5. Centaur

Combination of human + machine intelligence

AKA Intelligence Augmentation
AKA Assisted Decision-Making
AKA Human-In-The-Loop

Examples

  • Spotify Discover Weekly
  • Amazon Fulfillment Centers
  • Netflix Recommendations
  • Formula-1
  • Voice Assistants
  • DaVinci Surgery
  • Uber (Marketplace)
  • Electronic Music

Quotes

That’s why asking whether technology causes inequality is the wrong question. Instead, we should be asking how advancing technologies have changed the relative demand for high-skill and low-skill workers, and how well we are adapting to such changes.

David Rotman

We are all centaurs now, our aesthetics continuously enhanced by computation. Every photograph I take on my smartphone is silently improved by algorithms the second after I take it. Every document autocorrected, every digital file optimised. Musicians complain about the death of competence in the wake of Auto-Tune , just as they did in the wake of the synthesiser in the 1970s.

?

The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.

J. C. R. Licklider, Man-Computer Symbiosis (1960)

Sources

  • https://www.oreilly.com/ideas/inside-the-washington-posts-popularity-prediction-experiment
  • https://spectrum.ieee.org/computing/software/the-secret-of-airbnbs-pricing-algorithm
  • https://www.technologyreview.com/s/612388/a-robot-scientist-will-dream-up-new-materials-to-advance-computing-and-fight-pollution/
  • https://product.voxmedia.com/2014/12/17/7405131/algorithmic-design-how-vox-picks-a-winning-layout-out-of-thousands

Extended Examples (Source?)

  • Being a centaur in the workplace means taking advantage of the vast analytical capabilities of AI-enabled technology and adding human thinking. The applications for the centaurmodel in the workplace are potentially endless, but here are a few example fields that are well-suited for the combination of deep analysis and human creativity:
  • Security and network planning: The volume of cyber attacks will continue to grow and AI will become increasingly necessary in threat analysis. However, attackers will always be creative, launching non-computerized vectors to compromise business networks. This is why humans will be necessary to prompt machines toward new ways of keeping creative attackers at bay.
  • Visual arts and music: Collaboration will replace the linear nature of artistic creation that we think of today. Two different algorithmic versions of a music program could give a human enough content to combine the two and generate an entirely new genre. Or, like Google’s Deep Dream, humans can input seeds of information for machines to generate artistic products.
  • Film and television: There are enough test cases for us to truly understand what a well-framed scene looks like. Teaching a machine how to essentially direct means filmmakers can set up scenes in VR and focus more on storytelling and creative connections than the minute details of production.
  • Architecture and product design: Function over form has dominated each of these fields. However, leaving a machine to design based on function over form might have us living in buildings that are just white boxes. However, IoT sensors can teach machines how we interact with our environments to learn exactly what people need in terms of function, leaving humans to balance function with form — spending more time on the art and less on the details.
  • Software engineering: Development is often thought to be a non-creative discipline, but the best software code is also the most creative. The best developers of tomorrow willdirect computers on a certain problem, examine the output and continue to redirect machines until they have new ways to solve old problems.

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