3 ms·
I think this perspective overlooks how human expertise actually works. Humans in cutting-edge research also get things wrong a lot: most hypotheses fail, most e
by leakycap 1y ago
I think this perspective overlooks how human expertise actually works. Humans in cutting-edge research also get things wrong a lot: most hypotheses fail, most experiments don't pan out, and most novel approaches lead nowhere.
When we celebrate a scientist who makes a breakthrough, we're not crediting them for being right 100% of the time. We're recognizing that they were right more often than random chance and earlier in the process than would otherwise occur.
A researcher (or AI) who can identify promising directions at a 2/99 or 3/99 rate instead of 1/99 is genuinely valuable – they're effectively doubling or tripling the efficiency of the discovery process.
Imagine if AI can test theories in under 100 seconds AND is slightly better out of 99 tries at getting things right. Beats the human out of the water.
- heisenbit 1y agoThis is exactly how I use AI now daily: Provide it with relevant context and help me to troubleshoot some software issue. I found often it casts a wider net and provides good leads to follow up. It balances out my own bias and overall speeds up discovery of the root cause. Admittedly some benefit comes from having to explain the AI the context - half the problems are gone once one can explain them clearly.
- slaterbug 1y agoLike a supercharged version of rubber duck debugging.