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I found that Deep Research mode in Gemini was able to give me a well planned 4 day trip to a major city. I told it my preferences and of the group members, whe

I found that Deep Research mode in Gemini was able to give me a well planned 4 day trip to a major city. I told it my preferences and of the group members, where we arrived and departed, at what times. I gave it my itinerary and then asked it to plan two new itineraries and also suggest a location to book a hotel that was convenient for the early flight on the last day. I went away for 20 mins and gave me a 20 page document with a good summary and decent options. I did choose some of the activities it suggested. I did this 10 months ago. It’s probably better now. But Gemini has access to google maps, so it can estimate travel times, and know which lunch places are near which sites and which hotels have good reviews. So if you want AI to work for travel panning you need to ground it in good data.

grey-area 3mo ago
Or maybe there are just a few complete trips to major city in the training data that it could copy from? I imagine major destinations are much easier.
- steve1977 3mo ago
  I think that's a large part behind the "success" of LLMs... people vastly overestimate their uniqueness.
  - JohnBooty 3mo ago
    It’s really one of the most flabbergasting things about discussing LLMs with the naysayers. There are a lot of extremely legitimate concerns, like the environmental impact and so on. But I just laugh when they point out that LLMs are merely clever regurgitators of their previous inputs… as if this isn’t how we as humans operate nearly all of the time. People realllllllllly want to think they’re special snowflakes.
    - grey-area 3mo ago
      It is not in fact how humans work at all. Ask a human to plan a trip: They do research, Pick destinations led by their own experience/likes/dislikes Compare to other guides Plan itineraries so they can get there Check and share Ask an LLM to plan a trip: It takes the prompt and continues it based on weights in the training data. If there is no data it picks the most likely thing (maybe made up). If there is it’ll mostly add things from that data. Maybe it’ll make tool calls and pull in data that way too but you can’t actually trust all the details. These two processes are so different, it’s important to understand how they work, which is nothing like a human.
      - rpdillon 3mo ago
        I think even if what you say is true, it doesn't address parents' point that both humans and machines regurgitate what they've consumed. But I'd also want to point out that the way you're characterizing an LLM planning a trip doesn't have any structure to it, which indicates that in your scenario you're not using any kind of harness. I've been amazed at how capable even 30 billion parameter models are when I put them inside of a harness that provides structure and task management. If you consider that scenario, especially with the ability to search the web and use skills, suddenly the LLM looks a lot more like what the human process looks like.
        grey-area 3mo ago
        Agents and harnesses don’t change the fundamental nature of LLMs, as is demonstrated by their terrible performance at real world tasks.
      - kijin 3mo ago
        There are plenty of humans who plan trips by concatenating destinations that appear the most frequently in their instagram feed. Not that different from how an LLM does things. Where humans and (current) LLMs differ the most is their failure mode. A human friend could be bad at planning trips, but that's kinda predictable, we're used to it, we know how to catch that Exception. LLMs on the other hand still have failure modes that come across as really wacky, like, what are they smoking in Mountain View? Which might actually serve as better evidence of different internal workings at a deeper level, than just parroting well-known superficial features of stochastic whatevertheysay.
- riffraff 3mo ago
  I used LLMs last year to plan an multiple week itinerary through Japan with the family, I wasn't super happy with the result so I tweaked it but they provided a useful template and some surprising ideas. As you guessed, there's a ton of info in the training data on this topic, but there's some value in being able to see it on one place with different options.
  - DrewADesign 3mo ago
    I think your experience with that trip echoes mine in a lot of areas. It’s a decent start. It takes care of some of the initial blue sky thinking to lay the groundwork. The problem is I think that’s the funnest part of a problem and I hate working on the details… it takes most of the creativity out of most problems as if it was drudgery, while leaving me to do the nitty gritty, which I consider the actual drudgery. I just don’t see LLMs’ contribution to tasks like this being anywhere close to being worth what they’ll cost after the VC subsidies run dry.

grey-area 3mo ago

Or maybe there are just a few complete trips to major city in the training data that it could copy from? I imagine major destinations are much easier.

steve1977 3mo ago
I think that's a large part behind the "success" of LLMs... people vastly overestimate their uniqueness.
- JohnBooty 3mo ago
  It’s really one of the most flabbergasting things about discussing LLMs with the naysayers. There are a lot of extremely legitimate concerns, like the environmental impact and so on. But I just laugh when they point out that LLMs are merely clever regurgitators of their previous inputs… as if this isn’t how we as humans operate nearly all of the time. People realllllllllly want to think they’re special snowflakes.
  - grey-area 3mo ago
    It is not in fact how humans work at all. Ask a human to plan a trip: They do research, Pick destinations led by their own experience/likes/dislikes Compare to other guides Plan itineraries so they can get there Check and share Ask an LLM to plan a trip: It takes the prompt and continues it based on weights in the training data. If there is no data it picks the most likely thing (maybe made up). If there is it’ll mostly add things from that data. Maybe it’ll make tool calls and pull in data that way too but you can’t actually trust all the details. These two processes are so different, it’s important to understand how they work, which is nothing like a human.
    - rpdillon 3mo ago
      I think even if what you say is true, it doesn't address parents' point that both humans and machines regurgitate what they've consumed. But I'd also want to point out that the way you're characterizing an LLM planning a trip doesn't have any structure to it, which indicates that in your scenario you're not using any kind of harness. I've been amazed at how capable even 30 billion parameter models are when I put them inside of a harness that provides structure and task management. If you consider that scenario, especially with the ability to search the web and use skills, suddenly the LLM looks a lot more like what the human process looks like.
      - grey-area 3mo ago
        Agents and harnesses don’t change the fundamental nature of LLMs, as is demonstrated by their terrible performance at real world tasks.
    - kijin 3mo ago
      There are plenty of humans who plan trips by concatenating destinations that appear the most frequently in their instagram feed. Not that different from how an LLM does things. Where humans and (current) LLMs differ the most is their failure mode. A human friend could be bad at planning trips, but that's kinda predictable, we're used to it, we know how to catch that Exception. LLMs on the other hand still have failure modes that come across as really wacky, like, what are they smoking in Mountain View? Which might actually serve as better evidence of different internal workings at a deeper level, than just parroting well-known superficial features of stochastic whatevertheysay.
riffraff 3mo ago
I used LLMs last year to plan an multiple week itinerary through Japan with the family, I wasn't super happy with the result so I tweaked it but they provided a useful template and some surprising ideas. As you guessed, there's a ton of info in the training data on this topic, but there's some value in being able to see it on one place with different options.
- DrewADesign 3mo ago
  I think your experience with that trip echoes mine in a lot of areas. It’s a decent start. It takes care of some of the initial blue sky thinking to lay the groundwork. The problem is I think that’s the funnest part of a problem and I hate working on the details… it takes most of the creativity out of most problems as if it was drudgery, while leaving me to do the nitty gritty, which I consider the actual drudgery. I just don’t see LLMs’ contribution to tasks like this being anywhere close to being worth what they’ll cost after the VC subsidies run dry.