simulation
By a "superintelligence" we mean an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills. This definition leaves open how the superintelligence is implemented: it could be a digital computer, an ensemble of networked computers, cultured cortical tissue or what have you. It also leaves open whether the superintelligence is conscious and has Entities such as companies or the scientific community are not superintelligences according to this definition. Although they can perform a number of tasks of which no individual human is capable, they are not intellects and there are many fields in which they perform much worse than a human brain - for example, you can't have real-time conversation with Superintelligence requires software as well as hardware. There are several approaches to the software problem, varying in the amount of top-down direction they require. At the one extreme we have systems like CYC which
(It would be interesting to examine in more detail to what extent this holds true for all of neocortex. Now, on the other hand, we can foresee the arrival of human-equivalent hardware, so the cause of AI's past failure will then no longer be present. AIs would help constructing better AIs, which in turn would help building better AIs, and so forth. While systems like CYC might be good for certain practical tasks, this hardly seems like an approach that will convince AI-skeptics that superintelligence might well happen in the foreseeable future. Artificial neural networks in real-world applications today are usually trained through some variant of the Backpropagation algorithm (which is known to be biologically unrealistic). If one AI has achieved eminence in some field, then subsequent AIs can upload the pioneer's program or synaptic weight-matrix and immediately achieve the same level of performance. In contrast to what's possible for biological intellects, it might be possible to copy skills or cognitive modules from one artificial intellect to another. It seems quite possible that very advanced optimization could reduce this figure further, but the entrance level would probably not be less than about 10^14 ops. And we are not very far from knowing what these rules are. It thus seems likely that the requisite hardware for human-level artificial intelligence will be assembled in the first quarter of the next century, possibly within the first few years. For example, people who have their hippocampus removed, lose their ability to learn new episodic or semantic facts. However, it has yet to be explained how Hebbian learning by itself could produce all the forms of learning and adaptation of which the human brain is capable (such the storage of structured representation in long-term memory - Bostrom 1996). By the early 1980s, AI research had to settle for $100,000 minicomputers. Other cortical areas take over the functions that would normally have been developed in the destroyed region.
Common topics in this essay:
AI AI,
Eric Fingerman,
Schlaggar O'Leary,
Presumably Hebb's,
Quartz Sejnowski,
MIPS Suddenly,
Intelligence Robotics,
Hans Moravec,
artificial intelligence,
human brain,
visual cortex,
computing power,
moore's law,
Phillips Singer,
human-level artificial intelligence,
human-level artificial,
neural network,
technologically feasible,
hebbian learning,
backpropagation algorithm,
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Acceptance Essays
