Welcome, everyone, to the promise of progress. I sort of struggled saying it because I can't say that I love the title of this session. Largely, I think if I had my way, I would have called it the threat of progress. And I don't mean to be Debbie Downer, but I do want to make sure that at a place like the World Economic Forum where so many smart people are gathered, we're actually having conversations about things that aren't necessarily being discussed so broadly everywhere else. And the issue here is the threat that progress poses to employment, to work, and to labor, and how we should think about those things. We've got quite a panel with us today. Starting over on your right is Andrew McAfee. He is a principal research scientist at MIT, educated at Harvard at MIT, and he studies how digital technologies are changing business, the economy, and society. He's the co-founder of the initiative on the digital economy at MIT and the co-author of a great book that I really think you should all read if you haven't already read it, The Second Machine Age, Work, Progress, and Prosperity in a Time of Brilliant Technologies. Andrew, good to have you here. Thank you. Thanks for the plug, Ollie. Yeah, there you go. Arnie Sorensen to, well, Arnie's over there. I'm going to get this left-right thing confused. Arnie is the president and chief executive officer of Marriott International, and he's going to have a lot to say about his business and how work is affected in the challenges and the accomplishments that he's experiencing. Laura DeAndrea Tyson is a professor and the director at the Haas School of Business at the University of California, Berkeley. He's also on the Global Agenda Council on Gender Parity. You will, of course, have heard her speak and spent good time as a senior economic advisor to President Clinton here in Washington, not here at all. Vishal Sikha is the chief executive officer and managing director of Infosys India, and Guy Ryder is the director general of the International Labor Organization. So welcome to all of you. Thank you for being with us. The question I put out there to my panelists as I tried to hijack what the session was actually about is innovation and technology are creating great leaps in productivity, but far less energy is being devoted to solving the problems of those who are displaced. I'm going to start with you, Laura. There are very few people who tell me that's wrong, that we are not sure we're devoting enough energy to all the people who are displaced, but I never quite understand what we're supposed to do about this. And I get similar responses, whether I'm in Silicon Valley or in Washington or in capitals of Europe. How are we supposed to think about this? Is this something that just fixes itself? So first of all, I want to say that you talked about your concerns about the title of this, so the promise of progress or the threat. I would talk about it as the sharing of progress because one of the key points I think in Andrew's book with Eric is I think that we can be optimistic that the technology is going to create a huge what they call bounty, a huge efficiency gains for the world. How are they shared? How are they shared is a huge issue because there is the dislocation effect, which is very real. It's not new. It's really not new. There has been a declining middle class and middle income jobs and mid-level jobs in the United States for 30 years. And it has been driven largely by technology. There have been good jobs with complimentary high skills, college educated workers, PhDs, MAs, getting jobs that are complimentary to the technology, but at the same time the technology and now at an increasing pace is taking out the middle. Their estimates at something like 47% of the tasks or 47% of the jobs, but the tasks that people do right now can be done right now with the technology that we have right now. So yes, there is significant dislocation. Retraining and education is part of it, but you know what? It can at the end of the day be the whole solution because if the technology is really taking out those jobs and not going to replace them with others, there are things like minimum wage support, collective bargaining. Who's going to bargain for the shares? How are you going to divide up those shares? It's going to depend upon workers having a voice in this. So collective bargaining, minimum wage. And then ultimately I think tax policy and longer term, a kind of basic income or negative income tax approach will be required. All right. And these are the kinds of ideas we want to talk about. What should we be actually doing about this? Vishal, I don't know where I was reading my studying for this. Foxconn, we all know about, if you've got an iPad or iPhone, they assemble them. Foxconn has 10,000 computers, right? 10,000 robots at the moment that do things that humans were doing. By next year, I believe they'll have 300,000 robots.  We have one in our booth serving beer tonight. Well, there you go. So I would have thought, I would have thought serving beer was safe. The German way, you know, with the inclined glass and everything. Right. So the point is, and we'll get to this, if that's not progress, nothing. So I guess my issue is when Laura talks about minimum wage and in the United States, there's a big movement toward a $15 minimum wage. The president has embraced $10 and changed as a minimum wage, but people are saying $15. Well, if we already are outsourcing jobs because we want to pay $15 to people, in your outsourcing jobs to computers, and Foxconn is laying off peasant workers, what hope is there for labor? My view on this is very simple. Automation is inevitable. The advance of AI is inevitable. We have to embrace that. We have the world needs more intelligent systems, more of these dynamic adaptive systems. And there is no way. I mean, it is an unstoppable force of nature. And we have to treat it that way. And we have to embrace that. And frankly, my view on this is quite simple. There is no limit to human creativity. There is, for the foreseeable future, my own education is in AI. And I don't see that for the foreseeable future. There is any shortage to the kinds of things that people can do, that there isn't a fundamental irreplaceable reason why people won't have jobs if we don't gear ourselves up to enable and to provide those kinds of jobs. So the answer lies in enabling people to become more creative, more innovative, going after innovation, going after the construction of these kinds of intelligent systems. And the way to enable that is quite straightforward. That is education. The way that we educate people, and we have to provide access to jobs. We have to provide much more transparent. And there are all kinds of marketplaces that are available now that lower the friction between supply and demand. We have to go after ideas like that. But at its root, the issue is one of education that has to enable people to find their own creativity, their own purpose. Okay, so let's talk about that. Laura, you mentioned retraining. Guy, at any given time in most modern Western markets in the United States, there's a certain amount of churn. There are always jobs available. But since the Great Recession, we have had far more people available than jobs. So if you retrain them all to be software engineers, every manufacturing worker in the United States, we still don't employ them all. So to what degree is this about educating people properly, people who are not educated for this knowledge economy that we're in? And to what degree is this something else where we are eliminating jobs faster than we can create the capital to create new work? Yeah, I think the first point to be made is we're starting from a bad place, never helpful in a debate. We're starting in a very sluggish global economy with very high levels of unemployment already and the trends going in the wrong direction still. And now if we see an unfurling second or fourth revolution, it depends whose book you read, we're going to have to deal with some very difficult situations from a bad starting point. Now, I spend a lot of my time railing against techno-determinism, as I call it. I don't know if you have any techno-determinists on the panel. But the point is the following. I think, and I like the title of the panel, because I think progress is not to be measured in technical advance or innovation, it is to be measured by what we make of the application of that advance and that application. I think there's one fundamental question where the jury is out, or at least I hear, very contrasting opinions. If we are going round this carousel for the second time or the fourth time, let's look at the lessons of what happened previously. And I think the optimists would say, well, we all know about creative destruction and we all know that after a period of turbulence, which people did either pretty well or very badly in managing, we came out ahead in terms of better living standards, distributive mechanisms and jobs. So just like it happened, I even heard somebody we see last week say, it's going to take seven years to get out ahead. It's sort of nice to have that sort of guarantee. Or is it different this time? Is there some qualitatively different? And I think there's a lot of reasons to think that it could be different, but not just measured on that sort of spectrum of creation and destruction. I think there's something qualitatively different, which is that this technological revolution, if that's what we're talking about, has inherently within it the capacity to transform fundamentally the way work is performed, not just about the number of jobs, but the manner in which work is undertaken. And of course, we talk a great deal about platform economies, the gig economy, all the rest. But I think this technology has a capacity to intermediate the way work is performed, which means we have to re-look some of our traditional categories of labor market institutions. I will sign off happily on more money for training, more money for all sorts of traditional job instruments, negative income taxes, income support. But the question I think is, do we have to actually get out of those familiar categories and start thinking in very different ways about the way work is undertaken in society? And feel free to offer those up, because I still recall that our basic education system in this country is modeled on the fact that we needed time off to do certain things. And so we maybe have to rethink every part of how we work, what a work week looks like. And when you say this country, which one do you have on? I'm sorry. Well, actually, all of our countries, all Western countries have got that. All developed countries. All developed countries have got that. I think that would be a good point. But you're right, which country we're in is a question we have to keep coming back to. Arnie, let me ask you something. I read that corporate profits through 2010 were 23.8% of corporate income. That's a high. But compensation for labor is at a 50-year low, and that includes CEOs. So most people will tell you, particularly in America, CEOs get paid way too much and it's right out of proportion with everything. But even when you add the CEO pay in, compensation's at a 50-year low and profitability is at a high. So we're not doing poorly as economies. We somehow are not valuing labor the way we're valuing other parts of the economic model. Yeah. Is that a question or a statement? Well, I... So let me just pause for a second. And I know we're not giving introductions, but I make my living in the second oldest industry on earth, the hospitality space. And so my first... We're yet to actually have somebody on a panel who makes their living on the first. First. Yeah. The oldest industry. Maybe one day. And so my perspective might be... But the room would be full. My perspective might be a little bit different. But if you look over the... And as a consequence, I'm probably more optimistic and probably more comfortable with the word promise. Now, part of that is also a global perspective as opposed to simply a developed world perspective. So if you look at over a couple hundred years, the amount of labor that was expended in feeding ourselves and taking care of ourselves to what is spent today, labor, to feed the planet, it is a tiny fraction of what we used to spend. And we still have labor, but that labor is engaged in other pursuits. It has been to the benefit of the hotel business, which is a much bigger business today than it was 200 years ago, because people have the resources to travel, whether it's in the developed world or the developing world. You look now at the more recent last 20 or 30 years, you've had technology which has increasingly impacted the way we do business in the West. But as it has impacted the way we do business in the West, it's created tremendous jobs in the developed world. And it's created a huge new middle class of hundreds of millions of people that are traveling for the first time. And those jobs are transforming lives. So I think we can say, yes, there is a threat to labor, but when you look at what's happened over the last decades, there has been an enormous promise of labor from a global perspective. What happens next? We're gonna have more wealth and we probably have the need to spend even less labor doing things which are routinizable, able to be made routine, right? And so what will we... It's interesting in your industry because hospitality, if you studied hospitality, if you went to the best schools for hospitality, it was about a personal touch. It was about the things that happened. And yet even in your industry, it's a remarkable amount of automation. Yes, but much less, I think, than in some other spaces. So you might, within a few years, many of us might open our guest room door with our phone and not have to get a key at the front desk. We're all experimenting with that right now. We've gotta make sure we get it right. It's inconvenient to open the wrong door which has happened in some of the tests. But that is relatively few people. You're still talking about a human experience. People come to the restaurants. People come to the bars. People go to the fitness center. This guy's got a guy pouring beer so I'm just wondering what can happen. And they wanna be together and they will be together. So most of those jobs will still exist. We will, though, as a society, I think, find that technology is freeing us up to invest labor in things that 200 years ago we didn't anticipate we'd be doing today. And we can anticipate, necessarily, what we'll be doing 30 years from now. But we will find value in it. Now, it might be back to the future in the sense that we'll have more time and so we'll go back and spend more time in growing our own vegetables, right? Or engaging in activity that we've had the wealth to free us from over the last, certainly, 100 years. And so, again, I think there will be jobs created. I think the policy is important. We do need to get minimum wage, right? We need to do some other things. We gotta be thoughtful. If you go back to your Foxcom example and you say overnight you've just doubled the cost of labor, there will be more economic incentives to replace labor with machine. And again, you gotta get that balance right. To me, that doesn't mean you don't start to move on these things, but you move in a way that is as smart as you possibly can. So you're actually building on incomes but not costing jobs. Andrew, we talk a lot about your book and you're an Eric's book and there are a lot of interesting things there's a lot that we've said here that I'd like you to comment on. But something I read earlier about how technology is destroying jobs, an article from 2013 by David Rotman, where he's, I think he's talking about yours and or Eric's work about technology, destroying jobs, then productivity increasing and creating jobs. And there's been this cycle that since the year 2000 seems to have stopped. So we continue to be more productive as an economy. We continue to grow to the earlier point corporate profitability continues to increase, yet we don't see substantial labor increases. Or if we do, as we've seen in the last few years, job increases, we don't see wage increases at the same time. There are some who say this is just cyclical. It'll be okay. But I think your argument is that it's different. Those people are wrong. But I want to start off by restoring- Don't hedge. He's not hedging. Can I make a more unequivocal statement? I want to start off by restoring, Ali, your hope about this panel because the title of it is absolutely correct. I couldn't agree more with what you just said. If you took all the people in the world and you lined them up from poorest to richest and then you looked at how much their incomes and their livelihoods have improved over the last generation, what you would notice is amazing increases for almost everybody all across that spectrum. And that picture would have one big divot in it where the people saw their incomes and their livelihoods decrease over the past 20 years. And that divot would be right in the middle class of the rich world. So Switzerland, America, Germany, UK, the classic middle class of those countries are where we have seen the jobs going away and the incomes really under threat. Now, why is that? My simple technologist explanation for it is there are a couple factors. One of them is technology. And my explanation for it is we have technologies that are really, really good at doing routine work now, both routine physical work and routine knowledge work. And those technologies are rapidly getting better at work that we used to think of as a little bit less routine at recognizing patterns, at understanding human speech and responding to it. Now, my point is that those routine jobs, jobs doing that kind of routine work, they are not coming back. Strengthening the labor movement will not bring them back. Raising the minimum wage will not bring them back. A bit more controversially, entrepreneurship will not bring them back because the companies that entrepreneurs are starting up today are not employing people to do routine knowledge processing work. That work is better off automated. Unfortunately, our educational system, again, in most of the rich countries in the world, is doing a really good job of turning out people who are qualified to do routine knowledge work. We need to change all this. So the policy prescription that I want to put forward is increasing entrepreneurship, facilitating innovation, doubling down on the research behind these technologies, not walking away from them or trying to throttle them, and then encouraging all kinds of experimentation to see what kinds of new jobs, what kinds of new employment opportunities can be created.  And if we're unhappy with some of the outcomes of that, then I want to underscore something Laura said, let's think hard about tax and transfer systems that can help out workers who are seeing their livelihoods slip away from them via things like a negative income tax. Okay, I want to go back to Laura then because you have been in government and you've been in academia, so you understand the policy discussions. Now I'm covering the US presidential election, I'm in every one of these debates. And I have to tell you that we're not having this level of sophisticated discussion about those kinds of policy prescriptives. This stuff's a little complicated. We're having other discussions that are remarkably entertaining, but not these. What should the public start to expect of policy makers just to show that they even understand it? I don't think anybody's saying there's one prescriptive here, but we've talked about tax implications, we've talked about wages, we've talked about guaranteed wages. Talk to me about some of these things, how they might work and why we should think about them or discount them. Okay, I agree with you on the general tenor of the political discussion in the United States but I will say that these issues are being touched upon. So for example, in the area say of should community college be free? And what should community college be about? And how can we link the community college training to the relevant needs of both the individual getting the training and potentially their employer or their entrepreneurial venture? Or what can be the role of tax and transfer policy in encouraging the development of accelerators? There's a lot of really interesting work going on politically in the United States at the level of state and local governments doing this. So while it might be not front and center of the political national presidential debate right now, it is being handled in those kinds of ways. And I will say another area where we're beginning to see policy considerations discussion is what should we do with if it's true that we're gonna have an increasing number of people who are not in regular employment relationships with an employer but they are independent workers, quote unquote, entrepreneurs and independent workers? What should society, what should be the mechanism for providing benefits for such workers? What is it we say about healthcare and social security or pension coverage? In most of our, certainly in the developed world and in some of the developing world as well, the traditional mechanisms for delivering those kinds of benefits have been through employer contracts. Well if employer contracts are a reducing share of the workforce, we have to start to think now. And one of the things that has gotten me and very involved in this conversation is Andrew makes a case and that book makes a case and everything I've seen since then. The technology is accelerating at rates which even the technologists are surprised at. So the extent of things that can be automated right now and can be automated a year from now, no one predicted this, okay? The policy discussions around the world even when there is a discussion of community college or minimum wage or benefits for the sharing economy is not grasping the potential magnitude of the change. And I wanna say my last point here just on something you raised. The big debate amount technology optimists and pessimists is actually about the fact that historically speaking the big revolution everybody refers to is the industrial revolution, the industrial revolution enhanced productivity, that created the demand for all new goods and services, that created more employment. The thing about the industrial revolution was for reasons of policy and technology and people like Henry Ford, the benefits, the income generated by the revolution was widely shared. That led to growing demand of the middle class of those who are the major engines of consumption and that led to the demand for new goods and services that employed people. Well, here's the problem. One of the things we can see it's most developed, most clear in the developed countries is that the productivity gains are not showing up in the incomes of the average or median workers. They're showing up in the profits. They're showing up in that very small sliver of the population whose talents are enhanced by the technology not substituted for by the technology. Well, if this is the case, this is a huge problem because as the technology takes out jobs, it takes out income. Where does the demand for future goods and services come from if the income of the consuming class is not growing commensurate with productivity but actually significantly slower than productivity? I wanna remind all of you out there to use the hashtag new jobs when you tweet us. I'm getting some good tweets in here, but if you use the hashtag new jobs, I'll be able to get your tweets and put them out there for you. Can I jump in? Absolutely. Laura's identified some really important issues. There's an even deeper issue going on with the dynamics that she talks about. At least in the States, access to really good education and all the benefits that come with that is increasingly becoming the preserve of the upper middle class and above. You have the top 20, 25% of the population. When you look at the high tech entrepreneurs, they come disproportionately from exactly that part of the population. Is it because nobody from 75% on down has a good business idea? That's ludicrous, of course that's not the case. One of the reasons we should all be concerned about this is we are turning our backs on ridiculously large amounts of human capital if we are either benignly or deliberately excluding them from the process of gaining skills, getting into a position where they can go out there and launch themselves and come up with things that make all of our worlds better. In the end, and let me ask you, Michelle, because you experienced this, we're all talking about in Western developed countries and in the United States, some of the examples we're using about the disconnect. If we can't share the value that's created, the new capital that is created, there's no dispute anywhere in the world that such new capital has been created by innovation, such as the world has never seen. But if we can't do this in a way that lets it cycle down to the consumer, we're gonna have problems. Your wage gap, your income gap in India is substantially bigger than it is in the United States. How do companies like yours, and I know you do a lot of your work in the United States, but how do companies like yours, major Indian companies that led to the employment and the increase in incomes for working Indians deal with the idea that you might be putting them back out on the street now? No, we are not. I think that the progress of technology does create much more opportunity. And we can look back to the Industrial Revolution and we continually see this. We are, I think, particularly threatened this time around because we see that more and more of the cognitive or the knowledge-oriented jobs start to go away. But in reality, I think, as Arne mentioned, if you look back on the opportunities that are created, I mean, the profits that we are talking about, yes, it is true that profits are very high, but those same profits are as ripe for disruption by disruptive new ideas as they have ever been. In fact, the opportunity over the weekend in India, there was a big startup event. People, entrepreneurs who have unprecedented access far better than ever before to new ideas and new economics and new opportunities that could displace those very profits that are at all times high. So I think the root of the issue is can we educate people for the way the world is going to be? I think it was Arne who mentioned that we used to have a society in the US, for example, or anywhere in the world that was dominated by agriculture. This is not the case anymore. I think in the US 100 years ago, 97% of the population was in one way or the other involved in farming, and now it is 3%. So do we prepare people for the way the world is going to be or do we prepare them for the way the world used to be? I think that is the fundamental question. If we create a culture of makers, we create a culture of people who understand software authoring as basic a skill as writing, we will not have a lot of these issues. That is my Arne belief, because somebody will invent the marketplaces that bring these jobs together. Somebody will invent the opportunities that connect people because there is disruption to be had there. So the root of the issue is can the large numbers of people who are susceptible to these issues, can they be trained for the way the world is going to be, not the way the world used to be? Arne, let me ask you, how has the employment situation, given your growth at Marriott, how has it been affected? Per whatever measurement you wish to use, are you employing more or fewer people for your revenue units or however you want to? I know in the hotel industry you have different ways of measuring it. Yeah, and again, the experience at a luxury Ritz-Carlton hotel compared to an economy fair field in, for example, would be very different in terms of the staffian model, but here too, so I think somebody, Ellie, you talked about compared to 2000 or over what period of time we've seen that percentage of revenues go to compensation. Since 2000, we have, I'm guessing here a little bit because I don't know every year's comparison off the top of my head, but I suspect we have added 200,000 jobs in hotels that have been added to our system around the world. It may be that the percentage of revenues going to compensation hasn't changed, or maybe it's even declined a little bit, I don't know, I'd have to go back and check that, but there are lots of jobs that have been created because you've got new hotels. Because we've got new hotels, but that's because of a growing economy, that's because of a growing global middle class, that's because of a consumption. But you're certainly not employing more people to do the same work. That's not, only anybody here is doing it. No, so if you look at the hotel we had in 2000 compared to the hotel we had today, it'll be probably 5% more efficient. Nor should we ever ask Arnie and his peers to take one for the team by employing people that they don't need. That's just not going to work. No, no, no, that's all right. So let me ask you this then. When you talk about things like minimum income or guaranteed income, that in the climates that we're in, in the western governments and western economies, doesn't go over all that well, doesn't go over all that easily. We are seeing in the streets of Europe for the last few years, in the streets of America, we are seeing protests about people not being able to earn a wage, where you do a job that used to be able to live on and you now can't do that. Where do you, how do you get there from here? When if Andrew's right that it's not up to Arnie or Vishal to take one for the team and do the right thing. Does it fall to government to come up with policies and what do they have to do? Propose policies and get elected and do this? At the end of the day, I think you have to think about the perspective and it might change over time. The policy perspective might change over time. First of all, I would say that I think the 2000s are not a very representative decade. I actually think that macro conditions have been very important to the, we inherited a really bad situation to your point, Guy. And it may be that growth is going to be slower going forward than it was in the 25 years up to say 2007. So we are all grappling with a slower growth world. That's a macro phenomenon. So then you could sort of talk about what policymakers might do about that. You could, on the issue of minimum wage, I think it varies from country to country in place to place. I mean the minimum wage in real terms in the United States is where it was in 19, I don't know, 1979 I think, way, way back in time. And we've had minimum wage really does affect. The evidence is very clear here. The standard of living, not just of low wage workers, but it really feeds into the middle. I mean it absolutely does. So different societies can, this is about empowering through policy a change in the sharing of the returns to productivity. That's what it's about. And a lot of minimum wage jobs, in fact, most of them are not in the tradeable good sector anyway. They're in the hotel sector. They're in the hospitality sector. They're in the retail sector. So you're not gonna have the competitive loss through trade. You may get some competitive loss through technology, which I think your point is, you gotta balance this, okay? The issue of some kind of basic income is I think we have to look at this as it evolves. We really have to look and see to what extent are the pessimists about technology right? That is the technology creates great wealth. It's not evenly distributed. It leads to eroding income across most of the skill distribution. It leads to a social outcome which people are uncomfortable with. It leads to a slow down in consumption spending. If that's all right, if that is the correct, that turns out to be the case. Then I think there's a very strong case for a negative income tax or basic income. If the technology optimists are right and we can all be more creative and more entrepreneurial and find out all kinds of new ways to make our lives in a high quality job. See, we keep talking about jobs here in terms of numbers, but quality matters. Income of the job matters. It matters. So if it turns out that we can, through education and retraining and a whole bunch of other things, create real opportunities for people with the technology, we won't need that. But I think we need to start having the, and we're having it here, we need politicians, political leaders to have this conversation because it's evolving very fast right around us. I wanna say one other thing. I think the issue for the developing countries, we need to pay attention to what Andrew pointed out. Essentially, this technology so far has been a big win for the developing countries. But there are people now who are really starting to worry about that because, and the Foxconn thing is just a high level example of that. Most of the success in the world at reducing poverty and bringing people into the middle class has been a China traditional industrialization using people and capital investment as the model. If that's not gonna be the model in the future, that path of development is essentially closing off. So what is the path for development? And here I'm a little concerned that the notion that we're gonna have creative entrepreneurs around the world in very low income societies figure out a way to generate the kinds of income growth which China was able to generate with its model. I just, I don't see it. So I worry about this from the emerging market economies as well. Laura, it's right, isn't it that the way the countries that got rich in the 20th century, the way they got rich was by going through this phase of industrialization. Exactly, that's what they did. And China was the last one to do it amazingly rapidly and successfully and it brought along a lot of the world with it through its trade channels. I wanna go to Guy in just one second. I just wanna remind our viewers, you can see that we're populating these tweets very well. I'm gonna get to them. Please use hashtag new jobs. The other thing, for those of you interested in this conversation, I know sometimes when you arrive at a conference it's hard to read an entire book but Klaus Schwab's book on the fourth industrial revolution, chapter three in particular is about the implications on employment. It's very well thought out. There's a lot of studies that are cited in here. So it gives you a good primer on what the topic is and why this is interesting. I studied this a lot, not as well as the people on the panel but I do find that it's worthwhile. It's an easy and good read to get an understanding of what's going on. All of what Laura and Andrew and all of us have been talking about speaks directly to the work you do with respect to labor. It's fair to say other products are available. Yes, yes, absolutely. There's lots of stuff to read. You can catch all our products. Just to sort of step back, looking over our shoulders, I would take a much less sanguine view of the record of the last 20 or 30 years. Now, this narrative that we've done pretty well, haven't we, for labor and jobs? No, we haven't. I mean, really, we have not. We've seen a step back from political commitment to full employment. We've seen the growth of very high levels of unemployment and low quality employment. And we've seen something which has only been alluded to indirectly, extraordinary growth of inequality, hardening into exclusion. If these access to education, the people are really moving and shifting come from the higher echelons of the social ladder. That's no accident. This is a reflection of growing inequality and hardening of exclusion. I think we should not be too sanguine about where we've come from and where we're going. If the coming technological sort of unfurling revolution is going to accentuate those processes, I think we should all be very worried indeed or more appropriately reinforced in our determination to manage this process. I think one of the very good quotes from Professor Schwab in that book is that it's not a question, a binary question of standing in the road of technological from a neo-Luddite sort of position or just accepting it unconditionally and waving it through. We have to manage this stuff. And that's where labor market institutions very much matter. I do, as I said at the beginning that's been alluded to by others, have that concern that in the new way that work could be mediated by technology, some of those existing institutions are not gonna work so well as they used to do. If that employment relationship becomes less and less the generalized form, how do you deliver social protection? How do we deliver healthcare? Big questions, which I don't think that we've quite really got to at this point. The minimum wage issue, the minimum wage issue has become big very quickly. It's not yesterday's news, it's today's news. The country in which we are sitting is one of the exceptions. It doesn't have a national minimum wage. Germany has one as of a year ago, right next door. Look at Southeast Asia. The big issues are around the level at which the minimum wage is set. And this reflects the reality that we have for decades seen productivity, outstrip, wage growth, therefore the fall of labor share of income, therefore inequality, therefore inadequate demand, therefore all of the problems with which we're trying to grapple. And I think we have to see this debate against the reality of that context. Is the, and by the way, I wanna start taking questions from the audience. So do give me, raise your hand and we'll start here in the middle. There's a question here. And there's another mic right there, right beside where you are. Lady in the red. There you go. Okay. And do we have a mic in front here? Let's bring a mic right to the front. I'll save my question because we actually have a lot here and we've got a bunch on Twitter. So I will come back to my questions in a bit. Please, if you can stand up and just say who you are and ask a question. Thank you, Ali. I'm Carlos Represas from Mexico. It's been a most interesting discussion. When I look at the Western world that I mentioned, I conclude that, yes, there is a promise of progress in spite of some reservations that the guy has made, which I understand, but I see that. However, when I look at the biggest part of this world, I question about this promise of progress because it's not the problem of a minimum wage or it's not the problem of full employment. The reality in our emerging world is a tremendous poverty as a consequence of unemployment, under-employment and the marginalization of people and as a consequence, criminality and many other situations. And I do not see truly a promise there. And I would like to hear from the panel, could we be capable of giving some policy recommendations and Laura has been an expert on this, not having been an advisor, but the chief of advisors, President Clinton, I remember when we met, but what kind of policy recommendations can we make for creating truly a promise in our emerging world to give an opportunity? I know that there are structural reforms to be made. I know that we have to change the education system. Okay, but I think that there are so many recipes and so many people talking about it that we don't have a good coordination among the leaders to really pinpoint and say these are the key elements. So I think we don't even have these types of coordinations nationally. The idea, it's a big idea, it's a good idea, but I think we're, the one thing about this Andrew is it's fairly universal. We're talking to a lot of people around the world and they're all facing the same issue. But let's look at what's been happening without super high level coordination. Maybe it's been happening because we don't have super high level coordination. You mentioned the plight of some of the poorest people in the world, even as the world's population has increased a lot in the past 20 or 30 years, the number of people living in dire poverty has been decreasing more quickly than ever before in human history. Child mortality around the world is decreasing more quickly than ever before in human history and by most measures, violence, interpersonal and state level violence is actually on the way down. So when I look at the bottom of the pyramid, that's actually when I walk away, most optimistic, your question is what kinds of things can we do to accelerate that improvement at the base of the pyramid. The evidence that I've seen says that one of the reasons things are getting so much better so quickly for the people at the base of the pyramid is exactly because they now have access to technologies that used to be only available to some of the richest people in the world, even a generation or two ago. So my easiest and my clearest policy win is for heaven's sake, let's liberalize the telecommunication laws. Let's try to do everything you can to encourage that kind of technology diffusion down to the bottom. The evidence is really clear that it's helping a great deal very quickly. Right over there in the back. And if you, sir, can hand your mic over to this young lady in front. Jenny Gordon, Australian Productivity Commission. I'm really enjoying this conversation and it's very, very, very useful because we're worrying about these kinds of things too. Just something that nobody's mentioned, which is we're talking about education and upskilling people. But when we come around to this idea of sharing jobs, so if there's only so much work to go around, how does that actually get shared? And what we see in the world out there is smaller number of people working incredibly long hours and a lot of people working very few hours and the only reason household income is held up in Australia is because the women are now working, but both parties are much more likely to be working part-time than before. So there's two things. One is, how do we get that sharing of jobs out there if we educate people so they're capable of doing them? And then two, shouldn't we be focusing on educating people to make better use of their leisure time, to make really good productive, socially enhancing use of their leisure time? And why aren't we talking about that? Very good questions. Maybe I can take a short of it. The more that those asymmetries exist, the more there is an opportunity to bring those asymmetries down, to stabilize those asymmetries. I think that is exactly what technology does. If we take a longer view, if we take a deeper view on these things, I am absolutely convinced that the more access that people have to jobs and the more equipped they are to be able to work on those jobs, the more these imbalances will go away. I think to your question earlier, exactly as Andy said, connectivity in my friend Nicholas Negroponte talks about this, connectivity has to be viewed as a human right. We have to view it that way and enable access to people, enable connectivity to people and so that they are at least visible on the global grid of supply and demand. And then if we equip them with education and if we especially educate them in the right way about the way the world of the future is going to be, we equip them, all evidence indicates that every innovator that showed up out of nowhere and made something great happen did it based on a culture of making, based on a culture of experimentation, based on taking risks, adapting. Why don't we teach these skills at a massive scale? And I do believe that tens of millions, even hundreds of millions of people can be taught to be innovators. Innovation is not, we live in times where we view innovators and entrepreneurs as these mystical people, but in reality, the act of innovating is no more than identifying something that is not there. So why don't we teach people systematically to see what is not there that might be useful if it was brought into the world and so forth? So I am convinced that the longer-term solution is connectivity, it is access, it is education, and especially it is educating on entrepreneurship, on making, on creating things. And if those three things are in place, then these problems will go away. Of course, there is the matter of what Guy is talking about, what Laura is talking about. In between now and then, you have to put in systematic measures, structural policy measures to ensure that people aren't left behind and there is a cover for them. But that is something that we can come up with policies for, but that should not be the primary driver, that should be the exception. The primary driver should be the act of educating, of connecting, and of creating. Can I say while I agree with the, I completely agree with the issue of connectivity, I think this is really important. It's empowering people all over the world to trade. Another thing is access to your market, access to your suppliers, access to knowledge that you need to innovate, access to your suppliers. It's absolutely front and center.  So this notion that somehow the overwhelming majority of humanity that currently is not entrepreneurial will become entrepreneurial. Okay, I just don't see it. I don't see that people have the same extent of risk taking, the same extent of creativity, the same extent of wanting to design their life around innovation. Most people actually, historically speaking, have preferred being parts of communities or organizations which provide benefits and security and some sense of community. So I'm a little skeptical. I think we should do everything we can to educate entrepreneurs, but I honestly think at the end of the day, and this goes to your point about what do people do with their excess time. Andrew, you wrote about this. In the US we have this very dist... We have a little, we have a group. We can actually observe what's happened in the United States. High school dropouts, particularly men. What has happened to them over the last 35 years? Well, their health has gone to hell because they do not find meaningful productive ways. Depression is up with them. Depression is up. Suicides are up. Suicide is up. Mortality is up. Drug use is up. I mean, so it's not a simple solution. So here's the technology it might empower you, but to get from here to a meaningful life is I'm afraid for most people not going to be through an entrepreneurial venture. That is my guess. So I want to just jump in here and make a defense of jobs that we take for granted. And maybe this won't be surprising from somebody in the hotel business, but we talk about service jobs or we talk about hospitality jobs as if they don't bring with them dignity. They do, they can. And that's just wrong. And it sort of gets to your point, which is not everybody's gonna be Mark Zuckerberg or fill in the blank of a high-tech entrepreneur who makes a gazillion dollars and is on the cover of all the magazines. And if that's what we hold out as success, either you achieve that or your life somehow needs to be filled with plain golf, we're in a bizarre land. We have jobs that are in our business, but you think about society, we're gonna be taking care of our elderly in increasing numbers. Those should be human jobs. They should be filled with dignity. Part of them might be in the gig economy where people are not working full-time. How do you construct policies that allow each of those hours to contribute a fair share to social security? One of our problems is taking care of the elderly, which is a growth area is not a latter job, though. They're noble jobs, though. They're highly noble and they don't pay much. But that's where you get some policy around it. That's an addressable problem. Hopefully you get employers who, I think one of the advantages of our company, we have a very long-term focus, and so we say, let's build careers for people. Some people will grow into fancier jobs. Some will just get years in the same job. But how do you make sure that they're investing in their retirement plans? How do you make sure they're getting their healthcare? How do you make sure they're getting financial advice so that they use their resources? And in some parts of the world, those jobs are extraordinarily transformative gifts. Great jobs, yeah. Because it's a regular job. It goes back to this employment workplace. And in that place, there's so much dignity associated with the job. But too often in the global debate, we denigrate those jobs in a way that then puts us in a position where we're trying to rationalize them and apologize for them and build policies which essentially say we're adding something because they're failures and they're not.  I wanna address directly the question. I would suggest that when we're done here in Switzerland, hop on a plane or a train and go to Holland because there's a really interesting phenomenon taking place there. I think somewhere around half of working-age Dutch women work somewhere around half-time. And part of it is because they have a policy environment where your social safety net is not as directly tied to your full-time employment status as we would have in the United States, for example. But there's something else going on and I don't know what it is because even though in Holland, you have fairly easy access to some drugs, it appears these women are not doing what Laura just talked about with high school American male dropouts. They're not living lives of addicts and overdosing and things like that. I was just in Holland, it is a remarkably stable, prosperous, kind of a healthy society as you look at it. What do they have figured out? I don't know, it's a great question to start asking. That's a good one. And to Laura's point, I don't disagree with it. It is, but my point is that it is more a consequence, an effect of what we see in the world today. If you look at a child, any nine-year-old child is curious. It is, there are billions of curious nine-year-old child. And for some reason, during the course of the education, such as it is, we end up suppressing that curiosity and so then we see the effect that you see. None of us is old enough to remember the time when writing was one of these privileged skills and reading was one of these privileged skills. Now, everybody assumed to read and write and there were hundreds of years ago, but the act of printing something, you had to go to a church to get something printed and then Gutenberg came up with a printer and all of us were able to print. The common sense manifesto that Tom Penn wrote in the United States, that got to three-quarters of the population or something like that within 10 months. We talk about the adoption of Facebook today 230 years later. So my point is, we are similarly living in times today where the act of writing software, the act of creating a company is viewed as this extremely exotic showup on the cover of Time Magazine kind of an act, but that doesn't have to be the case. Each one of us can think of things that can solve problems that are around us and create a world that has that additional solution as a result of it and therefore get a meaningful, gainful employment, serve our creative purpose and so forth. It is not that we all become extraordinary entrepreneurs and so forth, but there is no doubt in my mind and design thinking, for example, is one of these techniques of teaching people how to be systematically creative. It is possible for all of us to find problems and to then apply our intellect and curiosity and solve those and as a result, find gainful employment, so. Can I say one thing, because I wanted to just add a policy issue that hasn't come up that, so one of the, one of the, I would say experiments or programs that I have found most interesting to watch over time that's had a large degree of success is the Goldman Sachs 10,000 Women's Program. I don't know if anybody's here, but it's really a good program. Now what they found there was, it's they identified something we haven't identified here, which is we're gonna train all these entrepreneurs and maybe the population is larger than I expect and we actually have to worry about access to capital. Okay, this is a whole other issue that we haven't mentioned because you might have a great idea, you might be a very creative 20-year-old, but just like access to high-quality education is pretty limited, it's really hard to get access to high-risk capital, really hard, so people have to rely on family and friends, they still do that in the United States, but when you're very poor, your family and friends can't really be the solution. So the 10,000 Women, the examples I've heard over and over again, I have a thriving business, I have three employees, I cannot buy a truck. I cannot buy a truck, which would allow me to triple my business and I'm very innovative in terms of how I deliver and how I make and I'm very efficient, I can't buy a truck. So just to add capital allocation, capital mechanisms that policy could introduce to help these entrepreneurs, it's not just human capital, but it's human capital plus. I wanna ask you, I wanna come to you Guy with a question and you're responsible, what we've been talking about, but I do wanna ask you a different question as well that's come in from the internet. I can't keep up, there's so many questions coming in. Gann for Youth has asked about youth unemployment. So Laura was just talking about this generation of American men not very well educated with increasing mortality and health problems because of their feeling of not being useful to society. We have an entire generation of youth in Western and Southern Europe and to a smaller degree in the United States, not well educated actually across the Arab world. It was the flame for the Arab Spring. Start in North Africa and the Middle East. This is a big problem. I mean, this is an entire generation of people who have not seen good employment. If you're under 25 years old, you're three times more likely to be without a job than elder workers. Very clearly, the, I would say, issue that we need to be thinking about. Can I just, though, just- Yes, please go ahead. I want you to pick up on what we said about work sharing, job sharing. No, the biggest promise of progress and technological progress over the years has been that the productivity and living standard increases it permitted and frees people from excessive working hours. We've gone from 60 to 50 to 40 and some have gone beneath. That sort of conveyor belt of progress seems to be broken today. Why does France get the reactions it does on the 35 hour a week? Has globalization and competitive pressure sort of put a stop to that conveyor belt of progress? First thing to think about. Second thing to think about. Technology, I think, is blurring the distinction and definition of what working hours are. There's the apocryphal story of, you know, the Taylorite, the Taylorist factory, written on the wall to workers, unplug your brain on entering the factory gate. You are absolutely disencouraged to think. We have the opposite problem today. You're not allowed to disconnect. Right. You're not allowed to disconnect because permanent connection becomes a requirement of the employer, you know, and the apparently utopian offer of take your holidays when you want as much as you want is either a utopia or a dystopia. But I think there's a really big set of issues around how you relate working time and truly free leisure time in that situation. The Netherlands, I can offer some insight into the Netherlands. Why is part-time work going down so well? Because the Dutch with the very strong labour market institutions and systems of dialogue that they have had since the 1980s have made sure that equal pro-rata treatment around part-time work is a reality. It's not involuntary. It's actually preferred by many in the labour market. So, no, they're not going on drugs. They're not going down the bar or going for a smoke at lunchtime. This is how they want to live. And it's been an extraordinarily positive achievement. The other thing that's happening in the Netherlands right now is they've got unprecedentedly high levels of self-employment, independent workers, about which views are radically divided. The head of the Employers' Federation says, this is the best thing that's happened to the Dutch labour market for 50 years. The trade unions think it's time for a campaign for real jobs. Just shows you can put the same thing and come to a different conclusion. This needed to be a two and a half hour panel because so many new and interesting topics have come up. I'm sorry we didn't get to more of you, but I hope you've learned something from all of this to all of our panelists, Vishal, Guy, Laura, Arnie, and Andrew. Thank you so much for joining us. Thank you for your great insights. And thank you for your great questions. And to those of you out there watching this from wherever you are, thank you for participating in this great discussion. I hope you enjoy the rest of the 2016 World Economic Forum.


Do we become more introverted as we age? Our personality traits are thought to be inherited. While this is true to some extent, research states that our personalities evolve just like our brains. We don't tend to remain an extrovert or an introvert through our lifespan. According to Christopher Soto, a research psychologist and the director of the Colby Personality Lab, our personality is 50% innate and 50% learned from the environment. This is very true. I guess each of us can reflect this with our very own personality and can state how we would have changed over the course of time. Personally, I can relate to this fact quite well. I was an introvert as a young teenager, but I remember that my personality changed during my early 20s where I tried to be more social and behave like an extrovert. I guess this trait remained close to two decades, but with the progression of age I realized that I was going back to introversion and it kind of mentally affirmed me that it suited me best as a personality trait. Another research published in the Journal of Personality and Social Psychology that evaluated over 100,000 adults between the ages 21 to 60 based on the Big Five personality trait model found that though personalities are set by genetics, they do evolve with age. It was found that people tend to change on the five personality traits of conscientiousness, agreeableness, neuroticism, openness, and extroversion. As people age, they change in aspects like agreeableness during their 30s and continue to improve through their 60s.  Perhaps this can be due to change in the family roles and responsibilities, change in careers or jobs, busy schedules, and other environmental factors. Our personalities change with the environment in which we live, for good or bad. What we learn over the period of years through experience and introspection become encoded within us and cause our personalities to change. It has been observed that when individuals become more conscientious, agreeable, and responsible, and emotionally stable with the advancing of age, with their personalities improved, psychologists tagged this as maturity principle. People who are high on agreeableness, responsibility, and openness are low on anger, anxiety, and vulnerability. From personal experience, I can agree and relate well to the fact that as we age we become more introvert in nature. When we become more conscientious, agreeable, responsible, and emotionally stable, we tend to be more inward. We don't tend to see happiness only by being admits of a large circle of people, trying to do things to gain others' acceptance and or appreciation and try to be the center of attention. As introverts, we do interact with people but with more meaningful and purpose-driven intentions. We feel comfortable with small groups, yet know how to draw from inward strength and resources. We tend to become more introspective in nature and see less need for arguments, neuroticism, and needless extroversion. What do you think about this gradual shift in personality? Have you or someone you know changed from being an extrovert to an introvert? Let us know in the comments below. If you liked this video, be sure to check out our other social media for more content. And don't forget to subscribe. Thanks for watching.


Bonjour, je m'appelle Marie. Bonjour Marie. Je suis de CoinTelegram. Enchantée. So we are very happy to see you here. Let me ask a few questions. Sure. So please tell us how you got involved in the world of cryptocurrencies. My name is Jérôme Gaillet. I'm Jérôme Manager for VeChain Europe. And about six months ago, I met this fascinating guy in Paris that was travelling for business. And we discussed, we had a great discussion about how blockchain can change various industries, in particular luxury. And then I decided to jump ships. I used to be in finance and joined the team of VeChain. I'm a fan of luxury living. So please tell me, do you want to distract my lifestyle? No, I want to make it even better than what it is now. So how? So from a customer standpoint, what I want to bring to you is a very easy way to check whether a product you have in hand is genuine or not to fight against anti-contrafeet. But not only that. Once you have the product and you know it's not fake, then I want to allow the brand that you are interested in to share more, more fascinating stories of the product with you. So they can share where, how the product was designed, what is the ideas behind the product. But also can share who is the designer. They can share what is the fabric that is used, how the fabric managed to be created for this product. So then you have more, a better feel for what you have in hand. So could you give me an example? So let's say, let's choose some luxury goods and then how it works, like the whole process. Could you describe the whole process? Sure. So as a customer you would walk into a store, you see a very nice bag that you're interested in. So let's say bag. Let's say leather bag. Leather bag, alright. I like it. You've dreamed about it for a long time and then you found it here. So first of all you want to make sure, you want to make sure this is a real bag. So you take your cell phone, you start either the V-Chain application or the luxury brand application and you scan the bag. The application will tell you automatically, yes, it's real or not real. Absolutely, as easy as this. And then the application will maybe display a video of the designer, an interview of the designer explaining why she designed the bag like this. You can maybe choose to know the bag you like is a limited edition. So you want to know maybe, why is it a limited edition? What is the anecdote behind the bag that created the limited edition? Legend and story behind. Absolutely. And then after that you say, okay, I can't resist anymore. And you just buy the bag. After you buy, you ask at the desk that they transfer ownership to you. So they show you a QR code, for instance, on their phone. You flash it with the app. And now for everybody in the blockchain, they know that this bag, which is the number six of the limited edition, only 100 pieces in the world, then we know this bag belongs to Maria. That's how blockchain works. So for sure, it's mine. For sure, it's yours. Everybody knows. Let's say one day you cannot find it anymore. That's the end of the world. So you're going to go to the app and say, somebody stole my bag and you're going to write it in the blockchain.  But fortunately, the bag was not very far away.  Then you can scan the bag again and say, okay, I found it. He was only lost for a couple of hours. So all good. All this is recorded in the blockchain. But we know that everything is safe. So for our subscribers, what is an ICO that you wish existed, but it does not actually? Yeah, what I would like to see actually is in the field of loyalty programs, for instance. You know, what would be very interesting is to see an ICO that would provide some loyalty coins for a brand. So like instead of giving me miles that I can only spend on flights or maybe some goodies, then maybe some tokens that I can use to purchase a hotel room as well as maybe some entertainment, something a bit more integrated across industries like a global loyalty program that would be a coin. That's something that would be quite interesting to see.


Yes. Okay. Great. Thank you for Sylvia introduction. Today I'm going to talk about an ongoing project in my PhD thesis. And this is this work is about a pair wise competitive network of bacterial communities. To start with talk I want to give you some about the motivation of this work. So, as Martina pointed out, the micro we are living in a very diverse microbial world. And these microbes are virtually everywhere. And they perform important functions in their habitats. For instance, they are important decomposers in the in the ecosystems. They can also help degrades the wastewater from the human industrial processes. And of course they also produce fermented food that we like to eat like cheese or wine. And there has been great interest in trying to assemble synthetic microbial communities. Which can perform the functions that cannot be easily performed by the single strength for instance if there's a hard to degrade toxin or like lignin it's hard for a one strand to to fully degrade the complex that he needs a full community to do it. So, we as a human want to apply that into like using the ability of a microbial communities to perform particular function. But before a community can do a function it has to coexist first. So recent interest has been in predicting the coexistence of a of a species set of community from the pair wise competition. So, for instance, Friedman and Gore, they proposed the simple assembly rule. So basically they competed a, a set of eight bacterial isolates, but there are species, and then the competing all the pairs and trios. And what they found is that a trio can coexist only when all of its pairs coexist. So, for example, in this trio of ABC, it will degenerate into a pair of AB, because in the pair wise competition, a complete see. So, in this case, ABC will degenerate into AB. However, many of the works in such microbial community has have been with species that may not coexist together in the community, or the environments that we use to culture and and compete the species or measure the pair wise competition. They are the same as the environment where the community was first assembled. So it has been hard to see how these effects, the new structure that we observed. And a natural microbial community is usually highly diverse. So a soil of a grain of soil to harbor more than 1000 bacterial texas. And the number of pair wise combinations increase in quadratic. So it's really challenging to measure all the pair wise combinations if we are, we try to disentangle the pair wise network from these kind of highly diverse natural communities. If we are able to assemble a microbial community that with a decent number of species that we can manipulate and measure in the in the lab. In this community, the species survival is mostly different determined by the competitive in actions. And in principle, the network of their pair wise competition competition should tell us something about their coexistence. So, quite arguing here is that in imagining the species pool there could be many possible topology among species, and during ecological assembly of a community. Those network that fail to persist will be purged by the selective force imposed by the community assembly. And the other the rest of the network that can persist, or the network of consistent community will be favored any and there will be present in a final coexisting community. So, to test this idea, I'm taking advantage of the previous work in the central slab, which shows when we start from a very diverse species pool. So these species can be from leaves so from a soil basically very diverse, diverse sources of bacteria. When we grow these species in a simple environment in the lab. So it's basically has only sugar as the only carbon source. So at the end of 12 serial dilutions. So you can think about it as a periodic system know environment. And in the end, many of these communities have converged to a similar taxonomy composition. So these communities not only have the same families, at least they are in the family level. So these families not only have the same family composition, but they also have the similar proportions. So here I'm showing you the relative abundance of 13 representative communities that we have assembled in the glucose environments. And, of course, this pattern has been observed also in the other 96 glucose communities and the similar pattern also appears in the other kind of neutral environments. So what we we can take advantage here is that we are able to isolate the strands from these communities. So we know these communities coexist by themselves, and we can take apart the community and then isolate each strand. These strength will allow us to be in a good position to ask a question here, which is to stable communities that show similar taxonomy composition. Do they also show general network structure. So, they have been several ideas being put forward to what network structure we should expect to see in a coexisting community. So the first one is what I just mentioned from a simple assembly rule. So it's basically saying everybody should coexist with everybody in pairs. So, so that's the trio can coexist or higher diversity community can coexist. And the other idea is that intensive cycle should be expected. So even though the species would not coexist in a pair, they are able to coexist in the trio or higher, higher diversity communities. So to test these two ideas, I'm doing experimentally measuring the pair wise interactions. So take one pair from the first in the least community has a force strands and take one pair for example, we manipulated the initial frequencies of these pairs from 95% 5050 to 595. So we can grow these pairs in the environment, which is exactly the same as the community was assembled because it's a very simple just and like glucose medium. So after also serial passage. In the end, we, we played it's these final cultures on the solid alcohol plates and then because they're morphology at different we can count the colonies and determine the final relative frequency. So we have the relative frequency in the beginning of the experiment and after their competition. So, one example I'm showing is, I'm plotting the relative of frequency of a three different initial ratios in the beginning and in the end. So if each species can invade the other from rare. We need a lot these community, sorry, this pair is in a stable consistent relationship, then we draw a mutual link to connect these two species. On the other hand, if only one species can invade from rare, and the other cannot, then these pair is in a competitive exclusion relationship, then we draw a certain arrows to connect the winner toward the loser. So we can repeat it for all the possible pairs from the 13 communities and only pairs we've seen the community are measured. So, I think maybe before I jump into the result I can take a post here to make sure that people are fully understood with with the method. Does anybody have questions. Yeah, and I can I can proceed to guess. Cool. So, after we've done with this experiment, we can test the first hypothesis, which is a everybody courses with everybody and these hypotheses will give us a very intuitive expectation, which is all the pairs should be coexistence we shouldn't expect any exclusion happening in a community. Because remember these communities coexist by lens ups. Here I'm showing you the poor the result of all the 186 pairs I have from the 13 communities. So, what we found is quite contrary so the coexistence is rather a minority of the pairs, because only less than 40% of the pairs are coexistence, but more than 60% of the rest of land are mostly exclusion. So, these same slots, the simple as very rule may not apply in our community, or at least they may not fully explain why our community coexist. So to explain why they are very high, there is high fraction of exclusion links, we turn to the second hypothesis. So, the intensity should be expected. And we can measure intensity for a network, or for one community by this ratio of intensity intensity the number of intensity triads over the number of old child possible trades in a community network. And I'm plotting one intensity motif, for example, so this mock community has three only three species, and because he only has one possible trial so, and it's an intensity trials. So, this network has an intensity equals to one. And for the for all 13 communities I have. So, surprisingly, that we don't see any of the intensity found in the network. So, um, similar to the simple assembly rule the intensity also cannot fully explain why our community coexist. We have tested to high policies that have been put forward to explain why network, why, what they will structure should be present in the coexisting community. But they all fail to explain why they coexist. So, can we have a more comprehensive way to describe neural structure maybe we are missing some neural structure that is present but we didn't measure. So we use a idea of a network motif, which is basically a recurrent sub networks that's frequently observed in a large network. So these ideas has been the network motif idea has been used in a large number of different kind of biological networks. So this isn't a new idea. So, never motive has been used to describe networks such as full webs, or transcriptional networks or neural networks, and depending on the network types. The number of network motifs are different. And in our kind of competitive network, they are seven of them. So there are three motifs, one and seven, which are I just mentioned before. And there are the other five types of motifs, they are in the intermediates between motif one and seven. I explained these motifs in more detail when I show the data. But here I will just walk through how these motif analysis works. So, basically, we can compare the observed network to the randomized network to determine whether the observed motif is most statistically significant. So here is a mock community of 10 species, and all the pay-wise interactions is either pay-wise competitions, either exclusion or coexistence. So we go through each of the triads in this network. For instance, the red, I know it's hard to see but the red triads is the motif that has two, coexistence and one exclusion. Then we count this motif one, and we go to the next one, and we repeat it for all the triads and match them to each of the seven motifs. Then we come up, we can come up with the observation for this network. And in this case, motif one is not observed, and motif two is frequent, etc., etc. Then we randomize the network just by randomly swapping the pairs by like, we keep the number of coexistence and exclusion the same, but we swap in the links. Then we get a randomized network. Then we repeat counting the motif for this randomized network, and randomize the network again and then count motifs again and again and again for a thousand times. Then we can have a distribution of the motif counts from these random, these 1000 randomized networks. Then what we're doing here is that we can compare the observation to the randomized network. So, if the observed motif counts, for instance, motif one is very low, is very rare. So if he falls within the bottom 5% of the randomized network, and then it's considered that this motif is underrepresented. First, motif three is very frequently observed. If he falls within the top 5% of the randomized network, then it's considered a overrepresented motif. So, for these overrepresented motif we shaded by green and for underrepresented motifs we shaded by red. So we can repeat it for all the motifs. Then, before I go to the next slides, I would just want to emphasize that this is just one network. So we can repeat states for all the possible, all the 13 networks I have and counts the motifs. So in the next slide, what I'm going to show is a poor result of all 13 networks and counts the motif from the observation that also randomized networks. So, the first result that we can see is that the intensity motifs are underrepresented. So motif one, that forms an intensity loop, and this motif is never observed in our network. So it's consistent with our previous results. And of course it's also underrepresented compared to randomized network, which expect that we should see some of the intensity. And motif four is an interesting motif that is kind of half intensity, because it has a bbbc but ac coexist. And these motifs is also very rare in our networks. So the total number of motifs we have 288, and among then we have observed less fewer than five of these network motifs. And the third also kind of intensity, but it has two coexistence, and these networks also underrepresented compared to rendered. So some of the motif seven, the simple assembly rule, and it's more enriched than expectation by random. So it seems that at the Paris level, the simple assembly rule does not apply but here it seems that if you look at the network structure, then these motifs are, it's more frequent than we expect by random. So it seems that the simple assembly rules works to some extent to explain our coexistence, the current is coexistence. But this isn't the only enriched motif. So from the chart, take it now on this. So yes, you said this results are pulled over 13 networks. How reproducible are these observations slash statistics across the examples. In other words, what are the error bars on the plot on slide 18, as in how big are the error bars. Very for the clarity. So the first question. We. So across all the communities, there are some are really small, for instance, there's three species. So for last communities, if all the pairs are coexistence, then there's no way to randomize the network. And in the large communities, there are more ways to randomize the network. So, yes, there's a sample, the sampling has different like effects, depending on the community size. But in general, for those communities larger than five or six species, we do see the same pattern in those large communities. And for the second question. So the, the bar in these plots means that the range of the top five to the bottom 5%. So if a observation. Yeah, I understood that I understood what the bar here means I just meant to ask what are the, if you added error bars on this plot, which would be how irreducible they are across maybe your large network examples, how big would they be like in this, for example, in your motif seven, you know, it's observation is just slightly above that range, if I put air bars on the red dots. Yes, Martina is helping me there. Yeah, what are the air bars in the red dots. And I, I'm not sure if I can draw the error bar for observation because they just one count for the total. But this is a poor result so there. So it's not the mean of the of the old networks is a total count of all communities. So I don't think there's a way to draw a robot, but if it's a the mean of the total communities I seen these pattern should be consistent with at least among those large networks. Thank you, we can maybe discuss later as well. Yeah, yeah. Thanks. Thanks for the question. So, for the other kind of of motifs. Motif two, three and five they are all over represented. So these three motifs share a same. Common is which is they are all hierarchical, which means that a we see them what if they're hierarchies. So what if two is a pure transitive train. And what is three is that one species beats the other two, and the other two coexist, and what if five which is the AB coexist together, but like, each outcome piece the third species. So these three motifs are all transitive, and they are all over the sentence in our communities. So, I think from this part, I can, I think I have shown some data that support the idea that stable communities. Exhibit generic network, combating network, and which has the features of species post from these communities tend to be exclusionary land coexistence, at least in our simple environment. So the motifs, then interest in motifs are underrepresented, whereas the transitive motifs are over represented in a in a coexisting communities. So, with these data, we can ask a second question I think is very informative, which is whether these network topology, a property of community assembly, or is it more constrained and the emergent from the ecological So, in the interest of time I just want to highlight the scars scarcity of the two interesting motifs. So motif one is never observed and, and, and the motive for is also rare as well. And I think part of the explanation for these motif can be attributed to the species pool. So, if we consider a species pool, and we connect all the possible networks in the species pool, then we have this global network, which is determined by ecological interactions and ecological interactions affects the species survival which shapes and structure of this global global network, and somehow, if these global network has the motif distribution, different from the expectation by random. It's possible that the interest in motifs are also rare in the species pool. So, even though communities assembly, maybe favor interest in motifs, but they are so rare so it's unlikely that the community assembly could favor the enrichment of these motifs. So to test this idea, one way is to go through all the species pools and then try to map out the network in the species pool. And it has been so much work so I resorted to use a modeling approach. And I'm using this microbial consumer resource model, which is basically captures the microbes will behave. For instance, what the resource competition among microbes and what new chance that the micro prefer and and the metabolism that each microbes has. And then metabolism will determine what metabolic by products that the micro produces and release to environments. So we could include the cross the common cross feeding features of the microbial community into the model. And with this model. We could simulate simulates a experimental scenario, which is star from diverse species pool and then culture all the monoculture in the in the environments that we provide with them is a simple environment with only sugar. And these materials some of them will not be able to grow, because there's a minimum media. And for those that are able to grow, we can take a sample of three and compete all the pairs, exactly the same as what I did for experiments, then we can match a network for these communities, sorry for this network of three. We can repeat it for 1000 times to get a sense of what the species pool network would look like. Because, of course, is we cannot map out all the pair wise combinations in the species pool if a point so large, but we could easily get the data that is more rich than the experiment can do. I'm showing only motif ones and motive for for clarity. And as a control I also ran a top down assembly, which is how we assemble the communities tested in experiments. And it seems like the model can could capture the result of of those experiments quite well because motif one is never observed and motive for is also rare. And each dot here is a mean of a 10 communities that I tested and I repeated for 20 species for so there are 20 points here. And these top down assemble communities. Motifs interest, the two interesting motifs are underrepresented. Especially like regarding their absolute motive counts they're pretty rare. Similarly, the species poor also lack also lacks interest of teeth motifs. So motif one is never observed and motive for is also rare and there's no significant difference between the species poor and community in a motive for motive ones never observed. These kind of support the hypothesis that the interest tip motifs is already rare in the species poor. And, of course, we, I haven't exhaustively explore all the possible model parameter choice of the model. Guess, there will be too many of them, but by tuning some of the parameters we sense that these features is general. There's not contingents on the parameter choice. And this pattern appears as long as the new chance competition is considered in the model. So, the supposed idea that's ecological interactions, in particular new chance competition would limit the appearance occurrence of the intensity motifs. This is my final slides. So to stable communities assemble in simple environments so generic network structure, the answer is yes, and the structure is generic, at least in the simple communities that we considered. I guess I think, in the more complex community with all the other ecological mechanisms in play, I will be, I will expect that the structure will be different, and will be less predictable. It's an able topology of poverty of a community assembly or is it emerging from ecological interactions. I think part of the answer is, yes, and could be explained by ecological interactions, at least for the intensity that we have the lack of intensity we observed, and these could be mediated by by ecological interactions, and in particular resource competition. In fact, I would like to thanks for ICTP for inviting me to give this talk, and I also like to thank for thanks everyone in a central slab, especially Georgia Bayek who helped me with like experiments, and I also like to thanks for the funding fellowship from the Ministry of Education in Taiwan.


Hello everyone and welcome to a special episode of Code Emporium where we're going to talk about chat GPT. So chat GPT is a language model that takes in a user prompt and then generates a textual response and the responses that we have been seeing are like one of the most realistic until date. So how exactly is chat GPT doing so well and also how is it incorporating a sense of non-generating nonfactual statements as well as non-toxic statements too. In this video we're going to look at the technical details to see exactly how. To understand chat GPT we kind of need to understand some more fundamental concepts. So chat GPT is built on top of GPT as well as the entire paradigm of reinforcement learning and the GPT models themselves are essentially language models and they are also built on top of transformer neural networks. So let's take a look at each individually. So language models. Language models are models that have some inherent understanding of language in a mathematical sense and I say in a mathematical sense because they understand a probability distribution of a sequence of words. So given some context or words that have preceded it, these language models can determine what is the most appropriate word or word token to generate next. And depending on the type of data that is used to train this language model and also the architecture of the language model itself, we can get different types of probability distributions of these word sequences, which means that these language models will try to generate different kinds of words depending on these different circumstances. And because of this, we can actually generate language models to handle very specific tasks like question answering, text summarization as well as language translation among others. Now let's talk about transformer neural networks. So transformer neural networks are a sequence to sequence architecture that takes in a sequence and outputs another sequence. Sequences in this case can be a sequence of words for language. The transform architecture consists of two parts, an encoder and a decoder. And so in order to do this translation from English to French, it will take all the words of the English sentence simultaneously. It'll generate word vectors over here for every word simultaneously. These word vectors will then be passed into the decoder. And then the decoder will generate the French translation one word at a time. And every time that there's one word generated, it's going to be provided as context to the decoder itself. Now I've explained much more in detail how this training works in my video on transformer neural networks. So please do check it out for more information. Now what's really cool about this architecture is that we have now two components, an encoder and a decoder that do have some sort of contextual understanding of language and can actually be used as a base for language models. And so if we stack the encoders, we'll get a bidirectional encoder representation of transformers or BERT. And if we take the decoder parts and stack them together, we get a generative pre-trained transformer. These are popular language models, which are typically pre-trained on just general language data. And then they are fine-tuned by us depending on the task that we want to solve. ChatGPT is a GPT model that is fine-tuned to respond to a user's request. And then it is also fine-tuned further by using reinforcement learning. Reinforcement learning is a method of achieving some goal via rewards. I'm going to explain reinforcement learning in general by using a typical example and explaining these concepts here, but I'm also going to explain just after how these concepts relate to ChatGPT. So at first, we have an agent over here. And the goal is to make the agent go to this end state. And in order to entice this agent to make certain moves, we use rewards. Rewards are these scalar values that you see in these squares. A high reward is used to entice it towards the goal. And every other place has a lower reward so that we can make sure that the agent actually goes as fast as possible and taking as few steps as possible. The state here is going to be the representation of the current step. So for example, this agent is in a position 1-1, so that position is the state. Action is what action is taken by the agent. For example, left, right, up or down within the boundaries here. In order to get to the final goal. And the policy thus becomes the sequence or one sequence of actions that the agent will take in order to try to achieve the goal. So one such policy, for example, could be the sequence of actions down, down, right, right, right. And in this case, the reward given total reward would be 10-1-1-1 which is 6. Another policy could be the sequence of actions, right, down, right, up, right, down, down. And in this case, the total reward would be 10-6 which is 4. And now we have two policies, each with its own reward. And so we can tell which policy was better than which. Now relating this to chat GPT, the agent is the model itself. The reward depends on the response that's given by chat GPT. If the entire response is a good response, then it's going to get a high reward. If it's not a good response, it's going to get a negative reward. Now in order to talk about state, every single action taken by an agent is essentially a time step. Now in the context of chat GPT, a time step occurs when every single word or word token is generated. And so we can define a state as a combination of the user input prompt as well as every single word that has been generated until this point. And this can be used to infer what action we need to take, which is what word we should generate next, because this is inherently a language model. And so the overall policy would be the sequence of actions taken, which is what is the sequence of words that are generated. So different policies would entail different responses. And if we have multiple responses, each of these are policies, that means that each of them will have their own rewards. And we can then start comparing them to see which was the better response and which was a worse response. And then we would fine tune the model in order to help it generate these better responses. Now that we have like a holistic idea of some of these foundational pieces, how do they all come together with chat GPT? So I took a screenshot of this from the open AI blog, and let's actually walk through each of these. So the entire process can be divided into three major steps. In the first step, we take a GPT model that has been pre-trained on understanding language itself. And now we will fine tune it in order to take in a user prompt and actually generate a response according to that prompt. And we get the data with labors. So we'll have essentially a few labors that will write a prompt and also write a response to how they want to see that prompt answered. And because we have the input and the output, this becomes a supervised fine tuning of the GPT model, hence SFT. Next we now take this supervised fine tune model and we'll take a single prompt, pass it through the model, and we generate a few responses over here. And now the labeler is going to be ranking how well these responses are. So for each of the responses that are generated, the labeler will assign some reward. And this is going to be used to train another GPT model, which is called the rewards model. And because this is a model, it is a function which takes some input and generates some output. The input to this model is going to be an initial prompt as well as one of the responses. And the output is going to be the reward that quantifies how good was this response. And now in step three, we'll take an unseen prompt, pass it through a copy of the supervised fine train model. And then we are going to generate a response. And this response is passed through a rewards model to get some rank that quantifies how good was this response. And now this rank is actually going to be used to further fine tune our fine tuned model. And the way that it's done is that this rank is going to be used in the loss function of this model to back propagate some updates to the parameters. Now what's really interesting is that this process actually helps a model incorporate non-toxic behavior as well as also create factual responses. And this is because that's how the reward was generated itself. The responses which were non-toxic and also factual were given a higher reward. And so incorporating the reward into the model in this way is going to help the model generate responses that are less toxic and also more coherent and factual. And that's kind of the overview of this entire process of how Chad GPT works. So I'm going to end it here, but I'm also going to be making some more videos on this topic and as well as other topics in deep learning as well as machine learning too. So if you do like what you saw, please do give it a like, subscribe, and I will see you very soon. Bye bye.