Yann LeCun’s version of AGI vs SingularityNET’s

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This presentation at the Paris-based AI Action Summit, given by expert AI researcher Yann LeCun, argues that true Artificial General Intelligence (AGI) will not be achieved using Large Language Models (LLMs), but rather through World Models like JEPA (Joint Embedding Predictive Architecture).

Key Takeaways:

  1. Move away from traditional AI methods
    • He suggests abandoning:
      • Generative models (like GPT) in favor of architectures that embed knowledge more efficiently.
      • Probabilistic models (which predict based on probability) in favor of energy-based models (which focus on optimization).
      • Contrastive methods (which compare differences in data) in favor of regularized methods (which make learning more stable).
      • Reinforcement Learning (RL) in favor of model-predictive control (which plans ahead rather than learning through trial and error).
  2. Limited role of Reinforcement Learning (RL)
    • RL should only be used when predictions fail, to correct the world model or the critic (a system that evaluates decisions).
  3. A Strong Message Against LLMs for AGI
    • He explicitly states: “IF YOU ARE INTERESTED IN HUMAN-LEVEL AI, DON’T WORK ON LLMs.”
    • This suggests that LLMs (like ChatGPT) are fundamentally limited and will not lead to AGI.

LeCun’s approach to AI development has some alignment with SingularityNET’s goals, but there are also key philosophical and technical differences. Let’s break it down:

Where They Align:

  1. Moving Beyond Traditional Deep Learning
    • LeCun argues that LLMs (like ChatGPT) won’t lead to AGI because they lack true reasoning, planning, and world modeling.
    • SingularityNET (SNET) also sees current AI as narrow and lacking general intelligence, which is why they advocate for decentralized AGI that is more dynamic and autonomous.
  2. Importance of World Models & Energy-Based Learning
    • LeCun’s JEPA model (Joint Embedding Predictive Architecture) focuses on learning abstract representations of the world, rather than just predicting the next word in a sentence (as LLMs do).
    • SingularityNET’s OpenCog Hyperon also aims to develop symbolic and connectionist AI that can reason, plan, and build mental models of the world, making it closer to LeCun’s vision than traditional LLMs.
  3. AI Needs to Generalize and Learn from Experience
    • LeCun supports AI that self-improves by interacting with the world, rather than just memorizing data.
    • SNET’s approach also includes self-organizing AI networks, where different AI agents interact and learn collaboratively.

Where They Differ:

  1. Centralized vs. Decentralized AI
    • LeCun’s approach focuses on building better AI architectures (like JEPA) but still assumes these would be developed in centralized research environments (e.g., Meta, large AI labs).
    • SingularityNET is fundamentally decentralized, promoting an open, distributed AI network where many independent AI systems can contribute to intelligence development.
  2. Symbolic AI & Hybrid Methods
    • SingularityNET’s OpenCog Hyperon is heavily based on symbolic AI and logical reasoning, integrating neural networks with structured symbolic models.
    • LeCun is more skeptical of symbolic AI, preferring energy-based models and predictive learning without explicit logic structures.
  3. Market vs. Research Focus
    • LeCun’s JEPA model is a research-driven paradigm, mostly focused on advancing AI architecture.
    • SingularityNET operates in a Web3 ecosystem, where AI services are offered on a blockchain-based marketplace, making it a mix of research and real-world deployment.

Final Take:

LeCun’s critique of LLMs aligns with SingularityNET’s vision for AGI, but SNET’s focus on decentralization and hybrid AI architectures sets it apart.

This article was written using ChatGPT-4, which is based on OpenAI’s GPT-4 model. Specifically, it was generated using the latest available version of GPT-4-turbo, optimized for efficiency and cost-effectiveness while maintaining high-quality outputs.

DeepSeek and the Future of Decentralized AI: Implications for Leaders at the Paris AI Summit

Introduction: DeepSeek and the AI Summit in Paris

As AI leaders gather in Paris for the upcoming AI Summit, they do so under the shadow of a significant recent development in the AI landscape: the emergence of DeepSeek, an advanced large language model (LLM) that has captivated the industry. While some have heralded it as a game-changing moment, Dr. Ben Goertzel, CEO of SingularityNET and a leading figure in artificial intelligence research, offers a more nuanced perspective. In a recent video, Goertzel analyzed DeepSeek’s technical advancements, its open-source approach, and what it means for the future of artificial general intelligence (AGI). His insights provide a critical roadmap for AI leaders in Paris, especially those committed to decentralization, openness, and democratization of AI technologies.

DeepSeek: An Efficiency Leap, Not an AGI Breakthrough

DeepSeek is impressive, but it is not a fundamental breakthrough toward AGI. According to Goertzel, “DeepSeek is a significant efficiency gain in the LLM space,” but it does not represent a disruptive paradigm shift. He draws a historical analogy, likening it to how computational advancements have rapidly reduced costs and increased accessibility, much like how high-end supercomputers from the 1990s have now been replaced by consumer-grade devices capable of performing similar tasks.

At its core, DeepSeek employs a mixture of experts model, a well-established ensemble learning technique that significantly reduces computational costs. Unlike traditional transformer architectures that activate the entire neural network for each query, DeepSeek selectively engages only the necessary parameters, making it far more efficient. The model also leans heavily on reinforcement learning, particularly in training for reasoning, which sets it apart from previous LLMs.

While these efficiency gains are meaningful, Goertzel emphasizes that they do not bridge the conceptual gap required for AGI. Transformer-based models like DeepSeek, ChatGPT, and LLaMA still lack core cognitive capabilities such as self-directed reasoning, compositional abstraction, and the ability to generalize beyond their training data (Marcus, 2022). This reinforces the view that AGI will require more than just better and faster LLMs—it will necessitate fundamentally new AI architectures.

The Open-Source Shift and China’s Role in AI Development

One of DeepSeek’s most striking aspects is its open-source approach, contrasting sharply with the walled-garden strategy employed by companies like OpenAI and Anthropic. As Goertzel points out, despite its name, OpenAI has rarely adhered to open-source principles. DeepSeek, in contrast, has made its research paper and model available, allowing researchers, startups, and developers worldwide to build upon its foundation.

This move is particularly notable given China’s evolving role in AI development. Historically, China has been a dominant force in AI research but has not been a major proponent of open-source AI. DeepSeek marks a departure from this trend, signaling an increasing willingness within the Chinese AI ecosystem to engage in global collaboration and transparency. “While the combination of open-source AI and China may seem uncommon, I am not surprised to see this emerge given the immense investment in AI research across China,” Goertzel notes.

The potential impact of this shift cannot be overstated. Open-source AI fosters rapid innovation, broader adoption, and collective improvement. It also levels the playing field, enabling smaller organizations and decentralized AI initiatives to compete with tech giants. This is particularly relevant for projects like SingularityNET, which advocate for AI decentralization as a safeguard against monopolistic control and the risks of centralized superintelligence.

Implications for AI Development Beyond LLMs

Beyond the immediate impact of DeepSeek’s efficiency gains, Goertzel suggests that the commoditization of LLMs will likely shift AI investment toward more novel architectures. “LLMs are becoming cheaper and faster, which is great for their economic applications, but this also means that investors will start looking for the next big thing in AI,” he explains. This could mean renewed interest in neuromorphic computing, neuro-symbolic AI, evolutionary AI, and decentralized AI models.

The economic implications of DeepSeek’s optimizations are also profound. With the cost of deploying powerful LLMs dropping significantly, the threshold for innovation in AI has been lowered. Startups, research initiatives, and non-profit AI projects can now develop competitive AI solutions without requiring billions of dollars in computing resources. This, in turn, increases the viability of decentralized AI platforms that rely on distributed networks rather than centralized data centers.

What This Means for AI Leaders in Paris

The AI Summit in Paris is the first major gathering of industry leaders since the DeepSeek announcement, making it a critical moment for reflecting on the broader trajectory of AI development. Goertzel’s insights underscore three major takeaways that should guide discussions at the event:

  1. DeepSeek is a wake-up call for decentralization – The fact that DeepSeek has demonstrated a viable open-source AI model suggests that the future of AI does not have to be monopolized by a handful of corporations. Decentralized AI projects must seize this momentum and advocate for AI ecosystems that are transparent, accessible, and globally distributed.
  2. The AI arms race is evolving – While DeepSeek is an advancement, it is not an AGI leap, meaning that the next phase of AI development must go beyond transformer-based models. AI leaders should collaborate on research that integrates alternative AI architectures, particularly those that emphasize self-directed reasoning and real-world interaction.
  3. DeepSeek validates the potential of open-source AI – By proving that open-source AI can compete with proprietary models, DeepSeek sets a precedent for future AI innovation. This should encourage AI policymakers and researchers in Paris to prioritize open-source AI development as a means of ensuring equitable access to AI’s benefits.

Conclusion: The Road to a Decentralized AI Future

DeepSeek is not a revolution, but it is an important milestone on the path to AGI. It exemplifies the exponential progress of AI, while also reinforcing the need for decentralization and global collaboration. As Goertzel puts it, “If we want the singularity to be beneficial, we need to ensure it remains decentralized, global, and open.”

As AI leaders in Paris debate the future of artificial intelligence, they must recognize that DeepSeek is not just a technical development—it is a strategic inflection point. It is a call to action for those who believe in a future where AI is controlled by the many, not the few. The DeepSeek moment should be leveraged to push for policies, funding, and collaborations that move AI closer to a decentralized, democratized, and ethically responsible paradigm.

For those committed to the vision of beneficial AGI, the message from Paris must be clear: The future of AI must be open, decentralized, and driven by global cooperation rather than proprietary control. DeepSeek’s success proves that this is not only possible—it is inevitable.

This article was written using ChatGPT-4, which is based on OpenAI’s GPT-4 model. Specifically, it was generated using the latest available version of GPT-4-turbo, optimized for efficiency and cost-effectiveness while maintaining high-quality outputs. The model integrates insights from various sources, including the transcript of Dr. Ben Goertzel’s recent video and supporting references from AI research and industry discussions.

The Otter.ai audio/transcript of Dr Ben Goertzel’s video is available here.