Cloud superintelligence

Anders SandbergFuture of Humanity Institute

James Martin 21st Century School

Oxford University

Austria has around821,000,000 IQ points

Total work = individual work – communications losses

Cheap superintelligence

Sometimes simple tricks produce amazing results

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Parallel trials (different agents)

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Wikimagic

• Basic model: visitors to a page improve it if they think they can do better

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Evaluating complex results

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Multiple agents as units

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Feedback for further improvement

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Scientific community

Evolutionary algorithm

Select best solution(s)

Variation

Information market

Price mechanism

Prices

Aggregated information

Why we need more people

• Why did the Tasmanians lose bone tools, fishing and warm weather clothing?

• Simple model– People imitate other people, innovate rarely– Number of technologies that can be retained

then depends on population size

Why blogs1 get smart

• Managing limited input channels• Model

– Agents read a limited number of information sources and have certain interests (many unique)

– Random agents post stories based on their interests– If one read agent has a piece of interesting news that source is

regarded as more useful by the agent– Agents write about the interesting news

with a certain probability, creatinginformation cascades

– Over time agents randomly shift to othersources if one proves useless

1: Or twitterers, journals, friends, etc…

Problem recogniser:Invoke suitable resource manager

Monitor/trouble-shooter

Resource manager: set up suitable problem solving structure

Superknowledge

• Often information can substitute for smarts

80 million tiny images

• http://people.csail.mit.edu/torralba/tinyimages/• Gather lots of example images, find nearest

neighbours of input image, deduce properties

Superorganisms and the posthuman realm