Trusting Artificial Intelligence for Big Business Decisions
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Trusting Artificial Intelligence for Big Business Decisions

Others - Trusting Artificial Intelligence for Big Business Decisions

Artificial Intelligence and related analytics capabilities offer the promise of better, more accurate decision making. As humans, we are still not ready to sit in a driverless, speeding car. Have we come to trust the machines to a point where we are willing to let Artificial Intelligence drive big decisions in business?

 

Top 3 Learning Points

  1. How to leverage the power of technology to add to your bottom-line
  2. Is AI really replacing humans or just primitive computer systems?
  3. Will the human brain that creates the AI programs ever be able to catch up with its learning abilities?

 

Show Notes

  • At the end of the day, the computers are just extensions of us. They’re just there to do what we want them to do. It’s for us to figure out what it is that we want to do.
  • Once businesses grow beyond a certain point, they have to use computers to help them make decisions for everything, from customers to production
  • People often think of AI as replacing humans, but it is replacing computer systems that were much worse before.
  • Do you think AI is going to be only as good as the data that you provide, or there is some other unique way by which it can profile me beyond this, even if I am not ready to share every part of my life?
  • Maybe in the very long term computers will become better than humans at everything but that’s going to pick up. In the foreseeable future, what we’re going to need is a very close combination of people and computers doing most things.
  • The CEO isn’t going to be replaced by an algorithm anytime soon because while algorithms are very good at some things, there are also many things which humans are much better and we need both.

 

Transcript Summary

While artificial intelligence is able to perform most business functions, deductions and tasks perfectly, and sometimes even more perfectly than humans, the truth is for 99.9% of companies out there, the CEO isn’t going to be replaced by an algorithm anytime soon because, while the algorithms are very good at some things but there are many things that which humans are much better than the algorithm, and we need both. So while there’s more promising machine learning happening in one year today, than it used to happen in a decade, we still have a million more miles to go. It might not take a huge amount of time to get there, but nobody knows if it will be decades or more. What will finally work is a grand unified theory of machine learning to actually have all the intelligence that human beings have, in one system. Once we have that, it is possible that the majority of jobs will end up being done by computers.

 

Transcript:

Sanjog: Our topic for today is Trusting Artificial Intelligence for Big Business Decisions. Our guest today is Pedro Domingos, Professor of Computer Science, University of Washington.

We shall be talking about something which is our reality today, but it is futuristic for many. Enterprise businesses are at all times making decisions, sometimes very strategic. We always thought humans would bring complex processing into the decision-making, but today artificial intelligence is also claiming it can either do equal or better than humans. Do we need to explore if we can trust artificial intelligence for big business decision-making? It will not necessarily replace humans but at least come close. But why is someone even looking at the need for using AI to drive such business decisions?

Pedro: There are a number of reasons, but the biggest driver is growth. Once businesses grow beyond a certain point, they have to use computers to help them make decisions for everything, from customers to production. The benefit of AI is that decisions can actually be made in a way that is much closer to or better than what humans would do. People often think of AI as replacing humans, but it is replacing computer systems that were much worse before.

And in many other cases, like in companies like Google and Amazon, it is making decisions that simply weren’t made by anyone before, because it didn’t even exist. There’s this whole new spectrum within which AI is being used today.

People often think of AI as replacing humans, but it is replacing computer systems that were much worse before.

Sanjog: You said the computers were not able to churn data quickly or as accurately but we cannot compare AI to a regular database management system. It’s a different animal, isn’t it?

Pedro: They are different animals, but if you have to market a new product, you can rely on your gut feeling on whom you want to market it. Traditionally, companies would rely on these broad demographic categories, which were very coarse. They did this partly because there wasn’t any better data than that earlier. But now we have so much data in such detail, as well as the computing power, that we can leverage AI to use that data to make better decisions. 20 years ago, the state of the algorithm, of machine learning and making the predictions and recommendations was much more primitive. All of these things have progressed enormously, and we’re now at the point where they’re actually ready to make the vast majority of the decisions that it didn’t make.

20 years ago, the state of the algorithm that to the machine learning and that make the predictions and recommendations was just much more primitive.

Sanjog: You’ve mentioned that the data wasn’t available, so that means somebody collected the data versus machine doing it by itself. Because some form of human intervention would be required to collect that data. Secondly, when you speak about churning that data and getting some insights out of it, that’s an analytics function. Was this analytics produced in the recent past, but was not allowing us to dig deeper? If that’s the gap, how are you defining artificial intelligence? Is it some machine logic which is going, to sum up, come up with an insight, where even a computer-driven analytics would not be able to find?

Pedro: Yes. Two parts to this. The first one is that data used to be gathered by telephone survey-calling a few thousand people on the phone and even that was expensive. In the 90s, there were databases of projects, and that’s when people actually started to do real data mining. But it wasn’t that much, and it was expensive together. These days your cell phone is continuously collecting data value. It’s everything that you do on social networks is being captured by somebody or other.

For example, when you’re on the Amazon website, it’s not just the part that you buy that generates data value; it’s everything that you click on. Amazon can actually see what the sequence of things that you did was. The data is available in magnitudes greater than before. It’s also much cheaper to collect. In many cases, these days the data doesn’t even have to be collected. It’s naturally generated by people as they go about living their lives which is done more and more online. And a lot of data gathering happens from the things that people in the physical world do. This is one aspect. But the other aspect that is equally important is as you mentioned, the machine learning.

The whole name of the game in machine learning is to generalize from your past behavior to your future behavior. And this is actually a very hard thing to do

The data that people generate doesn’t actually necessarily say anything about the future. This is just something that they did in the past. The whole question is, how can I use that to predict what you want in the future? Machine learning is used to generalize from your past behavior to predict your future behavior. Over the last few decades, but much faster over the last decade, machine learning has become better things and now to a point where it can actually do things amazingly well in many cases.

Sanjog: Essentially what you’re saying is the fuzzy logic which artificial intelligence would use to compute and predict behavior based on what they have done in the past is just an expanded version of multiple humans sitting together for years trying to figure the same thing out. Is the name of the game the speed, or the quality of decision?

Pedro: It’s both. It’s the speed and quantity, and quality of the decisions. You mentioned fuzzy logic. It is something that was used a lot back in the 80s, and it was called knowledge-based system. It was an expert’s system which responded to the program that you would write down to, for example, make a medical diagnosis. You have rules that said if this patient had these symptoms, then what you have is the flu.

True logic came to this very well, which is why in anything like the fuzzy logic you have information there’s more or less confident. But the problem with that and why AI failed, is that these systems were too brittle and they didn’t know enough. There was always more knowledge that they needed to have and that affected their ability to deal with uncertainty. It was very limited. As soon as you went outside the narrow scope of what you had taught, they failed.

Today, the big reason why AI is not really taking off is that we’ve replaced things like fuzzy logic and knowledge base systems with things like machine learning, where you use things like neural networks. And the neural network is essentially a simulation of a human brain that learns from experience from data to some degree in the same way that a human brain does. Another type of machine learning, for example, is based on simulating evolution, except that what you are doing is evolving programs, instead of evolving animals or plants. But there’s a same general idea of genetics and natural selection as if everything is on the computer. So there are a number of these caveats in machine learning that let us do things that you could do back in the 80s with the so-called classic AI.

Sanjog: To that end, my question to you would be the change in complexity that you programmed it, as you mentioned neural networks and many other types of logic. At the end of the day, the logic of how it would function, how would it learn itself is still coming back to some human beings who were working at it. Now the way the complexities are coming, or we’re facing the complexities in the business world as well as in our lives is about a whole set of parameters that were not even be dreamed of in the past. We’re continually evolving. So how does a human brain, which in turn is supposed to program that AI logic, ever is able to catch up, so you can make a decision for today?

Pedro: The big difference is that in computer science, it’s humans that write the programs. In machine learning the computers write their own programs, the computers program themselves. What makes them so powerful is that the computer is looking at that from the data, from the input and the desired output, and it’s figuring out what the program should be. The more data you have with machine learning, they’re correspondingly better, the programs get more output with no additional human work. It’s a little bit like a human being as a child that learns by playing around by herself. And to a large extent, this is what machine ... Read Full Transcript v  

Contributors

Pedro Domingos, Professor of Computer Science, University of Washington

Pedro Domingos is a professor of computer science at the University of Washington and the author of "The Master Algorithm", the best-selling popular-science introduction to machine learning. He is a winner of the SIGKDD Innovation Award, th... More   View all posts
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