[flagged]
By 2030 Europe will be known for croissants and colossal brains.
ChatGPT agent: bridging research and action
21–30 of 508 posts
Re: ChatGPT agent: bridging research and action
#22[flagged]
When your colleagues are accelerating towards a cliff being left behind is a good thing.
Re: ChatGPT agent: bridging research and action
#23[flagged]
Damn! This is why I can’t see it! In in the UK…
Re: ChatGPT agent: bridging research and action
#24[flagged]
Could you name which specific regulations that are applying to all EEA members those would be and why/how they also apply to Switzerland?
Re: ChatGPT agent: bridging research and action
#25I do not know what an agent is and at this point I am too afraid to ask.
Anthropic's breakdown is quite good: https://www.anthropic.com/engineering/building-effective-age...
Re: ChatGPT agent: bridging research and action
#26One the one hand this is super cool and maybe very beneficial, something I definitely want to try out.
On the other, LLMs always make mistakes, and when it's this deeply integrated into other system I wonder how severe these mistakes will be, since they are bound to happen.
Re: ChatGPT agent: bridging research and action
#27[flagged]
I would be happy to be left behind all these things. Unfortunately they will find it's way to EU anyway.
Re: ChatGPT agent: bridging research and action
#28For me the most interesting example on this page is the sticker gif halfway down the page. Up until now, chatbots haven't really affected the real world for me†. This feels like one of the first moments where LLMs will start affecting the physical world. I type a prompt and something shows up at my doorstep. I wonder how much of the world economy will be driven by LLM-based orders in the next 10 years. † yes I'm awar…
By "sticker gif" do you mean "update the attached sheet" screen recording?
Re: ChatGPT agent: bridging research and action
#29Re: ChatGPT agent: bridging research and action
#30The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the time consuming work, that will save you a ton of time"
It feels like either finding that 2% that's off (or dealing with 2% error) will be the time consuming part in a lot of cases. I mean, this is nothing new with LLMs, but as these use cases encourage users to input more complex tasks, that are more integrated with our personal data (and at times money, as hinted at by all the "do task X and buy me Y" examples), "almost right" seems like it has the potential to cause a lot of headaches. Especially when the 2% error is subtle and buried in step 3 of 46 of some complex agentic flow.