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ChatGPT agent: bridging research and action

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141–150 of 508 posts

Re: ChatGPT agent: bridging research and action

#141
post #117

Earlier quoted context omitted.

Why does an ecommerce website need a profit sharing agreement?

Why would they want an LLM to slurp their web site to help some analyst create a report about the cost of widgets? If they value the data they can pay for it. If not, they don't need to slurp it, right? This goes for training data too.

The alternative is the AI only telling customers about competitors wares

Re: ChatGPT agent: bridging research and action

#142
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…

>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 (and not model quality) is why I’m betting on Google.

Re: ChatGPT agent: bridging research and action

#143

I'm not so optimistic as someone that works on agents for businesses and creating tools for it. The leap from low 90s to 99% is classic last mile problem for LLM agents. The more generic and spread an agent is (can-do-it-all) the more likely it will fail and disappoint. Can't help but feel many are optimizing happy paths in their demos and hiding the true reality. Doesn't mean there isn't a place for agents but rathe…

> Can't help but feel many are optimizing happy paths in their demos and hiding the true reality.

Even with the best intentions, this feels similar to when a developer hands off code directly to the customer without any review, or QA, etc. We all know that what a developer considers "done" often differs significantly from what the customer expects.

Re: ChatGPT agent: bridging research and action

#144

I'm not so optimistic as someone that works on agents for businesses and creating tools for it. The leap from low 90s to 99% is classic last mile problem for LLM agents. The more generic and spread an agent is (can-do-it-all) the more likely it will fail and disappoint. Can't help but feel many are optimizing happy paths in their demos and hiding the true reality. Doesn't mean there isn't a place for agents but rathe…

Seen this happen many times with current agent implementations. With RL (and provided you have enough use case data) you can get to a high accuracy on many of these shortcomings. Most problems arise from the fact that prompting is not the most reliable mechanism and is brittle. Teaching a model on specific tasks help negate those issues, and overall results in a better automation outcome without devs having to make so much effort to go from 90% to 99%. Another way to do it is parallel generation and then identifying at runtime which one seems most correct (majority voting or llm as a judge).

I agree with you on the hype part. Unfortunately, that is the reality of current silicon valley. Hype gets you noticed, and gets you users. Hype propels companies forward, so that is about to stay.

Re: ChatGPT agent: bridging research and action

#145
post #70

I've been using OpenAI operator for some time - but more and more websites are blocking it, such as LinkedIn and Amazon. That's two key use-cases gone (applying to jobs and online shopping). Operator is pretty low-key, but once Agent starts getting popular, more sites will block it. They'll need to allow a proxy configuration or something like that.

Agents respecting robots.txt is clearly going to end soon. Users will be installing browser extensions or full browsers that run the actions on their local computer with the user's own cookie jar, IP address, etc.

I hope agents.txt becomes standard and websites actually start to build agent-specific interfaces (or just have API docs in their agent.txt). In my mind it's different from "robots" which is meant to apply rules to broad web-scraping tools.

Re: ChatGPT agent: bridging research and action

#147
post #136
post #70

I've been using OpenAI operator for some time - but more and more websites are blocking it, such as LinkedIn and Amazon. That's two key use-cases gone (applying to jobs and online shopping). Operator is pretty low-key, but once Agent starts getting popular, more sites will block it. They'll need to allow a proxy configuration or something like that.

Automating applying to jobs makes sense to me, but what sorts of things were you hoping to use Operator on Amazon for?

Finding, comparing, and ordering products -- I'd ask it to find 5 options on Amazon and create a structured table comparing key features I care about along with price. Then ask it to order one of them.

Re: ChatGPT agent: bridging research and action

#148

Earlier quoted context omitted.

Of course, Pareto principle is at work here. In an adjacent field, self-driving, they are working on the last "20%" for almost a decade now. It feels kind of odd that almost no one is talking about self-driving now, compared to how hot of a topic it used to be, with a lot of deep, moral, almost philosophical discussions.

The critics of the current AI buzz certainly have been drawing comparisons to self driving cars as LLMs inch along with their logarithmic curve of improvement that's been clear since the GPT-2 days. Whenever someone tells me how these models are going to make white collar professions obsolete in five years, I remind them that the people making these predictions 1) said we'd have self driving cars "in a few years" bac…

> said we'd have self driving cars "in a few years" back in 2015

And they wouldn't have been too far off! Waymo became L4 self-driving in 2021, and has been transporting people in the SF Bay Area without human supervision ever since. There are still barriers — cost, policies, trust — but the technology certainly is here.

Re: ChatGPT agent: bridging research and action

#149
post #3

I do not know what an agent is and at this point I am too afraid to ask.

An workflow is a collection of steps defined by someone, where the steps can be performed by an LLM call. (i.e. propose a topic -> search -> summarise each link -> gather the summaries -> produce a report) The "agency" in this example is on the coder that came up with the workflow. It's murky because we used to call these "agents" in the previous gen frameworks. An agent is a collection of steps defined by the LLM it…

I think it's interesting that the industry decided that this is the milestone to which the term "agentic" should be attached to, because it requires this kind of explanation even for tech-minded people.

I think for the average consumer, AI will be "agentic" once it can appreciably minimize the amount of interaction needed to negotiate with the real world in areas where the provider of the desired services intentionally require negotiation - getting a refund, cancelling your newspaper subscription, scheduling the cable guy visit, fighting your parking ticket, securing a job interview. That's what an agent does.

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