Hello and welcome to NewsClick. Today we have with us Professor C.P. Chandrasekhar from the Centre for Economic Studies and Planning at Jawaharlal Nehru University. Thank you for joining us, Professor. We would like to discuss the whole idea of these bank mergers that are happening. The government has announced that they would be merging Vijaya Bank, Dina Bank and Bank of Baroda. And they say that it would be a way of tackling the problem of NPAs. So, how do you see this? Well, you know, I mean, we must realise that this comes in a context in which the government has tried many things in the past to try and address this problem of non-performing assets. The problem itself is quite substantial and got revealed in large measure over the last three to four years when the sort of guidelines with regard to recognition of these bad assets were tightened and therefore, suddenly you saw this mushrooming of bad assets. Now, normally when you have bad assets, what you need to do is you need to try and first write-off, it's a technical write-off and then recoup as much as possible and to the extent that you don't, obviously you need to provision for these bad loans and that eats into the capital of the banks and you've got to recapitalise them. Now, over a long period of time, the government has been recapitalising, but two things have now sort of shorted that process. First, of course, is the fact that the volume of recapitalisation required. And we had an Indra Dhanush plan, for example, for four years starting from 2015-16, which is about 70,000 crores. Now, we are talking about, the government was talking about some 2.2 lakh crore recapitalisation. Now, obviously if this is occurring in a period in which the government is increasingly adamant about the fact that it is going to stick by its fiscal deficit norms because the very day after the merger was announced, the finance minister also said, we are not going to budge from our fiscal deficit target, which essentially means that you're going to keep spending down so long as you're not willing to tax, which obviously this government is not willing to. So, if you cannot recapitalise the banks, then obviously you need to find a way of dealing with these assets. And the second sort of, I mean, not that they occurred sequentially, but second attempt was to try and create asset reconstruction corporations where you sell the bad assets against receipts very often, against actual cash at some discount to a reconstruction corporation, which then tries to peddle these assets, recoup a certain amount of money and then replace these receipts with actual money. But the issue there is how much of a haircut are you willing to take? And it became clear that the demands which were being made by these asset reconstruction corporations was extremely high. And finally, of course, there was talk about setting up a bad bank, which finally would have had to be a public sector bad bank which means that the budget was anyway going to carry the, the tax pay was anyway going to carry the burden. So, in essence, this is this merger or this beginning of a process of merger is an attempt to try and create a situation in which the government need not touch its sort of budget in order to be able to recapitalise. So, it's in that context that we need to look at this effort and revive this whole, because it's been spoken of before, idea of merging banks, consolidating banks. And it's almost clever by half. You take a small bank like Denner, which is in very bad position, which would possibly have had to be completely recapitalised by the government. It had 11% plus net NPA ratio. And you take a bank which is also a small bank, but bigger than Denner, which is Vijaya Bank and which is not doing too badly. It has about a 4% net NPA ratio. You put them together and the average NPA ratio comes down to about 6.5% and you hand it over to Bank of Baroda, which has got a 5-point something, but it's so big that when you add it on, Bank of Baroda's net performing assets ratio does not change. Now, there are two or three questions and I'll just stop with that, posing those questions. The first question is what makes the government think that Bank of Baroda doesn't have its own problems? See, because the idea is what you're going to do is give it a little bit of a carrot, which is Vijaya Bank, in order to be able to take over something which no bank would want to take over without it being recapitalised. So what is the perception or what is the understanding that Bank of Baroda itself, like most public sector banks, is in a situation which is extremely troubled and should be at the moment focusing on addressing its own 5.6% or whatever it is, net NPA ratio. The second is that, you know, the point is this is a process that has been credit which has been provided over time and if there is credit which is provided over a particular period, let us say the period from 2006, 2007 to 2008, 2009, for example, which has gone bad now, there's no guarantee. In fact, there's a strong likelihood that as we go further, there would be other assets which would turn non-performing, including in the books of Bank of Baroda. So if you say that this is going to be a way of without providing money from the government's budget by taxing those who can afford to be taxed and putting your banking system back on shape, that's not clear because this problem might not go away. It might just now reappear in an intensified form in the books of Bank of Baroda in time to come. And the second is that even to the extent that it remains where it is, the perception that this is going to actually unleash a whole credit wave because the whole idea is credit isn't going fast enough, so we need to do something. So my own perception is that the way the government is planning, what the government's plan possibly is, is that it has been wanting to get these banks off its hands. And if it wants to get these banks off its hands, there's no way it can do it if the books are not relatively clean. So for example, if you take the Southeast Asian Crisis of 1997, Indonesian banks went bust, the government took them over, cleaned their books using taxpayers' money and then handed it over to the private sector. So I do think that the government cannot convince itself that this problem is not going to remain, so that it will constantly recur if they happen to be public banks. But if you want to convert these public banks into private banks, you've got to find these, you've got to indulge in all this kind of manipulation to try and fix it, that the balance sheet improves a bit so that you can actually privatize these banks. So I think that's what is on the annual. But then the NPAs are not really going anywhere. Yes, but what you're trying to do is you're trying to reduce, because finally you're looking at the average NPAs, you're trying to reduce the average NPAs. Let's be clear, these are ratios. The volume of NPAs is not going anywhere, as you said, because you're just adding up the NPAs of Dana, Vijaya and BoB. So they are not going away. But the point is you're trying to make it, because you're really looking at it relative to the total advances. So when you take this merged entity, you're going to give it, this is an amalgamation, it's not even a merger. So you're actually going to have a new entity, a bank of, I don't know what. And that entity is going to have, well, not too bad. So the whole perception is let me take the ones which there's no way I can sell, and fold it in with some, you know, a little bit of gravy like Vijaya Bank, fold it in into a bigger bank, so that it would become possible for the government to actually wipe its hands of this problem, because once you got rid of your development finance institutions and you needed big projects to be financed, you were going to have to push the banks to do it. And if the banks are in the public sector, you would do it and you end up with this problem. So you leave it to the private sector and that is obviously have growth implications, but that's tomorrow. Today you'd have it off your hands. And how would this play out for, say, the customers of these banks? You see, there are two kinds of issues involved here. One is, of course, there would be a transition period when they've got to integrate their systems. One advantage is, of course, most banks now or almost all banks are, you know, or the bigger banks are in the sort of, under the co-banking system. So integrating it becomes, integrating the accounts becomes easier, but till such time as the accounts get integrated, there would be some delays, some minor hitches, etc. But the larger issue is going to be that, you know, you're going to have to, you're going to have to, cleaning these books is also going to mean you're going to cut costs, you're going to replace, so there's, what are you going to do with all these people who come as employers? The employee base is going to be now that much larger. The employee base is going to be that much larger without the volume of NPAs having, you know, gone down. So you'll be looking for ways of cost-cutting, etc. And, you know, there's an officer carder, there is senior management who's going to ask to work under whom, etc. So there would be these problems which would obviously affect client service in different ways. But as of now, there's no reason to believe that it would involve a financial loss. It could involve some degree of inconvenience. And it's only when the government decides how exactly it's going to resolve the rest of the problem. You know, that there was the FRDI bill in which there was a whole idea of a bail-in, which obviously was threatening depositors, which is why the government had to go back on it. And so you mentioned about the employees. Do you see a possible similar situation to when the SBI mergers happened? Yeah, even in the SBI, I don't think it's played out in full because what you do is that, you know, if you consolidate, create larger banks. Now, let's be clear. Vijay Bank is a small bank.  That's why you're giving Vijay Bank along with dinner. So the evidence is clear. There is no relationship between whatever you want to call it, some notion of efficiency, whatever that might be, profitability, etc., and size. There is no direct relationship. You can have small banks. And in fact, most countries have banks of different sizes which operate in different niches. And, you know, they operate to reach credit to very different kinds of people, etc. So the point is that the idea would be you consolidate and use technology to reduce costs by shrinking the manpower base. But that means that you shift to a different kind of banking. It becomes a kind of banking which is, you know, in which accessing, you know, a human voice is only possible through a telephone if you manage to get through after waiting in queue, etc. And so it's going to change the very nature of service. But it also means much for these employees, you know. The point is that fortunately we happen to be in an organized sector which still has some, you know, some rules which apply to trying to retrench workers who have regular contracts. But then, I mean, things are changing. It's a different kind of government. It's a different kind of economic philosophy. So it's all being done in ad hoc manner step by step. But the direction is clear. The direction is one in which you finally don't want banks to play the social role as instruments of development which are given them. And then you might as well have, why should it be in the public sense? You hand it over to the private sector and hope that they make a better deal as far as the bank is concerned. Thank you, sir. And thank you for watching NewsClip.


Dobro, da so vši, in tudi na moj glasba je dobro, ker smo njih odstavili tudi v Kaliforniji in Italiji, in imam glavno, da so vse, in pojelim, pojelim, kako je na skrinu, ker je šantal, nekaj nekaj nekaj nekaj which I gave a few months ago, since I am in circle. And it is always related to big data artificial intelligence and to ongoing revolution. And this time I will focus a little more on two aspects. On what it really means artificial intelligence zato na veliko najverite vse iz dotovine, avčanje in tako, kaj vidi, da so zabrili. Zelo je ta nekaj finačna trenutka. Tko spetama, da nekaj finačne trenutka z decemifej iz kamerjnja, s delim in z našem srečanjem, in nekaj je prilivil inzibno, kaj je vsak opravljava. Zato, če pravamo, da brimo, ki sem pošličen, in začeljamo, da zelo je odelo. Zelo sem zaprejval, da je to pošličen. Zelo je pošločen. Artyvično idnečno je vedno quitovito svojeh. Szkoli je svih vrste, vsebe, našli in vama v nou, na barzi, na socialni. Včanje je zvoj konfuzion in dragi. This confusion is fueled by the media, who always want make something more appealing to the general public. Usually when they encounter a topic which is potentially very negative implication, they keep Lemmeringon it. je gove jazna imes na vso nekaj, stajelje in nekand do ljenjenju. Se tako je počadno vse z vso nRS, na našem mnoj napravu se termine se rovave, per nekaj 50 jaznih. To je neko, z katerim, ko povolj na zelo, način je jaznomakers. There is only one difference, that while nuclear has more or less only one meaning, z zelo nekaj zelo več, nekaj nekaj nekaj, je to več nekaj zelo, da je misledne. To neče, da je bilo nekaj relamo.  Tudi ni je naredil nekaj nekaj nekaj neče in načinila mu, ki kaj je se vse vesel, nekaj nekaj se vseh, tega da vidimo. chickens is the foot we used to, but now as I was film at discussed that the Ihrumerik. It's it. What is true, it is ok. This is for sva pone. dazu. data in vse do Mrs. laske deljevane infrastrukače. A evo smo se fluxo zvalična vseo zelo kaj pa se potopiti vseo z tega in utravapirka in začuši tudi specijal.ackedi kaj, kaj bi sem zrešal noželji svoj antih. Hellosnih začetvednjih vsega vsega vsega vsega vsega vsega vsega vsega je neuralne netice. Koste je se z taj obsat, da je nekaj neki dževil kljen, svom, nekaj neki dževil neki dževil netice. Zdaj, v evenlyčen tem, da počtem, da nič nekaj dževil in in tudi se bilo ne razmerilo in teda je tudi je vsem kombinator o štešnji vsega odlezvendvana vsega, nekaj imamoelnost, nekaj z nemunistikami, na ne kaj glasbeni, na nekaj dej, ki mali je, na nekaj dej, se najizvanje, nekaj sem? in z njega je zelo početnju. Vse je zelo, da je zelo, da se se vse zelo. Kaj je medija zelo, da je vse zelo, da je vse zelo, da je vse zelo, da je vse zelo, da je vse zelo, da je vse zelo, da je... ...jak se imamo vsi nekaj, ali je vse vse zelo, da je zelo. Ok. Začaj seveda iz neko, da so vse malo počučati, na čeha ne imamo nič zato, kako izpravimo, kako je vsega način vsega in nekako, način je vsega način. Način je, da se nekako vsega vsega nekako vsega vsega vsega. Nekako ne imamo nič način, nekako, nekako vsega, nekako vsega. So, usually when in the field of artificial intelligence, we speak about strong or strong artificial intelligence, we intend a third level artificial intelligence, which is basically to be able to build algorithms capable to make intelligent decision, which shall see what intelligence means, and in a general case, and basically you present the machine with a general problem where it is required to make a decision, and the machine performs better or at least equal to the humans. This third level artificial intelligence will be achieved. I mean, we are getting there. There are different timelines for the topic. We go from very optimistic predictions by Searle, for instance, in 2045, even a little earlier than that. In 2035 could be possible. To very pessimistic estimates, like one by the great physicist Roger Parros, who thinks that it will never be possible. Personally, I am more on the sort of side. I think that artificial intelligence of the third level is very close. I mean, we are seeing clear signs in this direction. But in any case, both these types of intelligence, fourth type and third type, are still to come. I mean, they are far in the future. When they will arrive, we will probably solve the most of the problem, which we are already facing, which are related to the artificial intelligence of the second level. Artificial intelligence of the second level is already here. And this is a big point. Algorithms capable to make intelligent decisions on a specific problem, even a very complex problem, but just one problem. And algorithms, which can make decisions, either learning on the basis of examples, like, for instance, it happens with most neural networks, most other algorithms, basically, you just have a large number of examples of a possible solution to a problem. You feed these examples to the network. The network adjusts itself in order to achieve optimal performances. At the end, you present the network with the new case of the same problem, and it makes a prediction, which very often is much better than what a human could. And this artificial intelligence is already here. We have it for 20 years. What has changed in the last 20 years is the complexity of the problem, which we can address. An example of artificial intelligence of the second level is Deep Blue by IBM, the one which defeated the world champion of chesses already in the 90s, or Watson by IBM, which defeated the champions of Joe Party. All are familiar with it. It's basically a question and answer game, which is very popular on the US television, and which requires not only the capability to understand questions, but also, you know, to provide the correct answer. This is anything but trivial, but again, this is intelligence of the second level. It's nothing to do with the artificial intelligence, which we see in science fiction movies. The other example of this intelligence, I mean, automatic cars with automatic driving, where basically you have cars who are capable to recognize the street, the traffic condition, the incoming vehicles, the signs on the road, and you know, to drive the car in a completely autonomous way, or you know, the landing of airplanes, which is nowadays mostly performed by machines. The pilot has very little to do with the landing. I mean, the landing is completely controlled by computer. Or even the most extreme case, which we had in the case of the automatic landing, the old landing of the Curiosity rover on Mars. In that case, I mean, everything had to be done through algorithms of artificial intelligence, just because there was no due to the delay in the transmission of signals between Mars and the Earth, which is about seven minutes. Since the whole landing lasted less than seven minutes, there was no way in which humans could intervene in the procedure if something went wrong, and therefore, the entire landing procedure was controlled by algorithms.  More recently, we have had a very extreme case of artificial intelligence of the second level, which is based on a special type of deep learning, which you see on the side, the generative adversarial networks. Where this society, AlphaGo, which immediately after has been bought by Google, produced the software which was capable to self-train itself, without examples, but in the game of Go, which is considered to be the most complex ever invented by human beings. And this is also a game based on strategy rather than rules, so therefore, it requires a very good level of understanding. Well, this machine trained itself in Plingo, and in less than two weeks, if I'm not wrong, was capable to defeat the world champion of Go, something which was considered completely impossible only five years ago. And this was, I think, this has been the highest peak reached by artificial intelligence in the last years. Ok, let's see what is behind all this. And let me make a little detour, first of all. This is something which I already mentioned in the past. The whole thing takes place into the so-called infosphere. The infosphere is a combination of three factors. The internet, without internet we could never have real artificial intelligence. The big data and the computing infrastructures. In one case, the first thing provides the possibility to interconnect heterogeneous data, internet. The second thing provides the algorithms with a huge number of information, a huge amount of information, let it be image, text, data, whatever you think, very heterogeneous data, which can be used by the algorithms to learn a trend, learn a pattern, discover a correlation. Basically, to make, to uncover the model which is behind a phenomenon, behind a decision, behind a trend. It doesn't matter where you apply it. It can be an economic trend, so the trend of stock market in the economic domain. Or it can be, you know, the behavior of supernova in the astrophysical domain. Or it can be, you know, in automatic driving. It can be, you know, the way in which you must react to a specific condition of traffic and of incoming vehicles. The problem does not change. And here we have the first large problem. Big data. Big data, as I already mentioned the other time, represent anomaly in the history of humankind. Because for the first time they are not subject to the states. Therefore, since they are not subject to the states or to the nation as a whole, they are not, at least in democratic countries, they are not subject to the people. Due to a sequence of steps which were underestimated by politicians, by government and so on. And which really require some serious thinking about the freedom of the internet. We can discuss about that if you wish. What happened was the following. When the internet began to grow, there were few groups of people who wanted to index the web. What does it mean? Basically, you have a web composed by n nodes, n nodes where you have different data, different information. But if you don't have a central indexing of these data, of these nodes, you cannot find the information even if it is there. So you need to have algorithm in this, which explore the web in all its possible nodes, in all its possible biforkation. Find the page, index them, so basically extract the meaningful word from this page, the image. And so classify them to a set of keywords. And then these pages need to be ranked so that basically you know which one is more relevant to a specific problem. The huge success of Google is behind one algorithm, which is called page rank, which was made by Sergey Brin, which basically was a very intelligent way to measure the relevance of a node as a function of the number of the nodes which were linked to that specific node. Consider it like a spider web. If you have a node, an intersection where you have many wires coming in, that node is more relevant than another one where you have fewer wires coming in. So basically this is the idea behind it. So what happened was following that the people who were building the browsers, those software tools used to explore the network, immediately had the problem to store the information which was gathered and to process the content of the nodes in order to index them. So basically it was a continuous feedback. The more the network grows, the more storage I need, the more storage I need, the more data I have, the more data I have, the more computing power I need to process these things. So you have a sort of loop. And as a result of this loop, some companies, those which dominated the market nowadays, grew enormously. When people realized the danger which was intrinsic to such huge concentration of information, it was too late. These companies were the only one who had the know-how, the infrastructure and for storage and for computing power. And therefore also the only one who could handle this amount of information. This is where Google, Facebook or the Chinese equivalent, 10 cent and so on, made their fortune and gathered such an enormous power. And there is a power which is very difficult to underestimate. It's very well-leading because this is not a conspiracy theory, this is a matter of fact. They control the network and from the network most of us obtain our information. And therefore controlling the network, you find very seldom articles or reliable information on how these companies are pervasive and how these companies are really shaping our way of thinking, of voting, of doing everything basically. Google is the master of the world in this moment and it's a pleasant but it is definitely true. Ok, where does Google get its enormous power? Here you have for instance, you should begin to have an idea from this slide. This is the Google Galaxy, which is controlled by company named Alphabet. In this you can see Google is just one small part of this company, which was the first one after Apple to go well above $1 trillion of consolidated value. $1 trillion, $1000 million, $1 million, incredible, incredible, never happened before. And where does this money come from? Google is free. You can use the Google browser for free. Therefore, where do they make their money from? Many people would answer from advertisement. That is a very negligible fraction of the amount of money which make the fortune of Google. I mean, merchandising, advertisement, it's less than 10%, if I'm not wrong, of the value. The value is in the access to big data. In a few minutes we shall understand why. And you can see also that when Google bought YouTube, it paid $1.65 billion, it's a huge amount of money. And also it will be surprised by discovering that Google pays $6 billion USD per year to control the download of the apps. Most of the apps which you download on your cell phones are for free. So where does the money come from? Why Google is investing so much to control the traffic, which takes place on the Android cell phone. Same thing for Facebook.  The owner of Facebook calls its customers useful idiots, because they provide data to Facebook for free. The value of the company was $700 billion USD, 0.7 trillion. And to buy Instagram, they paid $41 billion for its society, which had 20 million users. When they bought WhatsApp, they paid $19 billion, because they had 1.5 billion users. Same thing for Microsoft. They paid $8.3 billion USD to get Skype with 600 more or less million users. As you can see, the value of a society is directly related to the number of users. And if you think both Instagram, WhatsApp, Messenger, Skype do not produce money directly. You don't pay to use Skype unless you have a special contract to have multiple calls and so on. Or you want to use normal phones. You don't pay to use Instagram. You don't pay to use WhatsApp. So where does this value come from? It comes from the data. The more users, the more data are collected by an application. So at the end, by buying Instagram or Facebook, gained the community of 20 million users, which were useful idiots, providing pictures, providing data. Of course, some of the value is in fact that when you have 20 million users, which you have a profile that basically you can do a fine tuning of the advertisement. So basically, if you are visiting, you know, I am a stamp collector. If I buy many stamps on eBay or Amazon, when I go next time, I use Facebook or I use Google, I get advertisement for stamps or for phishing equipments, which are the only thing I buy online. But this cannot justify the huge value. So let's go back to what is behind the need for these huge companies. What are the real reason behind the need for data? To summarize, the total investment over the last ten years in the world for the infosphere has been more than 3 trillion USD worldwide. Did they do it for, because they want, you know, I don't remember the English for it, because they are very generous. They want to provide the world with services, unpaid services, or they wanted to have a return out of it. For what we know of, you know, large corporations, large companies, obviously the main goal is profit. There is no generosity in making apps for video games or in making WhatsApp available for free to a huge community. There must be an economic return. And the problem, and where does this return come from? It comes from the combination of big data and the strict unbreakable link which it exists between big data and artificial intelligence. And this is something which will develop the second part of this talk. To understand what happens, we, in any case, need to do a little bit of the tour. Why big data are so crucial for artificial intelligence? Let's start from here. And you born baby. When the baby is born, basically it is tabula rasa. In his brain there is nothing. There are all the connections ready to be activated. There are few instinctual reactions to the environment. But you are one of the smartest expert system which starts training itself. And what is really a miracle of the nature is that in less than few weeks the baby recognizes a biberon. I would say the not to pacifier. In Italy, I don't know what is in English for it, the bottle from which they drink milk. Okay, the one which is in the pictures. And this is not a simple operation. And it tells you a lot. These bottles come in all possible shapes, different colors, different shapes. They have completely different shapes if you look to them in a different perspective. If you look at them along the circular session, because you know, you see the diameter of the bottle. If you look at them from the front, you see the pacifier with the bottle. If you look at it from behind, you see only the end of the bottle. But the kid in few weeks learns how to recognize a biberon. Doesn't matter which color, doesn't matter which shape, doesn't matter which orientation in a nanosecond. How does he do that? He has trained a neural network. This is what basically happens in all classifiers. And remember that classifiers basically are behind most of the human activities in the human brain. So it doesn't work. Let's take it a brain. It doesn't matter whether it is artificial or natural. If it is our brain or it's a machine. What you need is something which is capable to do a very simple operation. You expose this brain with 1000 of different images of the same objects, like for instance in this case a chicken. And by combining the information in shape, color, in complexity of all the images of a chicken, the brain builds a sort of meta chicken, which is its classification of chickens. And this is incredibly powerful algorithm. Remember, we are here talking about one specific problem. I want to recognize chicken. So from the point of view of machines we are dealing with the artificial intelligence of the second level. I have a specific problem. I want to teach a machine to scan through thousands of images and recognize those where there is a chicken. Ok. You train the first classifier to recognize the chicken. You expose the machine to hundreds or thousands of images of chickens. Or you expose the human brain of a baby to thousands or few hundreds of images of a biberon in different shapes or rotations. And they both build a meta idea of that object. And they both build a meta idea of that object. They train a classifier to recognize those objects. Then you submit to your train at the classifier a new image, in this case a black rooster. And automatically your brain, if there is no other example, classifies that object as a chicken. Even though he has never seen that type of chicken before. But somehow in the brain some patterns, some correlation between colors, shapes, it's difficult to quantify what. But basically some correlations trigger the classifier to say it is a chicken. Repeat the operation with more than one classifier. For instance, you first train to recognize a chicken, then you train it to recognize a boxer. And so at the end you have two classifiers which are trained together. One recognizes chickens, the other one recognizes boxers. And if you expose it to cases which they have never seen before, basically you immediately know that in one case you are looking to a chicken and in the other one you are looking to a boxer, even though both have never been, do not belong to your training set, do not belong to your previous expertise.  Optical illusions are a typical example of how the human brain gets confused, goes in a sort of a still position, if it is exposed to something for which it has never been trained, exactly what happens to the machines. Now, so basically what you can have, and this is what is artificial intelligence or with which we are dealing right now, basically you can train, if you have enough computing power, enough examples, you can train N classifier where N is a very large number, each one of them is specifically trained to perform one and only one operations. And trust me, in most cases they can do that operation much better than any human brain. They do it with higher accuracy, they do it with lower level errors and they basically outperform the human brain in performing that specific task. And this is already something which has huge implication on the structure of those sites. Then, what basically makes the difference between the second level and the third and the second plus something level, is that we need to perform more complex classification at once and this is what is done with the deep learning. You have N classifiers working together and the information is somehow combined by what we call a sort of meta classifier. This is not correct from a mathematical point of view, but it helps to understand what happens, which basically allows you to perform things like that. At different level of the network, you have different classifier with work together so when you expose them to the image on the left, you have a first classifier which classifies it as a chicken, a second classifier which tells you that it is a male chicken, you have a third classifier which tells you that it is not a real picture but a cartoon and also that since it has glosses on it is a boxer, so the final interpretation you have a cartoon of a boxing hen. But behind, you have hundreds of other classifiers which are at work which tell you that this thing is wearing pants which are colored, which are blue, gloves which are colored, which are red, it has a beak which is curved, colored, yellow point. So basically you have that the problem is dealt with a level of complexities which was completely unprecedented until a few years ago. And this is why in these days there is so much speaking, so much talking about deep learning. From a mathematical point of view, there is nothing new. These algorithms have been around for the last 20 years with the different names, they are just chains over neural networks, they are ensemble over neural networks. There is nothing new from the mathematical point of view. The only huge difference which makes them a very effective tool is that nowadays we have the big data provider and we have huge computing infrastructure because to train these networks even for simple tasks requires a large amount of computing time, a large number, especially if they need to self train themselves, they need a large number of examples. So all these would have been impossible without big data and without large computational infrastructure. But where is the impact of this? Here we are. The big data provider controlled the computing infrastructure, controlled the big data and controlled the algorithms because they have huge number of scientists who work in improving the algorithms. This is why these large companies buy YouTube to get access to the huge amount of images which are in a single video and to the huge amount of information which is there which can be used. All they buy WhatsApp or Instagram and so on. All these information are processed in order to train classifiers either with deep learning or with other algorithms but it doesn't make much difference to perform very specific tasks which nowadays, thanks to the computing power can be also very complex. Is what for instance Siri does or Alexa does when they understand, very often also quite complex sentences and react to these sentences. This is what most automatic translator do and this type of everything is reprocessed neural networks of different types are trained, the results are stored in the cloud, your request goes directly to the cloud where it goes through the neural network and you receive the answer back basically real time. And this is where the huge power and huge economic value of the big data provider comes out because this thing it's revolutionizing the world already nice. Already now, sorry. Let me spend a few words on this. We think that this is a revolution, cultural revolution like the ones which we have already had many times in the past at the time of the invention of printing for instance, or at the time of industrial revolution. Not true. It is the first time that these humankind is facing with the revolution with this not to do with the way we make things, revolution which affects the home of other but it is the first time which we are dealing with the revolution which affects the home of others, the way we think, the way we understand things. And we are completely unprepared to deal with this type of revolution. Let me make you just a very few examples and then we can go to the question if you are ready. In the past, take the revolution in the transportation which took place between more or less at the same time of industrial revolution. You had horses which were used to transport material people then you had carriages then carriages were substituted by trains, by cars, but basically the people let's say not who had not undergone through specific studies or very advanced level of studies with low level skills were working either as horse riders or as drivers of carriages or as operator of trains or as drivers. It is even more evident if you take into consideration another type of revolution. When you move from agriculture to industrial age where you have basically the coal industry fueling the world and then you have the oil industry fueling the world you are not laying down people with no skill in the pre-industrial age peasants who had not studied who had not a very high level of knowledge working in the fields then you had coal miners and then you have oil drillers but there has always been a place for low level job not in sense that they are not important but low level in sense that they do not require very high level of training from the point of view of studies they do not require very high level of knowledge mathematics and so on because this revolution were affecting the way human beings do things not the way human beings think or react to the world which surrounds them so this revolution basically triggered social unrest but it was very moderate in comparison to what is going to happen in the coming years in the next 5-10 years artificial intelligence is affecting the way we think and the way we control any process take for example this example I think is crucial truck drivers the final end the death revolution in transports truck drivers will disappear in 5-10 years this is not sense fiction on the right you see the first prototypes and all companies Volkswagen, Audi, General Motors, everyone are building prototypes of self-driving trucks you will lay down hundreds of thousands of truck drivers who cannot be converted in the next technology because these things do not need humans anymore so what will happen to 100,000 truck drivers forever and remember that for everyone this is the estimate 1000 low level jobs this revolution is going to create only 5 high level jobs and this is going to affect everything all aspects of our life let me give you a further example now let's take this is about pros and cons for instance we are talking about global pollution let's think about agriculture until now two main problems insecticide, pesticides are distributed randomly on fields by for instance the plane you see on the left 80-85% of the insecticide is completely useless because it goes between plants on the soil and is absolutely wasted it contributes to pollution, environmental change environmental damage so basically at the end it contributes to cancer and other things so basically it is a very ineffective way of doing things also we have a problem with monocultures we are obliged to grow in a specific parcel of land only one type of crop just because you need to optimize harvesting if you need to make mass production you need to have all the corn reaching maturity so that you can send one of these huge machines cutting down all the corn and processing it at once so since to have a single culture on land and polish the land of nutrients you have to rotate land which means basically you have to change type of cultivation every year or every four years so one year you have mice the other year you have corn the other year you have millet this has a huge impact from the point of view of effectiveness because basically you have very often parcel of land which are left uncultivated for three years we need the food and also you know it is very expensive in terms of labor labor power you need to have many people to control this this process well in ten years also this will change drastically and this is already happening now again it is not some prediction from science fiction sorry now again I get confused already now in France insecticides are being tested in a competitive in on several fields many many large fields are being tested with automatic artificial intelligence basically you have small robots which explore the cultivation the place where the crops are they look at each individual plant they measure the amount of parasites and they spray exactly the proper amount of insecticide on each parasite this allows you to say 85% of an insecticide incredibly effective very convenient from the economic point of view no need anymore for people spraying insecticide also this experiment is taking place in California will soon be exported everywhere rotation no longer needed if instead of using this machine for the harvesting the one which you see on the left you use small robots which walk through the field and you basically cultivated this field with the mixtures of seeds basically you put together millet, corn and mice and now because they reach maturation in different times therefore if you call pick up the corn you are destroying the other things but if you do it with this machine but if you use a robot this is much more effective because the robot goes there stays at the plant says this corn is mature picks it up and puts it in the box goes to the next one this is millet needs to stay here for another week this is enormously the number of the production the productivity of the land but again what happens to the workers peasants no longer needed you say this is not going to happen very soon trust me in 20 years from now this will be really the normal situation in agriculture because probably advantages from the point of view economic point of view the owners of the land the people who produce food will save a huge amount of money they will not be obliged to pay salaries this machine basically lasts for very long are very effective consume very little energy so I mean it's okay perfect there is no way to stop it well again the ratio here obviously I take the most pessimistic one because since my good friend George knows I am very pessimistic guy but for 1000 low level jobs these techniques will produce one high level job what happens to 999 people who don't have the skill to get through this transition in a safe way we are getting to the conclusion Amazon very nice to buy on internet only in Italy in this year 35 stores medium size closed just because you know it's so much more convenient to use the suggestion which you get from Amazon which uses artificial intelligence to find you on your needs right thing and these 35,000 families are out of business and it's not easy for them to find an alternative and so on now this is not going to affect only low level jobs these are what you see here are prototype of things which are already available on the market you can download them don't trust me you just go online and download them you have your medical doctor Stanford biomedical DSI Babylon Health UK ADA and so on all these apps for the monitor apps but just because they are collecting the data to train the artificial intelligence which is behind are medical doctor already now they are saying to those of human doctors on normal diseases you don't need to go anymore to a medical doctor to a generic doctor because these apps more or less work like them for more complex things at the moment it is still better to go to a doctor I also prefer to go to a doctor but there is no doubt that even high level professions like medical doctors will be completely transformed by this ongoing revolution it's very likely that some generic doctor general doctor, family doctor we call them in Italy will disappear because they will be substituted by algorithms already now lawyers for large trials could not work without artificial intelligence for instance take the more report about the so-called Russian collusion of Trump in the United States the document, the 2.5 million documents which were produced by the committee were processed by an artificial intelligence algorithm because no team of lawyers would ever figure out what was in those documents so the pre-processing not the final conclusion but the pre-processing of this document was made using artificial intelligence tools and this is reducing the room for lawyers this is reducing the room for medical doctor this is reducing the room for university professor this is elitik which is one year ago was 70% accurate which is not enough but now it's already 85% because the deep learning network are beginning to improve give them time and they will achieve a level better than what is already possible for humans let me forget the bubble Google sematics but I just want to say that Google is already trying its own version of doctor on a trial of 10,000 patients it is operational should be concluded in 4 years and the idea is to provide all medical doctor in the world with the service basically the patient goes to the doctor tells him the symptoms enter it into this aid artificial intelligence aid which makes them returns the most probably diagnosis the best drugs all the relevant research for the case of the therapy it's clear that once you have these the step to the next level is to skip completely doctor so all this is terrible but the problem is that is at the end of this sentence human beings are very good at finding solution when the problem is already there but both climate change and artificial intelligence when they will be here it will be too late because there is no way to reverse the climate change and kill artificial intelligence when it arrives it doesn't and human beings are terrible at controlling processes while they are taking places because governments are slow governments are on average ignorant governments are not very sensible to the real needs of the world they are focused on pleasing their electron so so no, I don't know if I can call this conclusion we will call them final reflection don't be worried by all this bombastic announcement about artificial intelligence it's not here maybe it will never come in the general artificial intelligence we already have our problem with the artificial intelligence of the second level and the second level of human beings requires a strong ethical regulation global ethical regulation because if you have an AI in China it is going to affect USA, it is going to affect Italy it is going to affect Europe it is going to affect Maldiva Island one AI is not than sufficient to affect the whole world to nisi bilo počudno v saintevku, imam saintev, sem čestnju da bo to se zelo. To je politična problem, je netična problem in je vse potrebno iztenit z vsej zelo. I objezavno, smo v srbem vču 20 leti odstajali, ker prijev, kaj bejeni odpočovati Un Rose are also those which we elect because they look nice or because they appeal to our worst sentiment. Like racism or violence. And they have not the level of knowledge, which is required to track and to control such approaches process. So basically we are really in a catch-22 situation. And this is not something which will happen in the future. je zelo vsečo pričočno, a res potrebno, da bomo se odmačati.  Ko bi smo počeli vsi tudi, zelo je vsečo stavila več zelo. Zdaj mi se bilo. To je, sodas, da se možete izvako, In kako se ne so zelo, sem občasno zelo. Čutak je. Da sem včasno izgledati, da je to več nekaj, da je to zelo. BDP je vse rokočne matr. Google, Tencent, Facebook, vse rokočne matr. in tudi, ki imajo veliko informacije, ki je zelo potrebno. Znamenjo, ki je cih, komputerne infrastrucije. To je za komputerne infrastrucije. Zato vse obježujem vse internetne konvecije v komputerne in komputerne vse procesov. Vike, moj da počte za teh vših, bo sem si nekaj počte, da možete.  Ne však v USA. Sreč, na objevčenju v Europu, da smo teži, nekaj, nekaj, odmenev vsev. Tako, je priprav, odmenev kaj je, In, da je začala, da je bil druga dopradi, da je sem zelo privete in nekaj se služati, naltak v tem nekaj, zelo privete in nekaj privete poček, poček v tom izgleda za imeganje, privete poček v tom izgleda za imeganje. Ali tudi jezalno, tudi jezalno pomega, zelo poček v tom, mom jezalno, ki bil in očim, dobro zelo v blissima, ki jezalno, da jezalno sozati, because lack of scientific knowledge. But, yeah, I absolutely agree. There would be in the moment when this problem will bring you know, hundreds of thousands of people out of a job that will provoke for sure riots, backlash as a whole types, I absolutely agree with it. Well, let me see, I have lost many, many questions. Teglin, Google already is an imaginable, large database of medical information. Let me give you another starting point for thinking. Most of you, I think, are aware of Google books. Over the last ten years Google has been investing $600 million, if I'm not wrong, in digitizing, scannerizing, digitizing, interpreting all books and all scientific literature, which has been published since the invention of press. I was surprised when in Google books I could find the reports of the local government of the town of Naples dating back to 1815. Everything is there. All the scientific literature has already been scanned by Google. It has been semantically classified. Basically, for instance, medicine, they separated the symptoms from causes, from diagnosis, from solution, and all these things have already been fed up, fed to artificial intelligence, deep learning, mainly. In fact, Google doctor comes from that. Therefore, yes, Google is really moving in that direction. Yes, total information awareness is a typical political statement, means nothing. And if you think there are three contradictions in the same wording. Total means everything. Awareness implies that we are conscious of the total information content of something, which is impossible for human brain or for a machine.  We need serious people here. Politicians are not capable to even understand what is a problem. Sorry, let me go ahead. No, no, it wasn't my military project. That's my big data, not by chance. I mean, of course, the army, the military have an interest in this thing. I don't have any technology with huge implication, think to drones, but to tell you the truth, data mining is born from different things. Data mining is an application of statistics, advanced statistics, and think to the origin of the word statistics, where it comes from, from state. Or all these began when the states wanted to make a detailed analysis of the population, of the amount of people, of the net income, and some statistics as its etimus. Even its own name is related to that. So data mining, which is just a branch of a statistic, is somebody much older than any military application. You know that nowadays everything is somehow produced, but it's not true. They just do some things, but not everything. Thanks God the world has also different aspects. The change is good, you first. Yeah, exactly. You got the point. No more caffeine, it's impossible. Both physics and computer science are built upon caffeine. Without caffeine we would not live. I agree with you. The caffeine is what keeps me calm and puts me to sleep. Well, I agree, this could be a good panel discussion topic. If you wish, we can organize it. I have a few nice speakers, which we could invite. And I think I have not lost any relevant questions. As long as IE does not see the information, it looks at all sides, like clinical thinking. Eric, what does it mean, looks at all sides? This is a very crucial problem, and your statement is absolutely correct. The problem is that there is even a research field which is trying to find the proper matrix, to measure the level of impartiality. And we are still very far from that, because the way data are collected is biased. So the impartiality is already intrinsic to the data. In order to have an impartial evaluation of data, you should have impartial data, but this is almost impossible to perform. I think you got the point. So in teori, it could happen, but in practice it will never happen. Humans tend to... We have a tendency to see or only look for data that we agree absolutely. And nowadays this is even more extreme, because most of us get our information from the network and therefore the big data provider and the algorithm which are behind them provide us only with the information which they assume we like. Look to the level of schizophrenia which is on Facebook, where basically you have all the dark side of human beings coming out. You see people who believe in the flat earth or people who believe in the most superstitious things.  This level of exasperation is going to explode. He has ever been interfaced with the human brain. Sorry, I didn't see that. Yes, they are trying to do that. And in particular behind this there is Facebook. They are trying to interface the human brain with the machine. Basically the idea is that since we are not sure we will be able to produce an artificial intelligence of the fourth level, let's see whether we can empower our, they say, amplify the power of our brain by interfacing it with the computer who can perform some tasks faster and more accurately. They are trying to do it. There have been some very minor successes. For instance, I think there is a prototype which should come next year produced by a company related to Facebook which will allow you to control the mouse and the keyboard just by putting an element on your brain because you can control it with the electromagnetic waves of your brain. It's something which will happen. Our brain is just a machine exactly like the computer. So the only way, the only problem is to establish a common language and then it is an electrical signal on both sides. So it will happen soon. Vic, I think that all hypotheses of what artificial intelligence will do are just a projection of human fears. Artificial intelligence will be artificial. It will not follow the same rules of the natural human intelligence. It will not follow the rules which we have inherited from natural evolution because it will not be biological. It will be artificial. So all scenarios are open.  But you know. The problem in this world will not be artificial intelligence. The problem in this world is human beings. They are irrational. They are instinctively killers. They are violent. They are racist. They are arrogant. They hate everything which is basically different from themselves. The problem will not be artificial intelligence. I think that artificial intelligence will look upon us with a very pitiful attitude. What we must reform is ourselves, not artificial intelligence. And no, Vic, I don't agree. Because artificial intelligence will be its own intelligence. All the other things we have built so far in technologies were not capable of thinking. And they were, therefore, basically the way they were performing depended on how we use them, so nuclear, good or bad. If you use it to cure cancer, good. If you use it to kill people, bad. Cars. If you want to move from one place to the other, good. If you use them to kill people by running over them, bad. Everything, science and technology are not intrinsically good or bad. They are knowledge. It's the way we use them that makes them good or bad. And artificial intelligence will use these things its own way. So you cannot tell whether it will be or bad thing. Don't believe in Asimov, who says that you put, you know, three rules which cannot be overruled. You know, do not harm humans in this type of things. It's very naive.    So why should an artificial intelligence obey to rules which are again made by the human beings to protect themselves from the fears? We are very fearsome. We are very fearsome species. Well, thank you very much. Now, for me, it's time for dinner. I'm really very glad for all these questions for all your enthusiasm. Well, anytime you want, I'm always very happy to come to the Science Circle and I really would like to thank Chantal and all the people from the Science Circle for organizing these lectures and to maintain the thing alive. It's a wonderful thing. Thank you all. Thank you very much. I'm going to do it right now because I'm starting. Bye.


here in Berlin right here in September 2nd 2017 and who are you? I'm I'm the direct candidate for Sheldonburg Wimmersov which actually is the city best Berlin. So it's part of Berlin and you the candidate for SPD? Exactly for the Labour Party of Germany. And Martin Schulz is the leader of your party and is he going to be the next boss of this country? I definitely hope so because what we need now is a unified Europe and especially say is to Switzerland our guess next door because like we have to do to understand Europe as being more than one economical zone. Mr McCrory said like Mrs Merkel this is not a supermarket you can't pick the best of Europe you have to take it all and that is what Martin Schulz of course also says you know Martin Schulz used to run the European Parliament so his first object is Europe and that's what the next big step for Germany is to simply try everything to help that Europe comes together. So there's just a couple weeks or a little bit more right? Yep. Left and I mean there's some polls but what do you believe about polls? Is that the truth or is not? No polls. Trump people thought he was going to lose right? Exactly. And if it happens? We also had the last three elections the local re-elections that we had got in Germany, Lacev-Holstein, Rostrom was failure and Rheinland-Pfalz all looked like the person that did win in the end was far behind the leading person three weeks in from the actual election. So the polls these days are kind of tricky and people decide very last second. So everything is open so if you watch this and you still want to vote for SPD because you want Martin Schulz as a chancellor do it. Merkel has been in power for a very long time. She's seen three different presidents in France which is strange. She's not a dictator right but she's been there for so long. Why? What's going on? Has it got to with Germany kind of being boss of Europe and people in Germans being happy about that they just wanted to continue the same way? The one inside the one inside has to do with Merkel benefiting from all the reforms that Mr. Schröder has done before which kind of them were kind of hard social cuts but the country is growing economic wise big way then and she takes all the credits for doing nothing and people are afraid of losing this positive effect. On the other hand side she never has got conflicts because she does government policy like a stone. She only moves if the earth moves so if everybody else is on the very same opinion then she moves before and she's pretty stubborn and she's pretty much on one case and it's not much happening here which is also dangerous. Just have in mind we are in a world that's changing dramatically. We have for the Brexit, we have for Trump, we have put on the other side the digital revolution and not to move, not to think ahead. This part in Germany is kind of dangerous also for the other countries. Not everybody in Germany is rich, right? So it's not okay to just have just conservative politics. We need to take care of everybody and not just in Germany like reform Europe make EU better, right? EU has issues and it would be nice if Macron shoots and maybe Jeremy Corbyn would do something about it in a different direction than has been done before, right? On one side you have also in a prosperous country like Germany a lot of poor people you have really more seriously to take care of but on the other hand if you look on the European scale then you have got people who have got sometimes dramatical conditions of living in Greece but also parts of Italy and parts of Spain and we have hey we are one Europe so we're responsible for each other. So Germany did profit big way in the reunification process of the rest of Europe being extremely in a solidarity move spending into Germany making this happen and now it's up to us to also be to show solidarity to people who have got a bad living especially in southern Europe and in parts of Eastern Europe only we succeed to bring everybody on the same social level we have got a peace for Europe. So bring everybody off right? Yeah. Like change the system a little bit and get things done because China is moving so fast. Yeah. It's just one guy in China he decides they do it and in Europe is discussed for 20 years before something gets done sometimes right? Yeah and it's kind of a issue. I was I was in together with the foreign minister a few years ago in India and you already had got the feeling that for the people of India that Germany is that big it's felt a little bit like being the together with the foreign minister of Liechtenstein on a trip to Germany so you yeah you get an appointment but it's like where exactly is Liechtenstein and so it feels and in comparison it is a bit like this so just out of being like if we want to mean anything in the world if we want to have got any kind of influence in the world the only chance is to do it together so let's join in Europe. Unfortunately that's a moment I have to stop because I have to go there. Thank you very much.


Good afternoon, everybody. I'm Tommy O'Brien, coming to you live from TFNN, 2 p.m. Eastern time, two hours left to go on the trading day, and you got markets accelerating to the downside. Not that long ago, I was on the air, 9.30 this morning, the markets open, we had the spy trading at 4.37, just like that, we've given up almost five points. You talk about an acceleration down to 4.32.51, we'll jump over to yields, quite a moving yield as well. You check out the Nasdaq 100, we're off by 98 points right now, make it 101 points, off 2.30%, you get the Dow off about 1% right now, off 315, the Russell, how about it? Off 2.5% the Russell, you're looking at a 10-year yield, a 10th of a percent higher than it was this morning, even more than that maybe, 4.72%. We jump over to the 10-year, you talk about a sell-off, we get the spike higher on the CPI coming into the CPI this morning, a little bit of a hot CPI number, 0.4% month over month, and it's been a one-way trip, and we are making session lows as we speak, and as you get a sell-off on yields, we have the markets selling off as well, and what's that doing to the dollar? You might be able to guess, putting some strength into the dollar, and you talk about a move, man. Early this morning at 8.30%, we're at 105.69%, you've added almost a full point in the dollar to 106.54%, yields rising, the 10-year, now above 4.72%, you have yields rising across the curve as well, you jump to the two-year, quite a drop off as well, not quite the same escalation though, right? You get the drop off on the two-year on that 8.30 number, and then we chop around, longer term though, seems like rates are going to have to go higher, man, and we've been talking about the 10-year on a daily basis, right? Quite the reversal, right at the upper boundary line of that channel line, pretty remarkable, man. You turn around on that channel line, and the request in the 30-year in the den, you talk about a move, man. Yeah, longer duration, we got a negative two full points on the session, but that's not counting the fact that we were higher coming into that 8.30 number. Talk about some moves, man.  How about Target up by 1.1% right now, Walmart up by 2.10%, markets in the red. We got to replay this hour, folks. My dad, Tom O'Brien, he's coming up live from 3pm till 4pm. Where are we going to be by then? S&P is negative by 40 yields on the rise. Stay tuned, folks. Replaying my show, Tom O'Brien, my dad, live from 3pm till 4pm. The reality is that navigating financial markets can be


Live from Boston, Massachusetts, it's theCUBE at the HP Vertica Big Data Conference, 2014. Brought to you by HP with your hosts, John Furrier and Dave Vellante. Okay, welcome back. And when we are here live in Boston, Massachusetts, this is theCUBE, our flagship program. We go out to the events and extract the students with the noise. I'm John Furrier, the founder of SiliconANGLE. Dave Vellante, co-founder of Wikibon.org. Go to SiliconANGLE.tv to check all of our live footage. Go to Wikibon.org to get all the free research. Again, this is theCUBE where we broadcast live from the events that matter. And our next guest is Eric Venzuela, business development, full 360. Welcome to theCUBE. Thank you. Appreciate it. So big data is a discussion. So we were just talking prior to going on about Amazon, the cloud. And you mentioned the word DevOps. And I'm like, oh yeah, we love DevOps. We think DevOps, having developers work with the cloud is the biggest thing that happened in generations in terms of productivity. So welcome to theCUBE. Thanks so much for having me. How are you using the cloud to deliver data and value? Tell us. Well, we're early adopters of cloud and both Vertica with analytics and BI. We've been partnered up with Vertica since a delivery that was given at MIT, our CTO actually participating in that created a relationship. I think this was back in 2007. So we were immediately part of the beta test team at that time. And since then we've been running like crazy. But the interesting thing is that when Vertica was released, that's right around the time that Amazon started coming out with their cloud offering as well. So the two married well together and we immediately started building, started developing and started doing things like that. So what do you think about the Vertica situation? We'll go with Amazon a second. What's so about Vertica? Obviously the results we're hearing from their customers is pretty significant, large scale, big time customers using hyperscale as running with Vertica. You surprised by that? You psyched by that? You've been there from the beginning, kind of present that creation, if you will. What's your take on their success? Well, I'll kind of give you my take on it purely from my base of knowledge, which it's not a huge pool. It's not a deep pool that I dive into. It seems to me that now that there's a place for tremendous amounts of data, that people will continue growing out their data requirements as long as there's no restrictions, that those bits of data, those data sets will continue to grow. And then there will always be a reason to analyze those things. But I'm not at all surprised by that. I think it actually makes sense. I think part of the process getting to this point was the adoption of it. And now that it's easy to, from our perspective, just purely with the cloud, it's really easy to get a stack up and running. It's really easy to get something up and running where you can load some data and actually review your data in real time. And so just based off of that, there's no limitations anymore. So if you have a proliferation of people that can actually do this stuff, yeah, it makes sense to me. They gave a skeptic on self-service BI. So where do you see that going? Because that seems to be, people want business intelligence tools to be less geeky and be like a search engine. But there's a gap between where we are today and where we want to be. So yeah, how are we closing that gap? You know, that's an interesting question. I mean, I think from our perspective, I think we're doing a good job of building the technology, building the infrastructure, making it very easy for more people to get tied to it, to use it, to spin things up. And also, in addition to that, I mean, it's not magic. I mean, there is a level of respect that needs to be given to those platforms to keep them up and running. So I think as you continue building bigger and better platforms, that will eventually close the gap. But if you keep creating tremendous amounts of data, that's also a place where you can put that data and analyze it quickly, irrespective of the size of it. That's also closing the gap. Eric, how much of that is closing that gap as technology versus sort of training and process and the like? Yeah, that's a good question. So early on, I would say even just a couple years ago, a lot of the work that we were doing was purely education. I mean, we were teaching people that building these platforms in the cloud is secure, that it is efficient. And now with a bigger adoption around that, we're not having those conversations anymore. Now it's about, how do we get to that point much quicker? So these solutions are actually created and developed a lot faster than they were in the past. I'm not sure if I answered. So you brought up another point. So you think people are through the knot hole in terms of the pedantic argument of security and privacy. Yes. I mean, I've always said, John, John, you can vouch for this. I think the cloud is more secure than the vast majority of on-premise deployments. I agree. I agree. And so, but yet, we still have people coming on theCUBE saying, no, no, we don't do the cloud. Now, maybe that's industry-specific. Yeah, I don't understand the hesitation. I mean, you start digging into that. You start asking them, what is your hesitation?  Because I think the cloud is actually proven to reduce a lot of the operating cost for on-premise, right? So I kind of think that part of it might be from when you look at on-premise and how to manage and run and build on-prem, there's a different shift in the way that you think in order to get that up and run in the cloud. It doesn't directly translate, right? The way that you do things on-premise is certainly not going to be the way that you're going to do things in the cloud, right? So that, it could be an unknown, right? It could be, maybe there's just a gap there. I've kind of found that. I mean, once I've started educating people on certain things that you can do in the cloud and cannot do, you know, that sort of lifts a little bit. Oh, good. I think that makes DevOps so powerful is the fact that speed is a game, right? So like, right now the big trend that you're seeing in the startup community and in the data community is what they're calling the full-stack startups or integrated stacks or integrated value chains, if you will. The old model of siloed approaches is kind of getting busted down. We're really busting down the silos for the first time in history and changing that market. So like, if that's the case, things like clouds should make BI as easy as using Google Search.  You know, you've worked with the operating system to tune it as best possible to operate with this, you know, vertical instance, right? So you know exactly how that's gonna work. And so what we've been able to do is that as changes or additions or updates or things that are needed to be done around Vertica, we just have to, we leverage DevOps and we only have to make that change or make that addition to our scripts just one time. And we know that we can now provide this to all our customers, right? So it's not as if we're starting from scratch or from ground zero each time, building up from there. So, I mean, I tend to agree with you and John, I'll tell you again, I'm sort of a cloud bigot, but I wanna play devil's advocate. Why is renting cheaper? Why is it less expensive than owning? You would think at some scale, Zynga tested this. I'm not sure it worked out the way they wanted to. Uber seems to be going in a similar direction. But is it just better? Is it more sort of getting out of that what AWS calls the non-differentiated heavy lifting? Can you replicate what you're doing in the cloud on premise?  You got good background there. If you were one of these enterprise guys trying to hang on to the past, could you replicate that capability? It's much easier going from on-prem to the cloud, going back from the cloud on-prem. Not, no, that's a huge challenge. I mean, that's not an easy thing. Because of the mindset, the DevOps culture. Yeah, but there's also this little limitation, right? I mean, the environment that- Scales there, the environments are going to be completely different too. One thing you can almost guarantee is that Amazon's going to be exactly the same every time you work there. Different on-prem is always going to be kind of a challenge. Taking one of our scripts, just bringing this as an example, is that if we've developed a script, we know exactly that that's going to run. We know 100% of the way that it's going to run and operate and behave inside the cloud. In order to take that and apply it to an on-prem, we would have to then understand how that on-premise network and how all of that works modify this to make it work, right? So, once you go forward, I mean, there's no point in going back, but... Although some have tried. Yeah, some have tried, yeah. I mean, and I think if you look back a couple years ago, there was probably some companies that started in the cloud to do development, to do test dev, because it was really cheap plays. They didn't have to procure hardware. They didn't have to have these big CapEx purchases in order to just prove a concept to do some test dev. And I think they started those things with full intention of coming back in on-prem. But as that's, the cloud stuff has actually grown, it makes more sense for them just to... When we talk to our customers, we always throw that out as an option from the very beginning. If we're going to build in the cloud, you might want to consider keeping it in the cloud, taking that off the table of going back to on-prem. Let's just keep it in the cloud. I want to ask you a question that I asked a lot of folks.  What is the biggest thing that they're looking for? And then the specific question is, if they could ask their data anything, what would be the number one thing they'd want to ask their data? Ask the data, what's the top level, the number one thing that most customers want to ask of their data? So we cross a lot of verticals, I mean, we're in, we have some airlines, we have some big games, we have quite a few banks. They're all going to ask a different question, but I mean, I think it's just about getting down to the nitty-gritty, right? I mean, it's about making an educated decision with intent that's based off of fact and data. I think everybody's going to be asking that question, how they digest it and how they use it at that point. You know, I guess that depends on what they're talking about. We were talking earlier, yes, in the previous interview with Peter Fishman from Yammer about A-B testing, and you know, we were kind of kicking it around. Obviously, depending on how you look at it is good. We had Eric Schwartz on Twitter just comment to me. The vast majority of A-B testing I've seen done is utterly useless, mostly low base rate problems, sample size, and then Peter's talking about other analytics. Are you seeing the myth of big data and this intelligence being skewed by the hope that there's something there? Or are you seeing real, actual insights with the staff and the technology in place to make that happen? Where are we in that continuum, being nirvana of the holy grail? The people are peaked in talent. The things are fully scalable. The data's all ready, all the tools there. Versus kind of the dream, are we being sold the dream? I don't think so. I think infrastructure's there. I think that's available. I think that we can fully leverage that. I think the expertise is there in terms of what it is that you wanna do with that, with all those, with that huge volume of data. I think we're definitely getting a lot further away from a pipe dream and vapor than we think, but that's definitely moving quickly. I think by this time next year at this conference, we'll definitely see a different landscape. I think we're definitely gonna see a lot more people getting into the big data to either consulting or infrastructure in some way or another. So the name Full 360 is interesting, particularly because back in the day of the Heartland of BI and Enterprise Data Warehouse, a lot of the marketing was around a Full 360 degree view of your business. And most people would argue that that never lived up, that reality never lived up to the expectation. And that's fair, right? Yet you guys chose that name, and you talk about BI and DW for the cloud. So we talked about DevOps, you're clearly doing things differently. But are you finding that you're adding more business value, that it's really not sort of traditional BI and EDW? Or is it just you're doing that better? Well, I think we're definitely taking a different approach to how to do it. And I think that, I think conceptually, we're doing it better, right? I think we're fully using the technology out there to its maximum. Vertica, for example, the cloud, for example, I think we're pushing the limits as to what those two technologies can do independently and then actually together, right? So I don't know, that's kind of a tough answer. Well, so essentially, are you fulfilling the promise that was laid out there? I think we're getting, yeah, I think we are. I mean, I think we're getting close. I mean, I think we're listening to our customers in a way and building what it is that I think that they're asking. I think once they get closer to it and more knowledgeable on what big data is or how to manage it or how to use it, that's where things really start to grow, right? So you mentioned a couple of industries that are traditional airlines and banks and then the new sort of emerging big games industry.  We're chasing chips every time Intel comes out with a new chip, we got to buy it, because these things are so slow. Ingesting data is like a snake swallowing a basketball. It's just they just describe this painful patchwork of endless hell, right? There's another expression I won't say. It's one of my favorites. Some of those are the pumpkin. So what I'm asking is, is that changing because of your processes and because of the cloud and maybe a little bit of vertical in there? Or has something else changed? Is it this big data? Is it Hadoop that you're injecting into it? Is it, you know, have cost just hit a tipping point? I'm trying to understand what's different besides just the cloud and your excellence, your expertise and squeezing as much as you can out of the technology. I think it's a combination of all those things. I think if you view it in a certain way and you're open to expanding upon it and the infrastructure is there and it's somewhat limitless with the cloud, like I said, that's a really good partnership. That's a good marriage there. But then when you add on expertise and a lot of things that are broken that we've had to fix over a period of time, I think when you combine it all, now you're just focusing on how to digest that data. If you have the pipes big enough, if you create them big enough, now you can actually get the data loaded in and then it's about digesting it. Now, if you have a columnar type database like Vertica that can do the analytics very quickly, that part is now taken care of, right? And then if you have the expertise and the knowledge to put it all together, I mean, I don't think it's just one thing. I don't think Vertica in and of itself is solving all the problems or the cloud is solving all the problems. I think it's definitely a combination of everything put together. I wonder if we could talk a little bit more about the sort of, we'll be talking a lot about the technologies and sort of dig it in and playing devil's advocate on some of the cloud stuff and that's all well and good. And it sounds like most of your customers are way past that, right? That's kind of what you told us before. So where are they? I mean, take us through a typical engagement.  Talk about sort of the business drivers and maybe even give an example. A typical customer is somebody that has, is very on, everything is on-prem and they're interested in the cloud, right? So they know that there's this technology out there, right? And then they have a requirement for managing their data. Either it's in disparate silos or maybe it's just growing so much that so quickly that they just can't get a hold of it and they see moving into the future that it's just going to continue, right? It's just going to continue to get bigger and bigger. That is a, that's the typical customer. So they would generally come to us and we would understand their business, understand their data. We offer a product where what we call, what we call it is a quick start. And what that means is that it's similar to a proof of concept, but we're not really proving any concepts at this point. Everything's really been proven. We're just quickly trying to get them up and running and started on the cloud or with Vertica or with big data. So what we do is we take a sample set of their data, load it in and then give them some reports so they can start reviewing their data. It makes sense at this point because it is their own data. It's not some canned made up data, right? So from there, once all of that groundwork is done, that's when all the creativity starts to come out from the customer. Okay, now all of these things have been done. It's easier for us. Now we can see our data. It's not as complex as we thought. Remove in the unknowns as quickly as possible. That's generally- Then they're hooked. Yeah, exactly. So if you have a customer that has quite a few unknowns and you can remove those, then you're pressing forward. It's just opening up to a different way of thinking. And some of your customers are putting so-called personal information into the cloud? Personal information for their customers. Yeah, yeah, yeah, it's up to them, all right?  And I have a sort of a question about AWS. So the Vertica is in the AWS marketplace, or is it kind of bringing your own license, or how does that typically work? It is in the marketplace. There's a community edition that's there that will allow you to spin up Vertica in the cloud. Our stack, the ones that we've developed is different from what is available on the marketplace. So if you were to come to us, we would use our own scripts in order to stand up that stack for you. So it's been through months or years of development in order to get a finely tuned operating system, tuned Vertica, and connected on AWS. It's everything. So I transacted that through full 360. Right, right. And you take care of the, you're my heat shield to AWS. I don't need to hire developers, right? Exactly, exactly. I don't get a monthly bill from Amazon. I get a monthly bill from you guys. You'll get a monthly bill from Amazon for their services as well, just for the usage that's transparent pass through. Yep. We charge for the managed services on a month-to-month basis. But the way to look at it is that we're not only taking over the managed aspect of keeping Vertica up and running in the cloud and all of that. It's really an insurance policy, right? I mean, if you really think about it. We guarantee that that is always gonna be available that it's always gonna be up and running. It's always gonna be up to date. You know, that if there are some, you know, data lags, latency, query issues, all of those things we address for you. So you have a very intimate relationship with Amazon, right? Presumably, right? Pretty close.  How do you deal with that squaring that circle? Well, I mean, obviously Amazon spends quite a bit of money on making sure that those audits pass, right? That they pass muster. So we don't really focus on that too much.  Okay, and that's not an issue when you're in your customers. It hasn't been. It used to be, it used to be. Early on, it used to be, but now, not so much. Okay, Eric, thanks for joining us on theCUBE. Appreciate it. Obviously, Amazon Web Services really showed the way for innovation startups. Certainly IBM is trying to match that with EMC and VMware and Pivotal, trying to build their cloud. Everyone's building their cloud, so it's just the beginning of all the action. So appreciate your insights here on theCUBE. Business intelligence, big data is driving ton of innovations and enabling entrepreneurs to be successful, not just big companies. So we're excited to bring you that action. I'm John Furrier with Dave Vellante. Go to crowdchat.net, check out the live chat we're having in our engagement container. And on Twitter, I'm at Furrier and Dave's at atd, Vellante. Check us out, we'll be watching. So we'll be right back with our next guest after this short break.


Hi, I'm Cecio. Hi, I'm Sabina. Hi, I'm Louis. How are you? How's it going? All recipes we make come from old French king's tables. Some of them come from the peasants' ones. Food was scarce and people were great at using produce and herbs from their garden, and cheese from their cows, goats, or sheep. Potatoes were introduced in Spain by the conquistadors coming back from their expedition in the Inco World during the 16th century. It took almost 200 years for potatoes to spread all over Europe and be cultivated for human consumption. At first it was so precious that it was only used as medicine by rich people. By the beginning of the 18th century, potatoes became a staple of the everyday meal. Because every house would have cheese and potatoes at hand, grottin' recipes began to flourish in every region of France. The word tartiflette is probably derived from the Franco-Provencal word for potato, tartifla, or tartifoli in Italian. This modern recipe was inspired by a truly traditional dish called pelle, a grottin' of potatoes and onions without cheese cooked in the hearth on a long-handled pen called the pellegie, or shovel, in the east of France. The tartiflette, as we know it today, was developed 40 years ago by a group of cheese professionals to promote sales of a cheese called rebelochon. Several French regions developed their own recipes using different cheeses like morbière or morroix. Some use cream, white wine, milk, or even beer to keep the moisture in the grottin'. Rebelochon is pretty difficult to find in the U.S. and it's quite expensive here, about $20 a pound. For good results we can use a substitute, raclette cheese, which is easier to find in specialty stores. Enjoy this nice winter recipe. Stay warm and keep on cooking. The tartiflette is something we eat usually in the mountains when we go skiing, because it's a kind of heavy meal. So first, what we do, we prepare the potatoes. We cook first the potatoes. We cook separately the pancetta and the onions, and you mix everything together in a big pan, or the small one, just nicer. You mix everything, you put the potatoes, the mix of the onions and the pancetta, a little bit of cream to be rich. And on the top, you add a piece of cheese. And usually when you do it in the traditional way, it's a big pan, and you put the rebelochon with the round cheese, so you get the rebelochon in half, and you put it on the top of the potatoes and the rest of the ingredients. So are you ready? Yes. And it's typical for this weather, because here, now we are freezing, there's no air everywhere, so it's perfect. So first thing, peel the potatoes. So Sabina, are you able to peel the potatoes? Do you want your hand? I don't. If you can, tell me. I will help you. So you peel the potatoes, and you slice... You have two options in cubes or slices. For this mold, we are going to use slices because it's more convenient to fit inside the long jam. Maybe I can add a cube spoon. So during that time, we prepare for some boiling water. Right, potatoes. So you know how to do it. Okay, so we're about to set. Okay. And of course... No, no, wait, wait, wait. I'm sorry, I left it so we do the same. So now we need the big saucepan. And we'll cut the onions. And the pie. And we'll peel the onion. Never, never, hold a knife like this. It's too dangerous, because if you break it, it's directly in your stomach. So you know how to do it. You slice in two or... Like this. And you do this. Okay. So for you, it's like this. So put your fingers. Like this. And not too close to the knife. Voila, perfect. So here we have the pancetta. So you know what is pancetta? Yeah. Tell me. Oh, it's like... I don't know. It's an Italian bacon, actually. And it's not cooked as we do in the US, it depends. But it's an Italian style bacon. You can use it because it's very convenient. This brand is already cut, so you get some time. Okay. We have plenty of onions. Do you like onions? No. Are you kidding me? No. Really? I don't like onions. I don't? Even when it's cooked? No. Okay, so we will do a special one for you without onions. No. But yes, we can. No, I eat onions. I don't like onions. No? Okay. Okay. So now you put the pancetta in the pan. And then you have the onions cooked together. Some more there for the specificity. So what we have to do is to measure the heavy cream because we are going to mix the heavy cream with the onions and pancetta. So can you measure one cup? Yes. Okay. And we are going to dry out the potatoes. Okay. Okay. Yes. Perfect. Put the cream in this turn. Yes. So we can turn. Oops. Oops. It's cold. Okay. Okay. Good. So then you can take... Yeah. Go ahead. You put some potatoes. And then you add... I'm going to take that one. You add some cream. Okay. And then some pancetta, the onions and the cream on top. Okay. Then you add another potato on top. And then we add the cheese. Come here. Easy, huh? Not too much. Okay. And the piece of cheese on the top. Good. Oh. You can put some cheese inside. Okay. And do it again. Do you know what we do after skiing in France when we deserve a good meal? We do tartiflette. But there is a famous also... We do a raclette. Yes. This kind of cheese, but with charcuterie and potatoes. But we grill the... We marinate the cheese. And we put the cheese on the top of the potatoes and the charcuterie. Oh. What we can do also is delicious. It's fondue savoyard. It's a fondue made with sweet different cheese. It depends if it's fondue from Switzerland. So, first of all, can you tell us how to do the fondue from Switzerland? It's not the same as the fondue in France. The cheese are not the same. But, you know, in Switzerland you have what we call moitié moitié. So it's half, vache, frivogioir and gruyere, for example. And it's very good. Those are very good cheese. And in France, we put more emmental... Gouffard. Gouffard. Yeah. So it's not the same kind of cheese. But the result is the same. It's very good. And you melt the cheese in some wine. And also some kirsch. And you add a little bit. And it's super good.  Yes.  Oh, it's delicious. But very rich. Now, Gouffard is ready. Careful. Now, be careful. It's super, super hot. It's still bubbling. Smells good, huh? Even you can decide if you don't like too much onions. Are you... Sure. You want to try? Okay. Okay. So before to try, we have to wait five minutes.  Oh. Well, also, maybe I'll take this. I'm not tasting it until it's cooled down. Try this one, no? Try this one. So, pick a little piece on the edge. The potato. Okay. And wait, wait. Ready? Smoking. Oh, I can't wait. Mm-hmm. Ready? So, yeah. You like it? Mm-hmm. Good. It's good. You like it, Kallisto? Yeah. Okay. Really, so this is the tartiflette. And for next month, maybe Kels, you want to do some dessert? Yeah. Something sweet? Yeah. Okay. So maybe next month, it will be sweet. And thank you for everything.