Sheri asks, “What is AI good at? What kinds of problems is it good at solving?”
There are two sets of answers to this question. First, we need to know what AI delivers as an outcome:
- Acceleration of results
- Accuracy of results
- Alleviation of repetitive work
Second, we look to understanding what we know and don’t know about our problem to determine whether AI is a good fit or not.
- Known knowns: we know the destination and how to get there
- Known unknowns: we know the destination but not how to get there
- Unknown knowns: we know the area but don’t know where to go
- Unknown unknowns: we don’t know the area or where we’re going
The less we know, the less AI helps us to solve those problems.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Sherry asks,
What is AI? good at? What kinds of problems is a good at solving
AI delivers three major benefits, acceleration, accuracy and alleviation. I call them the three A’s and acceleration is getting to the answer faster. Certainly, machines can think and compute much, much faster than humans can. The second is accuracy, any kind of interview dealing with mathematics and statistics and probability and stuff like that, you’re going to get better answers from machine a fair amount of time, then you will from a human, we’re seeing examples of this, where
machines using imaging are diagnosing cancer more accurately, then
the Newman doctors are. And of course, the third bucket is alleviation, offloading relatively low value tasks or lower value tasks to machines so that the humans are freed up to do more valuable work.
Now, we consider the kinds of problems that AI is good at solving other kinds of problems that’s not good at solving
go go back a real long way to something from 2009 to 2010, former defense secretary Donald Rumsfeld was criticized in a press conference for saying that there are known knowns and known unknowns and unknown unknowns, it was a kind of a dodgy answer to a question at the time. So the criticism was valid, but the framework is actually not a bad one. So let’s bring this up here,
known knowns, our problems to which we know we have the answers, we know how to get the answers. And what we’re looking for, is not the answer we’re looking for how to get the answer better, or faster, or, or have something else do it again, this is the sweet spot of AI, the three X, known knowns are things that we know how to automate, and we know how to offload and we know how to validate the answer is correct. So it’s a great candidate, any kind of problem where we know what the answer is, we know how to get the answer that we have the code, the tools or the data to do so let AI do those kinds of problems.
The second category of of is, we we know,
we know the answer, we don’t necessarily know how to get it
the known unknowns,
this is analogous to, if you have a GPS or a Maps app, right? You have a destination, you have a route to get there, if you know where you’re going, but you’re not sure how to get their GPS is a helpful thing to do, right? Certainly, a GPS, and that that were you know how to get there. And you know, the end, you know, the destination, that’s something that
you may not even necessarily need AI for. But in this in this example, knowing where you’re going with not knowing how to get there is the unknown unknown. And that means that you know, the destination know the outcome, you know what you’re looking for, but you’re not sure about the code, you’re not sure about the data and things like that you’re not sure about the ingredients to get to the outcome that you want,
but you still have a general idea of where it is that you’re going to destination, you’re going to, again, AI can be helpful here,
unsupervised learning and machine learning would be a good candidate here
for helping understand some of these questions. But you still have a lot of unknowns, you still have a lot of need for what’s more data science than it is machine learning and deep learning, and so on and so forth. The third category in the upper left hand corner is the unknown knowns. So in that GPS example, this is, you know, sort of the, the means, but you don’t necessarily have a destination set, right? So, you, you know, the area, you know, the area really well, and you’re not sure where you’re going. But wherever it is, within that, you know, 25 mile radius your house, you know how to get that, you know, the roads like the back your hand, so, you know,
your data, you know, you have great code, but you don’t have a business answer
that you’re trying to get to. And again, this is where some forms of, of machine learning, like supervised learning can be helpful. But at the same time, you know, you you,
you have a problem that AI is not necessarily going to be great at solving, right, if you setting the destination for the GPS is much more a human judgment call than it is machine call a long time the machines can tell you
in this area around you hear the 25 places that currently have no traffic
getting to them. So wherever you want to go, it’s fast to get there. But you still have to make a choice. So that’s another area where, again, ai can’t be hand off the entire thing. But it can help data but you we’ll need a data scientist to an a business person to help you get to the answer you want, what destination Are we going to set and then the machine we can help you figure out how to get there
contrasting to the previous answer, where you know where you’re going, you just didn’t know how to get there. The last is the unknown unknowns. Where we don’t know what we don’t know, we don’t know what kind of data we have. We don’t know what kind of technology we have. We have no business goals and things, this is something that AI isn’t going to help you fix, right. So if you if you don’t know where you’re going, and you don’t know how to get there. And you don’t have any sense of the lay of the land, you know, you get dropped off in a foreign city that you have no knowledge about, you don’t speak the language
AI is not going to help you there. There. You can use very tiny use cases like language translation, for example, to read the signs. But fundamentally, you don’t know where you’re going. And you don’t know what it looks like when you get there. And this is something that I can’t help with. This is a pure business problem that where you’ve got to either pick one of those two, you pick, you know, learning the lay of the land, or you got to pick a destination and then try and figure out how to get there in order for you to start making progress. So maybe you’re entering a new market, maybe you’ve got a new audience are trying to tackle and you haven’t the foggiest idea what that’s where you’re going to need true exploration, true data science, very little AI is going to help you there because you don’t know what you don’t know. So think about it from those perspectives of the four categories for talking about, you know, the destination and you know, the route you know the destination, but you don’t know the route, you know, all the routes, but not the destination or you don’t know either the destination or the or the lay of the land of those categories. AI is great at improving what you know,
and this terrible right now at trying to solve for things that you don’t know. So when you think about what kinds of problems you’re solving for, and you think considering artificial intelligence as a solution,
think about what you know, think about the types of code or the data you would need to get to what you know,
and ultimately you’re going to need to retain and integrate your business users your data scientists your engineers and and your and your
I guess the the the overall strategists to put all the pieces together No one person at in at least in larger enterprises is going to be able to do everything right.
You will have people who can, you know, dabble in each of the areas and help you make some progress, you know, set intermediate destinations but at the end of the day,
the problems that AI can solve the things that it can do our supplementary to what we provide as humans
our judgment, our experience our cross domain knowledge, but she’s can’t do that yet. And it’s gonna be a long time for the can. So great question, very complex question and we’re using kind of a silly framework here as a way to to help bring some clarity to it. As always, please subscribe to the newsletter and the YouTube channel and talk to you soon. Take care
if you want help with your company’s data
and analytics. Visit Trust Insights calm today and let us know how we can help you
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