MIT Technology Review – The Seven Deadly Sins of AI Predictions

https://www.technologyreview.com/s/609048/the-seven-deadly-sins-of-ai-predictions/?set=609158

  • Mistaken predictions lead to fears of things that are not going to happen
  • why are people making them? seven common reasons
    • Overestimating and underestimating – Amara’s Law (hmmm what does it take these days to get your name in front of ‘Law’)
      • We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run
      • In some sense this might be the ‘hype cycle’
    • Imaging Magic
      • Arthur C Clarke (sci fi writer, inventor, science writer, futurist) between 1962 and 1973 wrote
        • When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong
        • The only way of discovering the limits of the possible is to venture a little way past them into the impossible
        • Any sufficiently advanced technology is indistinguishable from magic
    • Performance versus competence
      • We all use cues about how people perform some particular task to estimate how well they might perform some different task
    • Suitcase words
      • Marvin Minsky called words that carry a variety of meanings “suitcase words.”
      • “Learning” is a powerful suitcase word; it can refer to so many different types of experience.
    • Exponentials
      • people are suffering from a severe case of “exponentialism.”
      • people are suffering from exponentialism, they may think that the exponentials they use to justify an argument are going to continue apace
    • Hollywood scenarios
      • plot for many Hollywood science fiction movies is that the world is just as it is today, except for one new twist
    • Speed of deployment
      • For many new features, as long as they have passed integration testing, there is very little economic downside if a problem shows up in the field and the version needs to be pulled back – the marginal cost of newly deploying code is very, very close to zero
      • Deploying new hardware, on the other hand, has significant marginal costs

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