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Ai and autmoation


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How to think about automation, job security and the future of innovation in age of AI

Published in: Economy & Finance
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Ai and autmoation

  1. 1. Automation and AI Truth, Paranoia and the Human Advantage Nitzan Hermon — Studio VV6, New York
  2. 2. Automata • Our recent attempts to compute AI, or rather AGI (artificial general Intelligence) are not new • When Marvin Minsky and John McCarthy started MIT’s AI Lab in 1959 they were very much set on building machines that can think
  3. 3. –John McCarthy to Jeffery Mishlove, Thinking Allowed “There are 2 ways of looking at computing artificial intelligence, you can look at it from the point of view of biology or point of view of computer science You could imitative the nervous system as far as you understand the nervous system or you can immediate human psychology as far as you understand human psychology”
  4. 4. • “looking at it from the point of view of biology…” means the kind of “wet” programming the brain does, neurons, axons and so forth. Simulating such process is likely to be a reference to neural networks • which are (broadly speaking) sets of networks modeled around the way the brain works: namely taking a stab at clustering logic in proximity and generating hierarchies of information • “Looking at it from the point of psychology” is equally as provoking, as it refers to human intelligence as buckets of knowledge
  5. 5. –Marvin Minsky, part of a monologue in Machine Dreams “In order (for a machine) to be intelligent we have to give it several different kinds of thinking, when it switches from one of those to another we will say that it is changing emotions” “Emotion (in itself) is not a very profound thing, it’s just a switch between different modes of operation”
  6. 6. • This is the other way of computing a brain • Buckets of knowledge, and sets of actions • compartmentalized in different vertical buckets, and switched by emotions
  7. 7. Beyond being offensive to humanities, this argument is also easy to debunk using a quick thought experiment.
  8. 8. The Sitting Person • Imagine an intellectual person sitting in a chair, and doing nothing at all, starring into thin air • That person is clearly consciousness, and intelligent • Their lack of action does nothing to rob them of the consciousness and intelligent title • In other words, intelligence is not conditioned by action. It need not be modeled around goals, nor operational switches.
  9. 9. Intelligence is Not Conditioned by Action • This is an important point to stay on • This idea that we can compute human intelligence (we can’t) , or that the brain is a computer (it’s not) is the underpinning belief that has been fueling generations of researchers
  10. 10. Intelligence is Continuous • Let’s say that I could somehow peer into your brain and start creating a map of your intelligence. It may take me up to a few decades, but eventually I succeeded in computationally solving your intelligence • The problem is that I only solved the computation of your brain, and only at one point in time • In other words I only solved one instance of intelligence. Anchored to a point in time, and a subject (you). • The brain–as is conciseness, and its intelligence–is an on–going analog frequency. Not instance based digital permutations
  11. 11. No AGI – What then? • If you’re still reading, it is safe for me to assume that that you’re onboard my pro–human view • That is the view that the brain is not a computer problem, and hence we should abort the idea of AGI. • Narrow AI on the other hand is alive, and strong.
  12. 12. Opportunities in Narrow AI • think of your calculator as a narrow expert in calculation • think of a medical journal scanner as the best tool we have to consume terabytes of medical journals • And machine vision algorithms as the best possible face recognizer
  13. 13. We Can Perform the Same Tasks the Machine Does, But Only to a Certain Extent.
  14. 14. Zero to infinity on a single task
  15. 15. • On the left there is ‘0’, no intelligence is being used, and nothing is being done • We can learn to perform a task, and as a next step we might relay this knowledge to a machine, so it could hyper mathematize it • Examples of a singular plain: writing, browsing, coding, dancing, driving, reading, loading boxes
  16. 16. • We learn new tasks, and some of them graduate to automation. New domains are added, while other become obsolete • For example self–driving car technician, or a horseshoe maker respectively • We can think of the system of our modern life as made of an infinite amount of these single line trajectories
  17. 17. Singularitarians believe in infinite skills in infinite domains
  18. 18. The Human Advantage • In the absence of AGI is it up for us to navigate in this bend • Crossing, and linking disparate skills and disciplines • We hold an intellectual monopoly in that regard
  19. 19. Machines are incredibly capable in their unique domains but are blind to anything else, they only compute and extend the steps we relayed to the domain
  20. 20. How We Automate? • When we come up with a new skill or a technology, we will slowly improve it, maturing the domain for other people to participate • As part of that on–boarding, sets of instructions need to be written. In which point the domains is ready for a machine to excel it.
  21. 21. Focus on the Bend • That machine we just made is domain specific • Its algorithms could reach speeds of computation we can’t event imagine, but the bend is uniquely creative, and can’t be mechanically produced
  22. 22. If your product, or service is narrow by design then you’re open for a machine to excel, or replace you.
  23. 23. • This is absolutely not limited to lower grade jobs, as the machine hold no interest in your prestige. It holds no interests at all. • The system, can perform tasks efficiently • And if you’re job is on a narrow domain, and can be broken into steps then you’re not using your human advantage properly
  24. 24. Our unique, innate ability as creative humans is an important distinction. It will reveal currents in automation, AI, and innovation opportunities.
  25. 25. Thank You Nitzan Hermon