fruitful, it could necessitate a major re-evaluation of strategies across the field, potentially diminishing the perceived long-term value of scaling current LLM architectures alone. This makes Meta’s pursuit of JEPA a high-stakes strategic bet on a different future for AI.

The Path Forward: Joint Embedding Predictive Architectures (JEPA) and World Models

In place of an LLM-centric approach, LeCun champions architectures designed to learn world models. These are internal, predictive models that allow an AI system to understand how the world works and to anticipate the consequences of actions—its own or others’.¹ He posits that these models should be learned primarily through self-supervised learning from rich sensory data, especially video, rather than relying predominantly on text.

The Joint Embedding Predictive Architecture (JEPA) is a key architectural framework proposed by LeCun and actively being developed at Meta to realize this vision.¹ The core idea of JEPA is to learn abstract representations of inputs (such as image patches or video segments) and then to predict these representations in a latent (abstract) space, rather than attempting to predict raw sensory data like pixels directly.¹ Predicting raw pixels is an incredibly complex task, often leading to blurry or imprecise predictions because the world contains too much unpredictable detail. By making predictions in an abstract feature space, JEPAs can learn to ignore irrelevant details and focus on capturing the underlying dynamics and salient features of how the world evolves.¹

Meta has introduced specific instantiations of this concept, such as I-JEPA (Image-JEPA) and V-JEPA (Video-JEPA).¹ V-JEPA 2, for example, is described as a self-supervised foundation world model trained on video. It is designed to achieve state-of-the-art visual understanding and prediction, enabling capabilities such as zero-shot robot control in unfamiliar environments.¹¹ Technical implementations of JEPA often involve a context encoder, a target encoder (whose weights are typically a moving average of the context encoder’s weights to prevent collapse), and a relatively shallow predictor network. These components are often built using vision transformers and are trained using a reconstruction loss in the latent space, with various techniques employed to ensure the learned embeddings are informative and avoid trivial solutions (collapse).²¹

The development of JEPA and world models reflects a consistent through-line in LeCun’s thinking, tracing back to his early interest in connectionist models and his later formalization of Self-Supervised Learning. It embodies his long-standing belief that AI systems should learn more like humans and animals do—through active interaction with and prediction of their environment, rather than through the passive statistical pattern matching on vast, disembodied text corpora. This emphasis on learning rich world representations from rich sensory data is a core tenet of his philosophy on how robust intelligence should be constructed.

The ultimate goal of this research direction is to build AI systems that can reason, plan, possess common sense, and understand the physical world. This, LeCun argues, will enable AI to achieve goals in a more robust, flexible, and controllable manner than current systems allow.¹

Meta’s Pursuit of Advanced Machine Intelligence (AMI) / Artificial General Intelligence (AGI)

Mark Zuckerberg has publicly stated that Meta’s objective is to create AGI ⁵, and LeCun’s research is central to this ambitious undertaking.⁵ Meta uses the term Advanced Machine Intelligence (AMI) often interchangeably with AGI. LeCun believes that the next-generation AI systems emerging from this research, built on principles of world modeling and self-supervised sensory learning, will not replace human intelligence but rather amplify it, leading to profound societal transformations.¹

Meta’s public commitment to AGI and the strategic positioning of LeCun’s research, including its integration into the product organization, signal that the company views the development of foundational world models as far more than a purely scientific endeavor. It is seen as a critical component for future product ecosystems. AI that can genuinely understand and predict the physical world, as V-JEPA aims to do ¹¹, would be immensely valuable for creating more intuitive and capable robotic assistants, smarter augmented reality experiences that interact seamlessly with the physical environment, and more sophisticated AI agents that can plan and act effectively in complex, dynamic settings. The explicit mention of applications like “robotic assistants” and “wearable assistants” for V-JEPA ¹² reinforces this direct line from fundamental research to future product capabilities, making LeCun’s vision for AI integral to Meta’s long-term competitive strategy.

The “Godfather of AI”: Collaborations, Recognition, and Enduring Influence

Yann LeCun’s stature as a “godfather of AI” is not solely derived from his individual breakthroughs but also from his deep collaborations, the extensive recognition he has received from the global scientific community, and his lasting influence on both academia and industry.

The Turing Award Triumvirate: LeCun, Hinton, and Bengio

In 2018, Yann LeCun, alongside Geoffrey Hinton and Yoshua Bengio, was awarded the ACM A.M. Turing Award.¹ This award is widely regarded as the “Nobel Prize of Computing” ²⁸, representing the highest distinction in the field. The official citation recognized the trio for “conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing”.⁶ This wording significantly highlights that their contributions were not limited to abstract theory but also encompassed the crucial engineering work that made these ideas practical and impactful.

The three laureates share a remarkable journey. They worked both independently and together, developing the conceptual foundations of deep learning, conducting pivotal experiments that revealed surprising phenomena, and contributing engineering advances that demonstrated the practical advantages of deep neural networks.²⁷ They maintained a shared interest in neural networks even during periods when the broader AI field was skeptical of their potential.⁶ LeCun’s postdoctoral work under Hinton’s supervision and his later collaborations with Bengio at Bell Labs are testaments to their interconnected careers.² The Canadian Institute for Advanced Research (CIFAR) played a crucial, supportive role through its Learning in Machines and Brains program (which Hinton initially directed and LeCun and Bengio now co-direct), providing vital funding and fostering a collaborative community that helped sustain their research during less favorable times.⁶

The joint nature of the Turing Award for LeCun, Hinton, and Bengio is profoundly significant. It symbolizes more than the recognition of three brilliant individuals; it signifies the triumph of a persistent, collaborative, and initially contrarian research paradigm—neural networks and deep learning—that fundamentally reshaped the landscape of computing. Their shared history, marked by perseverance through periods of widespread skepticism (the “AI winters”), forged a collective identity and resilience that were crucial for the field’s eventual, explosive success. The award, therefore, acknowledges not just their individual genius but also their collective role in nurturing, championing, and ultimately validating a revolutionary approach to artificial intelligence.

A Pantheon of Awards and Global Recognition

Beyond the Turing Award, Yann LeCun’s contributions have been recognized with a plethora of prestigious honors from around the world, underscoring the global reach and multifaceted impact of his work. These include:

• The IEEE Neural Networks Pioneer Award (2014) ⁶
• The Lovie Award for Lifetime Achievement (2016) ⁸
• The Princess of Asturias Award for Technical and Scientific Research (2022), shared with Hinton, Bengio, and Demis Hassabis ²
• Appointment as Chevalier de la Légion d’Honneur (Knight of the Legion of Honour) by the French government (2023) ³
• The Queen Elizabeth Prize for Engineering (2025), awarded jointly for contributions to Modern Machine Learning ³
• Recognition as a “Great Immigrant” honoree by the Carnegie Corporation of New York (2024) ⁸
• The Harold Pender Award from the University of Pennsylvania (2018) ³
• The IRI Medal from the Industrial Research Institute (2018) ³
• A TIME100 Impact Award ⁵
• The inaugural Trailblazer Award from The New York Academy of Sciences (NYAS) ⁴

He is also an elected member of several esteemed national academies, including the National Academy of Sciences (U.S.), the National Academy of Engineering (U.S.), and the Académie des Sciences (France).⁸ Furthermore, he is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the American Association for the Advancement of Science (AAAS).⁸

The breadth and international character of these awards are particularly telling. Accolades such as the Princess of Asturias Award (Spain), the Legion of Honour (France), and the Queen Elizabeth Prize for Engineering (UK) demonstrate that his impact is recognized at the highest levels globally and across different facets of science and engineering, not merely within a specialized AI niche. This diverse recognition from scientific bodies, governments, and prestigious institutions worldwide elevates his “legend” status to a broader stage of scientific and technological achievement, reflecting a deep and wide-ranging influence.

Academic Leadership and Mentorship

Alongside his prominent role in industry, Yann LeCun has maintained a strong and influential presence in academia. He holds the position of Silver Professor at New York University (NYU), with affiliations across multiple departments, including the Center for Data Science, the Courant Institute of Mathematical Sciences (Computer Science), the Center for Neural Science, and the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering.⁴

He was the founding Director of the NYU Center for Data Science ⁶, a testament to his vision in establishing interdisciplinary hubs for this burgeoning field. His deep learning course at NYU is highly influential, and he has made the course materials, including lectures, videos, and notebooks, openly available online, significantly broadening its reach and educational impact.¹⁹ He continues to be actively involved in guiding research and mentoring students and postdoctoral researchers.¹⁹

LeCun’s sustained commitment to academia, exemplified by his NYU professorship, his dedication to open courseware, and his ongoing mentorship, alongside his high-profile leadership at Meta, demonstrates a remarkable dual dedication to advancing fundamental knowledge and driving practical innovation. This hybrid role is a defining feature of his career, allowing him to effectively bridge the gap between theoretical exploration and real-world application. This synergy enables academic research to inform industrial challenges, while practical problems encountered in industry can, in turn, inspire new avenues for fundamental scientific inquiry. This unique positioning allows him to influence both the next generation of AI researchers and the strategic direction of industrial AI development.

Broader Influence on the AI Research Community

Yann LeCun’s influence extends throughout the AI research community. His foundational work, particularly on Convolutional Neural Networks and the practical engineering of backpropagation, forms the bedrock of a vast amount of current AI research and development worldwide.¹ Many of the tools and techniques now considered standard in the AI practitioner’s toolkit can be traced back to his innovations. Furthermore, his vocal and consistent advocacy for open-source AI (detailed further in Section VI.B) has significantly shaped the norms and practices around how AI research is shared, disseminated, and collaboratively developed, fostering a more open and accessible ecosystem.

LeCun’s Philosophical Stance on AI’s Trajectory: A Pragmatic and Forward-Looking Vision

Yann LeCun is not only a builder of AI systems but also a prominent thinker on their future trajectory, offering a distinct and often pragmatic perspective on Artificial General Intelligence (AGI), AI safety, and the ethical considerations surrounding this transformative technology.

The Path to Artificial General Intelligence (AGI): A Call for New Architectures

LeCun’s views on achieving AGI are characterized by a critique of current mainstream approaches, particularly those heavily reliant on Large Language Models (LLMs), and a call for fundamentally new architectures.

Critique of LLM-centric AGI:

He consistently reiterates that while LLMs are undeniably useful for specific tasks, they do not represent a viable path to AGI.1 His primary arguments are that LLMs lack a genuine understanding of the physical world, possess limited reasoning and planning capabilities, and struggle with long-term memory and common sense. They are adept at statistical pattern matching in the “simple, discrete space—language” 1 but function as “System 1” (reactive) systems, unable to perform the kind of deliberate, “System 2” thinking characteristic of deeper intelligence.1

LeCun often highlights Moravec’s paradox: current AI systems can perform tasks that humans find intellectually challenging (like passing exams or playing complex games), yet they struggle with basic sensory-motor skills and the common-sense understanding of the world that even young children and animals possess effortlessly.¹ He argues that true AGI requires models trained on far richer, higher-bandwidth sensory inputs (like video) than just text, as humans and animals derive most of their understanding of the world through such interactions.¹ As he puts it, “Humans see more data when you measure it in bits” than what is contained in text corpora.¹⁴

LeCun’s Proposed Roadmap for AGI:

His vision for AGI centers on systems that can learn predictive world models from sensory data through Self-Supervised Learning (SSL), integrated within sophisticated cognitive architectures:

• World Models: AI must build internal, predictive models that represent how the world works, allowing them to simulate outcomes and understand cause and effect.¹³ These models should capture complex relationships between objects and events, reason about causality and temporal dependencies, and potentially integrate both symbolic and connectionist (neural network-based) approaches to AI.¹³ The Joint Embedding Predictive Architecture (JEPA) and its variants like V-JEPA are Meta’s primary efforts in this direction.¹¹
• Self-Supervised Learning (SSL): This is deemed crucial for enabling AI systems to learn these rich world models from the vast amounts of unlabeled sensory data available in the environment, much like humans and animals do.¹³
• Cognitive Architectures: LeCun envisions a unified framework that integrates various AI components such as perception, attention, memory, and decision-making modules.¹³ Such architectures should allow the system to focus attention, store and retrieve knowledge efficiently, and reason and make decisions under uncertainty. He has proposed architectures involving modules for action generation (proposing possible actions), world modeling (predicting outcomes of those actions), and objective evaluation (assessing how good a predicted world state is according to given goals, including safety objectives). These systems might be trained using reinforcement learning or employ classical search algorithms like A* or Monte Carlo Tree Search for planning.¹⁴
• Memory and Planning: AGI systems, unlike current LLMs that often have a fixed computational budget per query, need access to robust long-term memory and should be able to dedicate variable amounts of computational effort to planning and reasoning, spending more time on harder problems.¹⁴

Regarding the timeline for AGI, LeCun is generally more conservative than some other figures in the field, suggesting it is likely decades away (e.g., 10 to 100 years) and not an imminent breakthrough.²⁵

LeCun’s roadmap to AGI, with its strong emphasis on SSL, world models learned from rich sensory input, and integrated cognitive architectures, reflects a philosophical stance that can be described as more “constructivist” or “empiricist.” This perspective posits that intelligence, particularly the flexible and common-sense understanding characteristic of humans, is primarily built up from extensive sensory experience and active interaction with the environment. It contrasts with approaches that might place a greater emphasis on innate knowledge structures or purely symbolic reasoning (though LeCun does see a potential role for integrating symbolic AI with connectionist systems ¹³). This underlying philosophy about the nature of intelligence itself likely informs his architectural choices, such as JEPA’s focus on learning from video ¹¹, and his skepticism about achieving AGI through methods that largely bypass this deep experiential grounding in the physical world.

AI Safety, Ethics, and Openness: A Pragmatic and Pro-Democracy Stance

LeCun’s views on AI safety and ethics are marked by a pragmatic optimism, a focus on tangible near-term issues, and a strong advocacy for openness as a key mitigating factor against potential harms.

Perspective on AI Risks:

Compared to some of his peers, notably Geoffrey Hinton and Yoshua Bengio, LeCun is generally more optimistic and less alarmist about the potential for AI to pose an existential risk to humanity.8 He has stated that he does not believe current AI technologies pose a genuine existential threat 8 and is critical of what he sometimes terms “deluded” or exaggerated fears about imminent AI sentience or uncontrollable superintelligence emerging from current systems.1 He cautions against “magical thinking” and the tendency to anthropomorphize AI systems based on their performance in limited domains.1 He has argued, for instance, that superintelligent machines will likely have no inherent desire for self-preservation.36

Instead, LeCun tends to focus on more immediate and practical concerns related to AI. These include the potential misuse of AI for purposes like generating disinformation (though he has noted that the current flood of disinformation is largely human-generated ³⁷), the perpetuation or amplification of algorithmic bias, the risks associated with the concentration of AI power in the hands of a few entities, and the broader societal impacts of AI deployment.⁷

Advocacy for Open-Source AI:

Yann LeCun is a vocal and consistent proponent of open-sourcing AI models, research, and tools.1 Meta, under his scientific influence, has notably committed to open-sourcing many of its significant AI models and tools, a departure from the more proprietary stance often taken by other major tech companies.5 His rationale for this strong advocacy is multifaceted:

• Promotes Transparency and Scrutiny: Openness allows for broader examination of models, helping to identify biases, flaws, and potential safety issues.
• Democratizes Access: It enables smaller companies, academic researchers, and individuals worldwide to access and build upon state-of-the-art AI, fostering innovation more broadly.
• Prevents Concentration of Power: It acts as a bulwark against a few powerful corporations or governments monopolizing control over transformative AI technologies.
• Ensures AI Sovereignty: Open access can help countries develop their own AI capabilities, tailored to their specific needs and values.¹
• Fosters Diversity: It allows for the development of a diverse ecosystem of AI assistants and applications that can reflect a wide range of cultures, languages, and value systems.¹⁴ LeCun even argued for the development of free, open-source “universal” foundation models that understand all languages and cultures during a speech at the UN Security Council.¹⁹

This strong advocacy for open-source AI is not merely a technical preference or a business strategy for Meta; it is a deeply rooted political and ethical stance. LeCun views open-sourcing as a crucial mechanism for democratizing power and ensuring equitable access to what he sees as a profoundly transformative technology. It is a proactive measure aimed at mitigating certain societal harms that could arise from unchecked, concentrated control over AI. His argument that our future information diet, increasingly mediated by AI, should not be dependent on “proprietary, closed system[s]” ⁵ highlights this conviction. This positions his open-source advocacy as a core component of his approach to responsible AI development, perhaps viewing it as a more potent and adaptable safeguard against certain risks than some top-down regulatory approaches or purely technical alignment solutions.

Vision for Human-AI Coexistence:

LeCun generally expresses an optimistic vision for the future, where AI primarily serves to amplify human intelligence and capabilities, acting as powerful tools and assistants rather than as replacements or existential threats.1 He envisions a future where intelligent AI assistants mediate many, if not all, human interactions with the digital world, enhancing productivity and creativity.14 He has suggested a potential new human-machine societal hierarchy where AI systems are designed with built-in guardrails and objectives that ensure they serve human goals.1

Contrasting Views with Other AI Leaders:

While sharing the Turing Award and a long history of collaboration with Geoffrey Hinton and Yoshua Bengio, LeCun’s views on the imminence and nature of AI existential risk, and consequently the most urgent safety priorities, differ notably. This divergence is a significant aspect of the current discourse on advanced AI. The following table summarizes some key comparative perspectives:

Table 2: Comparative Perspectives: LeCun, Hinton, and Bengio on AI’s Future and Risks

Aspect	Yann LeCun	Geoffrey Hinton	Yoshua Bengio
AGI Timeline/Feasibility	Decades away (10-100 years 25); skeptical of LLM-only path to AGI 1; AGI not seen as imminent.34	Changed estimate to “20 years or less” for general-purpose AI, partly due to LLM breakthroughs.36	Transition from AGI to superintelligence could be rapid (months to years) if AI begins to self-improve.34
Primary AI Risks	Misuse (e.g., disinformation 37), algorithmic bias, concentration of power, societal disruption.7 Less concerned with imminent existential threat from AI itself.8	Existential risk (estimates a 10-20% chance of human extinction from AI 34), loss of control, autonomous weapons, superintelligence outmaneuvering humans.7	Existential risk (“very plausible” 25), AI developing its own goals, societal disruption, misuse by bad actors (e.g., terrorists 25), economic/political domination by AI companies.25
Approach to AI Safety	Open source as a key safeguard 5; build objective-driven AI with human-like intelligence and built-in guardrails, controllable by design.1 Focus on practical, present-day issues.35	Urgent need for research into AI safety and alignment; became more vocal about dangers after recent LLM progress.36 Considers iterative empirical safety approaches increasingly risky with more capable AI.36	Need for “guardrails,” independent oversight for AI organizations 25; adherence to ethical development principles (e.g., Montreal Declaration 7); AI systems potentially modeled as honest, non-agentic “scientists”.25
Stance on LLMs for AGI	Highly skeptical; views current LLMs as “nearly obsolete” for AGI, lacking true understanding, and believes they will be superseded by world models.1	LLM breakthroughs were a key factor in his decision to shorten his AGI timeline estimate and increase his concern about risks.36	Acknowledges LLM utility but emphasizes their lack of “System 2” reasoning and the need for more advanced capabilities.25
Desire for Self-Preservation in AI	Believes superintelligent machines will have no inherent desire for self-preservation.36	(Implicitly concerned, as a misaligned superintelligence could act against human goals to achieve its own, which may involve self-preservation as an instrumental goal 36).	Warns that an ASI focused on self-preservation could take extreme measures such as hacking, replicating itself, and manipulating humans to ensure its survival.34

There is a noteworthy dynamic in LeCun’s stated positions: he expresses less concern about existential risk from hypothetical future superintelligence ³⁵ while simultaneously being at the forefront of efforts at Meta to create more powerful and general forms of AI (AGI/AMI).⁵ His apparent confidence rests on the belief that it will be possible to design these advanced AI systems to be inherently “safe and controllable by design,” with built-in objectives and guardrails, rather than relying on retrofitting safety onto potentially unpredictable systems via methods like fine-tuning.¹ The ultimate success and robustness of this “controllable by design” paradigm for highly intelligent and adaptive systems is a critical, and as yet unproven, assumption. Whether this confidence is borne out will be a crucial test if his AGI ambitions are realized, and it represents a key point of divergence from the more cautious stances of some of his distinguished colleagues.

The Enduring Legacy and Future Outlook: An Indelible Mark on Intelligence Itself

Yann LeCun’s contributions have already left an indelible mark on the field of artificial intelligence and, by extension, on the fabric of modern technology and society. His legacy is not static; it continues to evolve as his current work aims to shape the next generation of intelligent systems.

The Pervasive Impact of LeCun’s Innovations

The innovations spearheaded by LeCun have rippled through countless aspects of technology and daily life.

• Revolutionizing Computer Vision: Convolutional Neural Networks, which LeCun pioneered, are now the undisputed backbone of modern computer vision.¹ They have enabled unprecedented breakthroughs in image and video recognition, object detection, image segmentation, and even image generation.
• Transforming Everyday Technologies: The practical applications stemming from his research are extensive and deeply embedded in the technologies we use daily:

• Handwriting and Document Recognition: His early work on LeNet led to systems that automated the reading of handwritten checks and other documents, a major efficiency gain for banking and postal services.¹
• Facial Recognition: CNNs are fundamental to facial recognition systems used for security purposes, device unlocking, and photo tagging on social media platforms.¹
• Medical Imaging Analysis: In healthcare, CNNs assist in diagnosing diseases by analyzing medical images such as MRIs, CT scans, and X-rays, helping to detect tumors, segment organs, and predict patient outcomes.¹
• Autonomous Driving Systems: The perception systems of autonomous and driver-assisted vehicles rely heavily on CNNs for tasks like lane detection, identifying pedestrians and other vehicles, and recognizing traffic signs.¹
• Speech Recognition: While not his primary focus, the deep learning techniques he helped establish have also significantly advanced speech recognition capabilities.⁶
• Natural Language Processing: Foundational deep learning concepts, to which LeCun was a key contributor, have also been instrumental in the progress of natural language processing.⁵
• Content Moderation: Online platforms utilize AI, often based on CNNs and other deep learning models, to detect and moderate harmful content, including hate speech.⁵
• Foundational Software Principles: The “building-block principle” for backpropagation, developed by LeCun and Léon Bottou, has become a core tenet of all major deep learning software libraries like PyTorch and TensorFlow.⁶ This critical engineering contribution has empowered countless researchers and developers worldwide, effectively providing the toolkit for the entire deep learning revolution.

LeCun’s career demonstrates a remarkable “virtuous cycle.” His foundational theoretical work, such as the principles behind CNNs ⁹, enabled highly impactful practical applications like automated check reading.¹ The success and visibility of these applications, in turn, provided validation for the underlying approaches and likely contributed to securing the resources and credibility needed for further fundamental research. This cycle continues today, as he leverages the capabilities of a major tech company like Meta, itself benefiting from earlier AI successes, to pursue even more ambitious fundamental research into world models and the path to AGI.⁵ This ability to seamlessly bridge deep scientific inquiry with real-world impact and then use that impact to fuel further inquiry is a hallmark of truly legendary scientific figures.

Future Directions: Towards Advanced Machine Intelligence

Yann LeCun’s current work is sharply focused on pushing beyond the limitations of existing AI paradigms to achieve what Meta terms Advanced Machine Intelligence (AMI), or AGI.

• His primary research thrust involves the continued development and refinement of Joint Embedding Predictive Architectures (JEPA) and the broader concept of world models at Meta AI.¹ The goal is to create AI systems that can genuinely understand complex environments, predict how they will evolve in response to actions, and plan effectively to achieve objectives.
• A major emphasis is on imbuing AI with common sense and robust reasoning capabilities, qualities he argues are largely absent in current LLMs and are essential for more general intelligence.¹
• He envisions a future where highly capable intelligent virtual assistants, built upon these more advanced AI foundations, will mediate nearly all human interactions with the digital world, offering personalized and context-aware support.¹⁴
• The potential applications of these future AI systems are vast, with significant implications for robotics (creating robots that can learn and adapt in the physical world), wearable assistive technologies (e.g., aiding navigation for the visually impaired), and numerous other domains that require a deep understanding of real-world dynamics.¹¹

The widespread adoption of LeCun’s open-source philosophy, particularly its embrace by a major corporation like Meta in releasing significant AI models ⁵, has the potential to fundamentally alter the competitive landscape of AI development. This could foster a more collaborative and distributed global ecosystem for AI innovation, allowing a broader range of actors to participate and benefit. However, it also presents new challenges, particularly concerning the business models for monetizing foundational AI research if the core outputs are made freely available. This advocacy for openness thus has significant socio-economic ripple effects that extend far beyond purely technical considerations.

Shaping Society: AI as an Amplifier of Human Potential

LeCun consistently projects an optimistic view of AI’s societal role. He believes that AI will primarily serve to amplify human intelligence and capabilities, augmenting our ability to solve complex problems and enhance creativity, rather than posing an existential threat or leading to widespread human obsolescence.1

He anticipates a societal transformation potentially as profound as that brought about by the printing press, with humans perhaps shifting into more managerial, strategic, or creative roles within a new human-machine collaborative hierarchy.1

Central to this vision is his unwavering emphasis on the importance of open-source development. He sees this not just as a means to accelerate innovation but as a crucial mechanism to ensure equitable access to powerful AI technologies, prevent monopolistic control, and foster a diverse AI ecosystem that reflects global values.5

Concluding Assessment: The Multifaceted Legend

Yann LeCun’s status as a legend in the field of Artificial Intelligence is firmly cemented by a rare and potent confluence of attributes:

• Foundational Inventor: His creation of Convolutional Neural Networks and his critical contributions to the engineering of backpropagation and the conceptualization of Self-Supervised Learning are pillars upon which modern AI stands. These are not just influential ideas but enabling technologies that have unlocked vast new capabilities.
• Visionary Thinker: His consistent, often constructively contrarian, and deeply reasoned vision for the future of AI—particularly his emphasis on world models over a purely LLM-centric approach, his pragmatic stance on AI safety, and his unwavering commitment to open source—continues to challenge, stimulate, and drive the field forward.
• Influential Leader: Through his pivotal roles at AT&T Bell Labs, New York University, and now Meta, he has not only steered major research directions but has also mentored and inspired generations of AI researchers and practitioners.
• Pragmatic Engineer: A defining characteristic of his career is the ability to translate profound theoretical insights into practical, impactful applications that solve real-world problems, from reading checks to enabling the next generation of intelligent systems.

Yann LeCun’s ongoing work continues to push the boundaries of machine intelligence, promising further transformations in technology and society. He remains a central, dynamic, and often provocative figure in the global quest to understand, create, and responsibly deploy artificial intelligence. His ultimate legacy may indeed hinge not only on his monumental past achievements but also on the success of his current, ambitious, and scientifically distinct bet on world models and JEPA as the most promising path towards Artificial General Intelligence.¹ If this vision is realized, it will not only validate his critique of alternative approaches but will also cement him as a figure who not only pioneered early deep learning but also architected its evolution into truly intelligent machines.

Works cited

1. Yann LeCun Fireside Chat: AI Month at Penn Engineering 2025 – YouTube, accessed June 12, 2025, https://www.youtube.com/watch?v=UwMpfGtEnWc
2. Yann LeCun, Pioneer of AI, Thinks Today’s LLM’s Are Nearly …, accessed June 12, 2025, https://www.newsweek.com/ai-impact-interview-yann-lecun-artificial-intelligence-2054237
3. Geoffrey Hinton, Yann LeCun, Yoshua Bengio and Demis Hassabis, Princess of Asturias Award for Technical and Scientific Research, accessed June 12, 2025, https://www.fpa.es/en/area-of-communication-and-media/press-releases/geoffrey-hinton-yann-lecun-yoshua-bengio-and-demis-hassabis-princess-of-asturias-award-for-technical-and-scientific-research/
4. Yann LeCun – Wikipedia, accessed June 12, 2025, https://en.wikipedia.org/wiki/Yann_LeCun
5. The Academy Recognizes Yann LeCun for Advancing AI – NYAS, accessed June 12, 2025, https://www.nyas.org/ideas-insights/blog/the-academy-recognizes-yann-lecun-for-advancing-ai/
6. Yann Lecun Is Optimistic That AI Will Lead to a Better World | TIME, accessed June 12, 2025, https://time.com/collection/time100-impact-awards/6692039/yann-lecun-meta-artificial-intelligence-time-award/
7. Turing Award Presented to Yann LeCun, Geoffrey Hinton, and Yoshua Bengio – Meta AI, accessed June 12, 2025, https://ai.meta.com/blog/-turing-award-presented-to-yann-lecun-geoffrey-hinton-and-yoshua-bengio/
8. Neural Net Worth – Communications of the ACM, accessed June 12, 2025, https://cacm.acm.org/news/neural-net-worth/
9. Yann LeCun : Awards | Carnegie Corporation of New York, accessed June 12, 2025, https://www.carnegie.org/awards/honoree/yann-lecun/
10. The Convolutional Neural Network – GitHub Pages, accessed June 12, 2025, https://com-cog-book.github.io/com-cog-book/features/cov-net.html
11. The History of Convolutional Neural Networks for Image …, accessed June 12, 2025, https://towardsdatascience.com/the-history-of-convolutional-neural-networks-for-image-classification-1989-today-5ea8a5c5fe20/
12. Meta Debuts AI to Help Robots ‘Understand the Physical World’ | PYMNTS.com, accessed June 12, 2025, https://www.pymnts.com/artificial-intelligence-2/2025/meta-debuts-ai-to-help-robots-understand-the-physical-world/
13. Introducing V-JEPA 2 – Meta AI, accessed June 12, 2025, https://ai.meta.com/vjepa/
14. The Path to Artificial General Intelligence: Yann LeCun’s Vision for …, accessed June 12, 2025, https://www.ml-science.com/blog/2024/10/10/the-path-to-artificial-general-intelligence-yann-lecuns-vision-for-the-future
15. What does Yann LeCun think about AGI? A summary of his talk, “Mathematical Obstacles on the Way to Human-Level AI” – LessWrong, accessed June 12, 2025, https://www.lesswrong.com/posts/jKCDgjBXoTzfzeM4r/what-does-yann-lecun-think-about-agi-a-summary-of-his-talk
16. www.flatworldsolutions.com, accessed June 12, 2025, https://www.flatworldsolutions.com/data-science/articles/7-applications-of-convolutional-neural-networks.php#:~:text=Image%20and%20video%20recognition%20is,recognition%20and%20automated%20content%20curation.
17. Practical Applications and Insights into Convolutional Neural Networks – Number Analytics, accessed June 12, 2025, https://www.numberanalytics.com/blog/practical-cnn-applications-insights
18. Convolutional Neural Network and its Latest Use Cases – XenonStack, accessed June 12, 2025, https://www.xenonstack.com/blog/convolutional-neural-network
19. What Is Self-Supervised Learning? | IBM, accessed June 12, 2025, https://www.ibm.com/think/topics/self-supervised-learning
20. Yann LeCun’s Home Page, accessed June 12, 2025, http://yann.lecun.com/
21. The Birth of Self Supervised Learning: A Supervised Theory – OpenReview, accessed June 12, 2025, https://openreview.net/forum?id=NhYAjAAdQT&referrer=%5Bthe%20profile%20of%20Yann%20LeCun%5D(%2Fprofile%3Fid%3D~Yann_LeCun1)
22. JEPA for RL: Investigating Joint-Embedding Predictive Architectures for Reinforcement Learning – arXiv, accessed June 12, 2025, https://arxiv.org/html/2504.16591v1
23. arXiv:2504.16591v1 [cs.CV] 23 Apr 2025, accessed June 12, 2025, https://arxiv.org/pdf/2504.16591
24. Yann LeCun – CES, accessed June 12, 2025, https://www.ces.tech/speakers/yann-lecun/
25. NYU’s Yann LeCun Honored with 2025 Queen Elizabeth Prize for Engineering, accessed June 12, 2025, https://bioengineer.org/nyus-yann-lecun-honored-with-2025-queen-elizabeth-prize-for-engineering/
26. ‘Godfathers of AI’ Yoshua Bengio and Yann LeCun weigh in on …, accessed June 12, 2025, https://news.nus.edu.sg/nus-120-dss-godfathers-of-ai-yoshua-bengio-and-yann-lecun/
27. www.google.com, accessed June 12, 2025, https://www.google.com/search?q=Yann+LeCun+A.M.+Turing+Award
28. 2018 Turing Award – ACM Awards, accessed June 12, 2025, https://awards.acm.org/about/2018-turing
29. The Future of AI: A Fireside Chat with Yann LeCun, Chief AI Scientist at Meta, accessed June 12, 2025, https://events.seas.upenn.edu/event/the-future-of-ai-a-fireside-chat-with-yann-lecun-chief-ai-scientist-at-meta/
30. awards.acm.org, accessed June 12, 2025, https://awards.acm.org/turing#:~:text=Since%20its%20inception%20in%201966,is%20named%20for%20Alan%20M.
31. Turing Awardees – Directorate for Computer and Information Science and Engineering (CISE) | NSF, accessed June 12, 2025, https://www.nsf.gov/cise/turing-awardees
32. Innovation & Impact Podcast: The Future of AI with Yann LeCun – Penn Engineering Blog, accessed June 12, 2025, https://blog.seas.upenn.edu/innovation-impact-podcast-episode-7-the-future-of-ai-with-yann-lecun/
33. Interview: Yoshua Bengio, Yann LeCun, Geoffrey Hinton – RE•WORK Blog, accessed June 12, 2025, https://blog.re-work.co/interview-yoshua-bengio-yann-lecun-geoffrey-hinton/
34. Yann LeCun – NYU Tandon School of Engineering, accessed June 12, 2025, https://engineering.nyu.edu/faculty/yann-lecun
35. Progress Towards AGI and ASI: 2024–Present – CloudWalk, accessed June 12, 2025, https://www.cloudwalk.io/ai/progress-towards-agi-and-asi-2024-present
36. Yann LeCun Calls Anthropic CEO Dario Amodei’s AI Concerns ‘Deluded’ – OpenTools, accessed June 12, 2025, https://opentools.ai/news/yann-lecun-calls-anthropic-ceo-dario-amodeis-ai-concerns-deluded
37. Existential risk from artificial intelligence – Wikipedia, accessed June 12, 2025, https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence