[HN Gopher] Erik Brynjolfsson on automation, productivity, work,...
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       Erik Brynjolfsson on automation, productivity, work, and the future
        
       Author : feross
       Score  : 24 points
       Date   : 2022-06-17 18:47 UTC (4 hours ago)
        
 (HTM) web link (rootsofprogress.org)
 (TXT) w3m dump (rootsofprogress.org)
        
       | ethbr0 wrote:
       | Direct video link:
       | https://m.youtube.com/watch?v=JCwIwCK8jpI&t=3m3s
       | 
       | And context, if you're like me:
       | 
       |  _" Erik Brynjolfsson [...] is an American academic, author and
       | inventor. He is the Jerry Yang and Akiko Yamazaki Professor and a
       | Senior Fellow at Stanford University where he directs the Digital
       | Economy Lab at the Stanford Institute for Human-Centered AI, with
       | appointments at SIEPR,the Stanford Department of Economics and
       | the Stanford Graduate School of Business. [...] He is known for
       | his contributions to the world of IT productivity research and
       | work on the economics of information and the digital economy more
       | generally.
       | 
       | [...] He was among the first researchers to measure productivity
       | contributions of IT and the complementary role of organizational
       | capital and other intangibles. Brynjolfsson has done research on
       | digital commerce, the Long Tail, bundling and pricing models,
       | intangible assets and the effects of IT on business strategy,
       | productivity and performance."_
       | 
       | https://en.m.wikipedia.org/wiki/Erik_Brynjolfsson
        
         | ethbr0 wrote:
         | Summary, for those who don't want to watch (even at the 2x
         | speed I did):
         | 
         | - GPT3 and large language models seeming to generate text with
         | deep understanding is fundamentally difference than the past.
         | Aka foundation models.
         | 
         | - Most AGI prediction dates have been pulled closer to the
         | present.
         | 
         | - Brynjolfsson et al. assessed ML capability vs current tasks.
         | In almost every occupation, ML could do some of the tasks
         | better than humans. In none could they do all of the tasks.
         | 
         | - Q: Are we going to need to see regulation and law updated
         | when jobs transition from human to machine, and there's no
         | human to hold responsible for task failure? A: Law is lagging
         | practice. Human in the loop is the near future, for both
         | regulatory and practical purposes. Augment rather than replace.
         | 
         | - Q: What about humanoid robots? Specifically re: +10-20 years
         | labor needs vs demographics? A: Controlled settings (e.g.
         | factories) are the best first environments. General purpose
         | environments are much more difficult. We live in constructed
         | environments (e.g. street signs). Likely to see more of this to
         | specifically support automated task execution.
         | 
         | - _" Moravec's paradox is the observation by artificial
         | intelligence and robotics researchers that, contrary to
         | traditional assumptions, reasoning requires very little
         | computation, but sensorimotor and perception skills require
         | enormous computational resources."_
         | https://en.m.wikipedia.org/wiki/Moravec%27s_paradox
         | 
         | - Q: On productivity paradox, why aren't we seeing measured
         | productivity increases as a result of ML deployment? A: Part is
         | a measuring problem (e.g. zero price goods like Wikipedia don't
         | show up in GDP).
         | 
         | Potential explanations for the missing productivity that he
         | rules out: (1) Mismeasurement: We've always mismeasured things
         | though. See: "consumer surplus." (2) Many technologies are
         | shifting the pie vs making it bigger (e.g. targeted
         | advertising). This doesn't calculate up to missing
         | productivity.
         | 
         | A good explanation: economic equivalent of Amdahl's law. Speed
         | up a portion of the process, yet the metaprocess still only
         | runs at the speed of its slowest step. There is a fair amount
         | of linkage in the economy, and we're not likely to see huge
         | productivity boosts until ML is properly digested and problem
         | tasks are reengineered to leverage the new capabilities. Which
         | happens on the order of decades (aka as managers die / retire).
         | 
         | [... in progress...]
         | 
         | IMHO, meh. At least for most of the crowd here.
        
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       (page generated 2022-06-17 23:00 UTC)