General AI is Already Here

with Chris Boos

More Info

Show Notes

In this episode, we look at the global aspects of AI and the implications of generalized AI in enterprise settings. Our guest Chris Boos is an AI entrepreneur, angel investor, and advisor to the German government on the implications of digital technology.

About Chris Boos

Chris founded Arago in Germany in 1995, pushing existing boundaries in AI technology to build a general AI. Since then, Chris has led Arago to become a key partner and driver for the established economy, positioning Arago’s AI HIRO™ as a platform for companies to reinvent their business models in the digital age. But his ambitions go far beyond: a strong believer in integrating machine reasoning and machine learning, Chris is constantly challenging current thinking on AI. As a strategic corporate and political advisor, as well as angel investor, Chris’ multifaceted engagement for AI makes him a much respected public speaker and thought-leader on issues of global importance, such as the man-machine-relationship, the way societies deal with information and the future of labor. arago.co

Transcript

General AI is Already Here (with Chris Boos) TK: Welcome to Foresight Radio. I’m your host, Tom Koulopoulos. On each episode, we explore the many trends that are shaping the way we will work, live, and play in the future. Our focus is on disruptive and transformational trends that are changing the world in ways that are often invisible. Our objective is simple: to give you the knowledge and the insights that you need to better manage the future. Foresight Radio is sponsored by our good friends at Wasabi. You can learn more about them at Wasabi.com. Our guest today is Chris Boos, and we’ll be speaking with him about a wide range of issues involving AI from how it works to its evolution, and its impact on business. Chris’ self-acclaimed mission is empowering human potential by freeing up the time we need to be creative and innovative through the use of artificial intelligence and he has an extraordinary background on which to do that. In 1995, long before the “buzz” around AI, he founded Arago, a German company that pushes the boundaries of AI by working on what’s called “general AI,” something we’ll talk about more during our conversation. Chris was also recently appointed to the German Digital Council by Chancellor Angela Merkel where he’ll advise the government on the future implications of digitalization and artificial intelligence. Chris challenges our current thinking on AI as he paints a vivid picture for its opportunity and the way it will create entirely new business models and experiences. Here’s my interview with Chris Boos. Chris, let’s start with what I think is one of the most important areas to get straight which is this whole notion of, “How do we define what AI is?” There’s so much vocabulary here. We talk about machine learning versus AI. How does AI differ from machine learning? Are there different kinds of AI and machine learning? CB: I would say AI is the overarching science that includes many fields, and machine learning just happens to be one of them. Personally, I believe that this notion that we have from the science side to always pick one algorithm that should solve it all and that one algorithm, at the moment of that algorithm family at the moment seems to be machine learning is actually responsible for the many lows that AI has had over time. I mean, it’s an old science, right? It was created as a field in 1954, and since then, it’s gone through summers and winters, and all of the winters were basically created by the fact that people started running after one technology to solve it all. Machine learning as itself is a great algorithm or a great algorithm set. It basically rebuilds instincts that we have in the animal kingdom or with humans, so by repeating something that gets a positive or negative reward, you train to do more or less of it and that’s behind all the machine learning that we’re doing right now, and it’s just one part of AI. The misunderstanding that I think is in the system is this is the part where we’ve made the most progress because it was very clear that with the algorithms that were available, they would be very much usable once there was enough compute power, and the algorithms haven’t really changed that much in the last five or 10 years even. It’s more the compute power that has changed a lot. This is why all of a sudden, machine learning that was invented in the 70’s became [appliable] to a larger set of problems, and all of a sudden you had results that you previously simply could not compute and that’s why it’s out there. If you look at the field of AI, there’s machine learning, there’s machine reasoning, then there’s natural language processing, there’s semantics. There are many more parts, and they’re totally essential, so I believe that just boiling it down to machine learning is super dangerous because if you try to solve everything with machine learning, you consequently will hit the wall, and the wall for machine learning just to get this in here is super straight. There never is enough data to describe them now. As soon as you hit that problem where you cannot describe whatever you want to decide with data in a timely fashion, you’re going to make stupid decisions. TK: Here is an interesting point Chris, because I often hear conversations that focus on this notion of, “We need more data. With more data, we’ll have more intelligent AI,” but you’re bringing up an interesting point which is that there are situations when you just – you can’t have enough data. As human beings, we rely on intuition, we rely on instinct, we give this all kinds of names – our gut feeling, but we often make decisions without enough data. Can AI go beyond those finite constraints? How does it do that? CB: Absolutely. With AI, we’re trying to simulate the application of human experience if we go further into this general AI realm where we’re trying to simulate human problem solving, so that definitely can do more than just replicate the gut feeling. Actually, I like that you bringing this up because it really is the replication of instinct, and instinct is a very old part in our evolution or development, and this is why the instinct part does not have words. This is why we call it “a gut feeling” because we cannot express our instincts properly. They’re a gut feeling, they don’t have words. They live in the old part of our brains. TK: We often talk about AI in the context of board games like Chess or Go. One of the things I remember hearing many years ago was how Kasparov, when he played against Deep Blue got a little flustered and frustrated because he thought the machine at that point wasn’t AI. It was brute force. He thought it was doing things that were not predictable, that were uncertain, that didn’t make sense within the context of the humans playing each other. Now, today, we have the same thing. When Lee Sedol played against AlphaGo, you heard the same kind of reaction like, “It was exhibiting intuition. The machine was doing things that were very human-like.” Can you give us a sense of what does it mean for an artificial intelligence to have intuition? CB: I’m very sorry to lift the magic out of this. There simply is no intuition in AI and whenever it looks like an AI is intuitive, it just does something that we, for whatever reason don’t do. You could imagine the Go player that did something that the Go pro would never have expected. That’s not a Go move that humans have never made. It’s just experts have never made this move because for some reason, it didn’t occur to them, and then you have to watch how people learn to play Chess and Go, they learned it along certain strategies and those strategies evolved on certain set of mindsets and what a machine can obviously do is mix those mindsets and that’s what it’s doing. I mean, sometimes a beginner in a game of chess can beat a great chess player because he makes a move that the professional chess player thinks is so stupid or just unexpected that his whole learned professional game falls apart, and it’s pretty much that what happens here. It’s not that the machine was intuitive and seamless. It can happen in broader ranges of data. We, for example, played Civilization and we had the machine change the strategy from a normal trade and military strategy to a, “Let’s go leave the planet” strategy very early on in the game and we never understood how it could know that that was the only way to win, but when you really dig deep into the data, it’s simply because of micromanagement. It’s simply because it really evaluated every single city it had on the map, and every single unit it had on the map. That was pure micromanagement. Humans would’ve come to the same conclusions if we give ourselves the time to actually look at everything. Humans are the much better pattern-matchers. We see patterns much better, but that also means we sometimes overlook detail. TK: One of the things that as humans we often do is impose bias on a problem. Sometimes that’s good because the bias comes from experience and you can look at the problem through a certain lens that allows you to solve it much better than someone who has no experience. However, I have seen many cases where the very senior or smart people, where their bias will limit the field within which they ask the question. They’ll leave out certain areas they think are based on their experience not worthy of pursuing. Sometimes, especially in the scenario-based planning, you can leave out some very important scenarios that you should be evaluating, but you’re saying the machine doesn’t have that bias. The machine will evaluate all scenarios with equal objectivity because it can do so. CB: It will not evaluate all the scenarios with equal objectivity, but it will go into the detail. Here’s one of the main parts. We say like, “Machines are very dangerous in being biased.” Sometimes that comes up. Machines reproduce the biases we give them, either through direct interaction or through the datasets that we provide. If you look at it, a machine is just not able to express itself in any politically correct way, meaning that if you want to mirror actually what people are doing and the biases that are within us in our society, AI is a pretty ugly mirror because there’s simply no buffer, no softening in this. It just reproduces the bias in facts that we have in the datasets and in our opinions that we teach the AI. TK: What I know a great deal is, “With more computing power, with more data storage, we will have better AI.” Is that really what it’s about? Is it just the amount of data, the volume of data, and the degree of power that we have in the computer to process that stands between us and the advances in AI or is there more that needs to evolve here to truly get us to the next level of artificial intelligence? CB: There is more to AI than simply that the compute power and the data ability just comes plays into machine learning. There are also other places. For example, AI started with expert systems, right – where computer scientists had this idea that there would be one right answer for every question you ask, one solution to every problem, one exact solution to every problem. That’s a very computer science approach to life. The whole point is that obviously didn’t work because for most problems, there’s more than one answer and mostly, the problem definition is not good enough so you have to keep changing the problem definition and then you get problems in going down that logical decision tree. There, you also see a reflection how chess used to be played, and why Go couldn’t be played with the decision tree. This family of algorithms that were started then is called “machine reasoning” where you simply – you had an argument alongside, and once it was clear that you could not have one answer, one logical answer to every problem, you actually started to have to weigh answers against each other. This is basically what we call “rational thinking.” There are many ways to what you can do next or what you could look at next and what you can think about next, and what is the one that gets you closer to your end-goal. That is basically what is happening inside the reasoning space. The reasoning went from these expert systems to more knowledge graph system where you’ve had multiple ways of reaching an answer but there still was only one answer to more knowledge-driven systems where you actually had multiple different answers to the same problem. Unfortunately, all of those were based on the world being completely logical, and anybody who’s lived a bit, right, and does not completely live in the bubble of the valley knows that the world is not really logical. TK: You once said to me, “We don’t talk about the future, we promise the past.” In some ways, it seems as though AI, inappropriately envisioned could simply be relying on the past and not innovating the way we humans do a new different future. Does AI shackle us in some regard to the past by doing so, by using patterns that are legacy patterns? CB: It definitely does. In the good explanation of AI, you would say AI is applying experiences you’ve already made. I mean, when you’ve learned how to add numbers in school, you can add numbers in any kinds of ways and that gives you something very interesting, but you don’t come up with the idea that you could also divide numbers maybe. That, In AI, it’s inherently non-creative, meaning that it does not create new experiences for itself. It’s an optimizer for how experiences can be used in different circumstances and different contexts, but it does not create new experiences. It’s our job as people to create new experience. That is what we do. TK: What do you do at Arago is focus specifically on general AI. Now, I want to talk about this, Chris, because when we listen to the prognosticators of gloom and doom that talk about how AI someday is going to become our overlord and take over the world and be our last great invention as mankind, what they also say to us is don’t worry about existing yet because narrow AI isn’t a threat. It’s generalized AI that’s a threat, but you’re focusing on generalized AI, so help us develop a more objective view of the future of AI, and maybe bring us back from the brink of the apocalypse just for a few minutes so that we can have some perspective on what’s going on here. Why are people so impassioned about the fact that AI could be the end of civilization as we know it? CB: Good question, but let’s try and answer that. First, I have three categories to define this whole space of AI. I would say on the one end of the category, you have the narrow AIs. That means applying exactly one algorithm to exactly one problem. I jokingly sometimes call this as the programmer’s answer to McKinsey. It’s pretty much what these high-end strategy consultants do, one very extensively optimizing solution to exactly one problem. This is what narrow AIs do. They’re great. They get trained to do exactly one thing, and then they do that one thing over and over. You have to re-train them when the world changes, but otherwise why would you be afraid as a person to use that? It’s just efficiency that’s coming out of it, and most companies have been doing nothing but efficiency programs for a very long time. I mean, look at the innovations that we’ve already made. We’ve become so much more efficient. Not always more effective, that’s sad, but definitely much more efficient. Narrow AI does the same thing. On the other end of the scale, you have these science fiction AIs. I’m not a [dystopias]. Let’s describe those differently. It’s like the robot that will actually say, “I love you,” and understand what it’s saying and mean it, no one has the slightest idea how to start building that. I mean, absolutely no one. There is no one out there who has the slightest idea how to create such a thing because we don’t even know how human consciousness works. It’s certainly not going to happen by accident and we are not anywhere close to rebuilding anything that approximates the brain. Even if Moore’s law holds by 2029, we might be able to reproduce the electrical part of the brain, but what about the chemical part, and there’s most likely quantum – we’re missing whole dimensions of what was needed in nature to create consciousness and it’s certainly not happening by accident. [Music] TK: One of the examples I often use to describe the difference between narrow AI and the general AI that Chris is talking about is that of riding a bicycle. You’d stop and think back to when you first learned to ride a bike. There were myriad uncountable rules that you had to follow. Eventually, your body however adjusted and you figured out intuitively how to ride a bicycle. Then, you had to teach your kids, or your nieces, or nephews, or grandkids how to ride a bike. There’s no way you could possibly have gone down every single rule that you would internalize so deeply when you learn. Through experience, you taught them how to ride a bicycle as well. Narrow AI is understanding a very specific discipline. Even though we may be very complex and very rich, and deep in terms of its ruleset, you can’t take that same ability to ride a bike and then transfer it to driving a car or flying a plane. All of these are separate domains. Truly, generalized AI would be able to learn any of those on its own. Furthermore, it will be able to express this uniquely human ability to be curious - [Music] back to my interview with Chris Boos. CB: The machine that has its own goals might become our enemy because it chooses to be an enemy of humans, I would not say that is never going to happen, but it is not anywhere on the horizon. In between those two, you have more general AI. General AI in this case means that you have a piece of software that is comprised of many algorithms and potentially, one data pool like one semantic data pool to be able to attach itself to the understandings that humans have given it because machine themselves do not understand anything across multiple domains like industries or just context of life. The whole idea of using this one machine, one data pool approach is that you do not need the ramp up time for every problem anew to create a dataset, clean the dataset, train the machine with the dataset, see if you like the results, try and model around with the model and the algorithms until you get desirable results. It actually means that you build on all the experiences you’ve had before in a very close context and a very distant context at the same time because in the end, everything is interconnected. General AI means that you apply one engine and one data pool for all the different types of problems that you’re giving to the AI. Your goal with general AI is to cut off all the ramp up time like the time you need to actually make it productive and do something you like, which in the worst case like starting stuff from scratch is more than a year, maybe two years, and if you wanted to automate all the processes in your company, that would take you centuries otherwise. Obviously, no one has the time for that, so you’ll need these general AIs to actually get through the business problems that we’re presenting to AI today. TK: The fear factor that we hear around general AI, it’s at times hyperbolic admittedly, but does any of that resonate with you? Is there a point at which in the near future we should be especially vigilant of or especially concerned about the application of generalized AI? CB: No, but there are a few factors that we should be extremely careful with and look out for them and also go to our institutions and make sure that that stuff doesn’t happen. The idea of for example putting AI into killing machines and having those machines making a kill decision automatically, that would take the human factor out of war. I believe that there’s nothing more important than having a general to make those decisions and having a general who has nightmares when he made the wrong decisions. I believe that taking the thinking about it well in the context of everything, not just in the context of, “This is my mission parameter, and I need to get it done,” that is super important and we shouldn’t give this to AI. These are the goals that we give to the machine and we simply should not put machines into that field, or if other people do it, find a defense against this. This is really important because we can never guarantee that no one is going to do it, but maybe something we should avoid for the longest time. I very strongly feel that it is so much more dangerous – it’s much more likely that we wipe ourselves off the planet before any AI does that. Even if you get that super intelligence, I don’t know, maybe 200, maybe 500 years from now, why should it – that would be an entirely new philosophical discussion why should it hate us. Why should it hate us? If that’s very unlikely which I believe is, why should it accidentally get rid of us like we step on ants? If that thing really exists, it would be much more likely to simply leave earth, because it’s a machine. It won’t be bound by water and oxygen. If it’s a machine that’s self-conscious, it would develop its own goals, and typically, if you want to achieve your goals, you would need resources and there are way more resources out there in the Kuiper Belt than now on earth. TK: Let me switch topics a little bit if I can. You are recently appointed to a very prestigious council by Chancellor Angela Merkel to look at digitalization in Germany. From a governmental standpoint, tell me a bit about what you’re doing there and the relevance of that, vis-à-vis our conversation today about AI. CB: I happen to believe that while AI is not going to wipe us all out from the planet or take all our jobs in the near future, I very strongly believe that it will turn our complete economic system upside-down. I believe that the introduction of AI seriously into the economy is going to be much more powerful than introducing the steam machine and that was a pretty heavy shift in terms of society and productivity and so on that happened. With AI, we’re most likely going to leave the industrial age towards a knowledge age. It’s going to redefine our systems and it’s going to redefine what we as people do to make a living. There is a tremendous opportunity in this, but on the flipside of that, on the dark side of the coin, that means that all the economies that are out there – and Germany happens to be a fairly big economy – that are so great at this industrialization have a much harder time than a lot of others in changing their models because it has worked for us so, so well. That was my motivation to actually accept the nomination to this council to like, “We do need to change as a country, as an economy, as a society, and it’s going to be hard, so we need people who can actually imagine this and maybe point the right way for these changes.” That is the key when I went in there. The job of this council is to advice the government and maybe tell where things are going wrong, and review things that are happening, and point out the more or less obvious that needs to be done on a short and long-term basis. TK: When we look at companies like Kodak that went out of business even though they’ve built the technology that put them out of business namely, digital photography, what shackled them was not that they didn’t understand the future. Not that they couldn’t even see the potential of the future, but that they were shackled by an industrial engine, and industrial supply chain, and industrial factories and machines that represented too great of an investment to walk away from. Certainly, they couldn’t walk away from it in time to respond. In the same way, successful economies: the German economy, the US economy, many economies around the world which have built their stature on the industrial era model are similarly shackled in many ways. Now, we’d like to believe that we’re growing out of that industrial era into a knowledge era, but it’s part of the risk here that we have done so well in the industrial era that we simply will not have the foresight, or frankly the ability to set aside that investment and move towards a new model. Does that give developing nations some sort of an advantage in the same way that they might have had Eastern [Unintelligible] countries were much faster to move to cellphones because they didn’t have the traditional landline infrastructure? Does something similar apply here, or am I stretching this beyond its boundary here to try to make that analogy? CB: No. Actually, you’re not emphasizing it enough. It is the true danger that if we stick with what we’re doing, we might be out of business very quickly. I mean, [Laughter] if you look at this, 30% of any given economy in the industrial age is logistics. If you change the cost of logistics and go 90% down in the cost of logistics, that means that 28% of cash all of a sudden becomes available in an economy. If one economy does that and the other does not do that, you’re going to change the bounds of power dramatically. If you look at this, if Germany for example did not do this and changing the price of logistics is basically introducing self-driving shared cars or vehicles, deliveries, whatever, then a much smaller economy like Poland who would introduce the self-driving vehicles would all of a sudden have the same free cash flow [Music] inside the economy as Germany and that is crazy. TK: Wow. CB: I would imagine that to an economy like India or China. TK: You’re listening to Foresight Radio. We’re taking a quick break to thank our sponsor of this episode, Wasabi Technologies, the leader in the next generation of cloud storage. Find out more about Wasabi at Wasabi.com. [Audio Presentation] Now, back to my interview with Chris Boos. The change here is so great, Chris, that I wonder to what degree our attention’s being brought to it, because I’ve heard the term “silent industrial revolution.” You’ve used that term in our conversations in the past. With the industrial revolution, with the steam engine we saw the threat. The Luddites took their axes and their sledgehammers to the factory looms and the mills. It’s not as visible. AI is a very invisible factor and it changes things in ways that are not necessarily apparent to us in terms of the threat and the degree of response we should have to that threat. Is that part of what’s happening as well? Is it the invisibility of AI? CB: That is totally correct. It’s not just the invisibility of AI, it’s also our unwillingness to think about the future. If we go into this a little deeper, we have to say there’s a very negative thing and a very positive thing about this whole situation. Let’s start with the negative first and so we can end on a positive note on this question. TK: Good. CB: The negative part here is that we simply don’t talk about the future. For some reason, the future is not an option anymore for a lot of people but everybody can feel that there is a change, and I believe this is why you have the shift to the right basically in the entire developed world because everybody can feel that there’s a change coming, no one is talking about it. This is like little children. When that happened to you with your parents, your parents would do something and you would exactly feel something is in the bush, something is happening here, but no one would talk about it. Mostly, what came out in the end was bad, like grandma had cancer or divorce was looming or something like this. We kind of have the same behavior patterns meaning that, “Something’s coming. No one’s talking about the future. Let’s please go to the guys that promises the past.” I mean promising the past, that is the only thing in history, if you look at it, that has never worked. It never worked to bring back the past. The positive part of this is we already have the future. It’s already there. You can see it. Our established economy is under tremendous pressure from the new platform companies that have come out of Silicon Valley, and if you don’t want to take a global view that have come out of China as well, those companies greatly threaten the models of basically any industry right now, but they exist side by side already. It’s not like, “The factories and the steam machines are so small, they’re unimportant, no one cares, and the rest of the economy can laugh it off for a while, and the Luddites can destroy a few machines until they can’t anymore.” We already have these two models and we should see the warning signs. I mean if you look at the largest five tech companies and their total market cap compared to all the other companies, I think that should tell us something how big they are and how much future we put into their hands financially. [Music] TK: Chris has pointed about the largest companies in the world is one that has gotten a lot of attention, especially as of late. Although these fluctuate, the companies that often come in as the largest based on their market capitalization are Apple, Microsoft, Alphabet, the parent company of Google, Amazon.com, Tencent, which is a Chinese conglomerate that invest primarily in internet-based technologies, Berkshire Hathaway, and Alibaba Group. Also, we’ll throw in there Facebook which often comes up in the top 10. What’s startling is that if you tally up the market cap for the top high-tech companies, Apple, Alphabet, Microsoft, Amazon, Tencent, Alibaba and Facebook, you’ll find that it accounts for about 20% of the market capitalization of all public companies in the US and just about 8% of all companies globally that are publicly listed. That’s an incredible indicator of how technology is dramatically impacting our economy for the future. Back to my conversation with Chris. [Music] CB: Because we already have these companies, I think that the hardest transition in this kind of phases that we have, the hardest part of the transition. The absolutely hardest part is the transition because it means that people have to change, jobs are changing, and a lot of times, that means that the efficiency that a new technology offers is only taken on by the entrepreneurs and by the shareholders and the workers don’t get any of it. That was the case with the steam machine. That was the case in the industrial revolution. That’s what caused a couple of world wars which were definitely not a good experience for anybody. We don’t have that problem today because our established economy is still very big and very strong. If we give a tool like artificial intelligence to the established economy to literally automate every process to a very high degree like, “I should limit this,” every process that is not entirely based on language and maybe we can have that discussion why I make this limitation. Anyway, every process that is not entirely based on language can be automated in the established industry today. That will give them a lot of money back and I don’t think they have the option of taking that money off the table. It will have to reinvest it immediately into people to create that new experience so they can compete with the platforms that are already out there. I think that because these two models already exist side by side, the old model can simply take the money of the efficiency and generate all these jobless people that we have, they have all to be reemployed to build the future for these companies and most of those companies have a very hard will to survive. TK: Who are the companies, or the industries, rather, let’s put it that way, that are most at risk as a result of AI disruption? Which are the ones that you see as needing to change their business model and perhaps their culture the most to be able to survive? CB: I’m not going to say anything really new here. The classification of how impactful AI is going to be or what it means for different industries is A, how accessible is that industry to AI? That, today, still means that how technical is it already? An industry like banking is very technically accessible. An industry like oil and gas where you still have to turn a lot of valves and drill into the ground, that is not completely accessible. Then the question of how much impact can an AI have in an industry also means - it defines how easy it is to deploy AI there. At the top of the disrupted industries are, first of all, computer companies, software, and I mean you’re looking, what’s happening with IBM and also the Indian large IT companies, you probably think that I’m right. The second one is the telecom industry, and you see that since Facebook bought WhatsApp, telecoms have been struggling to move beyond text messages as a general value proposition. Those have the largest impact of AI. With AI in a telco, we’ve done cases where you get 5% network efficiency without building any new cell towers or opening the ground to put new lines in. The banking, the whole financial industry is entirely accessible to AI because it is literally very technical, and we can move down and I’d say, at the end of the line of industries that are accessible is probably the Swiss watchmaker and the very much end of it is the Irish pub because yes, the only IT they have is the cash desk. [Music] TK: [Laughter] That’s great. [Music] One of the things that you said, and I want you to expand on this a bit, is how data should not belong to the collector but should belong to the source. Now, I may be not interpreting this correctly but does that mean that my data should belong to me, not to Facebook, not to Apple, not to Google? What does that look like from a practical standpoint? How do we actually do that? CB: At least you should make the trade consciously. With Facebook and Google, you’re consciously giving away your data and instead of getting money, you get a free service, you get search, you get mail, you get YouTube, you get acceleration of gossip which you get through Facebook and all these things. You basically pay with your data for a service and you think it’s for free because you didn’t pay any money. I would think about this much more in an industrial sense. If you look at an airplane, an airplane collects a lot of data. Currently, that data belongs to the airplane manufacturer because that guy, that company is collecting the data. It should probably belong to the airline company because the airline bought the plane from the manufacturer. The question is “Who owns the data?” Yes, I would go into “It should go down to the person level.” You should be owning your data and maybe if it became more conscious to you, you would not trade your data away so cheaply. On the personal privacy level, I simply have to fear that a lot of people simply don’t care. TK: Right. CB: Companies, on the other hand, do care because they know that if we believe that data is the future and the future in business typically means data becomes a tradeable good, who owns the data is kind of the pre-requirement for doing any of the trade but if you don’t know who owns it, how can you trade it? I think despite the really strange legislation the EU brought out here in terms of how to handle privacy, I mean it’s a clumsy bit of legislation but it does one fundamentally great thing. It defines who owns what, and to have that in a legal framework kind of is a very good starting point to build this kind of new business models that rely way more on who owns what data, who owns what knowledge. [Music] TK: Chris has pointed about the ownership of data is one that’s especially relevant. If we think about the way the world operates today, of the seven billion people on the planet, easily four billion don’t have an identity that would allow them to open a bank account or own anything specifically in their name. That lack of identity is a huge impediment to the growth and progress of our economy. The Peruvian economist, Hernando de Soto, in a book called The Mystery of Capital, which he published in the year 2000, talk about the importance of ownership in creating the foundation of a democratic system and a capitalist economy. Without ownership, we lack the ability to create that sort of future for the vast majority of the world’s population. Interestingly enough, one of the most advanced nations on the planet that is providing identity to every one of its citizens from birth using block chain technology is the country of Estonia. Back to Chris Boos. [Music] CB: As much as we can iterate and improve these kinds of laws to make them less clumsy, the primary definition to find out who owns what, I think that is extremely important. TK: Staying on the topic of AI, does AI somehow enable or otherwise provide benefit for these disenfranchised, for the rest of the world that today is still coming online, what’s the role of AI in their lives, moving forward? CB: I would prefer to actually look at our society as the very rich and industrial societies because that’s where we live and we have to focus on the question “Tomorrow, are we going to be a developed nation or are we going to be a progressive nation in there? What is AI going to bring for us?” Then we can talk about the people who might have a much easier path down that way because, as you said before, they simply don’t have this legacy of holding onto something. They can simply develop it on there, and if you look back at history unfortunately, having a legacy slowed a lot of people down quite a bit. Let’s focus on the people who have the harder job which is us as large economies in the world, the US, Europe, Germany is part of Europe, we do have a lot to gain from AI as countries and especially as societies. Why do we have that is because the industrial age is not very human. We tried for the last 150, maybe 200 years, we’ve tried to make people work more and more like machines. TK: It’s dehumanizing, if anything, I think might be a more appropriate way to say it in many cases. CB: Yes. I try to be polite. You’re totally right. We even see it, by the way. If something works really well, we say it’s working like a well-oiled machine. TK: Yes. CB: People are not made to be machines. In fact, if you look at it, even in countries that have civil wars, the amount of depression you have in the population is lower than in the richest nations on earth, which I personally find depressing. Industrialization does not make us inherently happy. That means if we give that kind of work to machines, if we give the work that makes us unhappy to machines but we do not shrink our economies, there’s tons of money to be distributed or things that, for us as humans, are better to do. There’re two big traits that humans have that machines simply don’t have. One is creativity, and I’d say creativity comes in three flavors. It comes in this artistic flavor where someone is willing to swim against the mainstream, like someone who does something outrageously different. There is the inventor part where someone sits around and fiddles around with technology until they find the solution for something that’s not been solved before. Then the third part is often not mentioned is the pioneering, like people who need more risk to be happy. Currently, we bore these people so much that they start jumping out of satellites. It is really amazing. The biggest second group, and I believe that is probably more than 50%, are people who are just people. That means people who are happy about being good humans, and typically being good human is do something for other humans. I mean for us as people, we have in an evolutionary way learned that the group, the better the group works, the more we succeed, so typically it feels much better for us to give something to someone than to receive a gift. These kinds of service jobs, these “Do something for somebody else,” be a humanist, if you want, that is definitely going to be of huge value because machines simply cannot do this. Imagine if you were sick and you were in the hospital and we would do value-based pricing for a nurse, how rich would the nurse be? TK: [Laugher] The point you bring up is a very good one. There are people that are enormously passionate that do it because of the humanity involved in their work, and that is [their] satisfaction, that’s where they get their joy, and that’s why, the sense of purpose comes from that deep sense of humanity. In many ways, that’s what we should be more of as human beings are expressing that humanity. CB: Absolutely. This also has something interesting in it. For machines, everything that is hard for people is easy for machines. Everything that is easy for people is hard for machines. TK: I love that [Crosstalk]. CB: Today, we pay people the most that do things that are hard for people, and we pay people least that do things that are easy for people. That is going to change, because the stuff that is hard for people is going to be done by machines. TK: Taking the drudgery away, taking the administrative stuff away, taking all of that very basic mundane work that we do day in [and] day out away from us gives us opportunity to focus on those areas where we can create more value. That’s also the challenge, Chris. People constantly look at the future and say, “How will we redeploy the machinist? How will we redeploy the administrator? How will we redeploy all these people?” CB: There are two major groups. One is the creative people and one is the passionate people. Some are creatively passionate or passionately creative, but those are the main job groups, and I believe that everybody falls into one of those groups. Because everybody falls into one of those groups, the question is “How will we reemploy them?” or “Why should we employ them?” On the term of creativity, this is fairly easy, since machines are not creative and especially the established economy has to compete through innovation and through pioneering and through going against what they’ve done for a very long time, we need a lot of creativity in especially the old economy. That means that all these people that have a creative passion, those are super needed in there. The second part is if you look at the development that is going, is happening right now with the digital assistants - I don’t know about the penetration in the US market but I think in Germany, it’s already reached, it’s close to 20% penetration of digital assistants. I mean go down what a digital assistant really means. It means that I as a consumer talk to a device about my intent and desire and the device does whatever is necessary in terms of makes the decision of how to fulfill that desire, meaning, it actually takes the buy decision. If I say I want to go on a holiday to the south of France, that’s it. A device will decide where to book the flight, what hotel to book, what tour operator to book, and so on and so forth because whoever runs this device has enough data about me to know what I can afford, what I like, what I enjoy, and so on and so forth. From an industrial perspective, that means that the amount of customers just boiled from 7.5 billion down to 8 because there are only eight companies that have enough data to build high quality digital assistants. Now, something very interesting happens. We used to use service as the piggybank in the industrial age because every time we wanted to build a new machine or a new property, a new factory, have a new business line or whatever, we try to get the money to do so to run this to make the investment by creating efficiency in the area of service. We’ve introduced the ATM machine and got rid of the bank tellers. We’ve introduced the ticketing machine and got rid of the ground staff at the airports, and so on. We’ve introduced lots of machines that do things. Wherever it’s not necessary that someone go smiles at you because you’ve already purchased or made the purchase or you’re in the process of making the purchase, that is going to change dramatically. If you lose your customer contact at the point of sale, at the place where the buy decision is made, how are you to expect to hold your customers? The only way is to do service while you’re delivering the goods, like while you’re in holidays, while you’re on the flight, while you’re at the bank and so on, so service is going to have an immense rebound and service just means people who like doing something with other people. TK: I love your focus on that because it is somewhat counterintuitive - probably very counterintuitive to a lot of people, but when I think about my own experiences as a consumer, part of the benefit in the plethora of online businesses today, are those that actually have developed extraordinary ability to understand me and my buying habits and then insert the human being where necessary. When I get on the phone with a provider and a human being actually answers, is well-educated, and provides me with exceptional service, I notice it. I have to say this, it is primarily the small upstarts, the new companies that are doing that well. The larger companies, the mainstays of the economy, those brand names that I’ve worked with as a consumer for decades don’t know anything about me. I can never get a human being at those companies. It’s an incredible differentiator and I have noticed it and it does influence my buy decision. It influences where I want to put my dollars as a consumer. It’s not intuitive but it’s a very important development I think that companies should be paying more and more attention to. If you use technology to enhance that ability of a human being to service, then you are creating a clear differentiator for your business and your brand. CB: Absolutely. This is very important in the types of businesses. I’m in the business of automating stuff and we automate a lot. I mean our average automation rate in any kind of process is 70%, 80%. That means that a lot of people are out of those jobs that we only have - in the entirety of our client base, we only have one client that has really fired people. The other ones have gone through innovation and service, and service, you say it’s counterintuitive, that’s underestimated so far. I think it’s going to have a huge rebound. TK: We often hear this quip that AI will be the last invention of humanity, the implication being that when we invent AI, it will put us all out of business so to speak or out of existence, if you want to be extreme. Why will AI not be our last invention, if I can ask such a broad question of you? CB: Yes, it’s the Elon Musk story all over again. Right? TK: That’s right. CB: In the beginning, Elon Musk said how unsafe online banking was and then PayPal said, “Look guys, if I’m doing it, it must be okay. You know how skeptical I am,” and he’s doing the same thing with AI. He’s investing heavily into AI, giving a little bit of money on ethical research and then saying, “I’m such a skeptic,” and I believe that the message that comes out of this is going to be like, “Look, you know how skeptical I am. If I’m doing it, it’s okay.” That is basically what is happening all around us. Why should AI not be the last invention that we ever made? Look, it is extremely simple. We have to stop in thinking like how bad it is that machines that will outperform us. We should be very happy. I mean do you want to become a crane? Should that be your new job? I don’t want to do that. I don’t want to carry around bricks. [Laughter] Seriously, no. I want the machine to outperform me. I don’t want to fly myself. It’s been very hard for people to try and fly themselves, simply because we can’t get the proper propulsion. Airplane’s a good idea. Machines outperform us all the time. Then we have this question like, “Are we losing control?” In our lifetime, if I sat down at 10 years old and said, “All I want to do with my life is build a toaster from scratch,” like get the iron out of the ore and end up with a toaster, that would not be possible in my entire lifetime. That’s how complex our systems are and this is how complex our world is. We are not fully in control. The point is, we’re in control of the important stuff, absolutely. We’re in control of the vital stuff to society which typically means ethics and moral and truth and all these high-end virtues, virtuities that are out there, that we are entirely in control of because all machines ever do is mimic our experiences in there. Getting out of control on the detail - no, we have not been in control of the detail for a very long time, probably since we stopped doing agriculture with our bare hands. Thinking that machines should not outperform is - oh my God, they should outperform us all the time because we have better things to do than carry the bricks around ourselves, manually. This is why we outperformed all the animals on the face of the earth because we could build machines. The next generation of machines is no different. They will not take the control away from us on the important things, on the things that actually make us human. There is no reason to think that it’s going to be our last invention. The thing that we are in charge of is ethics and moral, and if we give that out of hand typically to a group of maybe not so great humans, that’s the dangerous part. The machines by themselves will not kill anyone. It’s people that are telling machines to kill other people that are the really bad thing. In the end, it all boils down to us as people. TK: What a great way to wrap this up. AI ultimately challenges us to be better humans, and what greater challenge is there in our purpose, in our contribution, in our work in every aspect of what we are as human beings. What a fantastic conversation, Chris, thoroughly enjoyed it. Thank you so much for joining us on Foresight Radio. [Music] CB: Thank you. It has been my pleasure. TK: That was my interview with Chris Boos on the topic of artificial intelligence. To find out more about Chris, his company Arago or the many activities he’s involved in, just check out his link on the Foresightradio.com homepage. Thanks again to our sponsors for this episode of Foresight Radio, Wasabi Technologies. Take a look at how Wasabi is changing the rules of the game when it comes to cloud storage at Wasabi.com. This is Tom Koulopoulos. I look forward to joining you again soon for another episode of Foresight Radio where we look at the future of how we will live, work, and play. - End of Recording -