Monday, July 12, 2021

g-f(2)367 The Big Picture of Business Artificial Intelligence (7/12/2021), Wired, Need to Fit Billions of Transistors on a Chip? Let AI Do It




ULTRA-condensed knowledge


Opportunity, AI is helping to design computer chips
  • AI is now helping to design computer chips—including the very ones needed to run the most powerful AI code.
  • Google, Nvidia, and others are training algorithms in the dark arts of designing semiconductors—some of which will be used to run artificial intelligence programs. 
Lesson learned, AI and the chip industry
  • Chipmakers, including Nvidia, Google, and IBM, are all testing AI tools that help arrange components and wiring on complex chips. The approach may shake up the chip industry, but it could also introduce new engineering complexities, because the type of algorithms being deployed can sometimes behave in unpredictable ways.
      Lesson learned, Reinforcement learning at Nvidia
      • At Nvidia, principal research scientist Haoxing “Mark” Ren is testing how an AI concept known as reinforcement learning can help arrange components on a chip and how to wire them together. The approach, which lets a machine learn from experience and experimentation, has been key to some major advances in AI.
      • “You can design chips more efficiently,” Ren says. “Also, it gives you the opportunity to explore more design space, which means you can make better chips.”
      Lesson learned, The powerful reinforcement learning
      • Reinforcement learning was used most famously to train computers to play complex games, including the board game Go, with superhuman skill, without any explicit instruction regarding a game’s rules or principles of good play. It shows promise for various practical applications, including training robots to grasp new objects, flying fighter jets, and algorithmic stock trading.


      Genioux knowledge fact condensed as an image


      Condensed knowledge


      Opportunity, AI is helping to design computer chips
      • AI is now helping to design computer chips—including the very ones needed to run the most powerful AI code.
      • Google, Nvidia, and others are training algorithms in the dark arts of designing semiconductors—some of which will be used to run artificial intelligence programs. 
      Lesson learned, AI and the chip industry
      • Chipmakers, including Nvidia, Google, and IBM, are all testing AI tools that help arrange components and wiring on complex chips. The approach may shake up the chip industry, but it could also introduce new engineering complexities, because the type of algorithms being deployed can sometimes behave in unpredictable ways.
          Lesson learned, Reinforcement learning at Nvidia
          • At Nvidia, principal research scientist Haoxing “Mark” Ren is testing how an AI concept known as reinforcement learning can help arrange components on a chip and how to wire them together. The approach, which lets a machine learn from experience and experimentation, has been key to some major advances in AI.
          • “You can design chips more efficiently,” Ren says. “Also, it gives you the opportunity to explore more design space, which means you can make better chips.”
          Lesson learned, The powerful reinforcement learning
          • Reinforcement learning was used most famously to train computers to play complex games, including the board game Go, with superhuman skill, without any explicit instruction regarding a game’s rules or principles of good play. It shows promise for various practical applications, including training robots to grasp new objects, flying fighter jets, and algorithmic stock trading.


          Category 2: The Big Picture of the Digital Age

          [genioux fact deduced or extracted from WIRED]

          This is a “genioux fact fast solution.”

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          Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).

          Type of validity of the "genioux fact". 

          • Inherited from sources + Supported by the knowledge of one or more experts.


          Authors of the genioux fact

          Fernando Machuca


          Will Knight


          SENIOR WRITER

          Will Knight (@willknight) is a senior writer for WIRED, covering artificial intelligence. He was previously a senior editor at MIT Technology Review, where he wrote about fundamental advances in AI and China’s AI boom. Before that, he was an editor and writer at New Scientist. He studied anthropology and journalism in the UK before turning his attention to machines.


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