Computing overhang

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There exists computing overhang when there is much more computing power available for a given process than can be taken advantage of by current software algorithms. If this is true for AI software, then new discoveries in deliberative or recursively self-improving algorithms could bring about an intelligence explosion.

As an example, imagine that humans had computers with which to add, but were unaware of the simple algorithm for multiplication. Naïve programs for multiplication would execute "228*537" by performing two hundred twenty eight additions of the number 537 with itself. With at least three operations per add, this would require at least 1611 operations. Once someone discovered the FOIL algorithm, the computation could be replace with "(200 + 20 + 8)*(500 + 30 + 7)", with about 30 operations. In the field of Artificial Intelligence, is possible that current algorithms for prediction, planning, et cetera use considerably sub-optimal computations, and that other algorithms could perform better on less hardware.

An analogous circumstance occurred in particle physics during the 1960s. Experimental physicists had discovered hundreds of subatomic particles with different properties. Physicists at the time joked that there was a "particle zoo", with far more inhabitants than theory could account for. It was considered unlikely that hundreds of particles were fundamental. Eventually the theory of quarks was found by two people independently, and the hundreds of particles were immediately explained. Later experimentation confirmed that six quarks composed all of the particles in the zoo.

Enormous amounts of computing power is currently available in the form of supercomputers or distributed computing. Large AI projects typically grow to fill these resources by using deeper search trees, like Rybka's solving of the King's Gambit opening in chess, or by performing large amounts of parallel operations on extensive databases, such as IBM's Watson playing Jeopardy. While the extra depth and breadth are helpful, it is likely that this simple brute-force extension of techniques is not the optimal use of the computing resources. The computational power required for general intelligence is at most that used by the human brain. Though estimates of whole brain emulation place that level of computing power at least a decade away, it is very unlikely that the algorithms used by the human brain are the most computationally efficient for producing AI. This is because evolution had no insight in creating the human mind. Neural algorithms have not gotten any better since they were first evolved; therefore Homo sapiens are in some sense just above the boundary for general intelligence.

External links


  • Muehlhauser, Luke; Salamon, Anna (2012). "Intelligence Explosion: Evidence and Import". in Eden, Amnon; Søraker, Johnny; Moor, James H. et al.. The singularity hypothesis: A scientific and philosophical assessment. Berlin: Springer.