Many in the AI field think the bigger-is-better approach is running out of road
Isn&#x27;t the fundamental problem that LLM&#x27;s don&#x27;t actually understand anything (as greater concepts), but rather operate as complex probability machines?<p>My 2 month active experience with ChatGPT-4 gave me the following takeaways:<p>- when it&#x27;s right, it&#x27;s amazing; and when you, the operator, can recognize the niche use case where it performs really well, it can be a game-changer (although you could have programmed a tool to do the same narrow task)<p>- when it&#x27;s a little wrong, you (the expert) can fix the issue and move on without friction<p>- when it&#x27;s any amount of wrong and you are less than an expert, or specifically you are completely unfamiliar with the topic, you can waste an immense amount of time researching the output and&#x2F;or iterating with the system to refine the result<p>Initially I thought it was a 10:1 ratio of performance to effort.  But finally (as a developer) I settled on (1 to 1.5):1.  It basically just changed the game for me from doing the actual hard work to working out how to tease the system into producing a reasonable result.  And in the process (same as with co-pilot) I started to recognize how it was leading me to change my habits to avoid thinking and effort and instead rely on an external brain.  If you could have a reliable external brain always available, that would be a fair trade.  But when the external &quot;brain&quot; is unreliable and only available via certain interfaces, it&#x27;s better to train yourself to be proactive, voracious (with respect to documentation), and tolerant of learning&#x2F;producing cycles.<p>I once thought it would be a game-changer.  Now I realize it is a game-changer, but in a similar way that offshoring was... it didn&#x27;t improve or solve any problems, but it merely changed the work.