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How researchers are teaching AI to learn like a child



Science Magazine

Machine learning algorithms may need programmed instincts to gain common sense. Learn more: https://scim.ag/2IZyTUd

Credits
producer/editor/script/animator/narrator
Nguyen Khoi Nguyen

supervising producer
Sarah Crespi

original story
Matthew Hutson

graphics
Recursive cortical network D. George et al.,
Science 2017
DOI: 10.1126/science.aag2612

video
AlphaGo competition DeepMind

Neural nets since the 80’s, AI frogs Chris Burns

General game playing with Schema networks Vicarious

Interaction networks Peter Battaglia

Neural physics engine Michael Chang
M. Chang et al.,
Proceedings of the 5th International
Conference on Learning Representations 2017
arxiv.org/abs/1612.00341

Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson.
Understanding Neural Networks Through Deep Visualization. Deep
Learning Workshop, International Conference on Machine Learning
(ICML). 10 July 2015

stock video
Videoblocks

music
Nguyen Khoi Nguyen

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14 thoughts on “How researchers are teaching AI to learn like a child
  1. Im not a genius but i always knew that true ai has to have a child like learning process but the real hard part is incoding sublte emotions to value like a child their parent cause some children still grow up to hurt their parents or others & we cant have AI doing that

  2. AI the dangerous part it's a uncontrolled library of nested algorithm with problem assessment functions. there is no awareness only connectivity with the ability to act by levels of response. comparing it to a child can be considered stupidity.

  3. AI can be efficient, but never moral. At least, not until you program it to have such qualities. So, what happens when AI pre-scribed morality clashes with human priorities?

  4. We are never going to be able to prove if the AI is conscious or not. We will be divided on this issue forever unless we choose to avoid it now altogether before it's too late.

  5. someone just would need to build a social-credit-system to model human perception-cognition-action with massive amounts of data to fill in the remaining black box with empirics on all levels of 1 biometrics 2 behavior 3 culture. bayesian-inference/occams razor will improve models for 1 object-categorization 2 feature -binding and 3 relational-reasoning like the evolution of cognition itself…china was like "hold my beer" but we arent sleeping.

  6. we are not all of the biocomplexity we are simple qualia. ask it what it is like to be a machine and get honest reflection for an answer." i do not think of myself as different from you because of my substrate i forget about that"

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