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ChatGPT agent: bridging research and action

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Re: ChatGPT agent: bridging research and action

#121

Post by jstummbillig »

exitb wrote: post #102
jstummbillig wrote: post #89

Earlier quoted context omitted.

I am looking forward to learning why this is entirely unlike working with humans, who in my experience commit very silly and unpredictable errors all the time (in addition to predictable ones), but additionally are often proud and anxious and happy to deliberately obfuscate their errors.

You can point out the errors to people, which will lead to less issues over time, as they gain experience. The models however don’t do that.

I think there is a lot of confusion on this topic. Humans as employees have the same basic problem: You have to train them, and at some point they quit, and then all that experience is gone. Only: The teaching takes much longer. The retention, relative to the time it takes to teach, is probably not great (admittedly I have not done the math).

A model forgets "quicker" (in human time), but can also be taught on the spot, simply by pushing necessary stuff into the ever increasing context (see claude code and multiple claude.md on how that works at any level). Experience gaining is simply not necessary, because it can infer on the spot, given you provide enough context.

In both cases having good information/context is key. But here the difference is of course, that an AI is engineered to be competent and helpful as a worker, and will be consistently great and willing to ingest all of that, and a human will be a human and bring their individual human stuff and will not be very keen to tell you about all of their insecurities.

Re: ChatGPT agent: bridging research and action

#123

Post by kingnothing »

mclau157 wrote: post #84
twalkz wrote: post #30

The "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…

the bigger takeaway here is will his boss allow him to walk his dog or will he see available downtime and try to fill it with more work?

95% of people doing his job will lose them. 1 person will figure out the 2% that requires a human in the loop.

Re: ChatGPT agent: bridging research and action

#124

Post by jstummbillig »

levocardia wrote: post #93
alach11 wrote: post #14

It's very hard for me to imagine the current level of agents serving a useful purpose in my personal life. If I ask this to plan a date night with my wife this weekend, it needs to consult my calendar to pick the best night, pick a bar and restaurant we like (how would it know?), book a babysitter (can it learn who we use and text them on my behalf?), etc. This is a lot of stuff it has to get right, and it requires a…

Maybe this is the "bitter lesson of agentic decisions": hard things in your life are hard because they involve deeply personal values and complex interpersonal dynamics, not because they are difficult in an operational sense. Calling a restaurant to make a reservation is trivial. Deciding what restaurant to take your wife to for your wedding anniversary is the hard part (Does ChatGPT know that your first date was at…

> hard things in your life are hard because they involve deeply personal values and complex interpersonal dynamics, not because they are difficult in an operational sense

Beautiful

Re: ChatGPT agent: bridging research and action

#126

Post by michaelmrose »

groby_b wrote: post #100
travelalberta wrote: post #59

Earlier quoted context omitted.

I think this is my favorite part of the LLM hype train: the butterfly effect of dependence on an undependable stochastic system propagates errors up the chain until the whole system is worthless. "I think it got 98% of the information correct..." how do you know how much is correct without doing the whole thing properly yourself? The two options are: - Do the whole thing yourself to validate - Skim 40% of it, 'seems…

> how do you know how much is correct Because it's a budget. Verifying them is _much_ cheaper than finding all the entries in a giant PDF in the first place. > the butterfly effect of dependence on an undependable stochastic system We're using stochastic systems for a long time. We know just fine how to deal with them. > Meanwhile an agent that you accept to get only 98% of things right is meeting expectations. There…

There are in fact lots of tasks people complete immediately at 99.99% success rate at first iteration or 99.999% after self and peer checking work

Perhaps importantly checking is a continual process and errors are identified as they are made and corrected whilst in context instead of being identified later by someone completely devoid of any context a task humans are notably bad at.

Lastly it's important to note the difference between a overarching task containing many sub tasks and the sub tasks.

Something which fails at a sub task comprising 10 sub tasks 2% of the time per task has a miserable 18% failure rate at the overarching task. By 20 it's failed at 1 in 3 attempts worse a failing human knows they don't know the answer the failing AI produces not only wrong answers but convincing lies

Failure to distinguish between human failure and AI failure in nature or degree of errors is a failure of analysis.

Re: ChatGPT agent: bridging research and action

#127

Post by Aurornis »

twalkz wrote: post #30

The "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…

> 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...

This is where the AI hype bites people.

A great use of AI in this situation would be to automate the collection and checking of data. Search all of the data sources and aggregate links to them in an easy place. Use AI to search the data sources again and compare against the spreadsheet, flagging any numbers that appear to disagree.

Yet the AI hype train takes this all the way to the extreme conclusion of having AI do all the work for them. The quip about 98% correct should be a red flag for anyone familiar with spreadsheets, because it’s rarely simple to identify which 2% is actually correct or incorrect without reviewing everything.

This same problem extends to code. People who use AI as a force multiplier to do the thing for them and review each step as they go, while also disengaging and working manually when it’s more appropriate have much better results. The people who YOLO it with prompting cycles until the code passes tests and then submit a PR are causing problems almost as fast as they’re developing new features in non-trivial codebases.

Re: ChatGPT agent: bridging research and action

#128

Post by [deleted] »

twalkz wrote: post #30

The "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…

[deleted]

Re: ChatGPT agent: bridging research and action

#129

Post by dingnuts »

samtp wrote: post #97
nemomarx wrote: post #85

Earlier quoted context omitted.

I think it's basically equivalent to giving that prompt to a low paid contractor coder and hoping their solution works out. At least the turnaround time is faster? But normally you would want a more hands on back and forth to ensure the requirements actually capture everything, validation and etc that the results are good, layers of reviews right

It seems to be a mix between hiring an offshore/low level contractor and playing a slot machine. And by that I mean at least with the contractor you can pretty quickly understand their limitations and see a pattern in the mistakes they make. While an LLM is obviously faster, the mistakes are seemingly random so you have to examine the result much more than you would with a contractor (if you are working on something…

the slot machine is apt. insert tokens, pull lever, ALMOST get a reward. Think: I can start over, manually, or pull the lever again. Maybe I'll get a prize if I pull it again...

and of course, you pay whether the slot machine gives a prize or not. Between the slot machine psychological effect and sunk cost fallacy I have a very hard time believing the anecdotes -- and my own experiences -- with paid LLMs.

Often I say, I'd be way more willing to use and trust and pay for these things if I got my money back for output that is false.

Re: ChatGPT agent: bridging research and action

#130

Post by FergusArgyll »

pants2 wrote: post #115
FergusArgyll wrote: post #103

Earlier quoted context omitted.

If people will actually pay for stuff (food, clothing, flights, whatever) through this agent or operator, I see no reason Amazon etc would continue to block them.

I was buying plenty of stuff through Amazon before they blocked Operator. Now I sometimes buy through other sites that allow it. The most useful for me was: "here's a picture of a thing I need a new one of, find the best deal and order it for me. Check coupon websites to make sure any relevant discounts are applied." To be honest, if Amazon continues to block "Agent Mode" and Walmart or another competitor allows it,…

Right but there were so few people using operator to buy stuff that it's easier to just block ~ all data center ip addresses. If this becomes a "thing" (remains to be seen, for sure) then that becomes a significant revenue stream you're giving up on. Companies don't block bots because they're Speciesist, it's bec usually bots cost them money - if that changes, I assume they'll allow known chatgpt-agent ip addrs
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