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.
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.
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 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.
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.
Global technological transformation and disruptive technologies create extraordinary opportunities – and magnified risks. Headline-grabbing hyperbole dominates each news cycle, and some forecasts and bewildering futuristic projections can mostly be dismissed. However, meaningful substance and catalytic disruptive change are permeating all industries.
A context to understand these developments – a broader, methodical, and disciplined way to think about disruption and transformation- shows that extraordinary opportunities on a highly competitive global scale are emerging.
Artificial intelligence and AI-generated tools, digital assets, blockchain-based businesses, gene editing, and DNA sequencing profoundly impact the world’s most important industries. New technological innovations and platforms enable unprecedented disruption to all business and economic models.
AI is a powerful tool under human direction, not an autonomous entity with consciousness or emotions. It examines artificial intelligence’s opportunities, limitations, and risks while giving a reality check to the hyperbole and fears.
The first and most crucial principle in understanding AI is recognizing its inherent nature as a tool, a creation of human ingenuity designed to augment, rather than replace, human intelligence. AI systems, at their core, are complex algorithms capable of processing and learning from data at a scale and speed unattainable by human cognition.
However, it is an unprecedented, powerful tool that can permeate every aspect of society, industry, and human ingenuity globally. Its impact can be equivalent to Prometheus giving humans the gift of fire.
Artificial intelligence is poised to significantly impact various fields and activities, transforming how we approach creativity, professional activities, science, and many more domains. Disruption will accelerate the development of new innovative businesses and strategies in finance, medicine, data management, systems engineering, materials science, art, and other industries. AI’s impact will be profound and multifaceted, driving innovation and efficiency and posing challenges regarding ethics, job displacement, and new skills and regulations. As AI continues to evolve, its integration into these areas will likely shape the future of human society in significant ways.
The era of artificial intelligence is here, and it’s generating despair and fear over the loss of control and the worry that artificial intelligence is about to unleash killer robots and enslave humanity. Perhaps…or, something else. Artificial intelligence will improve lives and generate greater access to education, improve healthcare, and advance climate science. Among many other improvements, AI’s benefits greatly outweigh its costs. AI has its costs since everything comes with a price (there are always both sides to the ledger), but the extraordinary benefits that artificial intelligence can unleash are worth the effort. Don’t slow down, pause, or restrict research, development, and AI applications. Prometheus gave the world fire and while we can still cause great harm, it was among the single greatest advancements for humankind. Artificial intelligence can be the same thing for our modern-day recipients of fire from the gods. But, we can’t be naïve. We can still burn the earth down if we are not careful.
AI will not destroy the world – and is more likely to save it. I’s trajectory points towards a future where it not only enhances technological capabilities but also enriches human lives. Its evolving role will be characterized by a synergy between human and artificial intelligence, propelling societal progress and opening new frontiers of innovation and discovery is not just a technological advancement; it’s a catalyst for a new era of human endeavor.
Its impact is vast, touching every aspect of our lives and work. As AI continues to evolve, its role in shaping our society and driving innovation will only become more significant, opening new horizons for growth, creativity, and human potential.
The book attempts to bridge the gap between algorithmic processes and practical decision-making in uncertain circumstances. It moves beyond mathematical solutions to acknowledge the complexity of real-world decisions. The author, Nicholas Mitsakos, argues that decision-making is essentially a form of statistical analysis that involves models or algorithms, and being thorough in this understanding confers a significant advantage. is a comprehensive text discussing decision-making under uncertainty and the algorithms that can enhance the decision-making process. It covers a wide range of topics, including applications in various fields such as air traffic control, drug discovery, autonomous driving, and more. The introduction sets the stage by discussing the importance of understanding and managing uncertainty in decision-making. It defines algorithms in the context of systematic thinking and approaches to problem-solving, emphasizing the significance of defining problems clearly to find obvious solutions.
