We’ve interviewed a whole panel of business leaders to ask how they are actually using and applying A.I. in their business today. In this episode, we asked the question: What is Machine Learning? What challenges have you experienced? What’s the future look like? How can someone apply machine learning in their business?
Watch our first episode on A.I. Explained (in 3 minutes) https://youtu.be/3CUAku_Zi_Q
This video is just a taste of our new series focused on Artificial Intelligence. We are exploring how it’s being utilized in business today and how it will be used in the near future.
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DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!
Alexander Ferguson 0:01
Welcome to UpTech Report. What is artificial intelligence or AI? One item we found was it can be seen as an all encompassing term that there really many subsets or facets of it, like natural language processing, computer visioning, machine learning and deep learning. In this second topical episode, we take a closer look at what is machine learning? How can it be applied in business? And what does its future look like? To help answer these questions, we’ve interviewed a whole panel of AI experts, business leaders and entrepreneurs. To start us off, we did find that machine learning was actually a term first coined in 1959, by scientist Arthur Samuel, who worked at IBM. But a lot has happened since then. Let’s go over to our experts now and ask them, What is machine learning.
Robbie Allen 0:52
So machine learning is sort of a fundamental, you know, definition is automating something and learning patterns with data. So essentially, you start off with the data set. And it’s essentially learning the software is learning patterns in that data. So that when you give it new data in the future, it can make a prediction, or it can tell you, you know, how it’s similar to what it seen in the past, I
Chris Hazard 1:15
would describe machine learning as showing a computer something and having it be able to mimic you and generalize it to new situations,
Bjorn Nordwall 1:24
being able to read a lot of data and create a map of that data. And understand the relationship between that data that you have is what the the machine learning is all about. And I’m totally ripping this off from MIT. By the way, I’m gonna make a plug right now, MIT has an open course for machine learning. But I love that they they point this out, they say that in traditional programming and traditional ways of solving problems, you build a program first, and then you bring in data. And when you get the program running with the data, then you have an output, machine learning is you have data. And instead of a program, you have outputs. So I know that I want this thing coming out. So I have data and outputs. And what machine learning gives you is the program, it says, based off this information, here’s how you get from that data to this outputs this program. That’s really what machine learning is. And it’s all based in statistics,
Robbie Allen 2:29
machine learning really acts much like humans do in terms of humans learn off of their experiences, they learn off a data, humans are probabilistic, they do not get everything right, they do not necessarily repeat the same result, given an input, and machine learning is the same way.
Alexander Ferguson 2:45
Technology is always evolving. So we asked our experts, what are some of the challenges with machine learning?
Robbie Allen 2:52
You know, by far, the biggest challenge with applying machine learning is just getting the data ready. And if the data has garbage in it, or if it’s very noisy, or there’s problems, especially ones that you don’t anticipate, or you can’t see yourself, then the algorithm is going to learn those, those, you know, learn that noise, or it’s going to start making Miss judgments, because it’s learned patterns that were incorrect. You know,
Bjorn Nordwall 3:13
just having your data structured properly, is a huge challenge for a lot of businesses just to overcome and understand, right. And having an all together, I mean, their entire businesses, that are customer data platforms, that their whole job is to take, you know, five or six different data stores, and try to create a unified view of their end customer. And so, you know, I think that would be the advice that I’d give to business owners is, you know, start thinking about how you’re capturing data, how you’re tagging that data within the data system, and ultimately, like what you want to achieve from it, right? How do
Richard Boyd 3:51
we get enough data? And that’s the real challenge today? How can I find enough data that’s relevant, that can train a system reliably? on whatever it is, I’m trying to teach it the organizational knowledge of a law firm? Or how to drive a car, how to, you know how to navigate to Mars, those sorts of things? And that’s that’s the issue today is how do we get enough data, get it into the right shape so that machines can derive meaning from it?
Alexander Ferguson 4:19
It’s clear that data is one of the main issues. But what about accuracy? And the difference between consumer and business use cases?
Robbie Allen 4:28
Machine learning is probabilistic? Meaning that you know, you can’t count on a specific answer every time the accuracy level that you get with the machine learning algorithm may be in the 80% or 90% range. In business, you only have to get to 80. But in consumer you got to get closer to 99 Which is why people are you know, Desirey because it’s like, oh, yeah, it didn’t it didn’t understand me that one time. Whereas with business, you can be there. It’s a little bit more pragmatic. You can be like, well, it didn’t work 100% of the time, but you know, eight out of 10 times i didn’t have to do anything.
Alexander Ferguson 5:01
So what are some of the things that are up and coming on the machine learning front, we asked the experts
Alicia Klinefelter 5:07
think there’s usually two categories I think about with this, there’s kind of the things we interact with in everyday life that sometimes a lot of people don’t realize how machine learning in the backend that are really interesting. And I think it’s kind of obscured because we don’t really talk about it. And then there’s really the kind of new and up and coming applications that people are working on right now. So, you know, I think things like image classification in our everyday life that exist on Facebook, or word recognition and classification that exists on a Google search, or even the ability for your phone to be able to learn on the fly like characteristics of your voice, that can identify something you’re saying better compared to somebody else,
Jeff Lerose 5:45
it’s very, very impressive. You know, especially in the medical field, how it can help with diagnostics, it can it can access the full internet, for every single symptom that ever occurred, and match it up against the symptoms of a particular patient.
Bjorn Nordwall 6:01
A real opportunity in this abstraction layer is is what does the design of building these algorithms look like? And can we make it a Squarespace for AI? You know, can we make it so that it’s so easy that you’re dragging and dropping, and now you’ve got, you know, a really cool, functioning prototype that’s only going to get learned, learn and get smarter over time.
Alicia Klinefelter 6:24
Um, but I think a lot of the interesting applications that are coming up are in this class of unsupervised learning algorithms, that’s kind of this cutting edge class of algorithms where you actually are telling a machine to learn on its own, um, you’re not really giving it examples of data to learn from, you’re kind of you’re giving it this kind of goal to meet in terms of accuracy. And then you say, learn on your own.
Alexander Ferguson 6:46
Last, we wanted to know how a business leader could actually apply machine learning in their own business, should you do it on your own or hire someone is it very costly and difficult to apply?
Robbie Allen 6:56
You know, it’s very difficult to go from, you know, basic regression to deep learning, without having, you know, more formal training or a lot more experience, because there’s more things that can go wrong. And if you’re not, you don’t have an awareness or an ability to understand where things went wrong, or why they went wrong, then, you know, you won’t be able to understand or interpret the results that you get. So I would say, it’s not too much of a stretch, to go to basic machine learning techniques. But once you want to get to something like deep learning that requires more of an understanding,
Bjorn Nordwall 7:27
a question that you kind of have to ask yourself, every business when they’re looking at software or services to add on to their core competencies and say, you know, do we build it? Do we partner with somebody? Or do we buy it? And I think that that’s kind of where companies should be looking is, you know, how do we take these frameworks are there services out there that allow us to enable these things,
Richard Boyd 7:51
we’re always scanning the internet, in a skill into conferences like SIGGRAPH and others and seeing you know, what people are doing, and seeing what we can, you know, borrow from, I don’t want to try to create some brand groundbreaking new research at my company, because it’s, frankly, it’s not a good use of money. When there’s so many other smart people spending lots and lots of money, like people at Google and IBM and elsewhere, once they solve the problem, we go Awesome. Now we’ll come up with a way to integrate some of those ideas and, and, and do that six month 10x return on investment. That
Alexander Ferguson 8:26
concludes the audio version of this episode. To see the original and more visit our UpTech Report YouTube channel. If you know a tech company, we should interview you can nominate them at UpTech report.com. Or if you just prefer to listen, make sure you’re subscribed to this series on Apple podcasts, Spotify or your favorite podcasting app.