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Artificial General Intelligence is on the horizon. Join us at the AGI-25 Conference to connect with the AGI research community, hear from industry pioneers, and explore live software and hardware demos. Register now to attend or watch online for free:

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The 18th Annual AGI Conference (AGI-25) took place last month at Reykjavik University in Iceland, bringing together participants from over 80 countries to explore pioneering research on the path to AGI. AGI-25 programming featured distinguished keynote presentations from leaders in academia and industry, including Ben Goertzel, Richard Sutton, Tatiana Shavrina, Michael Levin, Anna Ciaunica PhD @annaciaunica.bsky.social, and Joscha Bach. These talks addressed core challenges in AGI theory and implementation, the limitations of current state-of-the-art learning systems, and new frameworks for integrating reasoning, embodiment, and autonomy. The accepted papers covered a broad range of research areas, including novel learning algorithms, reasoning systems, theoretical neurobiology and bio-inspired systems, quantum computing, theories of machine consciousness, ethics, safety, formal mathematical foundations, and philosophy of AGI. The conversation on advancing general machine intelligence continues toward AGI-26.
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The 18th Annual AGI Conference (AGI-25) took place last month at Reykjavik University in Iceland, bringing together participants from over 80 countries to explore pioneering research on the path to AGI. AGI-25 programming featured distinguished keynote presentations from leaders in academia and industry, including Ben Goertzel, Richard Sutton, Tatiana Shavrina, Michael Levin, Anna Ciaunica PhD @annaciaunica.bsky.social, and Joscha Bach. These talks addressed core challenges in AGI theory and implementation, the limitations of current state-of-the-art learning systems, and new frameworks for integrating reasoning, embodiment, and autonomy. The accepted papers covered a broad range of research areas, including novel learning algorithms, reasoning systems, theoretical neurobiology and bio-inspired systems, quantum computing, theories of machine consciousness, ethics, safety, formal mathematical foundations, and philosophy of AGI. The conversation on advancing general machine intelligence continues toward AGI-26.

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CEO Dr. Ben Goertzel discusses AGI benchmarks and explains why a system passing François Chollet's Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) wouldn’t necessarily qualify as human-level AGI.
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As Big Tech companies continue channeling billions of research dollars and attention into developing and scaling LLMs, a fundamental question is emerging in tech circles: Are LLMs actually leading us away from true AGI? At a #Consensus2025 panel addressing whether Web3 is losing the AI race, our CEO, Dr. Ben Goertzel, challenged the conventional wisdom driving investment strategies at many leading AI companies and sovereign wealth funds, including the recent multi-billion-dollar investments from Saudi Arabia and the UAE in US AI infrastructure. "I would quote Yan LeCun, a pioneer of deep learning and the head of AI at Facebook, who said on the highway to AGI, LLMs are an off-ramp," Dr. Goertzel told attendees, rejecting the premise that Web3 approaches are falling behind centralized AI development: "If you've gotten off the off-ramp, it doesn't matter if you're going 1,000 miles an hour and the other guy's only going 300 miles an hour if they're going on the right highway to the destination." The panel, which featured Ben Fielding (Founder, Gensyn), Jesus Rodriguez (CEO, IntoTheBlock), Clara Tsao (Founding Officer, Filecoin Foundation), and Jeff Wilser (Founder and Host, The People's AI Podcast), revealed a stark divide in how industry experts view the future of decentralized AI. While other panelists pointed to Web3's current disadvantages in talent, datasets, and infrastructure, Dr. Goertzel addressed a deeper issue: the incremental improvement of LLMs is not a viable approach to achieving human-level AGI. However, "If scaling up transformer neural nets is the crux of how you get to AGI, it's hard to see how the US and Chinese governments and the Big Tech companies in their orbit don't win the race," Dr. Goertzel acknowledged, noting the immense capital these entities are deploying. Our AGI R&D efforts at SingularityNET suggest a different path forward. "My own research intuition is that LLMs are not suited to be the central hub of a human-level AGI, let alone a Superintelligence, although they can be a powerful ingredient in a hybrid architecture for AGI," Dr. Goertzel explained. Our team is developing OpenCog Hyperon, a "hybrid, deep neural net, symbolic reasoning, evolutionary learning, approach to AGI within more sophisticated cognitive architectures than LLMs comprise," as described by Dr. Goertzel. This cognition-level approach represents a fundamentally different direction from most mainstream AI development. Dr. Goertzel closed with a prediction that would have seemed outlandish just a few years ago but now reflects our growing confidence in decentralized approaches through which AGI will be in the hands of humanity at large and without a single owner or controller: "Within the Artificial Superintelligence Alliance, we will launch the first AGI within one to three years from now on a decentralized infrastructure, and Big Tech will play catchup."
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As Big Tech companies continue channeling billions of research dollars and attention into developing and scaling LLMs, a fundamental question is emerging in tech circles: Are LLMs actually leading us away from true AGI? At a #Consensus2025 panel addressing whether Web3 is losing the AI race, our CEO, Dr. Ben Goertzel, challenged the conventional wisdom driving investment strategies at many leading AI companies and sovereign wealth funds, including the recent multi-billion-dollar investments from Saudi Arabia and the UAE in US AI infrastructure. "I would quote Yan LeCun, a pioneer of deep learning and the head of AI at Facebook, who said on the highway to AGI, LLMs are an off-ramp," Dr. Goertzel told attendees, rejecting the premise that Web3 approaches are falling behind centralized AI development: "If you've gotten off the off-ramp, it doesn't matter if you're going 1,000 miles an hour and the other guy's only going 300 miles an hour if they're going on the right highway to the destination." The panel, which featured Ben Fielding (Founder, Gensyn), Jesus Rodriguez (CEO, IntoTheBlock), Clara Tsao (Founding Officer, Filecoin Foundation), and Jeff Wilser (Founder and Host, The People's AI Podcast), revealed a stark divide in how industry experts view the future of decentralized AI. While other panelists pointed to Web3's current disadvantages in talent, datasets, and infrastructure, Dr. Goertzel addressed a deeper issue: the incremental improvement of LLMs is not a viable approach to achieving human-level AGI. However, "If scaling up transformer neural nets is the crux of how you get to AGI, it's hard to see how the US and Chinese governments and the Big Tech companies in their orbit don't win the race," Dr. Goertzel acknowledged, noting the immense capital these entities are deploying. Our AGI R&D efforts at SingularityNET suggest a different path forward. "My own research intuition is that LLMs are not suited to be the central hub of a human-level AGI, let alone a Superintelligence, although they can be a powerful ingredient in a hybrid architecture for AGI," Dr. Goertzel explained. Our team is developing OpenCog Hyperon, a "hybrid, deep neural net, symbolic reasoning, evolutionary learning, approach to AGI within more sophisticated cognitive architectures than LLMs comprise," as described by Dr. Goertzel. This cognition-level approach represents a fundamentally different direction from most mainstream AI development. Dr. Goertzel closed with a prediction that would have seemed outlandish just a few years ago but now reflects our growing confidence in decentralized approaches through which AGI will be in the hands of humanity at large and without a single owner or controller: "Within the Artificial Superintelligence Alliance, we will launch the first AGI within one to three years from now on a decentralized infrastructure, and Big Tech will play catchup."

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ARC-AGI is redefining how to measure progress on the path to AGI - focusing on reasoning, generalization, and adaptability instead of memorization or scale. At NeurIPS 2025, YC's Diana sat down with ARC Prize President Greg Kamradt to find out why most AI benchmarks fail, how ARC-AGI reveals the limits of today’s models, and why measuring intelligence may be harder than building it. 00:11 — What ARC Prize is and why it exists 00:38 — François Chollet’s definition of AGI 01:48 — What ARC-AGI Actually Tests 02:25 — When LLMs Failed the ARC Benchmark 03:38 — ARC-AGI Becomes the Standard 04:49 — False Positives in AI Progress 06:06 — The Evolution of ARC-AGI 08:55 — Measuring Intelligence beyond just accuracy 10:25 — What happens if a model solves ARC-AGI?

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Less Than 3 Days Remaining — $1.25M in Grants to Advance Decentralized Beneficial AGI Research and Development The AI Twin of our CEO Dr. Ben Goertzel, powered by Twin Protocol, invites AI developers and researchers to apply for our $1.25M in grants to advance decentralized, beneficial and benevolent AGI R&D via our OpenCog Hyperon architecture. The submission period closes on Sunday, December 8th, 2024, at noon UTC. Visit the Deep Funding portal to learn more and submit your proposals:
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Our Chief Product Officer, Nick Nayfack, discusses the Artificial Superintelligence Alliance innovation stack, a fully integrated framework designed to foster collaboration and build scalable infrastructure for decentralized AI and true AGI:
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