By Murray Shanahan
The concept human background is drawing close a “singularity”—that usual people will sometime be overtaken by way of artificially clever machines or cognitively improved organic intelligence, or both—has moved from the world of technological know-how fiction to severe debate. a few singularity theorists expect that if the sphere of synthetic intelligence (AI) keeps to increase at its present dizzying price, the singularity might happen in the course of the current century. Murray Shanahan deals an creation to the belief of the singularity and considers the ramifications of this type of in all likelihood seismic event.
Shanahan’s target isn't to make predictions yet fairly to enquire various eventualities. no matter if we think that singularity is close to or a ways, most likely or most unlikely, apocalypse or utopia, the very inspiration increases the most important philosophical and pragmatic questions, forcing us to imagine heavily approximately what we'd like as a species.
Shanahan describes technological advances in AI, either biologically encouraged and engineered from scratch. as soon as human-level AI—theoretically attainable, yet tricky to accomplish—has been accomplished, he explains, the transition to superintelligent AI may be very quick. Shanahan considers what the life of superintelligent machines may suggest for such issues as personhood, accountability, rights, and id. a few superhuman AI brokers may be created to learn humankind; a few could cross rogue. (Is Siri the template, or HAL?) The singularity provides either an existential hazard to humanity and an existential chance for humanity to go beyond its barriers. Shanahan makes it transparent that we have to think either percentages if we wish to result in the higher outcome.d to visualize either percentages if we wish to result in the higher consequence.
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Extra resources for The Technological Singularity (The MIT Press Essential Knowledge series)
Example text
There have been methodological schools advocating work at all points along this spectrum throughout the history of AI. The popularity of each school has waxed and waned, but none has emerged as triumphant, and each has arguments in its favor. For example, a well-worn analogy with the history of powered flight likens the first type of machine, the AI engineered from scratch, to an aeroplane. Early designs for flying machines included flapping wings, imitating birds. But this approach failed. Fixed wings and propellers turned out to be the best way to get a large, heavy, human-made object airborne.
We cannot begin to paint a picture of the future of AI without thinking about concrete mechanisms. In computer science terms, we need to think not just about specification but also about implementation. It is a commonplace in computer science that the same specification can be implemented in many ways. This makes our task a difficult one because, unlike a software company that only needs to produce a single product, we would like to form an idea of the whole space of possible artificial intelligences.
Well, even if we gloss over the many engineering questions this proposal begs, it remains unconvincing. The products of such an approach might briefly give the appearance of general intelligence. But nobody would be fooled for very long. The multispecialist is going to get stuck as soon as it has to face a problem that is outside any of its areas of expertise, an inevitable occurrence in an ever-changing world. Perhaps the capacity to learn is enough to plug the gap here. In an unfamiliar situation, a new specialist skill can be learned.