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The pace of engineering and science is speeding up, rapidly leading us toward a "Technological Singularity" - a point in time when superintelligent machines achieve and improve so much so fast, traditional humans can no longer operate at the forefront. However, if all goes well, human beings may still flourish greatly in their own ways in this unprecedented era. If humanity is going to not only survive but prosper as the Singularity unfolds, we will need to understand that the Technological Singularity is an Experiential Singularity as well, and rapidly evolve not only our technology but our level of compassion, ethics and consciousness. The aim of The Consciousness Explosion is to help curious and open-minded readers wrap their brains around these dramatic emerging changes-and empower readers with tools to cope and thrive as they unfold.
Ben’s new book The Consciousness Explosion delves into a topic regarding which he and I have often presented at conferences: the accelerating journey towards a Technological Singularity—a future where superintelligent machines surpass human capabilities in every aspect. The book is not just about the technological advancements leading us to this point but emphasizes the necessity of evolving human compassion, ethics, and consciousness alongside our tech. It's a guide for navigating and thriving in this imminent future, blending scientific foresight with philosophical insights.
Current AI technologies like large language models (LLMs) and convolutional neural networks are focused on the effective absorption of large-scale training data and fulfillment of queries based on the information in this data. This sort of technology, on its own, seems clearly not capable of producing HLAGI but it does seem very promising as a component of integrated multi-module AGI systems. This sort of technology is also not capable of real moral agency or ethical understanding. The ability to answer queries regarding ingested training data, and to generate new products based on the probability distribution inferred from training data, is certainly valuable and will provide fascinating insights