JasonBee

3 ms·

1. 1.▲
   by JasonBee 1y ago

   My guess is the LLM was then an expert on the user guide…so as long as you asked it questions about standard by the book use cases then it was perfectly well trained. But building something from those use cases? If one of them was a iPhone
2. 2.▲
   by JasonBee 1y ago

   To me that makes it worse. Why would two people get wildly different answers to a simple factual observation query.
3. 3.▲
   by JasonBee 1y ago

   Maybe it’s due to cost? I’m curious to know whether Apple fine tuned the Siri LLM to run lean in order to save money, and in the same vein that OpenAI loses money on even the paid queries. It has to break even somewhere unless hydro becomes
4. 4.▲
   by JasonBee 1y ago

   How does that future work when the AI providers lose so much money on every monetized transaction?
5. 5.▲
   by JasonBee 1y ago

   That’s a little shortsighted I think. How many patents does Apple hold? This whole conversation feels like a zero sum discussion, as “if Apple isn’t the most innovative then they’re not innovative on any level.
6. 6.▲
   by JasonBee 1y ago

   Maybe it’s not so easy?
7. 7.▲
   by JasonBee 2y ago

   GPT can’t do math, don’t we already know this? It’s a predictive engine, not a computational engine.
8. 8.▲
   by JasonBee 2y ago

   I think it says more about their self perception of their abilities in realms where they have no special expertise. So many Silicon Valley leaders weigh on in on matters of civilizational impact. It seems making a few right choices suddenly