Great i think we can get started, so for those just joining, my name is anne connors i'm assistant, director, in the admissions, office, i'm joined by my colleague karen cunningham. Karen and i are thrilled to welcome, jonathan, wayne. All the way from ireland. Today. To talk about his, studies, and his teaching, here at mit. Sloan. So with that i'm going to leave it to you, to, kick it off. Fantastic. Well first let me just, double check that my technology, is working. So, karen, or ann can you just come on the line and just let me know can you hear me okay. Yep you're great, that sounds great okay thank you very much. Well, um, thank you to the admissions, team to, ann and karen in particular. For inviting, me on to talk a little bit about some of the work i do at mit. So i understand, most the people, who are on the call today are interested, in doing. One of the various. Masters, or postgrad, programs, here at sloan, so what i'm going to try and do is, really tell you a little bit about the type of work i do as, just a small insight, into. The. The the range of faculty, who are here and, the diverse, range of work that we do but i'm only going to shine a spotlight, in one very narrow area which is my domain. Um and i'm going to give you a little bit of a sample even of some of the content, we actually cover in some of my classes, and some of my research. To give you a better flavor, for, what. Practically, speaking. Students. At sloan, are studying today i'll actually even show you some of the slides, i've. Presented. In, class, over the last couple of weeks to. Current students. As. And said, we'll, probably take questions towards the end but feel free to put them into the chat. And then, we'll, um. We'll get to them towards the end, but. Um. So why don't i get started, and, you know maybe just, uh, make sure the technology, here is working. Tell you a little bit about myself. I. As you can probably hear i'm originally from ireland but i've been at mit, now for about five years, and so, i am a lecturer, in what's called the global economics, and management, group at sloan. And then i'm a research, scientist. At the initiative, in the digital economy, which is the idea. So. In an academic, environment, this is kind of a. Typical. Um. Setup, for. A. Scholar or an academic, where you have some teaching work and use some research, work, so just to break that down a little bit more and maybe. Tell you about my own. Day-to-day, job and and how i'm interacting, with students, and with the with. My academic, research. I currently, teach in this, calendar, year this academic, year, i'll teach three classes. That are broadly, based at sloan. And but you'll hear more about how they intersect, with other parts of the. University. As well. So the three courses, are the, global business of ai and robotics. That's. We shorten that to g bear so if you hear me talking about g bear that's g b a i or g bear, which is the global, business, of ai and robotics. And this you we've been teaching that now for three or four years, we were probably. One of if not the first. Um. Universities, in the world. In the business school, to start, teaching the course on ai and robotics, and it's, its economic. And its business impact, i'll talk more about the course and even like i said give you some, sample material. That has been such a successful, course it's one of the most popular, elective, courses it's loaned for mbas. And in fact we get a lot of non-mbas. Coming across, maybe phd's, in computer science etc. Who come across to slow and to study as well so, typical, numbers every year is give or take about a hundred students, so very popular electorate probably in the top 10 20, of electives. In terms of student numbers. That has been a huge success, and this year i'm just launching. Uh with a colleague of mine, from, the. Engineering, side of the house, a new course on the global business of quantum computing.
And We're going to. Explore, that topic for the first time this year, on top of that i am currently right now in the middle of teaching a course called collaborative, intelligence, ventures. And collaborative, intelligence, technologies. Essentially, are technologies, that involve. Human. And machine, interaction. So, a lot of the more advanced, robotics, that we're starting to see come into the world are hardware, versions. Of collaborative, intelligence, technologies. But also stuff like chat box ai. Etc. And this is a course on a, again that fits in very. Practically. Because it's got a, high. Entrepreneurial. Focus, on it it's about not just understanding, these technologies. And getting people in a room. That are wanting to discuss, them and understand, where they're going but also actually foreman, teams and ideally, even companies, around that, and i'll tell you a bit more about the, about the hundred thousand dollar. Um competition, that we tag on as part of that course so they're my three main courses that i'm teaching. On top of that i do teach in other areas and other disciplines, and even other universities. Imagine professor, trinity college dublin, i teach a course there called essentially, an entrepreneurship. Course, uh called disciplined, entrepreneurship, which is an entire methodology. And it's rooted. With a, colleague of mine, bill olette, in mit, so i very much uh teach this. At trinity but also, it's, heavily part of my discipline, in. Mit. Um and then i i, help out some of my colleagues, who. Are they primary instructors, and a number of other courses. So analytics. Lab i'm a i'm a mentor on that i, have taught in the past and a bunch of other, action learning, lab classes, such as, a global entrepreneurship. Lab which is i think, still, probably, the biggest. Action learning lab at mit. And then i teach a bunch of exec education, courses as well so that's kind of like all my teaching that's the kind of work i focus on on the research, side of things i'm part of the ide, which is one of the largest research groups at mit. And that really is a bunch of us who are interested, in studying. The impacts, of this. Period, that we're in right now of rapid, digital, transformation.
Of Our economies. Our societies. And i focus on a bunch of areas around like how, do firms. Change their strategy, to, bring in, both entrepreneurial. Firms and incumbent, firms. How do they adapt their strategy, to make sure that they're taking advantage, of, the opportunities. That are out there i also look at things like new digital business models such as platforms. Look at how the digital economy, is changing. And so you know stuff like that would be like how automation, is changing, the labor market and what that might mean for the future, so that's it may be a flavor for the kind of stuff that goes on in the research, um side of the house. Um. So, you know you've heard about the type of things that i'm working on in terms of the various, courses. Um, and the various research and hopefully you'll start to see a theme across, many of them, and so, why, why you know why do i teach these courses why does slow, and, um. Want me here to teach these kind of classes why are students, interested, in this and, for me um. The. You know, the simple way of looking at it and, is because. Technology. Is such a profound. Impact. On, our economy. On our businesses. And our industry, but also on the human condition. The, lives that we are living today have been fundamentally. Changed. Because, of technologies. That have been developed. Entrepreneurs. That have released those technologies. Into the world, and then firms, who continue, to evolve, and improve. And. Disseminate. And diffuse those technologies. Into the economy. So, a colleague of mine a very distinguished, professor. Robert. Solo. He talks he won then he's the nobel laureate, i think he won a nobel prize in, maybe, 1987. And he's a professor here at mit. He talks about technological. Development, will be the motor, for economic, growth in the long run and this is kind of an economist, way of saying technology. Is very very important. Um. And. Um. If we kind of. If we think about technology, being really important to our businesses, and our societies, and us as individuals. Um. The kind of, the. You know one on uh one overarching. Framework, i guess to some degree that supports, this. Is, how does technology, come into, markets, how does it come into our lives. Um because we know that technology, is constantly, changing, right but, i really like this concept that's been around, quite a while from. An economist, joseph schumpeter. Who talks about the, um, idea of creative, destruction. And this i think is really powerful, because it blends together, some of the main interests, for me, which is around, obviously the technology, but also the entrepreneurial. Action that kind of brings these, these technologies, to life, and so what's this creative destruction, what does schumpeter, talk about he called it the perennial, gale of, of creative, destruction, in other words that it's constantly, blowing, this is not something that you can turn off now he was talking about this, you know 50 60 years ago, and really created destruction, is all about new technologies, but it's not just about technologies, it could also be, new products, new methods of production, new means of distribution.
And It's all about basically, making. You know certain models, obsolete. Forcing, companies, to quickly adapt to new environments, or fail. And, and, when we zoom back out and think about this it's a very important, concept. Um, but what does it mean practically, speaking, well. Um, let me just make sure that i've got on the right slides yeah, um so let me give you a practical, example, of how that works over a long period of time and why we might care about something like creative destruction, well if you go back to the. 1900s. You're going to see us piano, sales we just take that one market and have a look at it what we're going to see is that pianos, were the center, of family entertainment. Essentially. In that era. In for a middle-class, home in the united states. You wanted to have a piano, there, so that every evening, you could spend your leisure time, maybe not every evening but multiple evenings a week spend your leisure time, uh uh, you know generating. Entertainment. And you know the number of pianos that were sold then were nearly approaching, 400, 000 per year. Then we fast forward to 1927. We see the piano sales are starting to decline. This. Is highly related to the fact that the national, nbc, national broadcasting, company in the united states, began, regular, radio broadcasting. In 1926.. So, here's the new technology, that came into the world that changed, the way in which. Uh families, were spending their leisure time they no longer needed the piano. So piano. Started, to, still start to decline as new technologies, came in but then we've seen this progress even further. There was another leap take another leap forward. When. And, tv, was. Invented, and started to come into people's, households. Again piano, sales start to decline. As we spend our leisure time differently. And then even if you go forward, all the way up to today, now we can see the u.s piano sales at 30 000 are give or take about, less than 10, of their peak sales. So if you've been in the piano, industry, of all this time. Um. You know there's been an awful lot of change, a lot of the piano manufacturers.
Are No longer in business. And so, we could look at this over the last hundred plus years and say well this has been really bad news for piano makers. And that's true but it's been good news for lots of other types of workers. There are far more people employed in the united states today. In other categories, of entertainment. Because of the new technologies. That have, replaced, the old technology, so, nearly half a million people working motion picture and sound recording, in the united states today. And that's far more than ever worked, uh in piano, making so from an industry point of view from a labor, in a it did an economy, point of view, this creative, destruction. Process. You know it does destroy. Some jobs, and some companies, but it creates, oftentimes. Much more. Um. And not only that for industry, but also for consumers, so, you know i, take the epitome, of, of entertainment, being the halftime, super bowl. Show so in 2020, that was shakira, and jennifer lopez and we think from a consumer's, point of view, the incredible. Technologies. That have come about in the last hundred years, since the family was sitting around the, piano, for their evening entertainment. And now what we can see is possible, something like that at the the halftime, super bowl, incredible. New technologies. And, and, that whole creative, destruction, process is messy, but incredibly, interesting. And brilliant for consumers, at the end of the day. So, this is what championer, talked about when he was on about um. The, creative destruction, house the essential, fact of capitalism. And but interestingly. You know the technological. Changes. Is is never pareto, efficient the losers, are not fully compensated. Either by winners or by society, in general. Okay. So this is kind of like you know underpinning. Of. Of how i look at technology, and entrepreneurship. So let me give you a flavor of some of the course content, that you would get if you're sitting in on one of my courses, so, let's start with the g bear class the global business of ai and robotics, this is we find like a you know a really pertinent, class at the moment because there's, a lot of noise out there about ai about robotics, they're talking about artificial, intelligence, is it gonna, is it gonna save us all is gonna fix all of america's, problems, or. Is it gonna start to, destroy, our communities. And you know, this is, across the board we're starting to see these kinds of, um, comments new york times here from a, couple of weeks ago, the robots, are coming prepare for trouble and this we've seen these kinds, of. Uh. Media, stories. Honestly, for 50 plus maybe more than 100 years so. People are interested. They think these are really interesting, technologies. But, there's a lot of hype out there but positive, and negative, and so, at mit. Our, students. Are trying to look beyond the hype and as faculty. What we're here to do is facilitate, that, you know at mit we're going to take a much more thorough lens, on understanding. What actually is happening there so give you a little flavor, of that how we look at that.
At My in my gbr, classes we have. We take these different lenses, okay so, one week we'll be focusing on what's the total economy, effect. And so, total economy, thing is like, you know will there be enough jobs in the future. But then we try not to mix that with the individual, so for example. You know if a student of mine is in the class and one minute we're talking about will there be enough jobs in the total economy. And then we flick over and somebody will say well, you know, my. Uh. Parent, is a, truck driver, and. That. Let me tell you about their individual, experience, we want all those stories to come out but if we try if we mix. The lenses, that we're using, too often, it can be very difficult, to have a thorough conversation. That's why we try and do it one lens at the time, and the way we look at the economy, for example will be very different, than by the time we're, zoning in and looking at the individual. And and there is linkages, between them all so for example we look at the social and ethical, impact, and we don't just look at this from a united states point of view we have the global context, and the international, framework, as well so these are some of the various lenses we use, and within each of these we break it down further and further and further and further, we bring in some of the world leading experts, i've had, um you know i've had the privilege to have incredible, guest speakers come into the class yeah. Jason furman who is um. Obama's. Chief economic, advisor. I've had the. Minister, of finance who is now the head of the european, group of finance, ministers. Um. I've, had we've had some of the. Uh, you know leading, uh venture capitalists, from addressing, horowitz, we've, you know as well as obviously some of the world leading. Roboticists. Artificial, intelligence, experts so, we've had amazing guest speakers come in and we and this is how we bring all these topics, to life, with both, hyper specificity. And being diligent, and disciplined, about how we analyze, each element, but then also bringing in the people who are at the practical. At the front line of these technologies. And, you know for those people who may be on the line you don't know that much about artificial, intelligence, what is it that's so interesting, about it what is it that we'll be actually looking about looking at this, you know if we're, at uh, at nit. And, you know uh, michael, uh palani. Is a, is a, was, um. An economist, and he talked about, we this idea that we know more than we can tell which is really important, and so, if you think about traditional. Computers. And essentially, how they are built. Is that. Say their software, is built. Is, rules-based. And, there's this great example, to say you know the the skill of a car driver if you think of somebody driving their car many of you at the moment. The skill of a car driver. Cannot be replaced, by a thorough schooling, in the theory of the motor car no matter how much i tell you exactly, how an internal combustion, engine or carburetor, works, that is not going to give you the skill, of driving, because most people don't know how an internal combustion, engine works, but they can drive a car, so. This is this talks about the difference between the skills that we have and the knowledge that we have, and can we if we're going to embed that knowledge into a computer, system, we have traditionally. We have to be able to write rules for it so this is why machine learning has come about because it's it's really important.
If We're going to, continue, to make progress, in computer, applications. So, you know everyday, life requires an immense amount of knowledge about the world to interact, with it not all of this can be bundled into rules, so let me just give you an example of how this works. Um. So if i was to try and, teach a computer. How to recognize, a cat, from a picture of a cat, first thing i would need to do is really describe. All the rules, that define, a cat, and how that cat, is different, from lots of other animals and indeed any other picture. So if we had you know in one of my classes we spent a bit of time, trying to. Tinker with this a little bit i asked my students to try and define, all the rules that they would use you know they'd start, using definitions. Around the hair, and then the whiskers, and then the pointed, ears, and for nearly every rule that we write we end up having to write a bunch of other rules to contradict, the original, rules, and the whole system starts to break down, we cannot, effectively. Teach computers. To recognize. Cats, from dogs and all other animals, by writing a set of rules, so then we've had these great breakthroughs, in machine learning which is really the big area of artificial, intelligence, we focus on and what we do is we show, the computer. And the artificial, intelligence, and machine learning algorithm we showed lots of pictures of cats. And it, starts, to. Look for features. Within the image, that are not the same. Um. Maybe features that we as human beings. Are looking for, so the way my two-year-old. Niece, knows what a cat is, is very different than the way that an ai. Machine. Learning algorithm, knows, what a cat is, essentially, it starts picking up all kinds of features. Within. The, let's call it a jpeg. Or picture. Um. Of the, of the cat, and it then trains, itself. By. Applying. Weights, over and over and over again, until it gets good and by good i mean statistically. Accurate. Um at identifying. Whether it's a it's a a, cat or a dog and so that's what, that's the fundamental, of how the technology, works but then. Why do we care about it at the business school well. You know, um. Businesses, and economies, are built on things got you know a lot of time. You know massive changes, in costs make a big difference, and so machine learning, is changing, the fundamental, cost of prediction, and making an awful lot lower, and so we see lovely little examples, of that like this is the. Muffins. Or puppies. Example, and we start to show loads of. Muffins, and puppies. To a machine learning algorithm, and we can actually get it to be better than human beings. At, predicting. Whether, an image. Is of, a. A, muffin or a puppy so that's great it's a little bit of fun but you know, so what okay this is making prediction. Cheaper, but do we really care here's another little example. Um, you know is this, bill murray or tom hanks, so if you just take a second and have a look at that picture. I'm going to tell you that we've trained a machine learning, algorithm. To be better than a human, and i answering, this question. Is this bill murray or is this tom hanks. Um, and so normally i'd ask for show hands we don't have we have too many people on the call right now so i'm just going to go ahead and tell you this is actually bill murray doona tom hanks impression. So these are all trivia and they're kind of interesting, but, you know trying to bring this to life for businesses. Um. This is when donald trump got elected this picture of donald trump when he got elected four years ago. And so, um. You know i've, i i know and i've had conversations. With, a, hedge fund manager, who has access, to some of the most, sophisticated. Ai, algorithms, and ai teams on the planet. And they would say that in advance, of the previous, election, they knew that donald trump was going to win even though, the general polls weren't saying that, but this is what's interesting, about this from business point of view, knowing, basically, means they're statistically.
Reliable. Um. Information. Um it's not known for a fact it's, you know statistics, point of view. But, they couldn't do anything with that information, because being able to predict, is not enough, it also requires, human judgment to know what to do with that information. And that's a hugely, important, part, of, taking this technology, and bringing it to life, and so to give you examples, of some of the things that we delve into and some of the things we look to explore. You know so what's interesting, about this from a business point of view like let me give you a couple examples. The importance, of interdependencies. So on the right hand side i've got netflix, with the recommendation. Engine that's powered by machine learning, when netflix, want to introduce, a new algorithm, to improve, their. Recommendation. Engine they don't have to ring anybody, else they have very low interdependencies. With any other firm they can just do it, but when, google or waymo or one of these companies wants to bring out a self-driving, car that's fundamentally, powered, by substantial, machine learning, they need to do a lot of interdependencies. With. Regulators. With insurers. With drivers. Etc etc etc. So the speed at which these new technologies, come about in our society, and our economy. Is heavily dependent, on interdependencies. So this is the type of precision, i'm talking about that we start to look at these topics, at mit. Another type of thing is understanding, the tolerance, for errors. So on the left hand side is you know google when they're using their predictive, text, and email most people who've used gmail, will understand, this. But then on the right hand side we have a picture of a google-powered. Self-driving. Car that had an accident, and so, from a human being's point of view, we are quite happy, for google to, introduce. And then accelerate. And improve, the gmail predictive, text. That's fine bang away because it doesn't really matter if it fails. But we have a very different. Tolerance, for. Error when it comes to self-driving, cars so trying to understand, all these, is what this is where, you know the devil's in the detail, final one i'll show you there. Is just because the technology. Is, available, and invented, does not mean that it is going to automatically. Be introduced, into the economy. So i go over and back to the united states and europe a lot i'd love to be able to go over and back on concord, it would have the amount of time it takes my flight. But concord, no longer exists not because the technology, but because the business model around it did not support, its ongoing, use and maintenance. And so you know delving, into all of these topics i'm just trying to give you a flavor, for right now of course, but we spend a lot of time delving, into these. Um. And so that's maybe how we look at the technology, but undermining. A lot of that and that, bringing those technologies, to life which we're so passionate, about at mit. Is the disciplined, entrepreneurial. Process, and the important words there are the discipline. We think about it very much being. A, a. An endeavor, in which you can build skills, upon, in which you can improve, and you can get better and we do believe that it's a process. We don't believe that entrepreneurs, are born with some magic gene we think everybody, can be taught, to be a better entrepreneur. And. Um, we. We focus a lot at mit, and yeah there's a lot of great people around who have great ideas, but ideas are only a small part of the entrepreneurial. Success, pie in total, so things like understanding, market, execution. And team we really, you know we've got a huge platform, here, in the martin trust center as well as all our education, programs, and using entrepreneurship, to bring all that stuff to life. Um, let me just show you this is just to give you a small, flavor. Of all the different courses. That are available, at mit. Around, entrepreneurship. Education. So there's all kinds of stuff where it's like the very beginning. Nucleation. Ideation. Of new. Ventures, all the way through to, defining, a product, and learning your, core skills, or it might be negotiations. Or communications. Or product design, we've got courses for all of that within that, i teach this one course called collaborative, intelligence, ventures. And really what that's about is bringing together, people, from, engineering. In various different disciplines. Design etc. With business students to see. Are there are there concepts, that we can bring to life in terms of. New ventures. So every year we uh uh this is the second year now but we have a hundred thousand dollar, prize fund that we. Allocate, to the best performing, teams coming out of all that process, we're very lucky to have an external, sponsor, on that, um, and so.
That's The kind of thing that you're talking about here's the technology. Here's the underpinning, of entrepreneurship. Let's get people into room let's come up with some ideas and let's see if we can bring something to life. That's kind of like the mit, way about thinking about things and then also about uh about doing as well. Um, so just to maybe, just before i finish up i want to talk about look i've talked about, myself, a lot but um, here in the work i, we et cetera et cetera but i think it's important that i also maybe just uh talk a little bit about some of the. People, i work with because, that is a huge part of what you come to mit, for. And frankly it's not just you for students but even as faculty, myself. One of the reasons that i love mit, so much and you know i i find it as my intellectual, and spiritual, home. Is because of the people i'm surrounded, by the amazing, students, you get to meet here incredible incredible. People that just blow your mind, the humility. The, work ethic the focus. And the intelligence. Just generally all rounded very, you know great people to work with. Um, but not just on the student side on the faculty, side as well. I've put up six of the people i, work most closely with, five of whom i teach with and one of whom i do a lot of research with to give you a flavor, of the caliber, of people, that are just everywhere, at mit i already mentioned nobel prize winners, who are based, at slo and, there are plenty of courses you can take from nobel prize winners, up in the top left-hand corner your screen, is simon johnson who i co-teach. And and work very closely with uh simon was the former chief economist, at the imf, and his understanding. Of washington, of international. Politics, international, economy. Is you know is incredible. Um. Next to simon we've got julie shah she's a professor. Of aeronautical. Engineering, here at mit. And this just shows you it's not just sloan people work with sloan people we interact, with, across, the university. And julie is the associate, dean. Of the new college of computing, and a very exciting, place to be, and an incredible, visionary, researcher, she's worked outside with bowen and many other people.
Um, And one of the leading lights in the united states in her field of robotics, at the moment. Beside, that i teach with julie beside that i've got, andy mcafee, a good friend of mine and someone i work closely with i've helped him on his last couple of books and, here at mit, he's you know he he's had a, hugely, successful. A book that was published, a number of years ago called second machine age and that's a big part of, a lot of, people's understanding, about how the economy, has been shifted. By the, um. By digitization. And new technologies. And the bottom right inside, i've got will oliver who i'm starting a new course this year he's a professor, at mit. And he focuses, on quantum computing, one of the most complicated. Interesting, areas i believe for the next couple of years and probably decades. And the two of us are collaborating, this year to start a new course. That's, for business, students so they can get to understand, this very complex. Subject. Not as engineers, but as business people are going to take this and bring it to life in the real world, and we're probably the first, school in the world. To start a course in quantum computing, in a business school, um. Beside will in the middle at the bottom. Is luis fitigray. Who is a now he's, a. I think a. Lecturer, or a visiting scholar, at slow on i can't remember his exact title but he was former minister of finance in mexico. So again, somebody, is a phd, from mit but again somebody really understands. International. Perspectives, and global, trade. He was a big part of nafta. Negotiations. So for you know for north americans, you'll understand, how big of a deal it is that and then on the final one i've got gary gensler, who is a. Professor, of the practice, here at mit, who's a former. A regulator. In the united states and i'm not going to make, any commentary, on where i think gary, is going to be. In the next. Couple of months but i will just say, that if anybody, reads the wall street journal, or reuters. Um, you may have read, about. You may have seen gary's name over the last day or two, as he is. Uh reputed. Uh i'm not going to give you my, personal, understanding, of the situation. But it has been reported. That, gary, is to. Head up, joe biden's, team. On, uh overseeing. All of wall street, which is going to be, you know incredibly, interesting, so that's the caliber, of people who are working, and have worked and are at the cool face. Of technologies. And the economy. Uh here at mit, and you know it's just a real privilege to be able to work with these kinds of people, so. On that i think, that probably, tease it up i've got a little bit over my my half hour plan but at this stage maybe i'll open it up to questions. And, i'm happy to, dig into, any of the topics, that have been. Um. Explored. In this or the people or whatever, else that might be useful. Great that was fantastic. So we have a bunch of questions. I'm going to get to as many, as possible, but. Continuing. On with the faculty. At mit. We sometimes, see people, wanting to switch careers, coming in from a business background. How, do you. Help students, bridge the gap between their business background. And their technical, talents and sharpening, those technical, skills, needed, to, work in this industry. Well you know it's a great question, and i think it it hits at the fundamental. Of where. Where business, and within that where managers. And leaders. And the roles that their ch their roles that they that they, hold within firms, are changing. And so, the manager, of the future, is not somebody who just has the. Um, skills, around interpersonal. Skills, and maybe, what traditionally, we would have seen is maybe just strategy, skills or leadership skills they're going to be incredibly, important. But also to have those analytical. Skills, so, you know the ability and that's really a big part of the way we look at management. Here at mit. And so for people who are coming from the outside who may have. A bunch of skills already but are looking to really refine, some of those analytics, skills, there's a whole host, of courses. You know that are, based here around building, those, for the manager. Now if there's somebody who's coming in and they've already, been, exposed.
Or To maybe, you know, some, quite heavy, quantitative, work in their undergraduate. Even. And you're coming to mit, looking to explore even further, well there's no shortage, of courses for the expert either, and you know we go we have maths departments, and electrical engineering, departments computer science departments. Um. And so. The ability for people to be able to cross-register. For maybe, those kind of expert, courses. Um or. Or courses where you're trying, to blend. Your analytical, skills, and maybe your finance, skills, but then also try to find, a. A a home outside sloan first, so i think there's like you know one part of it is the huge range of courses were out there but i also want to re-emphasize. Something i talked about at the start, which is that it's not just about. You know, maybe traditional, courses that are people who might be used to from their undergrad, where there's, a professor, up the front and they're talking to a bunch of students, but these action learning labs they're a vital, part. Of how sloan. Goes about its business, because again we believe in students not just, you know learning but also practicing, and demonstrating. It's a very, integral part of how mit, and sloan operates. So the action learning labs i'm i'm part of the uh the team that runs. Uh a lab which is analytics, lab. Um, for one and a whole bunch of other labs that are available. I hope that answers your, question. Great the next question we have is on creative destruction. How does it differ between national, outlooks, and economies. How do these differs. Affect you when you're teaching at trinity, versus, at. Mit. Well. That's okay it's a lot of stuff to unpack there how long have we got we need another few webinars. And. So. Uh, look. Creative, destruction. Is. A powerful. Force, but it underpins, capitalism. And there are a bunch of other things that are important, to. Market, economies, you know because, there is no perfect, version of capitalism, around the world there's all different. Different, versions of a different varieties, of it you know i do understand. Quite, well. I would say the western, european. Nations, and the united, states, i also have. Quite a bit of experience, i spent a decent amount of time in places. Across, asia including, china, and. I suppose. You know when we look at creative destruction, we also have to think about. Um. Institutions. Okay and so institutions. What we're talking about there is that like. How is it that the people, organize, themselves, in the country, in a country. Who do they give power to, that can then, set the rules, of the game and that's what institutions. Do, so, you know, how. The united, kingdom, sets, up, its, rules of the game whether it be the bank of england. As an institution, or whether the people vote for something like brexit. That's constantly. Changing, and so creative destruction, has to sit within that framework. And so, you know, when we look to places like china they do that very differently. But what's underpinning. It, at the end of the day, is the macro forces. That, you know the perennial, gale, of creative destruction. And also the institutions, how the chinese, decide to set up their institutions, are different so, the lens that we can use. To look at these, you know institutional. Lens is very important, technological. Lens is very important. And then there's a much more subtle one that most people will probably experience, of have some. Innate, understanding, of and that is the, the social, one different. Countries, of different. Uh, i'd say different populations. Have different attitudes. To, some of the underpinnings, here so. For example in the united states in certain cohorts, not across everywhere. But there is more, of an acceptance, of failure in fact in some quarters it's actually glorified.
And, You know the more you fail the more chances you can have, in entrepreneurial. Terms of getting a successful, venture. We're probably not as entrepreneurial, in western europe as. As the. Pinnacle parts of the united states that's just, maybe, one insight. Great, excellent. Um, so the next question that we see is about ethical. Ai, could you please help elaborate, elaborate. How it's incorporated. Into your curriculum, or the curriculum, at mit, in general. Well great question, i mean this is such a complex, and interesting, and important subject. Um. And what i actually noticed is, i used to teach a section, you know practically, speaking i'll tell you. How do i incorporate, it in i used to teach a section on this myself. In one or two of my classes, and i just found it, it could be an entire class and in fact in parts of mit it is an entire class. And so that's where i i collaborate. With real world class experts, thankfully for example, in my, collaborative, intelligence, ventures, class i work with julie shaw, whose. You know, core, research. Her, in, you know, focus, of, of of her academic, career really is all about. The. The i would call it ethical and social impact, of these technologies. That she knows so much about, so i think, it's never of case of like, how do i. You know. Um. Teach this as if i'm the oracle, of uh of, anathem. I think your role as an instructor. A lot of the time is to bring together the right people the right ideas. And then we you know we discuss, it as a classroom. Because. Um. There are, it's another great thing about being at mit. The amount you learn, as an instructor. Is incredible. When you teach some of these classes. On, importing. And interest and sometimes, difficult. Topics, we don't come to an answer there is no, final, answer what we're trying to do, is get people to think differently. Be exposed, to different ideas. And then they're going to take that into their careers. Maybe they're going to be managing, a huge business down the road and have, thousands, of employees, and how they're going to think about automation. Hopefully, will be will be broader. Excellent this question is on your teaching, style. Do you have a particular, teaching style, or is it a mixture between lectures, case message. Method and mix or another method. It is a lot of a mix, um. The. I'm not as heavy into the cases, for example you get at harvard business school certainly not on that, you know that's way over on one end of the spectrum. We certainly, use case, methodologies. Um but what we do is i suppose, we're, deliberately, interdisciplinary. If you think about. We're not trying, to. Discuss. Technology. In a business skills, silo. We're bringing technologists. Both academic, researchers, and practitioners. Into the classroom, with us and then we're, then using our skills, as. Business people business scholars, business, students. To take those technologies. And understand, them and apply them in different ways so, um it's very, current. I mean some of the. Like you said look, there is gary gensler. Who, i am teaching with tomorrow night.
And. The wall street according to the wall street journal, he is right now. A major, piece of joe biden's. Um, presidential. Transition, team, and so that's how live some of these things are we've had people, i've had, um, guest speakers, an incredible, lady who, runs an ai company. Who. Uh came into, our class. And. It had gone, it had only been announced a couple hours before. That she had raised i can't remember something like 55, million dollars. So she is coming in teaching in our class as a practitioner, or a guest lecturing. On the same day that you know they're raising these massive amounts of, money. So i think my teaching style would be somewhat, case study, case study cannot. Keep up with that. Pace, frankly, in rapidly, emerging, areas. Um it's not that they're not useful they incredibly are and i suppose to some degree we're using the case study of of real life, practitioners, with real life problems. And trying to bring them to life, and then i say just generally, my, my my teaching style, is, i believe, my. Job as a you know instructor. Is not just to transmit, information. Not a huge fan of that. Much more that i'm there to. Unlock. Knowledge, that the students are you know these are all graduate, students at mit. Um. What i'm there is to, you know provide, direction. Unlock. They, maybe challenge them a little bit, help them to unlock, knowledge that's there or that they think about things a little bit differently. And and i and in one part now you know i know it wasn't directly the question but. We spent a lot of work. And i really mean a lot of work at sloan. This year and other universities. You know are doing this but really it's been, you know being really proud of the work the faculty, have done at sloan this year, and adapted, to the online. Reality, that we're in because of covet. And you know there are there are multiple, hybrid, courses, but a lot of what i'm doing, is, is teaching remotely. And. I, you know. I would say i mean. Like, my experience. So far. Ha. Since we've met the adaptions. I honestly, think that the student, experience. Is. Similar, in some ways, better than it was before. Now look we're certainly, losing a few elements of the in person. But we've done a lot of work to compensate, for that, when covert hit instantly. And we all just switched over to zoom. If i compare the classes, i was teaching then in the middle of march when you know we hadn't properly, adapted, we hadn't considered the environment, and i look at now the way we're teaching, and the way that we're learning how to, you know engage, students, and to. Uh you know i'm working with a couple of students at the moment and as part of their work we're getting them to develop a podcast, so that they can explore, topics themselves. That's not the kind of thing that i was doing in um. Precovet. I think we're adapting. I'd like to think we're adapted very well. Excellent, the next question is about your g bearer classes. Do they cover, topics, or resources. Strategies. To help, awareness, of ai, and robotics, in developing, countries. Certainly, we do talk i mean, if you saw the framework, that i had there was a square there was triangle in the middle in the top right hand side we just said international, well okay, that's a you know big topic, we absolutely. Do explore. Um. Developing, economies, to give you an idea, we it's not a full case study because frankly. There's no case study out there so we. Have developed, this. You know one class. It's not a full class but it's a big bulk of it. Where. We introduce. The, idea, to the class. We set the we set the context, as. The. Economy, of bangladesh. And we look at within that, how much of their, entire, gdp. And workforce, etc. Are involved. In, textile, manufacturing. And then we bring in this, you know notional. Um, machine. Which replaces. Hand stitching. Et cetera et cetera okay so we, invent these. Technologies, that are not that far away and maybe we do have.
Small. Cases of where they're around, but they're either, not technologically. Capable, enough today, or they're not cheap enough today, but we introduced, this idea, to the students and then we talked about okay so what happens to this economy, what happens to these people, we take it from the economy, and industry, of firm and individual. Perspective. Um. I co-teach, that class with luis. Figueret. Who you know like i said is the former. Finance, minister. Of. Mexico he was also the former trade minister for mexico. So when you've got and like i said, simon johnson who's the former chief economist, the imf, so, we take a very global perspective, on this i am actually when i think about that there's none of us uh simon was, was uh it was originally, from the uk although he's been in the us a long time, luis is from mexico, i'm originally from ireland so we're thinking about this from a very global point of view that's very normal at mit. Great and the next question is since technology, advancements. Are likely to decrease, the number of jobs, available, in certain areas of the economy. While creating, jobs in other areas, can we soften, the wealth disparity, that happens in the short term. Very, good question very good question, you know one of the things. That the. Market, economy, has been very good at. Is. Increasing, the size of the pie, in general it's hard to argue about that if we look back over the past. Number of decades, but we what the american economy, in its current iteration. As uh societies, across, a lot of the rich world, have, implemented, it has not been good at, is splitting that pie up in, an equitable, way, and so. Um. I think it's one of the great challenges. Of our generation. And the next generation. We definitely don't have any simple answers but we spend a lot of time thinking about it and talking about it we also, try to position it within, a realistic. View of where the world is today, because we're starting from scratch and designing, a new system, maybe we could design a better system, or maybe we're not just thinking of all the unintended, consequences. Of this mythical, other, system that we would implement. And so we take it from where we are today. How can we make things better, what can we do practically, in the short term, to actually get to a more desirable, long term, very important part of what we do and i think you'll hear, a lot of conversations. About that. In class and car doors among students. Very important. Do you have any particular, reading lists or books, that um these prospects, can look into. Oh where do i start. Um. I really like i mean. Uh, i've helped andy, mcafee, with and eric winofson. With, the last two books but their most famous book is the one before that second machine age i think it really.
It's It's it's almost getting dated now 2014. Or 13 sometime around then i think it's a really good, underpinning. To. Their, the, um. The, rapid. An unprecedented. Change that technology, has caused. Um, and that and it sets it in the context, of. Of millennia. Frankly. Um, and i think it it gets some really good underpinnings, so, you know i like i like still like i still really like second machine age but then. Um. Prediction, machine. Is a, i think the best, business, book out there. On ai. At the moment. Um, a couple of guys out of toronto, business school wrote that. A really good. Yeah prediction, machine. That's on maybe because you asked specifically, about g-bear. Anybody who's thinking of starting a business, a good friend, and colleague, bill ollett. It's a very different type of book it's called uh. 24, steps disciplined, entrepreneurship. Anybody who ever. Personally, contacts, me. And says you know about business i had actually done a startup and sold it, myself. Um before joining mit. And anybody who reaches out to me thinking about entrepreneurial. Ventures, and you know wants to jump on a zoom or call i'd say well, i you know happy to jump on a call but, um before you do so please make sure you've read even quickly skimmed. Below that's book because then we'll actually be able to have a much more useful conversation. So, i'd highly recommend, it. Excellent. You talked about the podcast, that you're doing with the students, um can you give any more examples, of cool things your students have done as a result, of your class in teaching. Um. Yeah what we see um. I've had a bunch of students. Who, organized. Self-organized. I would say facilitated. But, you know, i was facilitating. Them. I didn't come up with the idea, of, a. Hackathon. We. Coming out of one of the g bear classes, we had a incredible, guest speaker another. Fascinating, thing when you come visit us at sloan. Okay maybe not right now, but when we're all on campus together in the sometime in the future. In. When we're, in one of the, classes, i teach in slow and, literally, across the road you could i mean, you know if you're a good enough arm baseball player could throw a stone at it, is the amazon, offices, where. They, have their lead team, for the development, and implementation. Of. Amazon's. Alexa. Is based and of course you know that's a technology, that's, most people a lot of people are familiar, with when it comes to ai. So we had one of the lead, guys, from. Uh. Amazon. Alexa, in speaking to us and the group of students self-organized. And, ran a hackathon. Where. They. Recruited, other students from across mit, and on a saturday. They collaborated. With amazon, alexa. Because. Alexa had this ability where in a very short amount of time you can create new alexa, skills. And so. There's a bunch of business, students who probably, most of them did not have any. Coding, experience. But, instead of being intimidated, by the fact that there's quite a steep learning curve to get from zero, to hero, of being a coder. Instead of just accepting, that they found. A capability. In which they could in a very short amount of time basically one day. Do something, it's not the same as being a brilliant quarter, but they could actually. Bring about. Amazon, skills that could be embedded into the platform. Released, for the public. And. And they managed to do it in a day so that's the kind of things that, you know that. Hackathon, that students would self-organize.
That's, You know really exciting. When you see it of course other students have actually started businesses. Um. Like um they're trying to think of who's been a successful, one, pillpack. They sold. Their business, for, a reported, over one billion dollars. They were back in, sloane doing their mba, well one of them what like, i don't know. Five or six years ago something like that, not in my class but uh certainly in bills. Okay i think this is the last question we can take but um here it is it's been greatly debated, that machine learning models, are biased, as sloan, shapes global, and inclusive, leaders. Are there any courses, resources, that focus, on. Inclusivity. And technology. Well. Uh at the ide. The, research, group i'm in rather necessarily, teach them but any student who comes to mit, we're very keen on you getting involved in, in. In, in our programs, at the research group as well, we run the inclusive. Innovation, challenge, which sounds. Very close. To what your. The questioner, just asked about. And that's a million dollar, prize. That we've been running for the last number of years. Where we look for initiatives. Social entrepreneurs. For-profit, entrepreneurs, who have a social agenda, from around the world. Who are developing. Technologies. Or initiatives. Or. Companies. Where they are addressing. Some of this. Inclusivity. Um. In, new technology, is a challenge. So it's something that's very close to our heart something, you know we have some very generous sponsors, who've, put several million dollars behind that over the last number of years. The program is evolving, this year and in fact will now encompass, a lot of other parts of mit. It's been expanded. Into a more broad, inclusive. Inclusivity. Innovation. Challenge. Um. And so that's why i just want to talk about the kind of stuff that goes on at mit, in terms of. Making sure that all this innovation, that's happening, this is all great but, as my. Uh. Colleagues. John gruber, the. Economic, school, and simon, johnson wrote a book last year called jumpstart in america. And it looked at like, you know how we're going to, make sure that the prosperity, that we're creating, is going to be spread out equitably. And they talked about, uh, the idea that we want a rise in tide and that's what technology, creates, over the long term. Going back to bob, uh solo's. Uh insights. Um we want to create a ryzen tide that's going to float all boats, not just the yachts. And i think that probably, is the insight, that i like most when we think about this which is this is not just technology, that will create more billionaires, i'm not against having billionaires. But what i am is i i'm. Also. Um. More concerned. About making sure that. As many people as possible in society. Are benefiting, from, the incredibly. Incredibly, powerful, and important. Benefits, that are to be had no it's not all perfect, technology, doesn't solve everything and it comes with some negatives, as well as positives. But in general, over the long term, you know i think it's it's been an incredible, force for humans, and i think it will increa.
You Know that will continue, into the future, and i literally. And i really do mean this not just because. I work here but i literally cannot think of anywhere better in the world, than mit, and cambridge, to explore this topic. Great i think any last, uh words, from you, advice. Or where you see, ai, and technology, going in the next year. And then it's unfortunately, time to wrap up. Oh, i think it's going to be a turbulent, time. For, technology. In. At the regulator. At the large, firm level i think we're going to see, as we've already begun to hear for the last couple years in europe and begin in the united states, as well, you know these ideas like is big is tech too big. And there's, going to be really. The kind of questions that are going to be, at the forefront. Of a lot of the, newspaper, coverage, let's say. But what i'm always reminded, of and. In another presentation. I give. Um. The. The day. That. The, paper. Was published. That. The tim berners-lee, another colleague here of ours at mit, who invented the world wide web let's call it invented or at least, he published the paper. I. Have the front, i i got the um, the. New york times, front cover, from that day and i think it was 1991.. I can't remember exact date. And what was on the front page, of the. New york times and the day that the internet was effectively, invented. Had absolutely, nothing to do with the internet, so, i suspect. That there's two things happening. Over the next year let's call it, two years, one is that there is going to be a focus, on big tech. On. Its place in the world, from a taxation, point of view. From a monopoly, point of view, regulatory. Regulatory, environment. But then there's going to be something that's going on probably it's somewhere like mit, with people who might be on this call, inventing. New technologies. That it will be another 10 or 20 years before we know how powerful and important they are so there'll be, very microscopic. Stuff that's going on under the radar, you you know if you get involved and you come to mit it'll be fantastic, we'd love you to be part of that, um and then there's the stuff you'll be reading about in the new york times. Excellent, well i just want to thank you all for joining us today, whether it's your morning, afternoon, or evening. As always, we, hope you all, are in good health we wish you all the best, and we hope to see you at our future webinars. And. Hopefully, someday, on campus, so thank you again for joining, us. Thank you best of luck.
2020-11-27