Artificial intelligence is a technology that is poised to begin disrupting every industry it touches--including heavy construction and agriculture equipment. And according to digital strategist and TV host Amber Mac, the sooner manufacturers accept the inevitable roles of A.I. in their business models, the better off they will be. 

Plus, Singularity University’s A.I. Chair Neil Jacobstein outlines the real perils posed by the rise of the machines--and they're not what you think. 

Learn more about how A.I. is changing the way manufacturers innovate.

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Amber Mac:

I would argue that we're in an era right now of hyper-adoption with artificial intelligence, and even if you don't want to be using A.I., you probably already are.

Dusty Weis:

Hello, and welcome to another edition of the AEM Thinking Forward podcast, advancing the equipment manufacturing industry. I'm Dusty Weis, AEM's professional nerd, blade runner, and podcast host, and artificial intelligence is what we'll be discussing today, specifically what is this emerging technology going to mean for equipment manufacturers, and where can they start incorporating it into their business models?

We'll talk to Amber Mac, digital strategist, TV host, and entrepreneur, to see where this technology is at in 2019, and what it means for our industry. Plus, Singularity University's A.I. chair, Neil Jacobstein, on whether you should start panicking about the upcoming robot revolution. The answer might surprise you.

But it's these sorts of expert insights we work to bring you here on the AEM Thinking Forward podcast. Each month we explore a new subject area to help keep your business on the cutting edge of the industry. So if you haven't yet, make sure you subscribe to our podcast feed so you get an update every time we put out a new edition. Just click the show name in your podcast app and scroll down past the episodes to the Subscribe button.

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You know, even though it's 2019, and that totally sounds like a futuristic year, it still sometimes feels just a little far-fetched talking about the impacts that artificial intelligence is having on our industry. I think that the movies have ruined us by having us look at A.I. through the lens of Terminator, RoboCop, and The Matrix for all these years.

And that's why I'm glad I got to talk with Amber Mac at AEM's Thinking Forward event in North Carolina. As a Silicon Valley consultant and TV expert, she's a pro at taking guys like me and kinda slapping them upside the head with real facts about modern A.I. And it turns out there are some real ramifications for the equipment industry.

Amber Mac, TV host, entrepreneur, prominent Canadian, also the president of Konnekt, thank you for joining us on the AEM Thinking Forward podcast. You are also the host of a new podcast, The A.I. Effect, which explores the rapid pace of A.I. adoption. How does what's happened in this space in the last five years compare to what's happened in the last 50, for instance?

Amber Mac:

That's a great question, and if you're familiar with the history of artificial intelligence, A.I. isn't something that's brand new. In fact, the term “artificial intelligence” was coined in the 1950s. However, we didn't have the computing power to be able to realize full artificial intelligence until the past few years, and that's why we're hearing about it so frequently. So I think that we're seeing this rate of change and acceleration with this new technology, and it's purely because of supercomputers that do exist in our world today.

Dusty Weis:

I think that there's a tendency among folks to look at new technology and say, “Well, I don't need this, I don't want this.” I apocryphally tell the story of when the iPhone came out. I know some people, not sitting in this room or anything, who looked at it and said, “Well, I've already got a digital camera and an iPod and a laptop, what do I want this thing for?” So how do you sell me, a Luddite, on what the benefits there are in artificial intelligence for me?

Amber Mac:

Well, I think we've seen that historically over the years with any type of new technology, this idea really of digital Darwinism, where people are slow to adopt new tech, they fight new technologies, and we've seen this with the mobile phone revolution, like you mentioned, as far as using new smartphones. But I think what's really important to remember is that this technology is not going away, and in fact things are going to continue to go faster.

There's a great quote that I like to share in my presentations from Graham Wood, where he says that “change has never happened as fast before, and it will never be this slow again.” If you think about it in the context of artificial intelligence, that's really the world that we're going in today.

Think about the smartphone revolution. It took more than a decade for most people to get a smartphone. I would argue that we're in an era right now of hyper-adoption with artificial intelligence, and even if you don't want to be using A.I., you probably already are, whether it's searching on your phone or using a voice assistant like Amazon Alexa. Much of that is powered by artificial intelligence.

Dusty Weis:

Okay, so take me through it then, how is artificial intelligence already impacting my life?

Amber Mac:

Artificial intelligence is impacting your life in a number of ways already. A couple of them I mentioned already as far as smartphone technology and how you're doing search. We also see this with technology like computer vision, we're seeing it more and more in the healthcare industry. When they're scanning for certain diseases, they will be using artificial intelligence.

If you're talking to a chatbot, and you're on Facebook Messenger and you're trying to book a hotel room through a bot, that's artificial intelligence, something called machine learning. So even though you may not realize that all of those different technologies fall under the umbrella of artificial intelligence, they're already around us in our everyday. And that's something to remember today, is that it's not about you choosing to use this new technology, you probably already are.

Dusty Weis:

I've got an old buddy of mine from college, he's a programmer and he's been working in this space more and more. He likes to tell you that artificial intelligence succeeds when you don't even know that you're using it, and it's when you're aware that you're interfacing with artificial intelligence that it's failing at some point or another. So how do we get artificial intelligence to that point where you're able to interface with it effortlessly, and it doesn't feel like you're talking to a robot?

Amber Mac:

I think we're getting to the point where we're seeing that artificial intelligence is seamless, especially with new voice technologies, using something called natural language processing where all of a sudden your smart speaker really understands what you're trying to say, no matter what your accent is, even understanding different languages. So we're getting to the point where more and more those speakers do understand what we're saying and what we want.

However, if you've ever tried to have a conversation with one of those smart speakers, they're not great at really managing more than one request or more than one thing, and that's where in some ways artificial intelligence fails. And to your friend's point, that's where we see the many holes and flaws.

However, I am hopeful that over the next few years that will change, and that's what I talk about the rapid acceleration of artificial intelligence, is it's not gonna take that long for you to be able to carry on an everyday conversation with a smart speaker or a bot, where they truly act much more like a human.

Dusty Weis:

At that point, you're talking about something like you'd see in Iron Man, where you're talking with Jarvis, the A.I. construct that lives in Iron Man's helmet, and he's able to just go back and forth with you like a human. How far away from that are we really at this point?

Amber Mac:

I never like to say predictions because they always come back to bite me, so I would say that we are pretty far off from really being able to have robots in our lives that truly understand us. But it will change, and I would say if you look to places like Japan as one example, a few years ago they developed a robot strategy, so they understand that robots are going to be part of their society.

Now, why did they do that? Not just because they love tech. They did it because they had to solve a problem, and the problem was an aging population and not enough people who are caregivers. They have deemed over the next few years that four out of five people who are in care, mostly in elderly population, will in fact be taken care of by robots. So I'd say five to 10 years in certain parts of the world. The reality is, we will have conversations with robots, whether it's them taking care of us or greeting us in a store.

Dusty Weis:

Distilling this down to a level that impacts our members directly, our AEM members build heavy equipment in the construction and agriculture space. And you had mentioned that your new podcast deals pretty heavily with agriculture as well, but how is this technology going to impact the products that our members build?

Amber Mac:

I think what we see with artificial intelligence, no matter what industry that you're in, is that it allows you to solve problems that are solvable because you have a lot of data. So if you think about an industry, like you're talking about today, where you have a lot of data that can be driven from a certain piece of equipment, and you need a better way to be able to manage that data, artificial intelligence might be the right tool to do that.

In the podcast that we just launched we did an episode on agriculture, and as one example we interviewed a company called SomaDetect. What SomaDetect is doing is they're enabling dairy farmers to have better access to better quality milk, and they're doing so by providing sensors that actually give the farmers access to information about the quality of the milk at the source.

And so all of a sudden you see these sensors giving data, and then there's analytics on the other end on the computer to understand the quality of the milk, or if the cow has any issues. So a whole bunch of data that wasn't previously as readily available. That's just one example of a very simple problem that's being solved, and again being fueled, by A.I.

Dusty Weis:

When it comes to these sorts of new technologies I think back to one of the biggest hurdles, I think, for people in adopting these is just that user interface, and presenting this data in a manner that's friendly and easy for them to use. Now, I grew up in the heart of dairy country, so I'm glad that you brought that example up, but I think back to the farm where I used to go help out and muck cow stalls from time to time on the True Hart Farm.

And they would tell you sometimes, “I'm too busy, I've got too much physical work to do around the place, I'm up to my elbows in cow stuff.” How can you present this in a way that's easy for me to use?

Amber Mac:

That's a great question, and one of the things I learned from SomaDetect is that the company actually was created in a very rural area, and they worked alongside dairy farmers there in a small community, and they were able to easily convince the farmers that this technology would help them on their farms.

Both from a cost saving perspective, but again focusing on the output and understanding that they would have better milk, but more importantly also healthier cows, which is something that of course dairy farmers care about and need to ensure that their herd is in fact healthy.

So I think it's about seeing and realizing those benefits, and I'll say that at the same time saying there's going to be some people who, like you said, don't embrace this new technology, think, “It's more work for me.” But I would argue that those people will fall behind.

We've seen it in other industries, if you think about businesses out there like Blockbuster and Kodak and Blackberry, the list goes on and on. So many examples of companies that didn't adopt new technologies that did fall behind and eventually failed. So I think there is a risk if you aren't adopting some of this new technology, if in fact your competitors are.

Dusty Weis:

You listed some great examples of companies that failed because they didn't adapt to new tech. But the tech landscape is also littered with startups that just didn't make it because, for whatever reason, they didn't present the technology in the right way to their users. So, if you were to look at some of these companies that marketed their technology the wrong way, who are they, and what are the takeaway lessons that our members can keep in mind as they try to adopt this technology?

Amber Mac:

Well, I think there's a long graveyard of startup technology companies that have failed and haven't been able to succeed, and I know I'm dating myself here, but I worked out in San Francisco during the boom in 1999, and from that time I saw plenty of examples of businesses that weren't able to succeed, and there were usually a couple of reasons. One is they grew too fast, one could be that they grew too slowly, and I think at the end of the day, for every single industry, it's a matter of timing. Is it the right time to adopt this new technology or create this new technology to solve a certain problem?

And there are plenty of examples of businesses that just were ahead of their time, and plenty of examples of businesses that just were ahead of their time, and plenty of examples of businesses that were behind. We're doing a podcast right now, podcasting has been around for more than a decade, but all of a sudden over the past two years everybody is creating podcasts and listening to podcasts, and you've seen-

Dusty Weis:

Listenership has doubled in the last four years.

Amber Mac:

Exactly. There is an amazing example of a technology that was just ahead of its time. It was early, and it wasn't necessarily able to realize its full potential at the time because people just weren't listening to podcasts. So we see this all the time.

I would say for people in your membership, the most important thing to think about with this new technology, or trying out some of these new companies, is just dedicating a small percentage of your time to piloting projects. We see this all over the world today.

I live in Canada, and we see this with our government. Even though they have their legacy systems and old ways of doing things, they're actually doing a bunch of pilot projects with artificial intelligence to see if in fact in certain areas there will be cost savings and more efficiencies. So it's really interesting to see even a government, which you would assume would be very slow, to actually start acting more like a startup.

Dusty Weis:

I worked in government once upon myself, which is why my eyebrows shot up when you mentioned that, because government can be very slow and ponderous when it comes to adapting to new things. But it sounds like they've really found the value in piloting these new projects.

Amber Mac:

Yeah, absolutely. With the Canadian government as one example, they are doing things in the healthcare space. They recognize they have issues with suicide that are on the rise in certain communities, so they're working with a small startup and they're actually developing technology that is fueled by artificial intelligence to do kind of sentiment analysis in terms of recognizing if there are ways to prevent and identify communities if there is a spike in suicide. So that's just one example. In Estonia also, they're using chatbots in government to answer questions from citizens.

So we're seeing these little pockets of experimentation, and I think that's what's really important to remember is you don't have to go all in right away, you can just try, experiment, pilot, and then decide if it's right for you.

Dusty Weis:

So you're presenting to our AEM members at our Thinking Forward event today, and the topic is going to be on technology adoption, what motivates people to opt-in, and what potentially holds them back. What are the key takeaways for the manufacturers of construction and agriculture equipment?

Amber Mac:

I think the most important thing for the people in the room today during my presentation is to think about the rate of change that is coming when we talk about artificial intelligence. But secondly is to think about culturally within the organization, how do you shift from a culture that is a know-it-all culture to a learn-it-all culture?

There are lessons that I'll talk about from people like Microsoft CEO Satya Nadella, who really recognize that, even at Microsoft, you would think a really successful company, there were issues with culture when he came in. He recognized that his engineers, his team members, they all believed that they knew how everything should work. But the reality is, when you have technology moving this quickly forward, you can't know everything, and it's important to embrace this learn-it-all culture, and that will be a lot of what my presentation is about.

Dusty Weis:

I almost feel like there's a certain trajectory that tends to happen with startups, where they come in and they're small and they're agile and they try all sorts of new things, and then they, if they're successful, grow to be the size of a Microsoft or a Netflix, and then they start to exhibit some of the same characteristics that they were able to avoid as a startup, where now they're large, they have organizational inertia. How do you stop that from happening in an organization as it continues to succeed?

Amber Mac:

Absolutely, I've seen this in the tech industry and beyond, I'm sure it exists in construction as well as agriculture, you have those businesses that are growing, growing, and then act like a startup, they're really nimble, they're able to adapt, and they get so large that they're no longer able to proceed in the same sort of way.

So I think we're seeing it now out there in the wild with companies like Facebook. Look at Mark Zuckerberg, he believes that his company at one point was the hacker way, where they just tried things, and if it didn't work they changed them. They can't do that anymore. And I think he's in many ways seeing some of the consequences of acting like a startup when in fact they're a large company that affects billions of people. So there's an example of a company that is really struggling because of its growth and success.

Dusty Weis:

We deal with this especially in the construction and agriculture equipment sector because a lot of our companies have been around for 100 years, the Caterpillars of the world, the John Deeres of the world, and they build equipment that can last 30 or 40 or 50 years. In your experience as a consultant and sort of an advisor for a lot of these sorts of companies, what do you tell a company like that about how to stay fresh, and how to continue to incorporate new ideas into a business model that's succeeded for a century already?

Amber Mac:

Yeah, I think if we look at some great examples of businesses that are able to experiment and try new things, Google would be a great example. They have their 20% rule that, internally, 20% of the people are working on side projects, things that they think may stick but they're not 100% sure. And I think that's a good model for a lot of these businesses, is just to start to try. Some businesses go as far as to create innovation teams, a small group that comes up with innovative ideas. No matter what you do, though, the most important thing is that it comes from the top, and you have leadership buy-in as far as understanding that change is happening no matter what type of company you are.

I speak to maybe 40 to 50 audiences every single year, and every time I go to an industry, it doesn't matter what industry it is, I get the same feedback, is that “we are different, we don't need to change, we've got all of this history,” and at the end of the day the reality is there are plenty of problems that you likely have in this industry that could be solved by using new technologies like artificial intelligence. And you have to be able to see those opportunities and think about “what problems can A.I. help me with, and how am I able to get a team together to figure out how to solve those problems effectively?”

Dusty Weis:

We've even talked through a workshop that we put on at AEM with a consultant who advises companies to create a C-suite level position, a chief innovator, something like that, who oversees a team. That way, the innovation is not just treated as a technology project, but an integral part of the company's business model moving forward. What other tricks for encouraging innovation have you come across in your time?

Amber Mac: 

I think one of the most important things for this membership is to think about the future of technology like artificial intelligence as needing innovators and people in positions of leadership who understand what it's going to take, but also having people who are the explainers of the group. There's a lot of talk in the A.I. industry right now that we need people who maybe don't actually implement the technology, but who understand how to bring teams together and explain how it can help. So look in your organization for the explainers, the people who are good at explaining new technologies, to share their knowledge with other people.

Another tip is to look at what your competitors are doing, and understand there may be opportunities for collaboration with people who you maybe never thought you would ever work with before. And although I don't see that as much, I think this is going to be something that's going to be essential if we are truly able to realize the potential of artificial intelligence.

And from a practical standpoint, I'll bring it back to something like healthcare. We can only get to the point where we can better detect and provide treatment for diseases like cancer if, in fact, we have a large data set. We can only have a large data set if, in fact, we get better at understanding how to share data. But then we have a problem because individuals want to maintain their privacy, they don't wanna share data, so all of a sudden that system collapses.

So I think that's a perfect example of a situation where we have to figure out better ways of collaborating in order to see some of the benefits that exist with artificial intelligence.

Dusty Weis:

Well, Amber Mac, the president of Konnekt, serial entrepreneur, and Silicon Valley expert, your website is, and your new podcast The A.I. Effect is available on iTunes and I imagine any other number of podcasting platforms. Thank you so much for joining us on the AEM Thinking Forward podcast.

Amber Mac:

Thank you so much for having me.

Dusty Weis:

So, that's a well-reasoned and thoughtful rundown of the impact that A.I. could have on the equipment manufacturing industry, but for those of you still hung up on the Skynet thing, here's a short excerpt from Neil Jacobstein at last year's Singularity World Summit. He's the chair of the A.I. track at Singularity University, and depending on how you feel about people, his answer might make you feel better or worse.

Neil Jacobstein:

People are really too focused on evil A.I., and not focused enough on human intent.

And I often say that I don't stay up late at night worrying about machine intelligence, I worry about human stupidity and malevolence. And so ... Yeah, so I think that we shouldn't underestimate that A.I.'s could go off the rails, there's a whole community in the A.I. community that's committed to building in safeguards, multiple redundant safeguards, of different classes.

One of the things that you want to be able to do is to detect malicious behavior. That's not random, if you have a life support system and suddenly you see an A.I. going after your life support system, turning off the oxygen, opening up the airlock, things like that, you wanna be able to shut it down very quickly. And if it starts interfering with your ability to shut it down, you wanna have another anular ring of control that can countervene that move. So you want multiple redundant layers of control.

And this is a predator/prey problem. In other words, the predators get better and the prey gets better. So it's not a problem that you can finally solve.

But it is a problem that you can address systematically, and we certainly ... If you're building an A.I. product you certainly should build it in a sandbox environment so that you test it under a lot of different conditions and not be surprised at the first 1500 things that it could do wrong, you have thought about those things at least. And then you're dealing with corner cases after that.

Dusty Weis:

Again, that's A.I. chair Neil Jacobstein at last year's Singularity World Summit in San Francisco, to which I was lucky enough to be invited. Thanks for that to the folks at Singularity.

If you enjoyed this month's topic, you should go back and check out the September edition of the show, by the way, How A.I. is Changing the Way Manufacturers Innovate. In it we explore how a specific kind of A.I. called generative design is allowing engineers to virtually prototype thousands of different designs using the power of the cloud.

And, I should add, Amber Mac was a presenter at one of our AEM Thinking Forward events. Don't you wish you could have been there in person, maybe gotten some professional development credit from the company too while you were at it? Well, our next event is February 21st at the Airbus Experience Center in Washington, D.C. We'll be exploring how your future customer will make decisions, starting with some insightful research AEM has undertaken with McKinsey & Company.

We'll also hear from Charlotte Blank, the Chief Behavioral Officer at Maritz, and Dr. Lonnie Love, additive manufacturing guru at Oak Ridge National Laboratory. Dr. Love was actually my first interview for this podcast a year ago ...

Dr. Lonnie Love:

Just call me Lonnie, only my wife calls me Dr. Love.

Dusty Weis:

Great guy, fascinating interview. They actually built that 3D-printed stealth sub for the government, by the way. I'm gonna ask him about it in D.C., so I hope to see you there.

There are six other Thinking Forward events in 2019, head over to to see the schedule and reserve your spot. And that is going to wrap up this edition of The AEM Thinking Forward podcast. If you're looking for another great way to stay on top of industry trends, make sure to check out our twice weekly e-newsletter, The Industry Advisor. Click over to to see what's happening and to subscribe for regular updates.

The AEM Thinking Forward podcast is brought to you by The Association of Equipment Manufacturers. Little Glass Men does the music, and for AEM, thanks for listening, I'm Dusty Weis.