AGI Is Not Coming Soon

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This podcast discusses my article on the current state of artificial intelligence, focusing on the limitations of large language models and the unrealistic expectations surrounding the development of artificial general intelligence (AGI). I argue that AI systems are not on a trajectory to match or exceed human intelligence because LLMs lack common sense and rely on regurgitating information rather than understanding. Despite the hype surrounding AGI, it is decades away, and the current focus on LLMs is misguided. Instead, I advocate for a different approach to AI that incorporates real-world interactions and visual data.

Burning Billions

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Many experts are exaggerating artificial intelligence’s power and peril. Most public commentary, ranging from politicians to prominent technologists, says AI – more precisely, AGI – is close to surpassing human intelligence. The significant risk is that artificial intelligence will supplant human beings. This speculation even comes from Geoffrey Hinton, a Nobel prize-winning AI pioneer. AI models are useful, but, as Yann LeCun, Meta’s AI head, says, they are far from rivaling a house cat’s intelligence, let alone humans. The talk that AI will become so powerful that it poses an existential threat to human beings is nonsense. AI is a powerful tool and is becoming enormously important in all aspects of the economy. But AGI, malicious or otherwise, will not happen anytime soon.

A New Vision for AI

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This is a new podcast based on my artificial intelligence research. I argue that the current approach to artificial intelligence, reliant on massive datasets and “neural networks” inspired by the brain, is fundamentally flawed. Instead, it advocates for a new vision that prioritizes cognitive architecture, mirroring the brain’s ability to process information dynamically and identify relevant data. This new approach would utilize smaller, more focused datasets, leading to more efficient, accurate, and scalable AI systems capable of true learning, knowledge transfer, and prediction.

Apple versus Visa

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Apple can disrupt global finance. Visa and MasterCard are now vulnerable. Previously, it was believed that the capital required for infrastructure, systems, and processing was an insurmountable obstacle to any new competitor. But things have changed. Innovation and disruption in the credit card business pose a threat to established players like Visa and MasterCard. Apple can leverage its ecosystem, user experience focus, brand trust, strategic partnerships, and innovative use of data to succeed in the credit card business. Over time, as it scales and innovates, it could challenge Visa and MasterCard’s market dominance.

A New Perspective on AI

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AI is not a data problem; it is a cognitive architecture problem. Data and computing power will become insurmountable hurdles for transformer-based models. A new generation of AI models requires fundamental breakthroughs. Large data models can’t learn, transfer knowledge or understanding, understand the relevance, or use analogous learning to transfer that relevance and predict. Current AI models require massive and increasing data and learn from reinforcement. This cannot scale and is massively inefficient. Real learning based on cognitive architecture, focused dynamic data, and referential data sets is a better solution. This is closer to real human learning, more effective and efficient, and offers a significantly better solution. Understanding the natural learning process — referential and analogous data, categorization, transferring and building upon that data, and creating knowledge applicable to new situations — learning builds upon itself and is exponentially effective. That is the real AI solution.