Wednesday, October 27, 2021

g-f(2)605 THE BIG PICTURE OF THE DIGITAL AGE (10/27/2021), TechTalks, Stanford reinforcement learning system simulates evolution

ULTRA-condensed knowledge

"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Reinforcement learning system simulates evolution (10/27/2021)  g-f(2)426 

Opportunity, TechTalks 

  • In nature, body and brain evolve together. Across many generations, every animal species has gone through countless cycles of mutation to grow limbs, organs, and a nervous system to support the functions it needs in its environment.
  • Hundreds of millions of years of evolution have blessed our planet with a wide variety of lifeforms, each intelligent in its own fashion. Each species has evolved to develop innate skills, learning capacities, and a physical form that ensure its survival in its environment.
  • But despite being inspired by nature and evolution, the field of artificial intelligence has largely focused on creating the elements of intelligence separately and fusing them together after development.
  • In a new paper published in the scientific journal Nature, AI researchers at Stanford University present a new technique that can help take steps toward overcoming some of current limits. 
  • Titled “Deep Evolutionary Reinforcement Learning,” the new technique uses a complex virtual environment and reinforcement learning to create virtual agents that can evolve both in their physical structure and learning capacities. The findings can have important implications for the future of AI and robotics research.

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                    Extra-condensed knowledge

                    Lessons learned, TechTalks

                    • Studying the evolution of life and intelligence is interesting. But replicating it is extremely difficult. An AI system that would want to recreate intelligent life in the same way that evolution did would have to search a very large space of possible morphologies, which is extremely expensive computationally. It would need a lot of parallel and sequential trial-and-error cycles.
                    • AI researchers use several shortcuts and predesigned features to overcome some of these challenges. For example, they fix the architecture or physical design of an AI or robotic system and focus on optimizing the learnable parameters. Another shortcut is the use of Lamarckian rather than Darwinian evolution, in which AI agents pass on their learned parameters to their descendants. 

                    Condensed knowledge

                    Opportunity, TechTalks

                    Deep Evolutionary Reinforcement Learning 

                    • In their new work, the researchers at Stanford aim to bring AI research a step closer to the real evolutionary process while keeping the costs as low as possible. “Our goal is to elucidate some principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control,” they write in their paper.
                    • Their framework is called Deep Evolutionary Reinforcement Learning. In DERL each agent uses deep reinforcement learning to acquire the skills required to maximize its goals during its lifetime. DERL uses Darwinian evolution to search the morphological space for optimal solutions, which means that when a new generation of AI agents are spawned, they only inherit the physical and architectural traits of their parents (along with slight mutations). None of the learned parameters are passed on across generations.
                    • “DERL opens the door to performing large-scale in silico experiments to yield scientific insights into how learning and evolution cooperatively create sophisticated relationships between environmental complexity, morphological intelligence, and the learnability of control tasks,” the researchers write.
                    • The work can have important implications for the future of AI and robotics and push researchers to use exploration methods that are much more similar to natural evolution.
                    • “We hope our work encourages further large-scale explorations of learning and evolution in other contexts to yield new scientific insights into the emergence of rapidly learnable intelligent behaviors, as well as new engineering advances in our ability to instantiate them in machines,” the researchers write.

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                    • Category 2: The Big Picture of the Digital Age
                    • [genioux fact deduced or extracted from TechTalks]
                    • This is a “genioux fact fast solution.”
                    • Tag Opportunities those travelling at high speed on GKPath
                    • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
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                      • Inherited from sources + Supported by the knowledge of one or more experts.


                    “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)605, Fernando Machuca, October 27, 2021, Corporation.

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