Scott Jenson
For #AI innovation, should Big Tech be our only choice?
The assumption so far is that AI is just too big for normal developers, so we have no choice but to let Big Tech figure it out. There is likely some truth to this, but I'd like us to live in a world without silos. Every single company pursuing AI right now is using it to buttress their own silo. This may indeed be the simplest solution in the short run, but I'd like us to have at least the aspiration of something bigger.
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Eaton
@scottjenson I’d clarify that the ability to compose text depends on the nature of the text — a lot of peoplle tend to conflate analysis etc with the composition of the text describing the conclusions, which is where we get a lot of confusion about “reasoning capabilities”
Scott Jenson
For example, it's clear that #LLMs are great at parsing, summarizing, and composing text (both human and computer languages). If we were to have a range of "input bots" that gathered data from various places (e.g., banks, calendars, the DMV) and a series of "output bots" that visualized that information, LLMs could be the glue connecting these together, creating an enormous range of applications, triggers, and assistants. But we have to want this to be an open system.
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Eaton
@scottjenson @jarango aha, yes! @karenmcgrane and I are doing some similar things for large scale site navigation reorgs, though right now the work is embedded in a yarnball of a larger project. The work of breaking it out into something demoable separately is one of those eternal “next weeks,” heh
Scott Jenson
@eaton Oh, I completely agree. I'm actually more encouraged by #LLMs ability to write code fragments. This allows us to escape 'text output' with anything that can be coded. For example @jarango analyzed >1000 web pages and was able to output the results (after a few steps) into a directed graph. This feels like a prototype of this approach.
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