Good evening. Good afternoon. I want to warm welcome to our panel. I think we're going to try to make a premise. It's going to be very difficult not to ask the question about the Eurozone, but I've been doing coverage for the last two days. And all we seem to talk about is a recession in the Eurozone. So I'm almost tempted to have everybody stand up and start shaking away and say we're going to talk about growth for the next hour. How's that? Why not? So can emerging markets deliver global growth? That's our premise this evening. It's a simple premise, but it should have a fairly straightforward answer. In 2011 we saw a substantial growth gap between, shall we say, the incumbents and the newcomers. The incumbents being the OECD industrialized countries producing growth of only 2% in 2011. And the emerging markets. The newcomers coming onto the market last year at 6.5% for 2011. Now the reality, if the projections are right, is that we're going to see the global economy go from $70 trillion as it is today in 2011-12 to some $308 trillion by the year 2030. The other interesting figure tucked away into that is that 68% of that growth should come from the emerging markets alone. Now the shocking figure in that sort of calculation is that 98% of the population growth will probably come from the emerging markets as well. So you see where the vibrancy should be coming between now and 2030. Long term, it's promising then. Short term, probably more challenging. In fact the World Bank last week came out with a report suggesting that the emerging markets should hope for the best in 2012 but prepare for the worst. They've lowered down the projections for the emerging markets. I'm a pretty solid believer that we've seen a lot of decoupling taking place in the last five years and certainly we've come a long ways from 2008 to 2012. The other kind of surprising news there between 2008 and 2011 when the West was contracting the global economy still grew $9 trillion and again two thirds of the growth came from the emerging markets. Half of that growth came from China alone. So we're talking about substantial tailwinds here pushing the global economy forward coming from the emerging markets. Very quickly on the format this afternoon we're going to have a two 15 minute block discussion making up the half hour special that's going to run Sunday on our new emerging markets program on CNN called the Global Exchange. It's going to run at 1700 Central European Time. We're going to have a short video report to introduce the first block which is going to be the global outlook and then the second block will introduce on the global risk for emerging markets in 2012. This is an entire debate, no opening comments, no speeches, no power points. The last 15 minutes will be coming from you from the floor so prepare your questions so we can have a very healthy debate at the very end. So to start our initial discussion let's take a look at the global outlook for 2012 for the emerging markets. We have a video report. If last year's financial turmoil shows us anything it is that the economic balance of power is shifting east. The world economy is more dependent on China's juggernaut economy than ever before. Emerging markets, the new drivers of growth, the one bright spot on an otherwise bleak horizon. In 2012 the shift east continues. Not that emerging markets have survived unscathed from the crisis that still engulfs the Euro area. Forecasts have been cut. Manufacturing is down. Fiscal policies will need to be tweaked. Standard chartered bank forecasts the world economy will grow 2.2% this year, still hamstrung by the Eurozone crisis for at least the first half of 2012. Emerging markets will be responsible for the bulk of that growth and the world's best hope to stave off a global recession. The developed world where growth will be anemic at best will cast envious eyes at the brick nations in 2012. According to the World Bank China's growth rate will continue to slow this year but still be over 8%. India is predicted to grow a healthy 6%. Russia and Brazil a solid 3.5%. But emerging markets are not immune to events in the west. An escalation in the Eurozone crisis in 2012 will spare no one and that includes the brick nations. But where once the emerging markets look to Europe and America to lead the way, this year the global economy will hope emerging markets can help fuel growth, stimulate spending and pull the world away from recession. Lee Dakoy is an economist from China and Martin Stroll of course is the chief executive officer of WPP. Gentlemen all welcome here. Let's start on the first question which I'd like to put forward to Mr. Bhavajan because I think it's very interesting that you sit and straddle between east and west. Do you think the tailwinds that you're facing right now from the Eurozone will slow down the economy as many are suggesting below the 4% that you're hoping to get for 2012? Turkish experience and the reform process has been quite a unique one. On one hand we are in the EU accession process trying to become a European Union member country but also we have many aspects of it developing and emerging market as well. What we have done at the beginning of the crisis was to go through a fiscal consolidation program and keep our banks strong and safe. So the two major areas of problems in Europe public debt and weak banks does not exist in Turkey. And that differentiated us and have very high growth rates for the last two years 9% in 2010 and 8% in 2011. But in order to make sure that this is a sustainable growth we started to do some tightening on the monetary policy side and also on the banking sector as well through some macro-prudential measures. Your question about how EU affects us is mainly two channels, the trade channel and the financing. 45% of our exports go to the European Union member countries and also we get a lot of FDI from EU member states. So when things go in a bad direction in the European Union we are affected through those two channels. But on the other hand the good part of the picture is that 55% of our exports goes elsewhere and those markets, the 55% non-European markets of our exports are now growing quite fast and that is playing a very good balancing role. Domestic consumption is also very strong because of high confidence of consumers and also high confidence of producers. So people are spending, companies are investing and banks are lending. But in order to make sure that this turns into an overheating problem or turns into an unsustainable currency deficit problem we have actually started to tighten things that's why our growth rate is going to be lower than the previous two years. Let me follow up quickly then. Are you confident you can produce better than 3.5% to 4% growth in 2012 after that stellar year better than 8% last year? A lot of things depends on what is going to happen in the European Union because we have projected a 4% growth for ourselves given a scenario that things will not go worse in the European Union. If things recover in the European Union if we get a better than expected outlook in the European Union then we might see high growth rates but if things go worse in the European Union then we might see growth rates blow 4%. So a lot depends on the European Union. How much of your GDP is X4? It is still 15% or so. 15%. So it's not a big percentage yet. Stephen, let me follow up if I can here. Just a suggestion, just a second please. Suggestion first is that we should hope for the best. Stephen is what the World Bank is saying but prepare for the worst. They ratcheted down the target for the developing countries down to 5.4% this month. Is that an overreaction to what we see with the growth coming from China, India, countries like Turkey, Southeast Asia, even Africa? Well, hope for the best, prepare for the worst. We've been doing that on Wall Street for a dozen years, it really hasn't helped. But I think in light of where we've been, John, over the last three or four years it would be absurd if you just pretended that the world was going to be fine. You definitely have to prepare for the worst. I think the best case, though, is much better than it was three years ago. Unless, of course, we have a very disorderly breakup of the EMU. We promised we wouldn't talk about that. But to me, that's the potential black swan event that we all have to think about. I don't think it's going to happen. But again, we can't pretend that an outcome like that would not occur. Global trade contracted about 12% in volume terms in 2009. That was a devastating blow to every country in the world, including my favorite economy in the world, China. It pushed China into the functional equivalent of a brief recession and evoked a very strong policy response. A big shock, this concept of decoupling is really irrelevant. I think the word should be stricken from the lexicon of macro and investment analysts. We need to look at, I think, a more meaningful construct, which is resilience. I think the emerging markets, the developing economy of the world has great resilience. But they're going to have to do things, change the mix of their economy, focusing much more on their internal markets rather than relying on these squishy external markets in Europe and in the United States. Luciano Coutinho of Brazil. Now you've had a case last year at this time growing about 7%. You saw that growth cut in half. What's interesting about Brazil is the unemployment rate hit a low of 4.7% in the last month. There's still job creation taking place, but the growth dropped substantially. Central banks started cutting interest rates, so you had to go into high gear here to resuscitate the growth. Well, that was largely on purpose because we had to decelerate the economy to cope with inflation pressure in the first half of last year. And now we are trying to expand again and markets are projecting 3.5% for this year, but I think we will do our best to accelerate investment and have a better number. 4.5 would be. But let me compliment what Stephen was saying that it's not just resilience, but it is a fact that if we have a moving average in the last five, six years, emerging economies show a growth differential of 3.5 to 4 percentage points to developed economies. And this is a long-term trend. I mean, you might see some short-term coupling, short-term variation go together, but the growth differential is there and the growth differential is there because we have in almost all emerging economies very attractive investment frontiers that provide high profits. Just, for instance, take the energy sector, take the housing, take the basic manufacturing and also very dynamic domestic markets that are able to create jobs, create demand and then the concept of a certain degree of autonomy to grow. And this is a changing paradigm. I think this could be dated back to 2006-2007 onwards. So this is a... To boost domestic demand. Let me turn to Martin Sorrell here. You're a very firm believer of the BRIC concept and you've expanded that into what Jim O'Neill likes to call Goldman Sachs, the next 11, the next wave of fast-growing economies. And what he calls missed as well. This year we have friends from Indonesia, South Korea and Turkey, of course, Mr. Babachan as well. So this is where you've taken WBP? We're betting the ranch. I mean, it's not... The CEO shouldn't say I bet and he shouldn't say anything about ranches, I guess, but I'm betting the ranch on it. And I just want to comment on your opening film a bit. This concept of moving to the east, I find slightly objectionable. Forgive the comparison. It's an Americanism. You can guarantee this will be added out of the program. Yeah, it will. Well, it doesn't go to New York anyway. So I think I'm risk-free. But if New York is the centre of the world, which I think generally we think it is, it's not just the shift to the east, to China and India. It's the shift to the south. This is the decade of Latin America. We'll have the World Cup in the Olympics in 2014, 2016. That will define Brazil. It will define Latin America just as Beijing and China was defined by the Olympics and South Africa was defined by the World Cup. It's a move to the southeast, and it all knows very well as he takes Airtel and by Zayn and Brie Brand's Airtel, a Zayn Airtel in South Africa. It's a move to Africa in the Middle East too. So that's number one. Number two, Turkey. If Turkey grows at four, we will grow at ten this year. In fact, our budgets, media, advertising, companies are under-branded in these markets. So they grow at double the rate of GMP, certainly as Stephen knows, in early stages of development. The decoupling word. I just want to pick up on the decoupling word. Stephen, Zou Min made a very interesting point at the HSBC breakfast. He said, financially, we're more coupled than we ever have been. I think Mr. Coutinho will remember this comment. From a trade point of view, we're less and less coupled. The South-to-South trade, for example, in Brazil, before President Lula took power, I think America accounted for about 30% of Brazilian exports. Today, it accounts for 12. Who is Brazil's biggest trade partner? China, followed by India. South Africa. Who is China's biggest trade partner? It's Europe and the United States. It's all segmental world. The struggle in China is to move them, as you well know, from export to consumption. Twelve five-year plans, Stephen, as you well know, lowers the growth rate to seven. Please God, the UK would have seven and Western Europe would have seven. Lowers it to seven. They consistently underestimate their performance in their five-year plans. They move to healthcare safety net, because that's why people save instead of spending. My hope is that these markets, certainly from a WP point of view, continue to grow and develop. I think they will, because I think we haven't managed to achieve a degree of decoupling and economic management, and the breaking event was Lehman in 2008. Prior to Lehman, people in Brazil, people in India, people in China looked to the West for leadership, very financially, because we really messed up royally in 2008. I think they now feel they have little defending to learn, and they are less dependent on us and more independent in their thinking, and that's to their benefit. Thanks very much. It's not by accident we have our Chinese colleague and Indian colleague sitting next to each other. The two pillars of the emerging market forces at play in this generation of growth that we talked about. Anyway, can China continue to manage to produce the magic number of 8% in the economy and still kind of bring some cohesiveness to the country? We know the vibrant East Coast and the western half of the country very, very different places. Well, I think speed is not the key issue in China. Most people in China agree that the economy has entered a stage where the potential growth rate of GDP is rather high. My estimate is close to 9%. The problem is structural. The problem is sustainability. Whether structural problems today can be resolved, can be reformed, can be dealt with in order for the economy to continue growing in the coming decade. That's the problem. So for this year, 2012, our forecast Center for China in the World economy is 8.5%, coming down from last year's 9.2%, and next year, most likely between 8% to 8.5%. Why is that? The economy is slowing down in order to, the reason is in order to repair. It's like a car. There's no way to stop the engine. Stop the car to replace the engine. Because the economy has to go out. Meanwhile, the car slows down in order to twist, in order to repair some of the problems of the engine. So that's the issue in China. So today in China, the key policy debates are about whether fundamental reforms can be or will be pushed ahead. That's the key issue. Do we have a break here for the emerging markets? We have that inflationary pressure, the food price rise pressure in 2010, 2011. That's come off. Does that give India, Sunil, some breathing room here to lower interest rates? In the position to try to boost growth, I know the Bolligarks as the CEOs of India, the leading CEOs are called there, were moaning because growth dropped below 7%. And as Martin suggested, that's a pretty good number. But how do we get beyond that number, beyond 2012? Well, clearly inflation has been a cause of worry for the policy makers in India. And we have seen over a dozen rate of interest increases in the last one year. And that has taken away some momentum from capacity building. Now, there are many economists here and I take an entrepreneurial view. If the capacity is not created, you impact the supply a lot. Whatever you may do to manage the demand by raising the interest rates, you're never going to get the balance. The good news is in the last few quarters, in particular last month, food inflation has tapered off. Now, that has given some room to the policy makers to lower the interest rates. Last week we saw a cut of CRR by half a percent. But will that follow through with the reduction in interest rates? I think the industry remains very hopeful. Well, but wait. I mean, you've got a currency problem that really constrains your central bank from being aggressive. India is the only economy in Asia with a current account deficit. So with a weak currency, current account deficit, and you've had one good month on inflation, I think the discretion to ease on monetary policy is pretty limited in India, especially compared to China. But I think it's been tightened a lot, as Stephen said in the last several months. And I think there is a scope for loosening that to some extent. At the end of the day, will two or three percent make a huge difference to investment? On the bottom line, in balance sheets, not very much. But on the sentiment, it's a lot. Because people then start conserving cash, they start worrying about the future. And I think the government needs to, therefore, give a direction and a signal that we will balance inflation and growth. And that is what the industry is looking for. Well, you bring up an excellent point. There's been a lot of complaints internationally about the mixed signals we've been getting from the government of Mamohan Singh. Multi-brands are going to go into the retail market and they retrenched and went with single brands that could go into the retail market there. A lot of infighting amongst the cabinets here. How do they tighten up the ship and kind of focus on what's important here to drive growth this year? Well, I think there's no differences amongst the cabinet members. The problem is the government did finally take the plunge on some of the key reform areas which had been stalled for a couple of years now. FDI is on hold. It's not been reversed, but there was a furor in the parliament, as you know, by some of the leading opposition parties and the prime minister had to, and the cabinet had to put this on hold. Part of that piece has gone through. 100% FDI and single-brand retail has gone through. The 51% FDI in multi-brand retail is on a pause and going through a consultation mode. We are very hopeful that in the next few months we should see that going through as well. Then there is the company's bill, the banking reform, the pension reforms, all are on the anvil. But India has a very difficult political situation by way of a coalition government. Prime minister is committed, the cabinet is committed, but I think they need the political resolution to many of these things. Final comment here for this first segment. I find these conversations particularly observations by Western observers somewhat silly. If you look at the momentous economic change that China and India, for example, and Brazil in particular as well, have gone through in the last few years, I think we're being a little bit picky to pick out a few months or a year. There will be cyclical corrections. Brazil, we were talking before, has Brazil overheated? Is the real too strong? Well, but the general trend is this way. And if we had a similar trend in certain countries in Western Europe or the bulk of Western Europe, we'd be in a far better position. And the boot is on the other foot. We have to get our minds around the fact that economic, political, and social power is shifting to the east, southeast, and south. And it's a very painful adjustment process for us as we've been experiencing here in Davos in the last few days, and it's very difficult to get our minds around it. And I think we should be a little bit less critical about what's happening in those countries and a little bit more critical about what's going on in our own. Okay. Final point I wanted to make here in the first segment is, would you say the number one priority for Turkey and for Brazil right now is to build out the infrastructure so you don't have bottlenecks to growth? Because this is the next wave of the big challenge. Do you want to start? For sure. I think our big challenge to raise our overall savings and investment as a ratio to GDP. That's below what we need to sustain growth. So we should raise from 20% to 25%, 24% to 25% in coming years. And the priority is to accelerate investment in infrastructure. Those projections that 3.5% implicit it is an investment growing by four or five. And we need to grow and I'm sure that we will reach a growth of investment around 8% which makes it a GDP growing for 4.5% and even to 5%. So priority of our president is accelerate investment as a big support for healthy growth for Brazil. Deputy Prime Minister, I know this is a big priority for Turkey. Are you bringing in the capital?  Well, for us what is happening is Turkey is growing fast but also our income distribution is becoming more and more fair. So the gap between the rich and the poor is narrowing in Turkey and that's quite an exceptional case. I think Brazil is another exception but all across the world in developing world or developed world income distribution is getting worse. But a better income distribution is also meaning in Turkey rising middle class. We are having a better and better income levels and ready to spend more. But then this good domestic market also means low saving rates for us and nowadays our focus is more probably somewhat like Brazil to increase the saving rates so that we are able to also invest more. Invest more as the public sector for infrastructure but also as the private sector to build more capacity. Invest more in higher value added facilities invest in more high tech production not just traditional industries but higher technology industries which is going to create more value added overall. I want to use the analogy when you look through the windscreen you want to be able to see all the potholes that are out there for you. So we learned since 2009 that you can always get surprised by a big event that throws off the big projections that I put into my comments. So to set the framework of our second segment here let's roll the report on the global risk for 2012 and we'll pick up the conversation from there. In 2011 the BRIC nations proved themselves if not decoupled then certainly resilient to the economic storm that engulfed the west. But in 2012 and already saddled with a stalled Eurozone, new dangers loom large on the horizon. A soft landing in China featuring slowdown in India a credit boom in Brazil political risk in Russia these we know but there are others. Rising tension over Iran's nuclear ambitions puts world energy supplies at risk the impact of higher oil prices would ripple through every nation. How elections in Russia and America and the leadership changes in China will affect the global economy cannot be predicted right now With global growth set to barely rise above 2% this year we can say the risk of currency manipulation unhelpful economic policies and protectionism will increase. Last year the Arab Spring was unexpected and quickly engulfed the region toppling leaders and breaking economies as it went. Any country that ignores the seeds of discontent this year, political alienation unemployment, rising fuel and food prices does so at its own peril. But perhaps the greatest risk to emerging markets remains the Eurozone a failure to resolve the sovereign debt crisis in Europe could derail the global economy bringing down with it the BRIC nations as well those in emerging markets will pay special attention to Italy and Spain over the coming months. In 2011 the BRICs proved themselves to be resilient but this year perhaps we will see just how reliant upon Europe and India they remain. Sunil Mital we've talked about the Eurozone you don't want to dwell on it here in this emerging market conversation but is that the greatest risk to global growth in your view as you see it today? Not really I'm going to build a very different picture here if you look at the emerging markets and let's start with India 1.2 billion people 55% below the age of 25 is a continent of consumers on the consumer side be that services, be that food, be that products people are still consuming a lot there is of course a concern that the government social programs are probably taking a lot of money out of the system as what Stephen said and fiscal deficit is growing and that balance needs to be done but in a worst case scenario for us we are just below 7% growth for the current year and for a trillion dollar economy that's a very very good achievement in a bad year India on the last count had 31 billion dollars of FDI and this number may get adjusted upwards by the time the final count comes through and that's still very solid so investments are coming through infrastructure build up is happening perhaps not at the pace at which we would want to get back to 9 or 10% but we are okay so in India you should say prepare for just below the best it's not going to be the worst now you go to Africa especially the sub Saharan Africa consistently now clocking out 7% growth of course on a base which is lower but it's showing tremendous improvement in its indices in growth and consumption pattern again are very strong my view is the emerging market economies are going to propel the global economy from the current problems that they have the western factories the technology are all going to find buyers and consumers in the emerging world and I remain hopeful that we will play a very solid role in giving support to the otherwise fledgling world economy Deputy Prime Minister the policy of Turkey has been to engage with Iran we heard from the US Treasury Secretary during his presentation today he mentioned higher oil prices with the risk because of escalating tensions and the straits of Hormuz do you think it's wise to move down a path of greater engagement or to apply more intense unilateral sanctions to get them to the bargaining table from the very beginning we had big doubts about the effectiveness of the sanctions better is the UN Security Council resolution or a unilateral sanction and so far we didn't really see any concrete results of sanctions whatever it was intended for it is not really working out that's why from the very beginning we thought engagement was going to be very important and being neighbors with Iran and understanding each other well and approaching Iran as a country which we should all I think show also level of respect and make sure that their dignity is conserved and when we talk with a country and if we really want to cooperate and have a negotiated outcome through diplomatic ways I think it's very important how we pick the style of approaching that country and we still believe that if there's going to be a solution it will have to be a diplomatic solution and no other means is going to get any favorable results we are against nuclear weapons in our region definitely but on the other hand using nuclear energy for peaceful purposes is the right of every sovereign nation we need to also keep the right balance of course it's important for Iran to be transparent to be cooperating with the international community with IAEA and so forth but on the other hand I think better mutual understanding is also something which is necessary simple pressure and expecting Iran to do something just because of pressure is probably not going to work it has not worked so far and we still have doubts if it's going to work or not if tensions do escalate and prices don't have a foundation of $100 a barrel but they go to $125, $130, $140 what would you say is the biggest risk for 2012? well you know these predicting shocks John is extremely difficult I mean last year at the World Economic Forum where we're sitting here predicting shocks and no one spoke about the Arab Spring so it's really presumptuous of us to try to pick out the next black swan in the film clip there was a long list of potential risks and I think it was a fairly inclusive risk but my guess is there's something out here that is not on that list one that was sort of alluded to here was the political leadership issue US, Russia transition in China a risk that worries me a lot though is again to get back to this global dimension of the problem and that is weak growth in the developed world still high on employment does evoke the potential of a protectionist assault developed versus developing and right at the top of that agenda is US, China trade frictions featured prominently by the way in the campaign platforms of Mitt Romney as well as something that has been mentioned several times today here in Davos by Treasury Secretary Tim Geithner and his deputy Lail Brainer so we can't take these US, China risks lightly and I think we need to think about it Before I go to Martin Sorrell Lidia Kui, your view on the biggest global risk here is a big transition coming up at the leadership for China a well managed transition that we don't have to worry about so anything else you want to identify that we should be watching out for? Well I think different countries have different risks because the emerging market economies are actually not a monolithic group for a country like China I think the top risk still comes from the Middle East, from Iran the crude oil price going up perhaps is not the worst scenario the worst scenario I worry is that when the oil price goes up the free market mechanism of the crude oil market may break down if the market price goes up to more than $150 a barrel who knows, it will be rationing from the upstream countries like India, countries like China without much access to the upstream oil may well be the victim of this collapse of the free market that to me is the top risk for China another risk for a country like China is trade protectionist movements not only from the US I now worry more from some of our friends, friendly countries I wouldn't name who are actually very worried about China look at the Chinese economy the Chinese economy's trade surplus is coming down from what used to be 7.5% of GDP to last year is just 2% of GDP and this year most likely to come down to 1% of GDP and in another year zero trade balance however, from bilateral point of view many countries don't perceive this way really look at bilateral trade surplus even industrial trade structure they are subject to industrial lobbies putting on various trade protection measures that would provoke certain kind of reactions from within China China is also politically quite fractured status today so that to me is the biggest risk now the political succession actually may imply opportunities because new leaders coming in putting new energy and these new group leaders are actually quite unique these guys went through the hardship during the cultural evolution growing up in the countryside and these people went to college when they were already early 20s in 1977-78 so they were educated in the years of the honeymoon years of reform and opening up really great opportunity with the political succession in China this is not a risk, it's opportunity good, Martin Stroll, you're spread out and they don't have to run for offers every two years which makes it a lot easier we know where China is going I want to give you a brief introduction before I ask you you have to spread your risk because of the strategy you've used so how do you try to manage the risk going forward now that you've gone into the emerging markets they're faster growth markets if you just frame the risk look at last year we budgeted 5% top-line like-for-like growth excluding acquisitions and currency market expects us I can't comment but 5% to 6% this year we're budgeting 4% so we've already taken to account a little bit the increased level of risk if we talk about the known black swans Steven, let's call them grey swans for a minute there is an inequality risk that nobody's mentioned there's an inequality risk that we see in the occupied Davos, Wall Street movement we also see an inequality risk in India in China, in Brazil there are these issues in Russia these issues are being raised, that's one second risk would be Iran I think in doing our budgets we put Iran to one side if that happens we'd have to reassess the position there's an explosion we put to one side if we can't reach agreement or the banks you're not expecting an explosion we're not expecting it, we think they'll muddle through I'll come back to that in a second Arab Spring is baked in last year if the Arab Spring has become an Arab winter which to some extent I think it has as we look at these Middle Eastern states and whether they will move in the secular Muslim model of Turkey or the more extreme fundamentalist model of Iran Turkey is going to have an implication but we saw dislocation to our Middle Eastern business last year and that again is baked in for this year Russia is baked in as well I'm a great believer that Europe has moved to the east as well and the growth the growth part of Europe is the axis forgive the word but the axis of Germany, of Poland and Russia events in Russia, political events in Russia brought that under a bit of pressure but President Putin looks as though he may be set at least for six years and maybe for 12 years we'll have to see how that comes out the China trade risk I think is a serious one Stephen mentioned two events, there's another event that happened this week of importance the State of the Union message mentioned specific action that America is going to take in relation to China so we're going to have more chicken and tyre incidents it looks like You don't think that's just political for 2012 I think the speech was a political speech I think it won votes particularly that President Obama would be re-elected the practicality is how do you tax a company that offshores jobs in America it's a very difficult thing very difficult thing to do the real risk I think and Stephen is absolutely right there definitely are things we haven't discussed here at Davos as we know every year that happen that we don't forecast the real risk that I'm worried about is not 2012, it's 2013 it's the post-American deficit that has to be dealt with by the re-elected Obama or Romney if it's Romney or Gingrich if it's Gingrich with the risk of the President Obama being re-elected in losing the majority in both houses which would inhibit this is a risk we have a year of many changes there's elections in France quite uncertain we have elections in Russia we have changing China and we also have in the US I mean we have of the five members of the UN Security Council four will change this year which makes the global governance in terms of security changing and about Iran I would wish to say that there is a bit of rhetoric that moves threat but if I agree with the comment made by the Prime Minister that if you press too much and it's important to have a channel of dialogue you have excessive pressure it may backfire I mean I think you should take the US-China the same I think the obsession now with the US changing US policy we should be careful in watching these great this is excellent nicely done gentlemen I would like to open the floor to questions if I may we have a couple of special guests that I would like to call them before we get to the other questions we have His Excellency the Finance Minister of Pakistan Hafiz Sheikh here in the front row we can get a microphone to him sitting with Ikram Sigal if we have the microphones out would you like to comment first about I'm sure you don't have a question for the panel but I'd love to hear a comment about how Pakistan we're thinking primarily in the political sense and the geopolitical sense but you were talking about structural reforms through the budget deficit long-term debt trying to restart privatizations where are you today okay thank you John let me start by first saying it's been interesting and illuminating discussion but part of the problem with discussions like this is that we have categories like emerging markets and then the discussion focuses on four or five of them when in fact there may be I don't know 50, 100, 150 so exactly you know what are we talking about similarly I think when we say emerging markets are going to carry the load are we talking of four countries or 50 countries because my sense is that we should get our categories right if we want to be getting to some kind of a handle on the operational part of the discussion second is from the beginning we know that the world is interdependent nobody, no single country even China will pull the weight for everyone else and the other point is in a globally interdependent world what happens to Europe or so-called West is going to sooner or later catch up with the growth rates of the so-called emerging countries so I believe that the big lessons we have learned in dealing with crises or in dealing with economics in general is that each other and nobody can go it alone the second point is that domestic economic management is important because if there are 50 emerging countries maybe 10 of them have very different growth rates from the other 40 and that is because the way they are managing their fiscal deficits, the way they are controlling their desire to spend they are managing their exchange rate policy and so on the third point I want to make is that irrespective of what happens to growth rates there are new stresses in the global system and I think apart from growth what's important and was discussed is what happens to people because the new stresses are emerging such as climate change for example we had floods in Pakistan last year which cost us 10 billion dollars and many countries of the world are experiencing this floods and droughts and dramatic rainfalls and other calamities second if as it was pointed out that good economics is going to lead to austerity there will be less to spend on international public goods like vaccination like money for Ida in the world bank similarly if there is going to be a political consideration to shift towards protectionism of domestic industry it means there will be less for trade and the interesting or rise in commodity prices will create food insecurity the reason I mentioned these things is food insecurity climate change protectionism and austerity and less money available for international commitments all are going to create new stresses for the poor and the greatest number of poor are in the two countries that are doing so well on growth and so on the one hand we could be talking about 6 percent 8 percent growth on the other hand we could have hundreds of millions of people getting seriously impacted by these black swans or grey swans or whatever so I think a balance has to be struck in terms of both the role of domestic economic management and international coordination to move towards very good Sunil I was going to ask you to comment on that because you are the two pillars that we are referring to here and then we will take a question from back there just wanted to comment on what the minister from Pakistan said I think the difference is between interdependence and over dependence and unfortunately I think that is the difference between India and Pakistan today we wish that you would take an equal load in the emerging markets but the fact is you are currently fighting with the domestic situation and your dependence on the world is far too high as compared to India so when we talk about the emerging markets we take the load obviously it is those countries which are able to do so well two quick comments on your very important points number one is that there is another aspect which we have not touched upon in our discussion that is the emerging market economies are actually helping stabilizing today's financial markets because a huge chunk of capital flow in the form of currency reserves are actually from the emerging market economies so one way or the other this money is working day and night currently they are stabilizing the U.S. economy mostly not flowing into the European countries so this is something I really want to emphasize so if we want to come up with a solution for the European situation the emerging market economies are actually part of the solution do not forget about this point second point is that I fully agree that the human aspect of the poor countries should not be forgotten in fact if you come to a simple calculation 5% of total amount of money going into the U.S. financial markets or European financial markets stabilizing the sovereign bound markets sovereign bound markets can easily be diverted into some poor countries and can make great progress in relieving many of the human sufferings so indeed I'm fully with you so we should not forget about human suffering of people in the poor countries and we should devote actually a small proportion of our money into these countries and the big progress can be made Martin, very quick comment and questions from the floor. Growth deals with the poverty issue and economic improvement. You asked for a definition let me give you a definition of the BRICS or the faster growth markets very quickly BRICS, Brazil, Russia, India, China, MIST, Mexico, South Korea, Turkey, Indonesia and I'd add the following between the next 11 Philippines, Vietnam, Nigeria, Pakistan, Egypt, Bangladesh, point out that Iran is actually included in Jim O'Neill's definition of the next 11 and I'd add Colombia, South Africa, Argentina, Thailand, Angola and Kenya. Those are the countries that we regard as being the fast growth markets that we're focused on as a result question for the floor. We've seen the issues of inequality and the backdrop of the French and US presidential election. How concerned of the panel that these types of issues that fight out in the fast growing markets could lead to governments doing things that could be anti-business? I've got to quickly ask Mr. Barbara Jean are you really as interested in joining the European Union now as you were when you first started the talks? Do you need to? I'm going to say Martin does like me saying let's point East but you pointed East in a very, very successful way the last 10 years. Well we are still in the European Union accession process we are still a candidate country and we still have a very clear target in front of us to become a member. Why? Because we believe that European Union is a entity of values and we think that by having the criteria, benchmark and the standards of the EU for our own political reforms it is quite interesting for us so that we can always move for better in the political reform area. On the economic side though I agree that European Union or the Eurozone doesn't really set a good example for us anymore and I think we are already overperforming in many areas but regardless I think the work needs a strong Europe. The failure of Europe means the failure of the values and ideals that the European Union stands for and I think the problems with Europe, European Union or Eurozone more specifically is because there is no longer good respect for the values themselves the countries themselves didn't follow the rules standards like the mass street criteria and very simple if Eurozone countries implemented the mass street criteria we wouldn't have such a problem right now about the Eurozone crisis nowadays so the criteria, the benchmark are actually ideally and theoretically they are good but problems are because of not implementing them so we still believe in those criteria but we don't believe in what the Europeans are nowadays doing. Stephen quickly? Can I go to the floor after that? I just need to inject a note of caution here the danger of Davos is extrapolation we always tend to be consumed by what's happening right now whether it's a Euro crisis or resilience in the emerging markets and presume that that is the future and yet what we know about reality checks is the extrapolator always falls on his or her sword this is a challenging period for the world for the developed world, for the Eurozone Turkey and for all the other countries involved here including my favorite economy in the world China. This is a challenging period and we cannot pretend that we are not going to meet these challenges head on. Do not extrapolate. We often talk about fast growing economies and we were in my reference as the host of marketplace at least we've been talking about Qatar but how about Mongolia so I saw the Vice Minister a little bit earlier I think it's 27% was the growth for 2011 so this is a whole new dimension for growth. Any comments of what you've heard so far? 27.8% in nominal terms so we are still struggling with the inflation. That's more than three times the rate of China and that's Stephen's favorite economy. What is in real terms? 17.3 so we have challenges but I think having a strong China is also I think favorite of all of us but having no neighbors except Russia and China I think we love seeing China grow and we are trying to build the economy on the back of the strong demand from China for our commodities but at the same time the challenges to balance and not lose sight of the need to grow our people, grow our infrastructure and focus on avoiding Dutch Curse and natural resources Dutch disease and I think when all is said and done and when the dust settles what I want to refer to when I'm granddaddy to the phenomenon that we see in Mongolia is wolf running in between the Russian bear and Chinese dragon in the 21st century very good question from the floor John Chipman, Chief Executive of the International Institute for Strategic Studies based in London I completely agree with Sir Martin Surrell that the great geoeconomic traffic is South European and Asian look at Asia they look at Europeans and Americans look at Asia they look at South America they look at Middle East but the important thing is that Middle East, Africa, Asia Latin America is looking at each other and the traffic is there traffic can sometimes come traffic accidents and that's why I have a question for Mr. Cortino and David Lee there is now a good deal of tension between Brazil and China when I was in Brazil earlier this month I got a big lecture from the Sao Paulo business community as to how China is artificially inflating the value of the real by buying lots of real in order to gain market share I hear politicians in Brazil are concerned about China buying lots of land in Brazil for food security purposes and the situation needs to be passed to prevent this how Mr. Cortino can you contain the risk of Brazilian Chinese trade or currency wars and how David Lee can you give confidence to Brazil that China can trade and act in Brazil in a beneficial manner that shouldn't frighten people Luciano, do you want to tackle it first? Thanks for the question China relationship with Brazil on balance is extremely positive because it is a tremendous market for the expansion of Brazilian exports on the other hand we also have a very friendly relation at the top of the political and strategic geopolitical and strategic relationship between the two governments if we have trade frictions this is absolutely a common thing to be in a rational way to be administered reasonably so I don't see this as a a dividing or a clash between Brazil and China Martin, just a second, I want to go to Lee D'Aqui first Lee there is a tremendous misunderstanding between the two countries from the Brazilian side I fully understand you are very much worried about China, such a huge country lots of people earning low wage rates and a huge amount of investment going into your country you don't know what may happen next I fully understand from the Chinese side many investors, many entrepreneurs do not fully understand your culture, frankly speaking so I think I really think it's very unfortunate indeed the two countries can work together the two countries' endowments are fully complementary to each other one country having huge amount of resources the other country having a demand for your resources, one country can produce something, the other country can also improve your manufacturing sector the issue is more exchange views, more traffic of human flow rather than capital flow human flow, more direct flights between the two countries, more understanding more understanding between the two peoples so I am optimistic with more understanding, more education of our investors I really think the two countries can work together for the benefits of the peoples one specific case which gives us hope Chinese car manufacturer truck manufacturer Jack JAC launched their brand in Brazil I think it was in March of last year the Brazilian government increased the tariff and import on imports and against foreign manufacturers, what did the Chinese do they opened a plant I think in Bahia within about three months and started producing Jack cars and trucks inside the tariff wall in Brazil the response was the entrepreneurial Chinese and in this case it was a Brazilian importer who we know well a response to but that's absolutely legitimate to defend jobs, attract foreign investment that's the positive agenda I believe that we have positive agenda to be built and I won personally engaged in G's so I go to China quite often, we have this cooperation we understand that we should arrange in a positive way we have to do our own homework, we have to make our manufacturing more efficient more innovative so we can't complain if we're not doing our homework but we should do it and we should of course protect our jobs but not just resorting to our pure protectionism with positive policies good final comment to Steven Roach you just put your finger on a key potential flash point we want to protect our jobs, you're not alone every country especially in the developed world where growth is not fast enough to lead to reductions in high levels of employment they want to protect their jobs and that is the arena of tension that I think will continue to play out not just in this political year but in the years beyond and you may not like the label of developing economies but I think the battle for jobs will be fought between developed and developing economies for years to come and we've got to figure out how to do that much better than we're doing now I want to thank the deputy prime minister the two ministers that contributed to our debate and the questions from the floor just a round of applause for our panelists thank you


So maybe I'll start with some kind of, like, how many of you know what's a SAS application?  That's great. That's nice. And how many of you heard of Split Neural Network? OK, three. That's good to know. So the reason why I'm asking is probably people are very familiar with CNNs or RNNs. In five years' time, SNN is going to be one of those padded to the bucket list. That's what is going to happen. So my goal today is to kind of introduce you to this why it is important and stuff. And before asking, I want to go to the next kind of slide. What do you guys think? What's the next big challenge in ML world? So just maybe a few answers. Access to data? OK, that's a good one. Why do you think so? We always had access to data. What's changing in this world? That's valid. Once it is there, getting access is another term. So if customer data is there, they can't access data. That's right. And it has become even more worse. So any other answers? Privacy? Yes, that's going to be, sorry? Compudation, yeah, to some extent, yeah. But the biggest challenge? Sorry? Model size, yeah, maybe. Yeah, that's a good one. But actually, from what's going on with respect to, it's not just GDPR. It's actually things were worse even before. Like, say, health care, for example. In the US, everything is protected by APA compliance, which means you don't really have access to any of the patient's data at any point of time. And imagine you want to, I'm just trying to motivate you to where I'm getting into. Imagine you want to build a machine learning algorithm, deep learning, whatever, just to predict cancer. Can you do it without access to any of the radiology data and stuff like that? Probably the answer is no. So if someone says, oh, I'm doing all this deep learning on health care data, and most likely it's probably done in what I call as a petri dish, where you have access to some sample data and you're building something, and that's about it. It cannot go into production. You cannot really scale it. You cannot really do anything with it. It's pretty much. So what's going on? The next five years, you will see more and more papers similar to what I'm going to talk about today. And its applications are actually varied. And some of you mentioned SaaS applications, and that's what I'm focusing on, because my company, Workday, that I come from, is a big SaaS provider. We are basically one of the leaders in the cloud space. I don't know many of you heard of Workday, but so we are pretty big. We have almost 80% of our Fortune 500 customer, people are our customers, so it's pretty big deal. So the next big challenge to me, I mean, like to a great extent is to, how do you handle privacy and the machine learning? You want to make sure that data, which you have in place, is accessible to the machine learning algorithm, whether it's deep learning or anything else. And the raw data still sits in some place. It never comes out, but it's still useful for building a model, right? That's going to be the biggest area of challenge, which is going to be, and there are a lot of people working on it, and I'll just talk about it and stuff. So that's a big piece. Privacy and ML, they don't go together actually. It's very, very difficult to do both together. And I'm going to basically talk about, pretty much my agenda for the day, is to talk about in general, machine learning. How do you handle privacy in the machine learning world? Because we need access to the data. And then what are the standard, some of the techniques, which have been invoked for a long period of time, and what are the drawbacks and stuff, and motivate you into what's happening in healthcare, and compare it with the SaaS applications, which is my world. And then introduce you to something very, pretty recent, I would say, in terms of imagine someone was talking about in the earlier talk in Microsoft, that they say they publish a paper in 2015, and it's implemented in 2018. So this one is even more recent. 2018 is a published, paper is being published, and they've been just testing it out on some healthcare data for now. So, but I believe that this is a lot of promises. That's one of the reasons why I wanted to kind of introduce this to the evangelize this, and now it can be applied in different ways. And then we'll talk about some small use cases, and then what are the lessons we learned? Go from there. Sounds good? Okay, so when you talk about machine learning in general, like, so you have data, you have to do something to the data so that if some hacker or an adversary jumps into this, you know, wherever the data is hosted, then it doesn't make sense. The data should, it cannot make sense out of the data. That's a real goal. You think about when you talk about privacy, and things like that, it needs different techniques to make the data private. That's pretty much in the sense if someone holds the data and they should not be able to make any sense out of it. That's like kind of the biggest goal, right?  Let's talk about one standard technique which everybody, it's been there for a long period of time. It's very well known. You have data, just anonymize it, right? So you can just blank them, like especially the, what is called a PII data, which is personally identifiable information. You want to kind of mask them or you can just hash them, which is very standard technique or you can also do masking. So various ways of doing anonymous, anonymizing data, right? But the problem in anonymizing, you probably, most of you heard of it, is it's easily broken. So when Netflix, how many of you know about the $1 million price of Netflix? Probably everyone, right? So Netflix thought, okay, we are going to put this data out and it's completely anonymous. What they did was they changed the user account name and then they also did something with the ratings and stuff. But the key thing is they also had the movie name, which, you know, and then this was exposed, this entire data, they had like 100 million ratings, almost 400 and K users, that's a lot of numbers and almost like 70 K movies and the movie ratings were publicly available for people to, you know. So this clearly talks about, right? So I just anonymized the data, I put it out, no one knows about who that person is and stuff. And there comes this paper and I think for God's sake, maybe 2012, I don't remember the date, by two people in University of Texas, Austin. They took the IMDB data, I'm pretty sure everybody is familiar with IMDB data and the Netflix data, put them together, they were able to kind of pretty much figure out what does this 480K users are. So in other words, there's no anonymity anymore. Everything is exposed. So we think, okay, anonymizing something is very, very powerful, but most of the time it doesn't work. So it's a hammer to solve privacy issues. It's very easy to implement. It's scalable quite easily, you can easily anonymize data. But the problem is de-anonymizing is also very easy. You just need to put in some effort. And there is a view even more bigger, you guys should take a look at it if you're really interested in this. There's an hospital in Massachusetts where the governor basically said, oh, I'm gonna open up this data for everyone. And that killed, it's like, his governor's data was basically, they thought he's gonna expose this data and everything was anonymized, but people are able to de-anonymize. And they knew the entire medical history of the governor. So you can imagine how bad that is. They know everything about you. So anonymization is not the best way to do protection, private, you wanna do privacy, right? And then the next one, which is kind of interesting, which is also kind of an interesting area of field is called differential privacy, which comes into the folder of what is called obfuscating the data. I mean, I wanna explain this example, but think of this in a simpler ways. You have data. So when you think of the adversary, which is the hacker who gets into this data and he sees him some data and then if he makes sense out of it, it's good. So what do you do is take the data and then add some noise to it and then expose that. So when someone gets exposed, they're basically getting an information where it's mixed back. You don't really have the real information. It's mixed with noise and you actually know what's the distribution you had and stuff. So you are the only one who can de-noise it, when they do not know whether you're actually done an obfuscation or not. So this is called differential privacy. This is an old, so each and everything which I'm talking about here is basically what it's like two, three days of talk. It's like very, very deep fields, but I just wanna introduce the idea that there is something called differential privacy, which is pretty big. It's all about adding noise and the whole amount of research going on and what kind of noise we can add. Lap less noise is very common and what are the things we can do. So in this example, what happens is there is a, the bad guy just wants to know how many people have a rating of bad. So if you just threw, there's like three count, like the problem with this, many of the data is sometimes the count is so low, it's very easy to figure out who that person is and stuff like that, right? But imagine you do had some noise to it and then whenever he queries, he gets an answer of 2.915 and next time he gets 1.882 and the next time it's one. He's like completely thrown off. He's not, the first time he's very close, but he's not given the exact number, like he's trying to do something. So one drawback for this, you can already see is that like over a period of time, it will actually converge to this, the actual count, but the problem, so in other words, the drawback of this, right? It's not going down. So drawback of this is like, you really want to add noise, but at the same time, the hacker is one guy who wants to get your data, but there are all the other users who also needs to do this. So you need to, as a balance between utility of what you're trying to do and then the privacy part. So you need to balance these both and you need to do, so it's kind of, you add up random voice, make it harder for the adversary, which is basically the hacker. At the same time, you need to do something so that it is simple to implement and understand and stuff. So you're caught in between, but the only problem you see in this differential privacy, which is a big problem, that's the biggest bottleneck of differential privacy. It's very scalable and stuff. It's very easy to implement. That's the whole notion of it, but the problem is over a period of time, if the hacker gets into the system over several queries, he's able to figure it out because the pattern comes out, that you had added a noise to it and stuff, so he's able to figure it out. So that's what it is. It's more and more queries. You can actually get what the true data is and stuff. So I mean, that's a big drawback. So that's another one, but it's very well known and things like that. So the next one, which I want to talk about is the third big one. So we talked about anonymity and why it's bad. We talked about differential privacy and then good, bad of it. The third one is, how many of you heard of homomorphic encryption? Everybody knows encryption, so yeah. So it's basically, it's actually working, it was done by a Stanford PhD student, this PhD thesis was on this, that you have encrypted data and then all your applications actually happen on encrypted data, which is amazing. You have data and you encrypted it, but usually what happens is okay, so you are encrypted to the data and if you want to work on it, you want to decrypt the data and then work on it. That's like natural notion that you're encrypted and it's all safe and it's, and in a very safe location, now, okay, let me decrypt and work on it, right? In the world of where we're living right now, no place is safe for data especially. You can be hacked anywhere, you put it in anything, anything can be hacked and once something is decrypted, all your encryption goes for a toss. But imagine you can actually work on the encrypted data. It's already encrypted and you can actually do stuff on top of it. That's amazing because you don't have to, at any point of time, the data is never decrypted and you're still able to do a lot of operations. But the problem here is that it's an infancy, I would say, although this is great, fantastic. What you could do is still, it's a huge literature. Again, this topic alone can run for three days. So you have very, very simple addition, like say two and three and then you can encrypt two, encrypt three and then you can add, act on the, so we can do only simple math, like addition and multiplication operations and stuff. There are some complexity which gets added, which is great, but still you can see, imagine you want to do this and do machine learning. You can already see it's not scalable. You cannot really do homomorphic encryption and machine learning. But there are a lot of research going on and it's very exciting field to come in. So in fact, all of the three things that you talked about, like differential privacy and this one is a beautiful area of research and where applying machine learning to, how do you take machine learning and apply these techniques is each and everyone itself is a huge area of research. So it's pretty exciting, right? So now comes, now I think I've motivated you on different ways to do privacy and things like that. And next goes to the big, very big thing which happened in recent years from Google. It's just called the federated deep learning. So probably I think there was a few talks in this conference too which talks about this on how do you do anomaly detection on edge devices using federated deep learning. It's a fantastic thing because this kind of, now that I've kind of given you different techniques, whatever I'm talking about, federated deep learning and the split neural network which I'm going to talk about are overall frameworks into which you can actually add anonymity, differential privacy and any kind of encapsulation. I mean that basically adding other techniques and make it even more powerful. So you can add the privacy but what federated deep learning does is solves one of the original problems I started with that I don't want to, as someone was saying, that I need to do my machine learning but at the same time I don't have access to the raw data. The raw data in your cell phone and other iPads and others cannot leave the iPhone. So you have a lot of private data in your phone and then my machine learning, deep learning, I'm referencing only deep learning because there's a lot of things which deep learning does which works for federated deep learning but I'll explain in the end how a logistic regression and other things can also be applied into this space but the goal here, federated deep learning, what happens is that your training starts somewhere, you have a neural net and then you just say, okay, deploy this model across all my cell phones and then the training happens, like each and every individual, you have a phone, a ESF phone, a ESF phone, every one of these phones actually train the neural net on their phones and their idol, okay? It's basically training happens and then the parameters are basically, the gradients are basically sent back and then the main server, the main machine learning algorithm retrains or in other sense builds a new model out of all the inputs it gets and this new model is again sent back and then they take this and then start retraining. So if you look at this, the computation on, it's great, it's all like you can basically distribute this deep learning into a lot of cell phones and stuff but the problem is the network bandwidth you can already see, like every, imagine like there are a billion devices and they have to send all these parameters back to one place, so there's a huge clock which could happen. So this actually has some impact on how federated deep learning actually is done. So if federated deep learning is great, it's a fantastic technique but it has some scalability issues. You can already see like the network bandwidth as some issues and I'll show you some examples why this is not ideally suited for certain situations and stuff. So now let's going back to this, the actual topic of this thing is about introducing what is called a split neural network. It was a work done at MIT by Pranit Vepakumar who's a grad student there and Professor Rusker. So what they do is a very similar concept like federated deep learning. They do a federated deep learning. It's basically distributed deep learning but they do it in such a way that some of the bottlenecks and scale issues we have with federated deep learning are actually solved in a very, very novel way. That's pretty much what it is. They just do it in a slightly different way. I'll go into it, I'll show differences about each and every one of them, right? So now to kind of summarize what I've done so far before I get to the split neural network world, this is what really happens right now is that you have differential privacy, you obfuscate with noise, that's what you do. And homomorphic, I mean these are the top ones right now. So hammers, so-called hammers to do all these things, right? And homomorphic encryption, it's simple operations but not scalable, as I said. And federated deep learning, what happens is the training happens with the client and then everything merges as servers, a huge bottleneck of when it comes down. But what split neural network, which is our topic of interest, which I believe in a few years would be pretty viral, is that it's actually, the old training actually happens in the server as well as in the client and it's a continuous thing. There's everything is sent to what is called a cut layer and then it's kind of exchange over the period of time. And right now the research is focusing on doing deep learning, but it's a work in progress where they want to extend this to other things like logistic regression and stuff. Because I mean deep learning has, so it sounds very, very fancy and great, but deep learning are pretty hard to implement and pretty hard to actually understand. So there's a lot of pros and cons. So it doesn't really apply widely to all the use cases we have. Logistic regression could solve like 90% of world problems. I'm just saying that from my experience. And XG boost is probably the only thing, algorithm you need to learn most of the time to do most of your work. It basically trumps everything in the world except for some of the deep learning techniques. So it's coming, but it's not there yet. So some comparison between why split neural network is kind of better than split neural network is, as I already told you, it's the bandwidth. It's huge difference. You can already see the computation in T-flops. You can actually see the convergence, actually with the validation accuracy hub. So you can see federated is somewhere here, but split neural network seems to converge much, much faster. And it's kind of makes sense because you're continuously training in both places. This so-called wait time doesn't really happen. The server is sitting like idle. It's not doing anything. Until the clients come back and say, A, I've done something. In the federated, it's just like a design. By design, it's like that. The server kind of waits, not so. It's not doing something on its own and stuff like that. It sounds like, but you can imagine when you're trying to do deep neural network training, it takes a lot of time. So it has a lot of wait time and stuff which gets added. So that basically results in federated deep learning being not that great. But of course, split neural network has been inspired from federated deep learning. That's really true. But the point is getting better. And in a few years from now, you can see even better things coming out of it. But that's pretty much, you know. So now let me get into the world of what is this whole thing about to do with me? So why I'm inspired by it and stuff like that. So SAS applications, as you all know, software as a service and a pretty big thing about. So maybe I'll go to the next slide. Kind of, you can always see, you know, data is the oxygen for machine learning. You want to have it. And so privacy concerns, access to raw data. You need raw data, but you don't have it. And SAS applications, we produce a lot of data. It's not about question of data availability. It's data accessibility. It's a huge problem. And it's going to be pretty big. Privacy, I don't know.  It's going to hit. And every company is going to, it's already happening. I can see it. Every company has to start doing things in private. And there is this AI world, which is saying, oh, we are going to change everything in the world. And there's this privacy, which is like, they actually, you know, the correlation is like, reverse. You won't have more and more privacy.  How do you do? So which means you're going to be caught in this kind of a problem that you need to do more and more towards improving privacy. At the same time, you have to do machine learning, whose whole notion goes about, I need raw data, access to raw data. So these are like going to against it. So we need techniques which converges both. We need to make them converge. Right now they are like this, if you just think about it. Like imagine if someone comes and tells you, hey, I want to build this machine learning thing. And then I will not give you access to any data. So what do you do? So you cannot keep working on synthetic data for a while. At some point of time, you need access to the data to test your models and stuff. So that's going to happen. Like, you know, all the machine learning models and stuff in glorified terms. And suddenly the GDPR and all these things is going to come and hit. And then says, oh, the current architecture you guys have cannot work because you cannot have access to raw data. So it's going to happen. It's coming. So in five years from now, I can bet on it. Like there will be more, in this conference itself, there will be more topics around privacy and machine learning than the other things. Because that's going to be pretty big. It's going to hit. So obviously, we want to build machine learning. And the paper I'm talking about, Lesplit Neural Network, actually talks about health care data. And in the US, there's something called HIPAA compliance, which basically says no access to customers, I mean, patients' data you can never have. If you want to build machine learning for cancer, you will not have access to it. But the same kind of what my thought process is, I was trying to solve a similar problem in SAS application. So my thought process was, oh, it's very similar. In because of HIPAA compliance, I cannot really have access to the raw data. And I still want to do it. And Split Neural Network does solve that problem because you can still have access to the raw data. But the raw data is never taken out of the patients' radiology centers or something. Yeah, I have a similar problem. I have a lot of customers. And every data sits in a tenant space, like their own silos. And I don't have access to it. I cannot export the data. I mean, traditional machine learning goes, oh, get me all the data in one place. I'll do all the things. I'll do training, get the models, and put it in production. It's all very good. But if you can't even train on any of the data because you don't have access to the data outside, that's system. So now, if I cannot bring the data, I was thinking if I cannot bring the data to me, let me go where the data is. And it's exactly what Split Neural Network does. It basically goes into the space where the data is. Does it make sense? That's the beauty of it. And just to give you a flavor, so I kind of said health care, which is what the paper was, and SAS application. They have very, very similar problems. We don't have access to the data. And this is something which I wanted to note about why is this even more a better application for SAS application? So you kind of need to know that. So if you think about federated deep learning, one of the problems I said was I have like, say, 10,000 cell phones today when I'm training on. But I would love to have from 10,000, I want to go to like a million. And from million, I want to go to billion. So look at this last one. You can kind of maybe look at that one. But look at the last one. Federated deep learning has 500 clients. It's basically, it's not really optimal. It's still going to be from a 3GB kind of thing. So when I have this number of clients, it doesn't really scale that much. But look at what happens in split. It's just the opposite. When I have more and more clients, my training speed increases drastically. Like it's unbelievable that I could actually scale. So this is so more optimal. So imagine you are a SAS company and you have 1,000 customers today. What do you want? You want 1,000 to go to 2,000 or like 20,000 or like a 2 million or you just don't want to increase. So obviously everyone wants to increase the client more and more, right? It's like adding more clients. Spirit Neural Network just solves the problem beautifully. And this is probably the same thing with when you have federated deep learning, when you have number of devices go up, you hit the bottleneck where things don't scale very well. And this particular one is what makes it very, very, what should I say? It's beautiful. And it actually, when you had more customers in the SAS world, it actually does a better job. I would love that kind of a feature to be in my machine learning, right? So it's one of the things. And let me just get into, I try to simplify it as much as possible because given time, there's a lot of math involved, a lot of things involved. My goal is to just motivate you on what it is. And there are other things which you can try. So what really happens is that, say, the raw data is not transferred between the customer and the server. It's very similar, similar to federated deep learning. And that would be true for any distributed deep learning. The real motivation behind distributed deep learning itself is that, yeah, I can't just go where the data is kind of a concept, right? Forgetting about privacy and stuff. It has other implications that you don't have. You already have a huge infrastructure for storing the data. Imagine for machine learning, you want to take the data and put it again. So it's like kind of redundant. It's actually, it's very expensive, right? So you don't want to do that. Let the data be where it is. I'll go and grab that whole note. Beyond privacy. It's another advantage. And then what happens is that, so there is a layer called the cut layer. It's true. So every client basically takes the raw data, they do some, you know, generate some features and then some gradients are sent. And then it's sent to the cut layer. And from the cut layer, so, and then the cut layer from the outputs, it is sent to the server which completes the rest of training. So this is not what happens in federated deep learning. This is the key part. Everything else would remain the same. Is that the server in a federated deep learning pretty much acts like an aggregator. It gets everything, puts it and then, okay, new model, go, right? It doesn't do training as such. But in this, what happens is that there's only some components of the training which is complete. The rest of the layers are actually done at the server level because it continues on that, right? That's what makes it very, very lucrative in the sense the server is continuously doing some work, right? And this is an example where I have one server, one client, I'll show you one other example where multiple clients and stuff. It's, which is a very classic scenario. And then the gradients are obviously, so there's one for propagation which starts with client, it goes to the server. And then now the server basically does a back propagation and it sends it back because it needs the information about the gradients to continue to the next. So this completes one forward propagation to the server. The server brings it back and now it completes an epoch. Now the client starts all over again. It basically does that, right? And the gradients are sent back to the customers. In this case, the only thing is you have raw data and then none of that raw data ever leaves the customer. It stays right there. So that's one of the biggest reasons why we want to do this. And so again, yeah, of course, you know, it converges till continuous, till number of epochs continues until it happens, right? So the finally, the built model is within the server. So the one thing which happens is the same concept, but now you have more clients stuff, but everything happens in the same way. It's basically whenever every customer, everything happens, stops at the cut layer and the gradients are sent. It's the same thing, it's an extension. Think of this. One example I can think of, it's like think of this as a relay race kind of between the clients and stuff. That's what really happens here. It's like a bet on is given to the other thing and then it continues. So it's like over a picture, the big picture is that it's a continuous stream, like the server, clients, everything is continuously working on it. It's not like somebody's waiting for it or anything. It's both are equally involved, you know? It's, I'm just giving the high level overview. Of course, there's a lot of things which can be done. So how does it apply to like a SaaS application in general? It's very similar. You have multiple clients or customers here. And then, so one of the things which can be an additional thing to it is that typically when you have a SaaS application, you don't actually have the customers raw data information. That's obviously blocked. But most of the time, say, if you're a SaaS application, you have a lot of customers, you do have some sort of an information related to, because you log a lot of data about the customer, like say performance. So there's some amount of information you actually have, but I want to use that too. I'm very greedy, right? Machine learning, you know, almost every data scientist is very greedy. Any data I have, I want to use it. Like, that's very normal. So I don't have access to raw data. Oh, okay, I want split neural network to solve it. And then I also have this data for which I have access. You know, I have the data. I want to use that too. So that is something you can actually combine where the server gets this information. There is no client or anything involved. Everything is merged. This is like classic machine learning. And you can actually combine this with split neural network as well. So it kind of, you know, like a thought process like where you could actually use other, whatever data available to you. It's not like I can use only this and not the other one, right? So it's kind of, it's a thought process. And there is one of the things which is initially we thought about was, okay, now that I have raw data, one of the things which you always want to do is can I add more privacy stuff to this raw data itself? Like I'm like very scared that I have raw data. I'm doing something in it. Maybe an hacker gets into it and then now you can, you know, figure out what the raw data is. That's what I'm always worried. So maybe I do something like an anonymization is bad, but I'm saying, can I have something else added to it so that I can add more protection to it, right? It's, it is what is called leakage. Leakage is a term used whenever you have all these distributed deep learning and stuff leakage basically means that someone can still figure it out what that raw data is. So like the amount of, if you have a lot of leakage which means it's really bad. The old reason why you started with gets you're not solving that original problem. Because the original problem is I don't want to have access to the raw data. I don't want to give it to you because if someone takes it, it's bad. So you don't want to introduce a process which actually does that to you. So one of the ways anonymized but that was the initial thought process but there was a paper which again the MIT team released which is fantastic on how do we solve this whole leakage problem in a better way? You know, like can we do something better? So I'm going to kind of go into some sort of layman terms so that I explain this in a way it's understandable. So what really happens is that it's very clear you have the server. Say imagine you're doing like some sort of a classifier, right? And you know, I see most of the people here are experts that there's one of the last function which is called the categorical cross entropy which is widely used in most of the deep nets. And that's what happens here. So basically that's the last function. But if you look at the goal, I have raw data, right? And then I have like multiple layers which is actually happening. Like say basically extract, do something, do features, new layers and stuff. At some point of time, this is taken over by the server and stuff, right? What the motivation is that if I have a layer right next to the, I'm simplifying it but it's not really for the way. But so you have raw data and you have another layer on top of it. What is that you want? I want no correlation between this raw data and the next layer, whatever data to be very different. I don't want to have any kind of a notion of similarity between these two data which means if I have access, I don't have access to the raw data maybe and then if it's access to some layer, I can actually figure it out what the raw data is. You always need to make the assumption that, okay, the hacker will get into it.  So that's a problem, right? So which means the motivation is that you have a raw data which is a layer and then you have the next layers. You want to make sure there's the correlation between these two data pieces. Just in the form of data is very wide in the sense it's a negative correlation. You cannot really, there's no similarity. So the similarity needs to be very far away. And there is this thing called log distance which is kind of not the Pearson. Pearson is very linear. This is like the log distance. I don't, you know, we can talk about the part but the idea is you want to come up with like a log distance correlation which is non-linear but at the same time which basically so it's, in other words, now you have a cross loss function share which you want to minimize and there is this log distance correlation. Again, you want to, this correlation to be, you know, negative as much. So it's basically, you know, you want them to be very far apart. So now you have two loss functions to basically optimize and once you include a diet, your data leakage kind of drops down quite a bit. So you can imagine like, so this, the reason why I am like bringing this up is you can imagine how fast this thing is evolving. And this data leakage, I think the paper was done in like March of 2019. It's like a few months old. So it's basically a great, if anyone is interested in this area, it's a great area of research and it's been done at MIT. So of course they've patented it and stuff but still it's a great motivational thing to start on what do you want to build? It's not just for SAS application. This opens up an old new world of doing ML and privacy. So you can imagine the data leakage depending on the, as number of epochs go, it actually goes down and the distance correlation kind of goes down which kind of technically says that it's doing its job and making the draw data not similar to the next layers and stuff. So even if there is a hacking happens, it kind of prevents it. So it's a very nice feature to have and date and this whole leakage thing itself is like pretty wild. And good thing about split neural, one more thing which probably some of you have, my questions is that, hey, can I apply to different scenarios because the scenario I talked about is you could do obviously, if you have labels then you do supervised. You don't have labels, you do unsupervised but you want to understand whether, there could be labels which sits in the server. There could be labels which are sitting in the client. The paper talks about the multitude of configurations and how you can actually put it. But the idea is to have a cut layer but how do you put the, where do you put the cut layer kind of changes and it basically gives you what I talked about was a vanilla configuration but you can have multitude of ways to put this mix and match so that you can make use of where the data. Because that's a problem, right? Once you want to start doing it, sometimes the clients have all the labels. Basically say someone has cancer and that's in the patient's report, that label is sitting within the radiology report. It's not outside. So all the labels are within the client. So that's one configuration. But there are some other cases where the server holds all the labels. The labels are sitting in it completely different. It's not within the specific clients, it's there. I can't think of a scenario but I'm just thinking. So it's very different sets of scenarios which does exist and the beauty of this is it gives you a lot of different options to do it. So it's kind of gives you different flavors. So it's not restricting you to okay, you have to do this only this way. You have different ways to do the split neural network depending on what is the need you have and stuff. And where the data sits, which is very, very important because again, there is no way you can guarantee where the data should be and where your label should be. You cannot really do that because your customers, the customer data could be in different way and you still need to have this flexibility and change the configurations in different way. And the paper talks about a lot around it. So you guys can go and take a look at it. So I mean, that's pretty much what I had but the whole notion of this is I want every one of you practicing machine learning to start worrying about privacy to a great extent because it's coming. It's because of all the restrictions and things like that, data value. The view is to start with the process. It's kind of very interesting in the world of machine learning. Initially we said we don't have data. Then big data explosion. We have all the data in the world collected. And then we said, okay, now we can increase the computation. We have Hadoop and stuff. And then that actually led to saying, okay, we need to have a better methodology.  That was the original thought process which has think about it because there's initially no data and then there's a lot of data. Okay, I want to bring all the data in one place. Let me do machine learning. Now things have started the other way around because nobody talks about machine learning or like privacy and things like that. No one cared about it actually, honestly. We were just doing it and we were like going. And suddenly these things come back and now it looks like, oh, I want to distribute data now. And I want to also, I let the data be varitors. And then I want to, it's just the reverse notion. It's like we have come a full circle, right? If you think about it, like we started with, okay, I want to get all the data in one place. Now we are saying, keep it there. I'll come and grab and do whatever I want. So this is going to be kind of the way, like more and more customers, especially the number of data breaches we have and the number of billions of dollars lost in. And it doesn't matter how much secure and things like that, everything can be breached. But at the same time, all these things forces the machine learning community to be, the first question people ask or the legal people come and say is, how safe is the data? You can do machine learning, it's all exciting. So I want to do artificial intelligence, change the world, but I won't give you access to data. So it's coming. So, it's basically my talk is just about motivate you on what's happening in the current situation so that you guys can get exposed to it. That's pretty much what I had. So thank you. Open to any questions. Federated deep learning, pretty much. Its comparison is with, so the whole motivation of split neural network started with federated deep learning. But they figured out what are the drawbacks of it. When federated deep learning was introduced, it was just, people are like, wow, this is just totally against how deep learning is. I mean, ever, this is not that, if anything about machine learning, I want my data and then I want to get going, right? This is different. So like it started it, but at the same time, he tired as I showed you some of the examples and differences that this actually has some nicer properties than federated deep learning. It's not like you cannot, one is there was another talk on federated deep learning. Of course it works in many of the situation. It depends on what's your situation. And the classic example I gave is like, say if you have 10,000 or 1,000 customers today, I really wish to have 10,000 customers. But I don't want my machine learning framework to not scale with it. I actually go down. That's a big problem. You know, like when you're doing especially enterprise machine learning, the biggest thing is to, how do you scale from small set of customers to more? And if you have designed, if you are using something which doesn't scale with more customers, then you are in trouble. So, you know, it has some nicer properties in federated deep learning. That's a good question we are. So it's, I mean, I really wanted that we would make progress in such a way that I could actually do a demo. But yeah, the use case is the anomaly detection. Like basically that's one of the things like, where there are some, you know, fraud behavior within our application and the problem is, because we don't have access to customer data, we don't really know what's going on on the other side. But so for example, maybe I'll put it in a different way. Say assume that you have, your account is compromised or something like that, right? Somebody is doing something with that application and your account is gone and it's gone and someone is just doing something. And how do I know? I cannot really know because it's all, someone goes inside, changes some configuration of your system and things like that. If I had raw access to raw data, then it tells me, hey, I was in this state and someone changed the state and I didn't make the change at all. Like, you know, all this information, it's pertaining to the particular user whose code has been hacked. I actually have those information and stuff. And that could be a lot of, you know, help in like generating features for me in order to do it. Is there a change in state and stuff? If the customer is not giving you access to the data, then you never know it. But if I know it, then my machine learning model can act on it and then say, okay, stop when it's actually happening. So he gets into the account and then he tries to do something and my machine learning is basically acting on it. It says, oh, compared to the last one, something else has changed. So I will not allow you to make this change at all. So you can actually block that guy. But for that, you need sometimes access to the actual raw data, which if you don't have, you may not have that feature developed at all. So in this way of doing deep learning, when you're training the model, is this specific to that particular customer or is this a combined knowledge of all the customer data that gets applied to then one customer? So, yes, it's across the board. So, but the reason why we say, imagine if you don't have access to the raw data, the bottleneck is here. There are certain features which you can never develop without that raw data with that specific customer specific data. And that is needed when you're actually making inferences. So that is what is good. And obviously the raw data will not be pushed when the model is being trained. It gets transformed, but that information is available to the model and it can actually do it. So it's picked up from the customer data. And then, see, right now, the problem is training is the biggest problem because you don't have access to the data. Once you're trained, the inference is not a problem at all. That at the point of the inference, you do have access to the product customer data. You see the difference? Once your model is trained, you can put it on the server, like the trained model is running and it has access to all the data in the world because that's not a problem. It's each edge. So similar to the federated deep learning example in the other talk. So you do the infra, the trained model, once it is deployed, does have access to all the data. That's not a problem, but only in the training it doesn't. So, but that's the delta. If you have access to the raw data training cycle, that's not a problem at all for any SAS application deep learning, but the training is the biggest problem. What it solves is the training part. The inference part is not a problem. You put that as part of the application. Application has access to all the data in the world. Nothing, your application will run if it doesn't have access to the customer data, but it sits within the customer space. It doesn't get out. So inference, when you just think about it, separate training and inference, so inference can happen. Yeah. Okay. Thanks a lot. Thank you, guys.