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15 results for “operational ai”
Because then you will learn some of the implicit preferences that would help you that would help towards, like, this full delegation model. Yeah. Trust is very important. I have an AGI working for me, and I I we're we're still working out the trust i
53:59 / 55:05
from transcript53:59 – 55:05
we are basically asking these agents to make indeterminate tasks. Right? Meaning, take a set of actions, look at the output. Based on that, define what the next sort set of actions should be. And so that leads to very interesting possibilities if you
2:19 / 3:22
from transcript2:19 – 3:22
Your mileage varies, depends on the store and depend on the quote unquote agent. The problem is that people have been using the term agent now. It's so seductive for anything that smacks of any kind of automation, and it'll come back to bite us. I ca
6:47 / 7:51
from transcript6:47 – 7:51
And, here's some research. We prepared to reply for you, and and here are the tasks, that we've already delegated. One of the tasks went to the sales team, another went to customer support, and another one went to operations and finance. When will we
3:05 / 4:11
from transcript3:05 – 4:11
“AI agents aren't just cutting costs - they're transforming business operations”
someone's coming in to ask a question. If you think about HR service management, like, can I take these people out for lunch when I'm in the Indian office? I wanna apply for leave or parental leave, w
51:39 / 52:22
market insight51:39 – 52:22
and it gets the wrong answer, it doesn't know how to solve the problem. You look at it and go, okay, I'm using AI to try to do a hard math problem. This language model versus the other one got it right, got it wrong. I my prompt was changed. It's all
19:20 / 20:35
from transcript19:20 – 20:35
You know, a workflow the way I distinguish the two, by the way, is that a workflow is very deterministic. It's set of nodes. It's if this, then do this, then do this, then do this in a very structured way, whereas an agent has a list of instructions
1:14:10 / 1:15:21
from transcript1:14:10 – 1:15:21
When you're talking about computer use for the open web, for example, or the open operating system, like, there's no it's a mess. So, like, the possibility I'm I'm always skeptical of any system that is tasked with interacting with the human world, w
4:35:10 / 4:36:18
from transcript4:35:10 – 4:36:18
Okay. You know? The you have planned time offshore. But then somehow when it comes to SCRs, there's always a lot of unplanned downtime for all the humans. And so I think that's something where since the AI works twenty four seven, I was actually just
17:59 / 19:01
from transcript17:59 – 19:01
All the questions. Every there's a lot of people in this room, but we will answer every single person's question on all of this when we're back and we'll do a follow-up or maybe even two live for folks in this room. Yeah. Hopefully, this is helpful.
47:13 / 48:00
from transcript47:13 – 48:00
“MIT Professor compares proactive AI agents to raising children until age 18”
But AI agents can convert words to actions. But proactive AI is the agents really being autonomous and doing actions on their own, you know, without we having to stimulate them or prompt them and so o
1:11 / 1:29
framework1:11 – 1:29
Hey, Sasser. Imagine having agents for every support task. One that triages tickets, another that catches duplicates, one that spots churn risk. That'd be pretty amazing. Right? Happy Fox just made it real with Autopilot. These prebuilt AI agents dep
55:20 / 55:49
from transcript55:20 – 55:49
So you are providing, like, the brakes and the steering for the AI models. Like, when we're talking about agents to make them work, I mean, this is something you hear from Microsoft, from OpenAI, etcetera. They want all the access. They want all the
3:00 / 4:01
from transcript3:00 – 4:01
I think with humans, like, you can you should need to show a consistent effort to each other. Like, consistent effort that you care about each other, is that you, like, worked together very well or something. So consistency and, like, collaboration i
53:14 / 54:17
from transcript53:14 – 54:17
you know, this LLM or that LLM. Sometimes you run code. Sometimes you use a tool. You know, you'll access the database. You'll call, you know, a weather API, what have you. And something need to orchestrate all that, and you're absolutely right that
5:11 / 6:19
from transcript5:11 – 6:19
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