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This is so true. We can see it very recently too. As a specific example look at evolutionary optimization. It's existed for decades, has a bit of a non mainstr

• letitgo12345 10y ago
  That paper, while interesting, is not really "evolutionary" optimization
• leereeves 10y ago
  How does that fit in this discussion? The stars of deep reinforcement learning haven't exited the picture. Have there been polarized discussions here between the two camps?
  • emcq 10y ago
    Up until the last sentence of the grandparents post, the point was "I think people are unable to challenge stars in their field." The fact that an alternative perspective that proves simple, effective, and popular in alternative communities is now popularized from stars in the field of deep learning rather than the alternative communities is exactly this. In fact, the resources available to the stars is much different than what these other communities may have access to such that they may never be able to demonstrate value themselves. It's not very often you have a 720 core machine to run these tests. Resource constrained science exists all over from chemistry, physics, and biotech to computer science. The rich get richer as they say. However it's also a great instance of solid science to test alternative approaches and I don't mean to take anything away from anyone, but simply to point to a case where that it is extremely difficult for people to challenge stars in the field.
    • leereeves 10y ago
      That OpenAI article is based on the paper: Evolution Strategies as a Scalable Alternative to Reinforcement Learning by Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever This seems like counter-evidence to the idea that people are unable to challenge the stars of the dominant paradigm.