Publié : 4 December 2025
Actualisé : 9 hours ago
Fiabilité : ✓ Sources vérifiées
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📋 Table of Contents
Following a period of media silence after leaving Meta, Yann LeCun, a leading figure in artificial intelligence, spoke at the AI Pulse event in Paris. His speech, far from the widespread enthusiasm for generative AI , highlighted the challenges and dead ends of current models, particularly LLMs (Large Language Models) . A necessary questioning in a rapidly expanding field.
🤖 LeCun’s Disenchantment with Generative AI
Yann LeCun, renowned for his major contributions to deep learning and computer vision, did not mince words regarding the prospects of generative AI. According to him, the idea that LLMs can achieve intelligence comparable to that of humans is an illusion. He strongly criticizes the “hype” surrounding these technologies, pointing out their inability to truly understand the world around them.
Key Point: LeCun insists on the need to overcome the current limitations of LLMs to progress towards more sophisticated and autonomous AI.
This stance marks a break with the current direction of many companies, including Meta, which are investing heavily in the development of consumer applications based on generative AI. The strategic divergence between LeCun and Mark Zuckerberg, the CEO of Meta, appears to be one of the reasons for his departure from FAIR (Fundamental AI Research Lab).
🧠 AMI: An Alternative Project for Comprehensive AI
LeCun ardently defends the development of AMI (Autonomous Machine Intelligence) , a project radically different from LLMs. AMI aims to create cognitive models capable of understanding and interacting with the physical world autonomously. This approach is inspired by how humans learn and interact with their environment, combining perception, reasoning, and action.
AMI is intended as an alternative to the current approach, deemed too focused on processing large amounts of textual data. LeCun believes that true artificial intelligence must be rooted in the real world, capable of learning through experience and adapting to new situations.
🌐 Advocacy for Open Source and Collaboration
Another crucial point in LeCun’s speech concerns the importance of Open Source in the development of AI. He criticizes the tendency of American companies to close their models and research, which hinders collective progress and limits access to knowledge for researchers and developers worldwide.
LeCun advocates for a more open and collaborative approach, where knowledge and technologies are shared freely. He believes that this is the only way to ensure ethical and responsible development of AI, avoiding the concentration of power in the hands of a few players.
📊 Comparison of Approaches: LLM vs. AMI
To better understand the fundamental differences between LLMs and AMI, here is a comparative table:
| Characteristic | LLM (Large Language Models) | AMI (Autonomous Machine Intelligence) |
|---|---|---|
| Main Objective | Generate text, translate languages, answer questions | Understand and interact with the physical world autonomously |
| Type of Data | Mainly textual data | Sensory data (vision, sound, etc.) and interaction data |
| Learning Method | Supervised learning on large amounts of data | Reinforcement learning and unsupervised learning |
| Capabilities | Coherent text generation, translation, summarization | Perception, reasoning, planning, action |
| Limitations | Lack of understanding of the real world, bias, hallucinations | Complexity of implementation, need for quality data |
Important: The table above highlights the fundamental differences between the two approaches, highlighting the strengths and weaknesses of each.
In conclusion, Yann LeCun’s speech marks a turning point in the debate on the future of AI. His call to overcome the limitations of LLMs and explore new paths, such as AMI, opens exciting perspectives for research and development in artificial intelligence.
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