Key points to factor in with Singularity forecasting;
[*] The Singularity happens when transhuman intelligence is created. Although the space between human and transhuman intelligence is continuous rather than discrete, we can specify several levels of transhumanity:
- As “smart” as humans or slightly less so, but possessing hardware advantages that signify de facto transhumanity.
- Smarter than any human that has ever lived, plus substantial hardware advantages that signify powerful transhuman intelligence.
- Much smarter than any human that has ever lived, plus substantial hardware advantages that signify de facto superintelligence.
Artificial Intelligences will always have substantial hardware advantages. Various forms of IA, on the other hand, will not necessarily. Brain-Computer Interfacing would probably be the form of IA with the most substantial hardware advantages relative to everyday human genius. Still, it is very likely that AI’s inherent advantages will make it the primary forerunner among Singularity technologies.
[*] If we assume that Artificial Intelligence will reach transhumanity first, then the transcension point will be when sufficient software complexity is mixed with sufficient processing power. The difficulty of the software problem will decrease drastically as processing power continues to increase exponentially.
[*] Nanocomputing (which could arrive as soon as 2005 or as late as 2020) will multiply available computing power by several orders of magnitude, and would greatly decrease the software complexity problem of AI if it became available to AI researchers.
[*] Basic-to-intermediate medical nanotechnology (which would arrive between a few months and several years after nanocomputing) would multiply the resolution of our human brain scans by several orders of magnitude, greatly decreasing the difficulty of the software problem.
[*] There is evidence that algorithm design for artificial general intelligence can be heavily nonbiological; i.e., engineered based on the central principles of general intelligence rather than biological inspiration. If so, a viable AI design might be orders of magnitude simpler than the human brain’s design.
[*] Intermediate general AI designs need not be impressive or newsworthy; there is no explicit evidence that the optimal waypoints between where we are now and general AI must be visibly surprising or solve common human problems. This generates the unfortunate possibility that we might be caught off guard by general AI.
[*] General AI technology rests upon the intersection of several exponentially advancing technologies; computers, brain scanning, data analysis, and nanotechnology. These exponential trends have mutually synergistic effects; the effect they have upon one another will be multiplicative rather than additive.
[*] Artificial Intelligence need not be conscious, possess humanlike aesthetics, emotions and intuition, or be popularly accepted in order to pose a threat. An Artificial Intelligence need not be eloquent in human speech or even fully sane in order to improve itself recursively; even a “retarded” AI might be able to solve the problems of nanotechnology and begin creating new hardware for itself, quickly reaching an unrivalled state relative to its human creators, on the basis of cognitive hardware advantages alone.
[*] Considering the massive threats and opportunities inherent in Singularity technologies, it is probably prudent to take the conservative position and assume that general AI will be here sooner rather than later. That way we can be better prepared for its arrival.
Quotes:
“The neuroscience community has advanced our collective knowledge of brain function to the point where it is now possible to build accurate and meaningful computational models of major brain pathways. I have focused on the auditory pathway, aided by direct collaboration with the world's leading auditory neuroscientists. It is now possible to visualize the responses of large ensembles of neurons to complex real-world sounds such as speech, music, and sounds moving through space, for the first time giving us the opportunity to see the computations we are effortlessly performing at a subconscious level. With care, it is possible to verify that our models agree with biological function -- once the principles of operation are known, it is in fact possible to build engineered systems that outperform the human system in quantifiable ways. [...] The next two decades promise an exciting period of advances in our understanding of the nature of human intelligence, and the development of increasingly intelligent assistants and prosthetics that enrich human life in ways we can now only imagine.”
– Lloyd Watts, 2002 World Congress on Computational Intelligence, Plenary Session
“As the computational power to emulate the human brain becomes available--we're not there yet, but we will be there within a couple of decades--projects already under way to scan the human brain will be accelerated, with a view both to understand the human brain in general, as well as providing a detailed description of the contents and design of specific brains. By the third decade of the twenty-first century, we will be in a position to create highly detailed and complete maps of all relevant features of all neurons, neural connections and synapses in the human brain, all of the neural details that play a role in the behavior and functionality of the brain, and to recreate these designs in suitably advanced neural computers.”
- Ray Kurzweil, “The Law of Accelerating Returns”
“As I discuss in Engines of Creation, if you can build genuine AI, there are reasons to believe that you can build things like neurons that are a million times faster. That leads to the conclusion that you can make systems that think a million times faster than a person. With AI, these systems could do engineering design. Combining this with the capability of a system to build something that is better than it, you have the possibility for a very abrupt transition. This situation may be more difficult to deal with even than nanotechnology, but it is much more difficult to think about it constructively at this point. Thus, it hasn't been the focus of things that I discuss, although I periodically point to it and say: 'That's important too.'”
- K. Eric Drexler, father of nanotechnology
“Phase 4: Complete the brain. This involves scaling up the computing resource by the final order of magnitude. Timescale: 15-20 years. These "plans" could easily turn out to be very cautious; all that is required is a major breakthrough in understanding neural encoding and appropriate abstractions and the whole lot could fall into place in half the time I suggest here!”
- Steve Furber, “A real-time computer simulation of the human brain”
“Computers have come from nowhere 50 years ago and are rapidly catching up in capability with the human brain, which hasn't improved in performance for hundreds of thousands of years. We can expect man machine equivalence by about 2015, perhaps even woman machine equivalence by 2016. But after this, the computers will continue to get smarter.”
- Ian Pearson, futurist for BT Exact, a British Telecommunications company
“It may seem rash to expect fully intelligent machines in a few decades, when the computers have barely matched insect mentality in a half-century of development. Indeed, for that reason, many long-time artificial intelligence researchers scoff at the suggestion, and offer a few centuries as a more believable period. But there are very good reasons why things will go much faster in the next fifty years than they have in the last fifty.”
- Hans Moravec, Carnegie Mellon roboticist
“For example, one plausible vision of the near future, based on current research, is shown in the vugraph below. There, it is suggested that there will be an intermediate stage, before "pure" nanoelectronics, in which nanometer-scale quantum-effect devices will be introduced as subcomponents embedded in microelectronic chips. Design studies show that this should greatly increase the density and flexibility of conventional digital logic. Fabrication work toward this "hybrid" approach is ongoing in the research community. If it continues to be successful, it could accelerate the arrival of commercially useful quantum-effect, nanoelectronics. Some experts believe this could make a form of nanoelectronics available for applications as early as the year 2005.”
- Daniel Mumzhiu, Michael Montemerlo, and James Ellenbogen, of the MITRE Nanosystems Group
