This is part of IEEE Spectrum's SPECIAL REPORT: THE SINGULARITY
Given the current state of computer science and robotics, it’s hard to understand how ”the singularity” meme has become lodged in the serious discourse of the technosphere. This is the idea that, as a consequence of exponentially accelerating technological innovation and continuously self-improving artificial intelligence, computer power will outstrip human brainpower, leading to the end of human culture as we know it. Not a century from now, mind you, but somewhere between 2030 and 2045, depending on whom you talk to.
The concept was framed in its most tech-savvy form by computer scientist and science-fiction writer Vernor Vinge in 1983 in Omni magazine. It has since morphed into a complicated ”theory” that for some, notably prolific inventor Ray Kurzweil, includes a posthuman afterlife in which we abandon our biological selves and are uploaded into digital and possibly robotic vessels, there to spend eternity as cybernetic Methuselahs. It is also thought by its followers to be inevitable, not merely one of many possible future scenarios.
The singularity represents an untestable set of assumptions about our near future. So why are so many willing to take it seriously? That’s what we set out to discover in our special report, ”The Rapture of the Geeks,” in this issue. Given that it’s the 25th anniversary of Vinge’s seminal work, it seemed like a good time to call upon the science and technology experts—including Vinge—to get a sense of the merits and the demerits of the singularity case. We were particularly interested to learn what, if any, technology supports the extraordinary claims made by the singularity’s proponents.
What we found is that there’s a lot of hyperbole distracting us from the real work under way in nanotechnology, brain implants, and machine learning. Researchers are, with some success, making machines more intelligent and responsive to solving real-world problems. The explosion of disciplines involved in these pursuits gives you some sense of their complexity. Robotics departments have now added developmental, epigenetic , or evolutionary to their names; control and systems are becoming more and more intelligent; AI is coursing through the blood of embodied cognitive science.
But we’re still a very long way from understanding how consciousness arises in the human brain, let alone figuring out how to re-create it in a machine. We’re even a long way from the much simpler goal of creating autonomous, self-organizing, and perhaps even self-replicating machines.