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18 results for “ai transformation”
Topic10 mentions across episodes
AI transformation
actually, if you look at the function of an army, at some point, it moves into people in the field make the decisions. And so you have to have the training, the strategy, recruiting to do that, and then you have to empower your teams to work. And I t
1:09:32 / 1:10:49
from transcript1:09:32 – 1:10:49
consistent approaches, but you need to empower all those teams at the edge to operate and do what they will. And so, actually, one of the big focuses I've made over the course of the year is to reduce a lot of our hierarchy, make the organization way
1:08:59 / 1:10:11
from transcript1:08:59 – 1:10:11
So it's not about just, like, dumping all the, list of help center articles that you have into the agent. You kind of understand what are the things that you can build. And so initially, the first step of it would be something like, you have your sup
14:12 / 15:26
from transcript14:12 – 15:26
Speaking about, AI is assisting with, like, command and control. I mean, I I I remember was hearing some story about, how on Wall Street, humans used to not be able to you always had a human in the loop for each decision. So at a later stage before t
12:05 / 13:07
from transcript12:05 – 13:07
And so the the that's really the problem that that that we solve. So we we we come we we either go on iconic use cases. So we we try to work backward from the outcomes. We we work with enterprises, tell them that they shouldn't be looking at AI as as
17:12 / 18:16
from transcript17:12 – 18:16
Where are we at on the journey right now? And I wanna go into your crystal ball as we start thinking about the shifts in society. Where do you think we're gonna be in three, five years' time? Georgia? Well, I went to MIT this year. Sorry. Then and I
1:03:00 / 1:04:11
from transcript1:03:00 – 1:04:11
So you can kind of determine which of these use cases should go through that human and the loop layer versus which of the use cases AI can conveniently handle. And then all through this process, you're also logging what the human is doing. Right? Bec
17:12 / 18:19
from transcript17:12 – 18:19
a game of chess with a computer, which is analyzing board positions and and applying a certain kind of intelligence to figure out what its next move should be. That's the subject of our program today, artificial intelligence. The good future of AI is
0:45 / 1:54
from transcript0:45 – 1:54
If companies decide to act very differently, there's the prospect of competing with China, or maybe maybe Russia will become relevant later. And as that happens, this constrains their behavior substantially. So, I I I've been interested in tackling,
2:19 / 3:21
from transcript2:19 – 3:21
stay functional and keep delivering value? I think the end goal is for us to to disappear and for the the the AI products and the AI platforms to be fully usable by the business users. Today, this is not the case. Today, this is still too complex. An
18:44 / 19:49
from transcript18:44 – 19:49
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
Everyone understood that unless you do that, you will not be efficient, and one way or another, it should happen or your competitor will do that. So that's what they were tasked with, and that's how Amazon got rid of or replaced many vendor managers
37:30 / 38:31
from transcript37:30 – 38:31
is using our AI safely. No jailbreaking, no prompt injection, making sure my chatbot doesn't recommend a competitor. Now the most recent layer is agents. The people are building these AI agents. They take on the authorizations and capabilities of the
2:14 / 3:15
from transcript2:14 – 3:15
Number two, from there, they start to say, okay. How can I take a process that already exists and apply AI to making it better? That might be, something like customer support or taking fraud down from fifteen days to kinda cure to to ten days. And, l
10:13 / 11:33
from transcript10:13 – 11:33
and introduce this to customers. There is a challenge, though. There is a caveat that I would make. As a very new technology for an established organization, there are risks associated with any launch. That's why at Amazon, we use MLP as an acronym,
12:44 / 13:51
from transcript12:44 – 13:51
Because when you get to a whole stack of things that makes AI kinda work, a bunch of it is major data centers with a lot of compute in it, both for the training and learning, also for the inference. And a bunch of that within those data centers is th
20:14 / 21:17
from transcript20:14 – 21:17
it sounds like, as I say, but it's a fundamental requirement for knowing how to operate in in the fourth industrial revolution. You need to be using it right now. Mhmm. And, Josh, you back that up by saying that 10% of AI is actually understanding th
5:12 / 6:19
from transcript5:12 – 6:19
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