AI generates research ideas faster than I can test them. Anyone else? [R]

Key points

• A single AI discussion (eg asking it to review 30 papers I had in my reading list, audit them, check for ideas related to the status of my project) branched into dozens of promising approaches, and with coding agents, exploring them in parallel is in principle feasible.1
• But as a researcher, I still have to manage experiments, launch training runs, evaluate results, and decide what to try next.1
• I'm wondering if others are experiencing the same thing.1
• Have you found good ways to manage this, or best daily practices that help in this era of "vibe-research"?1

Sources (1)

• [1]AI generates research ideas faster than I can test them. Anyone else? [R]
  r/MachineLearning (top, daily) · Oct 19, 05:50 PM

  I've been using AI extensively for ML research, and I've noticed a bottleneck: brainstorming research directions and generating smoke run implementations has become incredibly fast, but actually testing those ideas hasn't.

  A single AI discussion (eg asking it to review 30 papers I had in my reading list, audit them, check for ideas related to the status of my project) branched into dozens of promising approaches, and with coding agents, exploring them in parallel is in principle feasible.

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