[HN Gopher] Neural-control family: what deep learning and contro...
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       Neural-control family: what deep learning and control enables in
       the real world
        
       Author : sebg
       Score  : 81 points
       Date   : 2021-11-25 16:17 UTC (6 hours ago)
        
 (HTM) web link (www.gshi.me)
 (TXT) w3m dump (www.gshi.me)
        
       | mark_l_watson wrote:
       | Interesting part about taking advantage of invariances. There is
       | more to this article than what I can digest on Thanksgiving. Book
       | marked for later.
        
       | joe_the_user wrote:
       | This depends on which "real world" you're talking about.
       | 
       | Doing the behavior that feedback driven control-systems do but
       | even better is a nice and impressive applications. That seems
       | most useful for applications like the application that's being
       | described - swarms of flying drones. Flying generally already
       | yielded to various control system - autopilots work because the
       | skies are mostly empty and so your system working according to
       | your predictions is all that matters. A drone swarm is much more
       | complicated but is still under the system's control.
       | 
       | It's worth saying that the "real world" where a lot of robots
       | fail has different challenges. Whether you're talking self-
       | driving cars, robot dogs accompanying troops or wheeled delivery
       | robots in hospitals, the problem is figuring both what you're
       | looking at and how to respond to it. And this has the problem
       | that nearly anything can show up and require unique responses,
       | causing progress here to never quite be enough. And better
       | physics and better cooperation between controlled elements
       | doesn't seem that useful here and this approach might not help
       | this "real world".
        
       | narrator wrote:
       | What bugs me about most sci-fi is that the robots have bad aim.
       | Watching these neural control videos, it becomes pretty clear
       | that the robots will kill the people in a fictional sci-fi
       | setting from miles away before our protagonist even knows they're
       | there.
        
         | mdp2021 wrote:
         | Surprisingly?! You just set less need for false positives...
        
       | cs702 wrote:
       | Incorporating priors from physics into hybrid DNN-blackbox +
       | traditional models makes a lot of sense for these kinds of
       | applications. It also makes sense that regularizing the DNN
       | blackbox to make sure it's "smooth enough" (i.e., ensuring the
       | change in output in relation to the change in input stays below
       | some threshold) helps make these complicated models more stable.
       | 
       | However, I don't quite understand how the authors are encoding
       | "domain invariance" with "a domain adversarially invariant meta-
       | learning algorithm." I'm not sure what that means. If any of the
       | authors are on HN, a more concrete explanation of such "domain
       | invariance encoding" would be greatly appreciated!
       | 
       | Finally, I have to say: The field of deep learning and AI is
       | going to benefit enormously from the involvement of more people
       | with strong backgrounds in physics, specially the theorists who
       | have invested many years or decades of their lives thinking about
       | and figuring out how to model complicated physical systems.
        
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       (page generated 2021-11-25 23:00 UTC)