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I think this has a related outcome in the commercialisation of automated systems. It seems that it is much easier to create automated systems that outperform hi

I think this has a related outcome in the commercialisation of automated systems. It seems that it is much easier to create automated systems that outperform highly paid domain knowledge specialists at diagnostic tasks, than it is to get them to reliably deliver pizza as well as an average human with a moped.

jkuria 7y ago
Exactly. This is what Steven Pinker Says in the parent article: The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. The mental abilities of a four-year-old that we take for granted – recognizing a face, lifting a pencil, walking across a room, answering a question – in fact solve some of the hardest engineering problems ever conceived... As the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists, and cooks are secure in their jobs for decades to come.[7]
- shandor 7y ago
  To be fair it's not quite that simple. Automation has already been heavily cutting at the so-called low-skill jobs starting from Industrial Revolution. Or by some definition, from the Agricultural one somewhat earlier. But the examples of the gardener and cook that were in your citation are actually quite good, I admit.
  - toast0 7y ago
    The secret to automating repetitive jobs is often finding a different way to do it that provides a similar output, but is more amenable to machinery. A sewing machine doesn't sew at all like most humans, a dishwasher doesn't wash like you would with a sponge etc. Gardening is hard to automate, but harvesting of somethings (grains, mostly) is heavily automated. Cooking is more amenable to augmentation than full automation, but there are exceptions mostly around processed foods.

jkuria 7y ago

Exactly. This is what Steven Pinker Says in the parent article: The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. The mental abilities of a four-year-old that we take for granted – recognizing a face, lifting a pencil, walking across a room, answering a question – in fact solve some of the hardest engineering problems ever conceived... As the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists, and cooks are secure in their jobs for decades to come.[7]

shandor 7y ago
To be fair it's not quite that simple. Automation has already been heavily cutting at the so-called low-skill jobs starting from Industrial Revolution. Or by some definition, from the Agricultural one somewhat earlier. But the examples of the gardener and cook that were in your citation are actually quite good, I admit.
- toast0 7y ago
  The secret to automating repetitive jobs is often finding a different way to do it that provides a similar output, but is more amenable to machinery. A sewing machine doesn't sew at all like most humans, a dishwasher doesn't wash like you would with a sponge etc. Gardening is hard to automate, but harvesting of somethings (grains, mostly) is heavily automated. Cooking is more amenable to augmentation than full automation, but there are exceptions mostly around processed foods.