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michael_nielsen's profile
michael_nielsen
michael_nielsen
michael_nielsen
@michael_nielsen

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michael_nielsen

@michael_nielsen

Searching for the numinous. Co-purveyor of https://quantum.country/ 

San Francisco, CA
michaelnielsen.org
Joined July 2008

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    1. OpenAI‏Verified account @OpenAI 16 May 2018
      • Report Tweet

      AI and Compute: Our analysis showing that the amount of compute used in the largest AI training runs has had a doubling period of 3.5 months since 2012 (net increase of 300,000x):https://blog.openai.com/ai-and-compute/ 

      26 replies 472 retweets 1,116 likes
      Show this thread
    2. OpenAI‏Verified account @OpenAI Nov 7
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      We've analyzed compute used in major AI results for the past decades and identified two eras in AI: 1) Prior to 2012 - AI results closely tracked Moore's Law, w/ compute doubling every two years. 2) Post-2012 - compute has been doubling every 3.4 months https://openai.com/blog/ai-and-compute/#addendum …pic.twitter.com/ILN5MRrWYH

      24 replies 298 retweets 683 likes
      Show this thread
    3. Grady Booch‏Verified account @Grady_Booch Nov 7
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      Replying to @OpenAI

      Where would Deep Blue and Watson fit on this chart? They are - surprisingly - missing.

      1 reply 1 retweet 15 likes
    4. Jack Clark‏ @jackclarkSF Nov 7
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      Replying to @Grady_Booch @OpenAI

      We plotted Deep Blue on this early on in the analysis, but took it out of the chart as these results are ones which predominantly use ML. I'm not sure if there was enough info in the Watson papers to do a compute calc.

      2 replies 0 retweets 2 likes
    5. Jack Clark‏ @jackclarkSF Nov 7
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      Replying to @jackclarkSF @Grady_Booch @OpenAI

      Here is the deep blue number & method, hope helpful!pic.twitter.com/iQVqN4GKhW

      1 reply 0 retweets 0 likes
    6. girish sastry‏ @girishsastry Nov 7
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      Replying to @jackclarkSF @Grady_Booch @OpenAI

      Yep, note that this was an initial cut at deep blue and makes some assumptions re: utilization and “thinking time”. As jack said, for this particular analysis we looked at learning systems in particular

      1 reply 0 retweets 2 likes
    7. michael_nielsen‏ @michael_nielsen Nov 7
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      Replying to @girishsastry @jackclarkSF and

      Where did the 11 GF / sec number come from? IIRC, the papers says they could evaluate ~200 mill positions per second, and were calculating ~8,000 features per position. That makes it at least 1 TF / sec, and probably at least an order of magnitude higher.

      2 replies 0 retweets 0 likes
    8. girish sastry‏ @girishsastry Nov 7
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      Replying to @michael_nielsen @jackclarkSF and

      It comes from the LINPACK benchmark in 1997: https://www.top500.org/site/48052 

      1 reply 0 retweets 1 like
    9. girish sastry‏ @girishsastry Nov 7
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      Replying to @girishsastry @michael_nielsen and

      The quoted calculation does ignore the specialized chip for evaluation of positions though

      1 reply 0 retweets 1 like
      michael_nielsen‏ @michael_nielsen Nov 7
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      Replying to @girishsastry @jackclarkSF and

      The number doesn't seem relevant then.

      9:26 PM - 7 Nov 2019
      • 1 Like
      • girish sastry
      1 reply 0 retweets 1 like
        1. New conversation
        1. girish sastry‏ @girishsastry Nov 7
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          Replying to @michael_nielsen @jackclarkSF and

          Yeah, sorry I was imprecise: I'm uncertain as to whether the LINPACK benchmark number exercises the use of the chess chips, but probably not? (IIRC I was going to look into this more, but we decided to focus on training models)

          2 replies 0 retweets 0 likes
        2. michael_nielsen‏ @michael_nielsen Nov 7
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          Replying to @girishsastry @jackclarkSF and

          Yes, the LINPACK benchmark seems in the right ballpark for what you'd get if you ignored the chess chips.

          0 replies 0 retweets 0 likes
          3. End of conversation

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