An Investor’s Guide to AI
We’ve been hearing about how artificial intelligence is about to take off, any day now, for the last 40 years. But this time is different (really), and we can explain why.
In this week’s episode of Industry Focus: Tech, Fool.com tech analyst Dylan Lewis talks to Eric Bleeker about the current state of AI research and why investors may want exposure to it. Find out how the some of the biggest companies are using AI to power their products and services, the fascinating story behind how deep learning exploded in the last few decades, a few lesser-known AI-focused companies for investors to check out, and more.
A full transcript follows the video.
This video was recorded on Jan. 5, 2018.
Dylan Lewis: Welcome to Industry Focus, the podcast that dives into a different sector of the stock market every day. It’s Friday, January 5th, and we’re starting out 2018 by talking AI. I’m your host, Dylan Lewis, and I’m joined in the studio by the one and only Eric Bleeker.
Erick Bleeker: Good to be here! Starting out with a bang!
Lewis: Starting out with a bang! This is the first time I’ve ever had you on the show.
Bleeker: It’s exciting.
Lewis: I’m sure people that really follow the Fool know you, or have been following fool.com for quite some time. You were heavily involved there. Do you want to talk a little bit about what you do at the Fool? Because some people may not know you from the podcast.
Bleeker: Recently, I’ve been working on aggregating some of our subject matter experts around some of the biggest themes that we cover. One of those is artificial intelligence. Recently, blockchain. We have some more stuff coming up. It’s finding a way to build out our resources and create solutions based upon areas that our investors are asking us, “I want more information about this.” So, that’s the problem I’ve been working on for the past year or two. It’s been fun.
Lewis: So, you’ve been heavily involved in the Market Pass Premium reports in particular that we’ve been putting out. One of them was on AI. So, naturally, when I wanted to talk AI, you were the guy to turn to for the conversation.
Bleeker: Let’s hope.
Lewis: Let’s hope. You know, we can fool people into thinking so, if nothing else. Before we get too deep in conversation, I think it’s probably good for us to formally define AI and deep learning/machine learning, because those are two terms that are going to be coming up again and again, and it’s good to just get that out of the way.
Bleeker: You know, I hate to do a history lesson.
Lewis: I love history lessons.
Bleeker: But, you kind of have to start with it in AI. I think your average person has a lot of natural skepticism around artificial intelligence that’s completely warranted. The reason for that is, the term artificial intelligence was coined in 1956.
Lewis: That’s a long time ago.
Bleeker: Over 60 years. There’s been a lot of boom and bust. In the 60s, people were saying it was around the corner in the 80s. The biggest thing was expert systems, where you’re going to have this computer as your co-CEO telling companies how to run. So, if you’ve been a longtime market observer, it’s good to have some natural skepticism about this. But, we have had several incredible recent breakthroughs. And what the breakthroughs are really rooted in are that 50 or 60 years of progress, because a lot of that research was actually right, and it was finding the right ways to approach this problem. Now, the limiting factor was, in the 70s, 80s, computers were slow.
Lewis: Yeah, they just weren’t capable of doing everything that the can now.
Bleeker: Yeah, artificial intelligence requires tremendous amounts of, No. 1, data. And when we used to have entire rooms to store megabytes of data, that’s not cost-effective. Second, it required processing power. So, I think the root of the current AI boom actually goes back to the birth of the internet, because it poured so much capital into the broader technology industry. And I think a lot of the forward-looking companies that many investors see as something not related to AI, truly were thinking about it at that time. There’s a famous example of Larry Page at a cocktail party mingling —
Bleeker: Yeah, and co-founder. He was commingling, and at the time, he ran into Kevin Kelly, who worked for Wired. And Kevin Kelly expressed some skepticism, saying, “Hey, I just don’t get it. You guys are a great search engine, but you’re still a search engine that’s free and I don’t quite get this.” And his response was, “Oh, no, we’re really an AI company.”
Lewis: And that was back in the early aughts.
Bleeker: Yeah, more than 15 years ago. He saw this wave that was coming, and he saw that Google’s product was essentially an AI product. And we had a lot of work around machine learning at that time. The definition of machine learning is essentially code that writes itself. You input a lot of data and you have software that basically finds very advanced patterns and doesn’t need to be explicitly coded. So, what are the applications of machine learning? You’ve seen a lot of it in web 2.0. It’s Google, it’s how Facebook filters your feed, it’s how Twitter looks for trolls, it’s doing a lot of that work that’s really behind the scenes in every application. A company like Amazon (NASDAQ:AMZN) optimizing its entire supply chain, they use machine learning for that process, it’s the underlying factor that makes everything hum. But you might say, “Hey, Eric, that’s interesting.” Kind of interesting, maybe. [laughs]
Lewis: I think it’s very interesting. I wouldn’t be having you on the show if I didn’t think it was very interesting.
Bleeker: “But, everyone has been talking about AI changing everything, and I don’t know that it sounds like it’s changing everything.” And that’s an astute point, listener out there. I’ll say, the real breakthrough started around 2011, and that is deep learning, which is a sub-discipline of machine learning. And essentially what deep learning does is, it replicates the way the human brain learns, which is building connections between synapses. Essentially, though, our brains are hugely powerful. We have .15 quintillion synapses —
Lewis: I’m not going to even venture a guess at how many zeroes are in that number.
Bleeker: A computer, you’re talking billions of transistors. So, there’s still a huge jump between those two areas. So, what had happened was, Google had hired someone who was, I believe, an intern, of all things, and he was one of the literal dozens of people in the world who was still studying this idea that had come out in the 70s about recreating the human brain. He said, “I’m working at Google, a place where there is unprecedented data that didn’t previously exist, and unprecedented power,” thanks to all the power to process all their cloud architecture and products, “what if I started applying this to identifying,” what else, “cats on the internet?” And what we found was, all of the sudden a quantum leap in computers being able to identify cats. And taking this back to the start really quickly, what is artificial intelligence? Well, it’s teaching computers to think like humans. It’s bridging that gap. Why was a calculator smarter than all the way back in the 60s, and yet looking at a cat and saying, “Oh, that’s a cat,” the most simple, human thing that you do, fractional, without any thought, beguiles computers? And once you figure that problem out, what can you do with it? So, deep learning figures that out. And all of the sudden, this thing catches on like wildfire at Google. There’s a couple of employees from this guy’s division, they take over to their Google Translate division. I’m talking hundreds of the smartest people in the world work on this problem for a decade, and they essentially say, “We can do better than you.” And that group says, “No way, don’t even try. But, it’s Google, we have to let you try, but you can’t do it. Oh, and by the way, don’t do French. You’ll never beat us in French.” So, do you know what those two guys do?
Lewis: They go French.
Bleeker: “We’re going to beat you in French.” And I believe it only takes them a matter of months to topple the combined works of hundreds, as I noted, of the smartest engineers in the world working on one of the most complex problems. At this point, it was an all hands-on deck at Google, and in 2012, I believe, deep learning was in a handful of projects. I’m talking, count it on one hand. And within three years, it was in over 1,200 projects at the company. It literally invaded every single product. I mean, self-driving cars had actually plateaued. For all the hype in self-driving cars, they stopped updating their self-driving car progress at this time. And then they put deep-learning in it and it was like an explosion. So, it’s one of those enormous moments of serendipity in technology. If this intern hadn’t started at Google, the right place with the right amount of data, the right processing power to start this discipline that a few dozen people in the world were looking at, we might be years behind technology. And I think very few people understand this kismet moment and the explosion it started. And that’s where we’re truly at in AI, because now this is going everywhere. Most companies are a couple of years behind Google, I’ll say. And I remember meeting with Nvidia in December of 2014, and they had relatively recently started working on the product, and deep learning in their products, and they had said, “We’re using deep learning to identify 39% of objects in our self-driving car model.” Like, oh, that’s nice, but it’d better be a lot better than that.
Lewis: Right, yeah.
Bleeker: And then I met with them in July of 2015, and they said, “Eric, we’re over 90. And the progress is increasing.” And that was my holy cow moment. I went back and talked with a lot of people that owned Nvidia at that point, it’s up over 1,000%, for being a company on the cusp of it. In any case, there’s my explanation of why artificial intelligence, why this deep learning is this big bang moment that progresses to a whole new level, and why we’re now sitting here at the moment and going, “This is different than the 60s. This is different than the 80s.” That skepticism, I believe, needs to be gone. Now, the question is, how big is this, what can it change and how can I start getting my crystal ball out to see how this changes everything in five years?
Lewis: So, to distill that down to a couple of bullet points. I think what we have is, there’s been this long, maybe, ideology or philosophy of what AI could be or might become. That meets the computing power that we currently have. It also meets all these amazing data collection and Big Data practices that come into vogue once the internet really takes off. You have things like search engines and social media companies. And you have the, by nature, very rapid, exponential ascent of progress that machine learning creates, because you have machines learning and then being able to take the best-performing machines and replicate that out. I remember you had this presentation at Fool Fest, which is one of our annual get-togethers, and you showed, I think it was a clip from Nvidia testing, what was it, computers playing hockey?
Bleeker: Yeah, robots.
Lewis: And the explanation he had provided, I forget who was narrating —
Bleeker: Jensen Huang, their CEO.
Lewis: Their CEO. He was saying they would basically test out all these different robots, attempting to score a goal in hockey, in this very simple game of hockey. And they would take whichever one had the best approach, best calculated approach, give it to all the other ones for the next iteration and so on and so on and so on. And when you have that testing environment, that just means it’s not going to be a straight, linear line of progress. It’s going to shoot up real quick.
Bleeker: Yeah, and I don’t know how quickly I want to bring this into sci-fi land here, but what was amazing about that was, UC Berkeley had actually been teaching robots using these techniques in the real world. But it’s so hard to learn at that rate. You need to go reset the puck, you need to have someone hand it to them, and it can only learn from so many examples. So, what Nvidia had done was built a real-life simulator, and they called this reinforcement learning, and it totally revolutionizes the way that robots can be something completely new. Now, I want to note, where does this get crazy? Google, we’re always updated on how they’ve done three million miles and data is so important to deep learning. What they discovered was, they could actually start stimulating all their driving. So, they have three million miles. They actually went and built a city out in the real desert that’s like a real city, and what they do is simulate a thousand times more miles in their simulations to look for weak points where their self-driving car doesn’t know how to handle the real-world situation. And only when they identify problems in their simulation do they bring their cars out to their city and train it over and over again to figure out how to do that.
Lewis: So, they’re taking issues that they’re recognizing in digital simulations, and then forcing the computer system to actually deal with it in the physical environment of being out in the street.
Bleeker: Yeah. So, this changes everything. It’s truly wild.
Lewis: So, this is a massively transformative moment, it seems, in tech. And we’re going to talk about some of the stocks to watch in this space. We’re going to do that on the back half of the show. Eric, back to AI. There’s so many different applications of this. It’s such a wildly transformative technology. You have language processing, you have self-driving cars, you have drones, you have automation and manufacturing, and that seems to be kind of just the tip of the iceberg. We’ve mentioned Google a ton. I think a lot of people are familiar with some of their AI ambitions. And really, one of the watershed moments for them was their DeepMind project with Alpha Go, basically them creating this AI program that mastered this ancient board game and had it wind up beating the best in the world at this game. That was kind of a wow, big stamp of validation for what they had been working on. There are a lot of other companies in this space that are trying to develop this type of technology. Who else are you watching here?
Bleeker: I think one of the important things to remember is, I believe artificial intelligence becomes a foundational technology across which all companies are based. It’s similar to the internet and similar to mobile. So, you can go back and look at the internet. Microsoft wasn’t really an internet company, they built an operating system. Oracle wasn’t an internet company, they built databases. But what the internet did enabled them to grow at exponential rates. So, I think we’re looking at a very similar thing with artificial intelligence right now. You need to cast a somewhat wide net. A similar idea to this would be, when you looked at the beginning of the mobile age, if you just said Google and Apple, you’re limiting yourself. And they were the most obvious because they built the phones. But if you understood what mobile meant to the future of media, you could have had a better chance of finding Netflix, which return significantly, tenfold, larger. You could have seen what it would do to e-commerce. And let’s not just think about Amazon, let’s think about MercadoLibre. Do you have some?
Lewis: The primary impact there is, people are going to be buying phones. That’s the mobile revolution. But the tack-on to it is accessibility, and ability for platforms to reach more people, maybe people having the ability to buy things at their fingertips.
Bleeker: Yeah, I’m always looking for those secondary plays. So, let’s talk about a couple of AI companies to start. Maybe you’ll ask for a few more. One company I really like highlighting is iRobot. It’s been a company that has seen its shares decline relatively dramatically, around 20 to 30%, after a huge run up, mind you, on some short seller complaints. I believe the competing brand that people are worried about pinching margins is named Shark Ninja. But what you never see in these reports is the open-ended optionality of what deep learning could mean to a company like that. Household robotics with the advances could get exponentially better in an extremely short time, and they would be the established brand consumer. So, you’re looking at a space that could get 10-20X better in a short period. And they’re going to be the one that people understand. The example I like bringing up on this is, when Uber first started, a lot of people said, why would you ever buy Uber? It’s a $10 billion taxi cab market in the U.S. Well, I think Uber does $20 billion in bookings right now. When you create a foundationally new technology that does something completely different, the market rises to a new level, which most investors did not anticipate. I see the same thing happening in robotics and their capabilities.
Let’s take that to a second level. iRobot that would be my consumer play. On a more industrial side, I’ll talk about FANUC, which is a Japanese company.
Lewis: How do you spell that?
Bleeker: FANUC. And I never know of my pronunciation on these things. But, they are a tremendously run company. I think their P/E at any given time is in the mid-30s, but their operating margins are incredible for the industry they function in. They have long tenured and relatively visionary leadership. This company has been a stalwart. But, they go on to a potentially fundamentally new plane, and that’s because they built this FIELD software technology system as an Internet of Things play. They want to connect all their software together, so you can update it, blah blah blah, fix potential flaws really quickly. But what it does now is allows basically all of the robots to learn together. So, your robots become a hive mind, and you use some of these new deep learning techniques to improve robots, their ability to use machine vision to pick things out, to have better agility, all those things that a human presently does that a robot can’t do. And what it does, like I said, is their technology allows all the inputs from the robots to go back to a hive mind to learn to make better and push it out. So, they’re always getting exponentially better in a way that wasn’t possible, and recreating. I think this company is having the platform for that stands a potentially tremendous opportunity. And it’s one most investors don’t know.
Lewis: Yeah. Well, I didn’t know it, so I’m glad you mentioned it. So, they are primarily working in industrial manufacturing.
Lewis: And basically any business that would have a fleet of robots working toward some kind of task. Maybe it’s in fulfillment, with shipping centers, or something like that?
Bleeker: Yeah. Right now, there are two main markets, electronics and automobile manufacturing, those are their largest. But over time, their robotics will go down to cheaper levels. Right now, it costs six figures. And they use this really antiquated software program to do very precise movements. What happens in the future, it’s kind of like cars, if you want some precisely program it, a self-driving application is extremely limited. But if you can teach it to learn, it’s exponentially better. And the same thing will happen with robots, and that’s kind of the software they own.
Lewis: So, we talked about two, not quite pure-plays, but maybe companies that could benefit a little bit more from an AI future in a way that would transform them. I think something that’s worth noting with some of these big tech companies that are heavily investing in AI, I’m thinking specifically about Google and maybe Amazon here, is that it’s really tough to pinpoint what AI will be for their business. It’s not an operating segment for them. It’s something that’s kind of an operational efficiency that should bleed into almost everything they do. So, as you’re looking at some of the investments that big tech is making in this space, realize that it’s not going to be broken out like a nice little line item that has a profit and loss. It’s going to be something they say in the conference call where they’re like, “We reduced our energy usage and our servers by 40%, and that saved us $2 billion.”
Bleeker: Yeah. And I believe it allows them to attack fundamentally different futures. For example, Google with self-driving cars, and how they might try to crack that problem in a number of ways, which could be extremely substantial. I think mobility as a service could be a trillion-dollar industry. With Amazon, I believe it allows them to fundamentally rethink their entire supply chain. The only thing that allows self-driving cars, drones, etc, is these advances in artificial intelligence. So, if Amazon, led by the smartest man in the world, for all we know, with Jeff Bezos, if he understands exactly where this technology is going to lead in five years, he’s already thinking ahead that maybe fulfillment centers, as they’re currently constructed, you move those up closer to consumers, you have all these automated tasks, and that allows you to actually begin delivering in a lesser time for cheaper than someone getting in their car and driving to the store. And maybe that’s your final death knell of retail. So, I believe in both these cases, artificial intelligence allows completely new businesses that these companies are uniquely equipped to foresee.
Lewis: Speaking of the death of retail, [laughs] I think, to go back to something that you used as a metaphor for all this with the smartphone market, I remember looking at a presentation that you put together, and you said, what’s the Nokia of AI? Who are the players that seem to be stalwarts and got passed by by this huge shift in the space that they play in? And I’m going to put it to you. What companies or what spaces are you looking at as being particularly prone to getting disrupted?
Bleeker: I think it’s generally a rising tide in technology. It’s probably a little harder picking out losers in that, especially companies that have now been through two or three iterations of these huge master trends, as I call them — internet, mobile, AI is the next one. They may struggle to adjust to it. You look at a company like Oracle, that’s already been having to deal with cloud and now is dealing with fundamental shifts, potentially, to their products from the AI age, that presents a potential challenge. But, I do think, when you’re looking, the companies that have the most to lose from this, it’s easy. It’s this shifting to entirely new areas and factories. It’s going into car industries. It’s going into many areas that previously weren’t defined by software as their value add. Logistics is another one that’s very interesting. These companies, they just have to deal with a fundamentally different problem. They have to hire new employees who they might be equipped to handle. And it’s coming on in many ways so fast that, if you get behind by a few years, playing catch up is dramatically difficult. And you’re often competing against companies like Amazon that don’t have to deal with cost of capital. That’s the hidden thing to Amazon and Wal-Mart , of course. If their earnings decline by $2 billion, their shareholders scream bloody murder. For Amazon, it’s rewarded. That’s a very hard company to compete against. Like I said, if I’m in logistics or some of these other industries that have an entirely different time frame from my shareholders, and these companies are coming in, I’m extremely scared.
Lewis: Eric, I know that we have an AI event coming up next week. You wanted to plug that, and I want to give you a chance to do that.
Bleeker: Definitely. For members of Motley Fool services, that includes Stock Advisor, Rule Breakers, you name it, we’re hosting an event on artificial intelligence that’s going out on … Tuesday? I don’t have a calendar with me at the moment. An AI assistant would have told me the date by now. We need the future.
Lewis: Well, we’re not quite there yet, Eric.
Bleeker: Sorry. I’m pulling up a calendar in front of me. Yeah, I guess not. But, in any case, it’s Tuesday. You’ll have that in your inbox. You’ll be seeing invites to be able to RSVP and get reminders to attend that. So, if you’re extremely interested in this trend, the possibilities, what are the top stocks within our services, I really encourage you to attend. I know you have some on fool.com as well.
Lewis: Yeah. I do want to plug, before we wrap up, if you’re interested in how we got here with AI, one of fool.com’s editors, Ilan Moscovitz, recently put together this epic four-part series on the history of AI, Google DeepMind’s work with Alpha Go, something I mentioned earlier, and then his own lessons from taking a machine learning class at Georgetown and trying to build his own stock-picking AI. It’s a fascinating series. It’s like 20,000 words. If you guys want it, please write into the show, I’ll be sure to send it out to you. Eric, thank you so much for hopping on!
Bleeker: And just to do a plug, that last article, where Ilan built his own neural net, incredible. I think people need to give that a read.
Lewis: Ilan is not someone you want to bet against. [laughs] That’s all I’m going to say. When it comes to getting something done, whether it’s April fools jokes or these series, he’s up at 05:00 in the morning pulling all-nighters making it happen. It’s really incredible to see his work come to fruition, because he did an excellent job with it.
Bleeker: Yeah. I think the title is Planned Obsolescence, if people want to Google it.
Lewis: Yeah. And if people want all four of them, just write into the show, email@example.com, we’ll be sure to send it along. One other thing I want to touch before we wrap up, in our last show, Austin mentioned that he had some fun holiday plans. We got a note from a listener, Austin. He wanted a follow-up on Austin Morgan’s bottomless family mimosa brunch and party, and he wanted to know exactly how that turned out for Austin’s family.
Austin Morgan: It was great! Everything was great. My mom was a huge fan of the brunch because she didn’t have to cook or clean up. I was also a huge fan, because those waiters were on top of their game with the mimosas.
Lewis: They just kept coming back?
Morgan: If you finished it, it was full again. And it was wonderful!
Lewis: Might this be a new Morgan family tradition?
Morgan: My mom was all about it. And the food was awesome. We were at Sequoia, I think, in Georgetown. And the buffet was not a typical breakfast buffet. There was sushi and seafood, and they had a dude cutting prime rib, and another guy making tacos. Crazy! It was awesome!
Lewis: This is just an excellent last four minutes of plugs here. We’ve got the AI event, we have Ilan’s series on fool.com, and we have Sequoia in Georgetown.
Morgan: Highly recommended!
Lewis: Listeners, we’re going to give you a ton of value here as we wrap the show.
Morgan: They even had a Santa Claus walking around taking pictures of people.
Lewis: Wow! Look at that. Usually you have to go to a Macy’s or some retailer for that.
Bleeker: I’m signing up for next year right now!
Lewis: Oh, yeah? Well, this is going off the rails, so I’m going to cut it now. [laughs] Listeners, that does it for this episode of Industry Focus. If you have any questions or you just want to reach out and say hey, you can shoot us an email over at firstname.lastname@example.org, or tweet us @MFIndustryFocus. Of course, if you’re looking for more of our stuff, you can subscribe on iTunes or check out The Fool’s family of shows over at fool.com/podcasts. As always, people on the program may own companies discussed on the show, and The Motley Fool may have formal recommendations for or against stocks mentioned, so don’t buy or sell anything based solely on what you hear. Shout out to Austin Morgan for all his work behind the glass. For Eric Bleeker, I’m Dylan Lewis. Thanks for listening and Fool on!