Well, good evening and welcome. It's fantastic to see so many people here on a slightly stormy night out there. Welcome to the second of the lectures in this year's Gibbons series where we're looking at the steps towards the singularity, artificial intelligence and its impact. I'm Robert Amor. I'm Head of Computer Science Department here at the University of Auckland. And it's my really great pleasure to introduce Professor Hans Gusskin, who's a friend and colleague to many of us over the years. And I don't think Hans got a drink or any food because he was saying hello to so many friends in the audience as he came in. So, Hans is a Professor of Computer Science at Massey University and his qualifications come from universities in Germany and Bonn and Kaiserslautern and Hamburg. And he came to New Zealand in 1992, so he's almost lost his accent. And he came to the University of Auckland when he joined us there. And in 2007 he shifted to Massey University where he's a Professor in Computer Science. Doesn't seem like a decade that Hans left us. That just seems a few years ago that that happened, but ten years ago. So his main interests are in the era of smart environments, ambient computing and intelligence, knowledge representation and inference, constraint satisfaction and spatial temporal reasoning. And tonight he's going to talk about artificial intelligence as it can impact on society and addressing one very specific problem where with our aging population and a population whose mental and physical capabilities are declining. How can AI help in that area? And so there are some solutions. Hans is going to tell us about those solutions and the solutions that should give us a home sweet home. Thank you very much Robert. Thank you for this very warm welcome. It reminded me of my arrival in New Zealand 25 years ago where I received an equally warm welcome both in general from New Zealanders and in particular from the staff members in this department. And I will never forget that. Now when Robert said I had to catch up with a lot of friends and therefore couldn't have a drink, it's half the truth. The other half is that my wife says if you drink alcohol your accent becomes even worse and nobody will understand you. So that has to wait until after the talk or otherwise you wouldn't understand the word. Now I'm really honoured to be here. You were quite frank. When Bob approached me he said, well, look Hans, do you want to give a lecture in the Givens Lecture series? And I thought, oh, that's great.  But nevertheless I'm really, really happy that you're louder. Thank you. I think I should move this purpose a little bit. Is it better? No, it's not. OK, I will try to speak up a little bit. So I'm really delighted to be here. And just to give you a little bit of an idea. So it's almost my silver anniversary in New Zealand. And 10 years since I left the University of Auckland moved to Massey. I've done artificial intelligence almost my whole academic life, so to speak. There was an odd professor in Germany who said, look Hans, there is a topic. Nobody has heard of artificial intelligence. It's really hot. You do that. And I said, yeah, I do it. And since then I got stuck. And over the years I developed various flavours. And the most recent one is smart homes. Now when we talk about smart homes, there are a lot of things probably going through your head. And the term is actually quite overloaded. You can install your own smart home nowadays, which automates most of the things in your home. Or you can use it in various other ways. And the way I want to use it is to support elderlies. So this is my motivation. So here's some statistics. According to statistics, about a quarter of our population will be aged 65 or beyond. So that's one thing. So there's an expectation nowadays that we live longer. And of course, who doesn't wish to live longer a healthy life and an independent life. But at the same time that poses a problem because it's not always possible to live an independent life because as some of you might have already experienced and I start to experience it slightly, there are certain things that happen over age so I run much slower now than I run as a child, I noticed. I also find it difficult to do particular tasks. I didn't have a problem at all, like touching the floor of my nose when I'm sitting down or things like that.  So the question then holds, how do we actually support people because obviously it's not always possible to live independently or in other terms, who cares? So we need to find a solution and the question is what is that solution? We need to find an alternative solution. There are various solutions and the one you see on the slide is probably not one somebody prefers. But if you're a fan of Dan Brown, you probably noticed that in his recent book Inferno, he suggested a more subtle solution which is to make half the population sterile. Well, even that is kind of unsatisfactory. But even if we somehow manage to get a solution, there's another problem. When I think back to my parents, my mum married the guy from the next village. My grandparents never moved. Nowadays, people are very flexible in terms of moving. I'm now probably on the opposite side of the world, almost, from where my parents live. So with this flexibility actually comes a problem that we are not as easily prepared to take care of our parents or grandparents. So this is more a goal I'm looking at. Independent happy life and preferably not in a rest home very early, in the early stage. So how do we achieve this? Or what is necessary to achieve this? I'll probably do the following and have a short break and put my watch here because I otherwise go on forever. So there are various aspects in smart homes or what we could use a smart home for. Is my voice still all right? Can you hear me or am I fading off already? Okay, thank you. Give me a signed wave. So if you wave, I know I'm too quiet. If you wave, I know I have not switched on my microphone. I've seen no waves there, no waves there. Everything's good. So first thing, assurance. I think there's not something which is particularly related to older people.  I do from time to time. And then you go back and check and then you leave again and say, did I actually check that? Okay, so assurance is actually something that is quite welcome. Just on an individual basis, feedback to certain things to a person living in a smart home. The other one is support. Support can mean a lot of things. If you depend on medication, it could mean that you get reminders at the right time to take the right medication. You probably all have heard about these intelligent boxes that open and the pills come out automatically at the right time. I'm always asking myself, are the people then taking the pills or falling them away? So there are always supporting elements like that. But it could also be just intelligent reminders that something has cooked long enough that you have forgotten, for example, to drink enough during the day and so on and so forth. And the third aspect is assessment. So sometimes with becoming older, it goes along a diminishing physical or mental capability. And there's a fundamental difference in my view about the tool. So the physical impairments is certainly something that you probably notice very easily. You find things harder. The danger with cognitive impairments or, for example, diminishing mental capabilities is that you might not notice it. You might forget things and so what? It doesn't affect you immediately but it affects you in the long run. So for that, we need, of course, input from the outside. So these are the three supporting elements and the key word here how some of the research in this area tries to tackle these is what is called ambient intelligence. What is on the bottom of this slide is actually a phrase not I came up with but people usually refer to ambient intelligence in literature. A digital environment that proactively but sensibly supports people in their daily lives. So what is this beast, ambient intelligence? What are the features we are talking about? Obviously, you might have guessed. I haven't said so. It could be all kind of things but as you probably know I come from computer science. It's a computer science lecture so it has to do something with computer system. But what kind of computer system? It's a system where there's ambient intelligence in there as a player with words so there's the artificial intelligence in there. So it's an intelligent system computer system that's intelligent but it's not disconnected from the world. What does that mean? It is sensitive and you probably might interpret the word in a way I don't mean it in this case or researchers don't mean it in this case just means it senses the environment. It's responsive, it acts on the environment. It's adaptive. It has some transparency so you know what's going on and this is ubiquitous. I finally managed to actually say ubiquitous. It's a difficult word for me. I was actually considering not to do research in this area because of this word. Right, so that's what it is. And people came up with more sophisticated definitions. I just put up this slide so those of you who really want to know more about it there's heaps of literature around and some of the definitions tick certain boxes so in this column the S-R-A-A-T you might have guessed is what was on the previous slide the sensitivity, responsiveness, adaptiveness, transparency, ubiquitous intelligence and some cover more and some cover less aspects on there. So how do we go about this? Well, we try to create a computer system that can act, sense reasonable at the word but don't just want to do it in any way. There are various aspects that also play a significant role and it's sometimes overlooked. One is the what is usually called human-computer interaction and you probably have a feeling of what that means but what does it actually mean in a system that's ubiquitous? I've actually haven't said what ubiquitous is. Ubiquitous is you don't see it, right? It's all over the place. And that makes sense. You don't want to have your R2D to a standing in a corner or something like that, your mainframe. You don't want to see it. It doesn't want to intrude. You don't want to intrude have the system intruding on you. So it needs some but you want to interact. So traditional computer interfaces really don't do it, right? You can't expect an older person to go to a terminal all the time and use the mouse and select something. That is disruptive. So we need to find new ways of interaction with a a million intelligence. And the other thing is security. People are usually quite concerned about their privacy, right? You know? People are usually concerned about their privacy. Although they don't act on it just to give you some idea security cameras or internet security cameras are quite a hype nowadays. Who in the room has a internet camera? Yeah. Makes perfect sense. I installed one in my house. My daughter is living with us. Sometimes the parents are not at home and she gets frightened living in Parmi. It's not as populated so you can feel a little bit. It says somebody out there. I installed a camera and I looked around and I got one and I just browsed to familiarise myself with a few issues around it and so on. And it turned out it's amazing of how many security cameras you can access through the internet. There's a site in the US and it lists security cameras so you can look into living rooms into garages of people. How does this happen? Well, you buy this security camera, right? Where do you buy it? Don't want to name a particular provider in New Zealand nor overseas. Maybe you get it somewhere brought in from overseas by email, per mail. Sorry. You install the thing. It works. You're delighted. Perfect. Well, these things come with standard settings, right? And one of the standard settings is the standard password. And they are not so many manufacturers and they are not very creative about their passwords or logins. Let me guess. If you go into your router, right? Who locks in with admin? Yeah.  No, because if people call it admin and so if I have to guess that's the login, right? Now, up to probably a year or two ago when you bought a router from one of the major providers in New Zealand it had a standard password. Recently they changed it and now the standard admin password are the last so-and-so many digits of the serial number of your model. So at least it's personalized. So if you don't bother to change that I know your password. So I can lock into your router, right? I open a port. Perfect. Your camera has the same password anyway and one of the favorite passwords for the camera seems to be one, two, three, four, five, six, seven, eight. Okay, there might be another password so you're off. And the same thing of course in principle can happen in an admin intelligent. So let's look at something which is a little bit more straightforward which is the sensor side of ambient intelligence. Catch my breath a little bit keep my voice a little bit difficult so I can perhaps say something with a lower voice Robert mentioned the weather he actually ordered that especially for me, me from Parmesan North always being windy in a windy area so we ordered some wind so I feel at home. Very good. Okay. So what type of sensors do we actually use? You are all familiar with sensors to some extent. If you come home, I guess most of you have security light around your home, right? There's a sensor in there, a passive infrared sensor, switches on. Perfect. We have other sensors instead of motion sensors. So what we could sense as well is light sensors in your security light do that already because they don't switch on usually during the day. Some form of radiation, temperature all kind of things. We can of course have a sound sensor if there's noise in the house. Another thing is solids, liquid and gases. So think about the tap running. How do you notice that? Well, you put something in the pipe and if flow goes through it senses this and the system recognizes the tap is open. Now we had an interesting project I get a little bit sidetracked but I'm hoping that this is making it easier for you to relate to this type of technology. We had an experiment in Parmas north of an elderly gentleman in his 90s blind living independently in his home and we equipped the home with sensors. Now the problem is although there is a kivi ingenuity there are a few things you can't do in New Zealand. So you can't just go in as a computer artist and cut the pipes. First of all it's not good anyway but even if you think you can fix them later and put something in there it's not legal. So one of our colleagues invented something that listens to the pipe a little device we just glued on and looked for noises and he did some other things to filter out for example voice which is a different form of noise. So you can do quite a few of things to get around the problems. Okay, the last one I haven't mentioned position, direction, distance and motion. So one thing we are currently doing is trying to track people in their movements because these movements might give us an indication of whether something is wrong with a person. Now of course this is a non-trivial task, a passive infrared sensor doesn't really do the trick what people usually do nowadays or what has been suggested in the literature experimental setups around the world is putting pressure sets into the floor so these are pressure mats and they recognize if somebody steps on that. This is actually a nice thing here there are no pressure mats in this floor I was expecting one because this is recorded this lecture and when we do that at Massey University we have to stand on the pressure mat and the camera zooms in as soon as you go off camera zooms off and if you are a very moveable person like I am because a student recently complained not to me, to a colleague of mine with a short message you are making me sick. Alright so these are the sensors then the question is well they are all quite limited they just send certain aspects how about cameras talking about cameras people usually are not having to have cameras everywhere they are at least in some places perceived as invasive thank you although the prices have dropped quite significantly they are more expensive than these cheap sensors so if we talk about infrared sensors or just sensors doors they are less expensive and because you get an image doesn't necessarily mean that the job is done and I have the pleasure to have some in the audience who are quite familiar with computer vision and they build whole careers on this and not definitely not because it's a trivial task so just having cameras doesn't do all the work for you and it turned out depending on what you want to achieve it's not really essential okay so the other aspects you need to consider is wired versus wireless sensors and there are various pros and cons wired sensors they are a little bit cheaper but you pay for the wiring and if we think about getting smart home technology into houses in New Zealand we can't just wait until all the houses have been replaced with new houses that have the equipment wired in and retrofitting houses although I can tell you retrofitting anything in New Zealand house is easier than a house in Germany and do things like that you can usually access the space over your living room and things like that but it's still an effort so wireless is a little more expensive but it has downsides like it uses batteries and so on and possibly it's not as robust again a trade off people need to consider now reasoning this is actually where my work lies I am not an electrical engineer so the sensors we use are usually out of the box sensors except for the little developments my colleagues do I am interested in reasoning so making sense out of what comes in from this huge amount of sensors so on this picture which is incredibly small I realised that which is really amazing that I put something like that on my slide I had a talk yesterday to my students how to avoid presentation mistakes and I am just thinking ok this is number 12 at least that counts so far anyway the first one is actually not being anxious but when I stared at this room at the beginning I was kind of terrified but I am also at the same time very delighted so these are sensors what do these sensors produce these sensors produce some form of data they transmit some form of data this data goes into a magical box and this is our immune intelligence this is essentially a computer system and it's amazing what you can do nowadays on small computers so we are not talking about these rooms full of machines even with simple very small computers you can achieve quite a bit some of you are familiar with Raspberry Pi I guess quite a few interesting projects you can do and we let our students do which already goes in that direction and provides some aspects of immune intelligence so out comes some recognition of some activity so let's say the immune intelligence says oh yeah the older person in the house just got up and is preparing her breakfast and with this information we can do various things we can just ignore it if it's a normal activity or if it's an unusual activity let's say it happened at 2 o'clock in the morning we might give some support for example calling a carer so that's the general idea of the flow of what we want to achieve now we call this activity recognition some people call it behaviour recognition I don't know which is the most more appropriate term so we want to first find out what is going on in the house now what we get is something similar as shown on this slide this could be a typical data stream so really row by row so in comes a signal at approximately 5 past 6 that in the living room the television was switched off and the curtains were closed about 3 minutes later things like that come in of course if you look at that that doesn't well it might tell you something but if your computer system looks at it without any reasoning process it doesn't tell you anything it's just data it's no information at this stage no knowledge that comes out of it data that comes from the sensors these observations are not activities so we need to think about how we turn observations into activities how we can map a sequence like this to the occupant is making breakfast or having a shower and so on now this might sound easy but it's actually incredibly difficult and there are various things that get in a way and one thing that gets in a way is variation luckily it doesn't get in a way completely because people have their habits and it's amazing what kind of habits people develop but in principle if you think of a simple task like making a cup of tea with milk so do you put the tea or the coffee into your cup first and then the milk or the milk first and then the coffee it might not be the best example but you can see there are variations in there if you have your bathroom routine do you brush your teeth first and then shave first who knows so the observations as a result of that might be quite different second thing that gets in a way interwoven activities what does interwoven activities mean it means that sometimes we interrupt an activity and do something else for example if you put your clothes in the washing machine I guess you are not switching it on and then you stand in front of the washing machine and wait until it's finished most people don't do that I think they do something else and even if it's just having a cup of coffee but that gets in a way because the cup of coffee has nothing to do with doing the laundry unless you think doing laundry is really a ritual which involves coffee drinking but it could be something else you do in between and most people actually do so if you look at the observation what really fits together is just a part the observations that belong to these orangey errors on the slide and are labelled as laundry whereas there's something in between making coffee so we need to take care of that at one thing we did this and now it becomes technical hidden mark of model who are familiar with hidden mark of models wow that is great I wasn't well, I wasn't a long time ago but okay so it is what is called a probabilistic model probabilities you have probably heard about probabilities probably so it's something that's not quite certain to toss a coin so it's a 2050 chance that you see head when it comes down we know all these kind of things so somehow this approach incorporates probabilities sorry about you guys who already know about hidden mark of models and are now bored to death okay for you I have this slide the rest ignores it so what does a hidden mark of model actually do well imagine yourself getting blindfolded okay and I put you in my car which I haven't here but let's say I had a car here and I drive around and we get out somewhere in Auckland and you get certain signals okay so let's say you heard some church bell ringing okay then we walk along and you hear a ship horn right and you walk along and you get some other input maybe you smell something funny so what do you do the first thing you probably say where the heck am I right don't see anything where the heck am I okay could be any type of places now Auckland is a better example because there is a huge number of places but let's say it was somewhere in your house or in a restricted area okay I could be either in my garden or I could be in my living room or I could be in my kitchen okay I don't know probabilities but then you get observations in suddenly you hear something that ship horn suddenly you think ah so I must be now at the harbour and you develop a model over time we call states where you are and we want to find out or in your process while being blindfolded you want to find out what I have actually done with you in Auckland where I let you and you might or might not be successful at the end you might answer yeah I know you dropped me off Upper Queen Street then we walked down Queen Street we ended up at the ferry building then we went to I don't know the bridge and so on or you might say well we could have been Upper Queen Street or it could have been some other street in Auckland and it could have been 50-50 chance but then something else happened so I think in the end we ended up to that particular sequence that's what the hidden mark of models do so what does this have to do with activity recognition so I skipped this oops there's just a few features why we use these let's go to this slide it looks very technical but it's not really so on this what is this colour it's a pink pinkish in the middle you see these O's so here come your sensor data you see the cover door opening the fridge door opening you see some activity in the living room and so on so this is a signal to our system so our system does the same thing you did in Auckland when being blindfolded but it makes some assumption and it tries with these hidden mark of models to find a good match for a part in the observation so this is what the principle is about there are a few things to consider because this comes in as a flow at some stage the activity change so we need to find the right window so there was a little research around this so when is it actually relevant to listen to certain hours to certain sensor information and when do we stop but you can do a few tricks and that's quite a successful approach so after this then we have a good idea of hopefully a good idea for this going on in the house of course these things have to be trained trained means in AI lingo you just observe people in the house and you tell the system like you train or teach a human being what certain observations actually correspond to that's called the ground truth so we say this type of observation is actually breakfast but at some stage we don't stop this and we expect the system to actually do the job on its own okay right so let's give you some idea of how you can do it in principle now what does it actually tell you when it tells you some things for example if you don't observe that a person has used a shower for a few days you could imagine that this might be something that is not acceptable or maybe it is depending on your lifestyle I shouldn't really jump to conclusions here okay but you see where I'm coming from so certain things that are missing you could interpret as abnormal and potentially dangerous the same thing the other way around that happen too often so let's say a person opens the fridge every 24 hours maybe there is some eating disorder going on or maybe the person prefers white wine over red wine which of course you keep in the fridge but again then every 24 hours might not be something you want to accept but there is a more subtle way of things unacceptable abnormal behaviours and that's behaviours that happen in the wrong context okay so having for example dinner at 6, 7 o'clock at night quite perfect but if suddenly you realise the dinner is taking place at 11 o'clock and the next night at 2 o'clock in the morning it's still the dinner activity which is perfectly fine but the context is wrong or let's say the person puts on the heater and is in the middle of summer now this is an incredibly bad example given the late summer at least perhaps north one okay so I will think of better examples sorry about that but you see out of context context information spatial information everything with where are things happening do they happen in the right place so taking a shower in the bathroom is quite normal but outside it's not so normal temporal information we had that example in emotional states and so on all this information can feed in and then either can boost the recognizer process which I haven't referred to but what I referred to is to recognise abnormalities so that's the idea then the next question is where do we do this so we are now moving on from activity to context so the image that we have in mind is to use context maps these are maps where we record when certain things happen so let's say you have certain activities lunch, watching TV, ironing and so on and they happen at a certain time so whenever we observe that somebody is having lunch we record which day it is which month it is which season it is in which room it happens and so on now the thing that's important here when we talk about temporal data it's not just a time stamp right because some kind of abnormalities happen because they happen at the wrong time of the day some abnormalities happen because they happen on the wrong day that you suddenly do things on the weekend you did during the week or during the wrong month or the wrong season right so we have to distinguish between these and the same holds for spatial data now in the following I just mentioned a few things for those who are familiar with techniques of AI so there are various again probabilistic models around based on neural network approaches and one keyboard here is Boltzmann machines for instance a particular type of approach that can be used for that however I would like to go into something which I think is a little more intuitive and a little perhaps easier to get your head around with because these Boltzmann machines again like the activity recognizer you train and you hope that something is coming out that is right in most cases that is the case and sometimes not so let's forget about all this stuff and say how would we actually in a naive way tackle the problem and let's get back to these probabilities so I'm now assuming that you're all familiar with probabilities I don't assume anything so let's have a certain this so what is A and C and P so it's a probability of something happening A activity and C context what is the probability that an activity happens in a particular context that both happens so there are all these formulas around and you don't have to really be bothered about that but what we are actually interested in is what is on the bottom line which is so called conditional probability and there is some way to calculate this which I have put on there conditional probabilities what is a conditional probability well a conditional probability how can I best explain this it's difficult enough to find a coin tossing a coin ok let me try to find an example that brings it closer one day a man was caught when entering the aircraft because he was carrying a bomb and of course nowadays the assumption is t ends with ist right but this man had a perfectly valid explanation right he said I read in a newspaper that there is a certain chance that you enter an aircraft and there is a bomb on the aircraft right but it is very unlikely that there are two bombs on the aircraft so I thought if I carry a bomb on the aircraft I am safe conditional probabilities right so this person didn't know anything about conditional probabilities because the conditional probability is actually the same as probability of one bomb on the aircraft so conditional probability of two bombs given that I am carrying a bomb is the same as right that's what conditional probabilities are about sorry not bombs but that kind of stuff ok so we would need them because we want to condition the activities on our context so given that it's a Monday what is the probability that the person does the laundry the problem is you need a lot of these and you need to calculate a lot of these or first of all you need to observe a lot of these and that's computer scientists are notoriously lazy so they don't want that so we need to find something else so they came up with something that is called a Bayesian network what is this beast again probabilistic graphical model but I give you an example this example is in the middle of the slide so what does it mean I know something I know A and A influences B ok let's say A is me carrying a bomb B is a bomb on the aircraft ok now think smart homes A is observing that the light goes on in the living room B is me watching TV ok so obviously the light is going on or the TV goes on has an impact on how I feel about whether I would assume that the TV is on or not other things don't so if the security light goes on outside doesn't have an impact on whether the TV is on you would say they are independent so that's what people call conditional independence so in this case E is conditionally independent of all the stuff that is not directly related to E so let's see the in a nutshell what Bayesian network it's about there are not too many people in this room because otherwise I have trouble when leaving this room because they will tell me this was too not right but the reason I'm trying to give you the idea of it because I now want to build one so how do I do that in the smart home context weekday weather season we have our behaviours, watch TV, ironing lunch at home and we have sensor readings the TV is on, the iron is on so let's say you have a little sensor in your iron on your TV so we create something then mimics what is going on in the smart home so here is an example we made up Mary goes out for lunch in summer if the weather is fine, rarely in spring, autumn and nearly never in winter so if it's nice to go out if it's not nice don't go out we all know TV program isn't all the same every day so maybe she prefers to have in her view it's good on Monday and Fridays sometimes on Saturday and Sunday if she thinks otherwise it's not for her she would iron while watching TV rarely during lunch nearly never otherwise so if you have that kind of information and it's a little bit of a made up simplified one we construct something like this, this is our easy network and I was going fully through this but just to give you the idea there are these bubbles nodes in there and we have for example lunch at home and that means that signals to us behind this is a probability that will get a value it's likely it's not likely and that is influenced by the weather and the season so when we run this network we run it it does magical things behind seeing some calculations and we suddenly get something like these tables there and if you go back to the lunch at home example suddenly you say t0.78 and f0.22 and what that means is that is roughly 25% chance that is not the case and 75% chance is the case so these are the things we get out of this now just to I skipped the next time the next thing the details of the next slides but one thing you need to consider also if you deal with probabilities is that there is a requirement that probabilities add up to 1 so if I toss a coin there is a 50% chance that I have tail and 50% chance that I have head so together I have 100% chance that the coin comes down somehow right now the problem is if you don't have information complete information this might cause trouble for example let's assume I don't know anything about which is really fair to say because I actually don't know anything about your hygiene routines so so you ask me what is my belief anybody of you can ask me what is your belief Hans that I shower in the morning and I would say don't know so I say I don't believe it 0 so you think I'm not showering in the morning right no that's not what I said just that I don't have any knowledge yeah but if you say it the opposite must be true right no not really so then you say yeah Hans you did it all wrong you should have said 50% right because if you don't know like the coin 50% chance okay you ask me again I say 50% okay that's right so show in the morning show in the evening show on midday now the 50% don't work right because 50% in the morning 50% midday 50% in the evening wow 150% that you shower at all super clean okay so message here is there are formalisms that do that you can use and I skip these that do all that stuff okay and the last thing is you could even talk about imprecise concepts as opposed to probability oh now it's getting taken imprecise concepts are as opposed to probabilities what is he talking about okay example what is probability versus fuzzy logic okay give you an example again I want to talk about a fuzzy model and I want to talk about a probabilistic model and they both belong to their set with a probability or a degree of 90% so what is the difference if I say yet for example a bottle contains portable liquid with a probability of 90% or I say I have a fuzzy bottle that contains a subset that belongs to the set of portable liquids with a degree of 90% is it the same no it's not the same so here's a scenario a fuzzy bottle means that you filled it slightly from the slightly polluted well so it's not the best it's not your New Zealand spring water which we soon won't have anymore but maybe you fill it from the tap and that's reasonably good it's not your cleanest water so that's your fuzzy bottle the probabilistic bottle is I give you a 10 pack of avian bottles and in one of them is a poison that drops you dead so here's the difference both are 90% portable liquid but some is a degree some is a probability so again you can talk about this and rephrasing things in in terms of fuzzy theory so you could ask to which degree is a shopping event a Sunday event and you can play around with this and you get information okay the last thing I would like to talk about is dependencies between activities and then the formal stuff is over so what we did is what computer scientists often do they download from the internet what is called data sets so data sets is a kind gesture of other researchers that they make available observations they have made in their setup it's very convenient so you don't have to set up your smart home you get this data set and one famous sets of data sets is the so-called CASAS data set from the University of Washington and we played around with this they recorded a number of activities and then we analyzed them and not surprisingly some activities follow other activities more likely than others so if you have a particular activity let's say breakfast it's more likely that the next activity is leaving the house because you go to work and it's less likely that the next activity is going to bed well I run out of sensible examples because whenever I say something I immediately think maybe there are people who have breakfast and go back to bed which is totally sensible but anyway so they are given a particular individual and this depends so that's the form part so what is actually going beyond all this reasoning stuff where do we go from here detect if an activity is normal abnormal in context and initiate a reaction but how about privacy and security coming back to these topics that I had on one of the first slides so what type of information will be gathered who will this information interact with and how will information be encrypted and sent so security where does this reside and why is the information needed in the first place and these again probably not from a computer scientist point of view purely but these are sensitive issues these are issues that have to be resolved and there is actually a project I am involved in but I am really a little bit of an outsider here because this is actually a project that is run from the business school and sponsored by HRC which is a health research council and they want to look at how you can sensibly use that and now we are using sensibly not in the sense of sensing but how we would use sensibly use and how can you integrate it with social media technology so the picture that serves as a metaphor for this project is in the centre is your elderly person in the smart home and there is your circle of friends around close friends, relatives and then there are the wider circle like healthcare providers and so on and they are all connected and other things of communication and the idea behind this project or the question behind this project is to find out which kind of technology is acceptable where and to which degree and I guess one reason why this technology hasn't really spread widely is that there are still people are hesitant to just accept this technology so to conclude there seems of research and I am confident all of you are now so hyped up about it you will immediately go to the library which I think is over there and get the 10 news publication on smart homes and read all whole night about it tomorrow you go to any shop buy yourself some equipment and build your prototype like many people all around the world because there is no integrated package you can buy we are getting there there are still hurdles things like standardisation and so on and again there is another new research area but you can get already some smart devices for example some of you might have heard about NEST just to give you one example it's a controller for your air conditioning unit or your heat pump and it learns how you control the temperature over time and then does it automatically and of course I focus on smart homes but this is just one of the areas where this is used this technology can be used the other of course are smart cities controlling things in the city for example lighting probably for somebody living in Auckland not really an issue because the city is alive all night but if you go to Palmerston North the lights are on but nobody is home everybody is home that's the problem so there is a lot of waste of energy you light all the streets it would make much more sense to keep a minimal lighting for security reasons traffic picks up especially in more rural areas the lights come on a big thing for messy you probably know messy university was an agricultural college before it long ago turned into a university so part of the heritage is that agriculture is a big thing messy so smart agriculture or some people call precision agriculture as a hot topic and smart retail Amazon perhaps there has been some discussion recently New Zealand Amazon so there is a lot more and with that I should really stop and thank you for your patience and it was a pleasure talking to you thank you we do have a little bit of time for questions so if there are questions can you jump us up in the pan please I am going on out on a limp here a little bit because I am not an expert I am not a psychologist but reading a little bit in a wider area so one of the issues is actually emotional state and stress that people have through these diminished capabilities and they go hand in hand what you just mentioned the extreme case of the wish to live or the wish to die is sometimes influence by these factors and not only that sadly it also influences the person who is healthy so the person who takes care of a person with diminished mental capabilities there are studies that has a higher probability that they will die now with this technology this technology by no means make a decision about about these issues but it might help to ease a little bit on the emotional stress that people suffer through the onset of diminished mental capabilities because if you don't want to go to a retirement home and there have been studies that people actually want to live in their own home then the prospect of that might make things even worse and might actually lead to an extreme like you just mentioned that the people switches from now I'm happy to live to I had enough so it could but I don't have strong evidence about it I don't have data to back this up but I know that others actually do work in this area I think it was in Germany or some other European country where they actually built retirement homes where they equipped the rooms in a certain style let's say the 50s or the 60s or what the people are familiar with and the reason being if you suffer from dementia what goes are more recent events right so you suddenly feel not at home in a modern environment but if you recognize the room you have lived in or the style you have lived in as a kid or a young adult so they are they are going to see they are pursuing the same goal easing the stress on people but with different approaches I hope that answers some of your question I think there is a Japanese product which is just the Keto the only thing that sits in the house is Keto and that was good enough for a lot of people it's written that mum and dad had put the Keto on three times a day and they were okay not much a little bit so there are two issues one is driven by what goal do you want to achieve so it's just a particular assurance or something let's say you know that a problem would be that a person doesn't drink enough or doesn't do a certain thing enough a particular sense might give that indication it certainly doesn't help you to it's not enough to recognize complete activity what kind of activity is going on so from that point of view it's not sufficient as part of this project with the Hidden Markable there was a PhD project in the last phase the student actually tried to answer this question and the way to answer this is you have a set of sensors so you go the other way around you throw everything into the house so full throttle and then you analyze the information gain you get from computer science point of view with each of the sensors so the same in principle the same thing you do with decision trees and then you try to find a set of most informative sensors so that was the idea behind that and there was limited success we didn't get very far with it unfortunately but it's a very it's a very relevant question actually because we want to keep the cost down right so innovation and privacy computational resources and so on but I think that's absolutely correct and in a certain way these pill boxes are pretty much the same they focus on one aspect because if there's an issue, if there's medication it's really essential and that's what it's all about this does the job and I'm happy to share them we're putting up the slides of the lectures where they shade so they'll be on the given website excellent, great I'll do some first I have done a few attempts at data gathering for the message related stuff a few years ago as I hope we should have to interest it I might just need permission for the people to do it secondly I still I still have my old ACOS I'm in the mess in 1932 which was I think by the time when they attempted to publish it any possibility of possibly being published well out of blue eye it's difficult for me to say but if there's still something in there it's not for sure but it's something you need to look at it in more detail because the field progress quite dramatically in 1992 here we are with the 25 years okay can I ask you and joining with me and this isn't the end of it next week there's another one so Marcus Freen from Victoria University is going to be here talking about deep learning whatever that is


It's now time for a member's statements, the member from the Lannark front of athletics and Addington. Thank you, Speaker. Speaker, with each passing election we see an ever-decreasing amount of political participation at all levels of government. One of the main contributing factors cited by non-voters is their decision to not participate in elections, is their belief politicians simply pander to the electorate around election time, and are not held accountable for their words or actions through their four-year tenure. Who can blame them when parties campaigning on actions such as no cuts and a balanced budget only define a few months later deep cuts and increased public debt? My solution to this issue comes in the form of my legislation entitled the Election Amendment Act MPP Recall 2015, which I will be debating on May 7th. The purpose of this legislation is to give the people of Ontario a tool to keep their elected officials accountable by giving constituents the power to trigger a by-election when they feel their elected official no longer represents their interest, rather than have to wait for the next election. I believe with the electorate empowered in this way we would see a rise in people encouraged to participate in our democratic process, while simultaneously making the democratic process more direct, more responsible and stronger and restore the much-needed integrity to this province's political system. Thank you, Speaker. Thank you. Members famous? Members from Welland? Thank you, Speaker. Today I stand in solidarity with members of Opsu Local 294 in my writing of Welland and writings of fellow MPPs in Hamilton, Niagara, Haldeman, Brent, HNHB region. These members have been on strike for 12 days. My inbox has been flooded with emails from many of the constituents who are patients or families of patients that have come to depend on nurses for the irreplaceable health services they deliver day in and day out. Donna Fobert, who suffers from a destroyed skull plate from a brain tumor, describes them not as nurses but as family. She fully supports their right for a fair and quality work environment and would be on the picket line with them if she physically could. She says nobody wins when nurses cannot do their job and ultimately it's patients like her who are suffering the most. Speaker Donna is one of the 1,600 patients affected by this dispute. The CCAC responsible for contracting the work to the for-profit care partners is a publicly funded agency, but there has not been a peep from this liberal government about ensuring the hard-working healthcare professionals and support staff are treated with respect and dignity that they deserve. I stand today to request that the Minister of Labor urge care partners, a for-profit agency, to put patient care before profits and ensure patients go back to receiving the care that they need in the immediate future. Thank you, Mr. Speaker. I'm pleased to rise today to tell you about a great charitable organization in Halton. Halton is one of the fastest growing and most affluent communities in the province and I can't think of a better place to live, work or raise a family. But we still have too many residents who face significant difficulties making ends meet in their day-to-day lives. These residents need a lifeline, a helping hand to keep them in a warm home. Since 2011, Milton Transitional Housing has been that lifeline. In fact, recently I attended a fundraiser the coldest night of the year, also the empty bowls fundraiser where they hold events to raise funds for this very important cause. For years, this dedicated group has worked tirelessly to build bridges between the short-term emergency shelter system and long-term affordable housing. They do an incredible job in building our community up and making sure that our neighbors don't fall through the cracks. That's why I was so pleased to announce last Friday that Milton Transitional Housing had received a three-year $218,000 Ontario Trillium Foundation grant. This funding will allow the organization to grow its staff, expand support services and provide greater access to affordable transitional housing in Milton by more than tripling the number of housing units from three to ten. Mr. Speaker, nobody in Halton or this province should ever have to live without a stable roof over his or her head and Milton Transitional Housing provides more than just a roof over their heads. They provide a compassionate human connection to those facing difficult times. Thank you. Thank you. Member Stevens, the member from Geron Bruce. Thank you very much Mr. Speaker. I want to share today some of the repercussions of this Liberal government's mismanaged energy file, in particular the Green Energy Act, and the consequence that this act has had in my writing and possibly across the province. The Green Energy Act was introduced in 2009 and since then it has wreaked havoc across Ontario and it has increased all homeowners' yearly bills by $1,100. It has torn communities apart and really, Speaker, it's been nothing more than an absolute disaster. I want to refer to a release that I received earlier this week where it says wind lease holders may be on the hook for billions and it goes on to read a recent visit to the Registry Office in Godridge, Service Ontario, has received or revealed that a registration of $1 billion, and I'll repeat this, a visit to the Registry Office in Godridge has revealed a registration of $1 billion by a wind company on approximately 100 wind lease owner properties in Asheville, Colburn, Wallinosh, and certified public records indicate that some properties may be encumbered at least 20 times more than their farmland value or more. Speaker, this is of grave concern and I ask today if the Liberals really anticipated the results of their poor decisions back in 2009, it's time they repeal this Green Energy Act and the disaster that it has wreaked across this province. Thank you very much. Thank you very much, Mr. Speaker. Last Friday I hosted a celebration for the Seven Women and Girls Honoured by this year, the Leading Women and Leading Girls Building Communities Recognition Program in Kitchener Waterloo. Melanie Baker was a core organiser behind Voices Carry, an event which raised $10,000 for women in crisis in response to Bill Cosby's appearance in Kitchener. Georgia Cunningham is a vice president of SG Cunningham as well as other women in construction while donating her time to numerous causes and fundraisers. Carly George is a writer, producer and director who made a point to hire women for traditionally male technical roles when producing her play Fool's Paradise. Sarah Ingle is a wee day ambassador, student council and model UN member and was recognised by her potential by Blackberry's Build a Village Awards Program. Janice Lee is the City of Kitchener's 2015 Artist-in-Resident. She founded KW Poetry Slam, chaired Rainbow Reels Queer Film Festival and runs workshops teaching girls to express themselves through poetry. Fauzia Mazar is the founder and chair of the Coalition of Muslim Women KW which has grown to include over 100 active volunteers. She works tirelessly to encourage other women as they become community leaders. Kirsten Pendlebury is the founder of the Female Equality Matters Club at her school which fosters a passion for social justice and gender equity among her peers. I would like to congratulate all of the leading women and leading girls, recognise and thank them for the work they do to speak up, speak out and as Janice said on Friday, live a life with dignity and integrity. It was a pleasure to honour them in my community. Thank you very much. Thank you for the member's statements and the member from Newmarket, Aurora. Well, thank you Mr. Speaker. Today I rise in the house to say Aalaho Abha and joined by my colleague Reza Moridi MPP for Richmond Hill and to wish the people of the Baha'i Faith a happy Resvan. Resvan is a 12-day religious festival and is one of the most important celebrations of the Baha'i Faith, often referred to as the King of Festivals. It takes its name from the garden of Resvan located near Baghdad, Iraq. The site is historically significant as it's where Bahlaloo, the founder's faith, spent 12 days prior to his religious journey to Istanbul before declaring that he was a divine messenger in 1863. Beginning at sunset on April 20th until sunset May 2, followers of the Baha'i Faith honour the 12 days that Baha'u'llah spent in the garden of Resvan by celebrating spring and renewal of spirituality. There are approximately 35,000 Baha'i living in Canada, Mr. Speaker, more than half of them living here in Ontario. In York Region, which includes the great writings of New Market Aurora and Richmond Hill, more than 22 individuals practice the Baha'i Faith and celebrating the Festival of Resvan. I would like to wish everybody celebrating this 12-day religious festival a happy Resvan including the constituents of New Market Aurora who celebrate this festival particularly Mr. and Mrs. Jeffrey Farzanay, Peterson and members of the Assembly of Baha'is of New Market who are here with us today. Thank you. Thank you, Mr. Speaker. Today I'm pleased to recognize my nephew, Jason Pettipies. Jason is an avid runner and yesterday he competed in the prestigious Boston Marathon. He completed the marathon in three hours, 22 minutes and 51 seconds. This is an outstanding accomplishment. He's also proud of his fine finish and for all the work he has put in to make it to this point. Perth Wellington was well represented at this year's Boston Marathon. Local racers include Matt Feltham, A. Curl Guidi, Julie Nickel, Pete Wilson and Tammy Story. I would like to congratulate everyone who participated in the 2015 Boston Marathon and again I would like to congratulate Jason on his remarkable accomplishment. Thank you, Speaker. Thank you, Mr. Speaker. I rise in the house today to commemorate and remember those lost in the Katin Massacre of April 1940. The Katin Massacre was the mass execution of 20,000 Polish military officers by the Soviet Union during World War II. Mr. Speaker, for the people of Poland, Katin is the symbol policy of the Soviet system against the Polish nation. After Nazi Germany and the Soviet Union concluded their non-aggression pact of 1939 and Germany invaded Poland from the west, Soviet forces occupied the eastern half of Poland. And as part of this occupation, 20,000 Polish military personnel fell into Soviet hands and were interned in prison camps inside the Soviet Union. However, when the Polish government said that the Polish military personnel be released in order to fight the Nazis, the Soviets said they couldn't be found. And the fate of these missing prisoners remained a mystery until the Germans found the mass graves in 1943. This crime against the Polish nation decimated the ranks of the Polish military, the ruling class and its intelligence yet. And Soviet leaders insisted for decades that Polish officers found in Katin had been killed by the Nazis. It wasn't until 2000 that Russia admitted that the Soviet Union was responsible for this crime. Mr. Speaker, today I stand with the families and the victims of Katin, including my own family, having lost two great uncles there, as well as with all Polish people who still feel the haunting cloud of this atrocity. We shall remember them. Thank you. Thank you, Mr. Speaker. This is an inspiring story I want to share with you from my writing of Kitchener Centre. It involves a group of talented young people who won big at a recent national singing competition called the 5th annual Show Choir Canada Championship. The KW Glee Club was one of this year's 14 competitors. According to their artistic director Amanda Kind, KW Glee was definitely the underdog. While their competitors were costumed in a sea of sparkles, the KW Glee Ensemble was not. As many of the youth in the 72 member choir simply couldn't afford the flashy costumes. Instead, the students wore their own clothes and called it the Urban Look. The theme of their performance was called School of Pop and the songs they belted out over 20 minutes included Pop 101, Uptown Funk and Shake It Out. I can't say that I know any of these songs but I'm sure it was fabulous. It was clear that the audience and the judges were evaluating based on substance rather than sequence. On hearing their name called as the first place winners the young singers fell to the ground with relief noting that this was a wow moment in life. In addition to winning a big trophy KW Glee also received a gift certificate from Yamaha which will help them to buy microphones and other equipment. Mr. Speaker, I'm so proud to serve title of Grand Champions and I look forward to many more creative performances in the coming years. Thank you. Thank all members for their statements.


Welcome. In this lesson I want to teach you about long calls and so this is a very simple strategy where you're essentially just buying a call on the underlying position on the underlying symbol. Your market assumption is bullish so you hope the stock goes up. You want to do this when implied volatility is low because an expansion in implied volatility will help your position. Optimal time frame is always 30 to 60 days to expiration. Profit target 25 to 50 percent of what you pay for that call of the debit paid. Your downside risk is defined so you know exactly what your risk is on the position when you put it on and of course no upside risk as if the underlying symbol moves up you're going to be in the profit and that upside is not defined either. So there's unlimited upside potential theoretically. Now keep in mind nothing is going to go up exponentially you know infinitely so that's why we manage these trades at a percentage of debit paid because we want to bank that profit and then be done with the trade and these have a probability of profit under 50 percent which I'll show on the platform here in just a minute. So the trade setup is simple you're simply going to buy one in the money call. I would very strongly recommend staying away from buying out of the money calls and the reason is as I note down below time decay or theta negatively impacts this position and even more so on out of the money calls. We want to buy in the money calls kind of in that 80 to 90 delta range because that's going to minimize that theta decay component on the trade meaning it's going to act more like buying stock and it's not going to have that time decay working against us as much very very minimal amount. So let's go to the platform and take a look. One thing I do want to mention too is you know think about in a stock index. So these are things that I don't typically buy by a straight long call in and the reason is because if you if you buy a call in SPY for example you want that to go up. The problem is when the market goes up almost always implied volatility goes down. So you so you've got it's got to make a pretty significant move up for you to profit because that implied volatility decay or in time decay is working against you. So I typically look if I'm gonna buy a call and something which I which I very rarely do to begin with but if you're going to do it I like to do it in a commodity. You know for example in gold or oil or soybeans or wheat or something like that because a lot of times not always but more often than a stock when you buy a call a nose and that underlying symbol goes up you also get an expansion in implied volatility. So both of those are working in your favor. So let's take a look at an example in GLD. So gold had a nice run up and now it's kind of pulled back and in my assumption and this is just purely my assumption and that is that we could have it's kind of had its pull back and it may reverse and kind of continue to the upside and so this would be a candidate that I would look to potentially buy a call in. You can do a vertical you can do other strategies as well that will work well on GLD but this would be a candidate that you could also buy a call on. So let's go to the trade tab and go to that 45 44 days to expiration and then we want to like I said you want to buy a deep in the money call. So the call side is over here. So if you look at the Delta here you've got 80 84 87 90. So somewhere in this range would be the one that I would buy. So let's just kind of go in the middle and let's say the 84 Delta. Those are trading at 555 565. So probably somewhere around $5.60 is where we would get filled on that. So if you just right click on that by single that's gonna populate down here as you can see like I said right between 565 65. So let's kick that down see if we get filled at mid price and before we do that let's analyze the trade. Got a position on in GLD already so I'll click off that just to show this one. So so here's what you're here's what you're looking at. So you've got here's where here's where price is and as you as you can see the difference between the green line which is your expiration the teal line which is expiration and the and the purple line which is which is today there's there's not a whole lot of difference between those two at your entry right. So meaning meaning the theta decay or the time decay is very minimal as far as working against you. So if this price just stayed right here you know between now and expiration theoretically you could lose $55 let me hover it over and you can see again fifty fifty seven dollars something like that and and obviously the max you can lose is that debit you paid five sixty so five hundred sixty dollars. Okay now just to give you an idea of you know sometimes it's tempting to say well why would I pay five sixty for this when I could when I could do one of these cheaper options and do something out of the money like the 120 and only pay 56 cents or 57 cents well let's let's look at that on the analyze tab and if you if you change this from 110 to 120 and look at that out of the money call make sure you unlock your little box here now this is what the the risk profile graph will look like okay now look at the difference if price stays where it is or trades all the way up to that 120 mark in GLD but nothing above that you're gonna lose the full fifty nine dollars paying fifty nine cents for this so it's fifty nine dollars so you it has to trade all the way up past 120 and a half dollars at expiration for you to make money now obviously if it does that quicker you can make money but the probabilities are working against you so if we move this hash mark to the break even at expiration you only have a 15 and a half percent chance of making money on that trade if you buy an out of the money call okay and so that's the reason why you buy these in the money calls the probabilities are much higher for you and so if you move that hash mark back to where prices today are actually to the break even point so now you've got it over a 45 percent chance and you don't have that time decay that fated decay working against you so still under a 50 50 probability on this trade but again if you can get a quick move to the upside bank a quick profit and again we're trying to manage this take this off at 25 to 50% of the debit paid so if the total risk or debit paid is 560 we're gonna take this off when we have a profit of somewhere around 50% would be would be 250 so all it has to do is move about a dollar and a half or a dollar and three-quarters for us to bank that profit at 50% and and even less if we if we bank at 25% of max profit you know all it's got to do is move about a dollar in our favor so that's how you that's how you trade a long call I'm not gonna fill that right now because I already have a long position on in GLD but just like we teach in in all the other fills you simply right-click confirm and send send that order and and get it filled hope that was helpful see you in the next lesson


Hey, what's up? It's Carlos Miller in case you haven't heard we over here at the 85 South show have launched our own independent streaming service called channel 85 Once you sign up you get access to the podcast a whole day earlier than anyone on YouTube all of our live shows independent specials new shows like project Carlos 85 and we even got you know special offers and discount codes for merchandise and show tickets It's on the 850 a month or 85 dollars a year.  That's channel 85. Make sure you subscribe


Mindset don't change you feel I'm saying just stay ready to be ready to work out Had a few other workouts visits, but I mean just Always always been prepared And the advantages for you knowing where everything is knowing how this works You've been here and done all that so you can plug-and-play. Yeah, man. I mean, it's not my first rodeo I've been in some 17, you know, so um, so um, yeah, I mean just come back be with the guys, you know Do what I can do Tell team get away Fun to come in on such an important week, you know a winning in Yeah, man, I mean anytime get seven the field is always fun. You feel I'm saying so I mean whether it was week week now When I got released or or now, you know, just Come in be ready. Did you follow this team closely in the last few weeks or Did you did you pay attention any teams or hell?    You can do that wherever you feel I'm good. I'm good. I was a good when I when I got released Yeah, I mean, you know, he's definitely doing a great job, you know, as I was saying before, you know Just watching the guys watching the team go, you know, um, doesn't that's been fun to watch as a spectator You know, I'm just just being being back around these guys, you know, I'm definitely a good feeling D and how different is that? Why Sunday


Hi everybody, welcome to VMware Explorer 2023. We're here live at the green set with Rob Streche, Cube Analyst Dave Vellante here. This is our 13th year doing VMworld slash VMware Explorer. Really the same event, they just changed the name. Double sets, it's actually quite an impressive show. Last night, Rob, we were walking the show floor. A lot of customers here. The ecosystems may be a little bit smaller, but there were some big booths. You had some of the bigger partners that you would expect. Dell, HPE, IBM, Lenovo, NetApp, et cetera.  And it was packed. I mean, the customers are here in force, maybe trying to figure out, okay, what's next for VMware? Maybe they're up for renegotiating their ELAs. Maybe they want to hear what the future with Broadcom looks like, or maybe there's just more of the same, but it was actually more vibrant than I had expected. Yeah, I think it was a good showing early on for them, pre keynote and pre understanding what's going on. I think getting in there with some of those actual customers and talking to them about what they're thinking about and how they see this playing out to your point, what their expectations. I think there is a lot of learning going on. I think there's also people looking at what their job is today and what their job may be tomorrow.  And how do you make that transition? And I think they're here to hear the multi-cloud story that's really front and center. Rob, you and I in ZEUS wrote a breaking analysis this week. It's published on siliconangle.com and of course on our YouTube channels and LinkedIn and everywhere. But we said our expectations were, we said we're not going to hear much on the Broadcom acquisition. We heard more than we expected. Haktan was here, he actually was on video and then he stood up and waved in the front row. He basically said, hey, we believe in this multi-cloud era and a lot of opportunity and we're going to invest. I think he said $2 billion in R&D and et cetera. So very strong message and indicator that obviously things are going well. We had spoken to some folks privately yesterday who said, yeah, there's a couple little things we have to settle in the Asia pack countries. We're going through that. The FTC as we know, and we started out our pieces continues to drag its feet, but there's no reason why they won't approve this thing. The European Competition Committee, the UK rather Competition Committee approved it, I believe yesterday, gave their final approval. They had already given preliminary approval. So that's no surprise. So that's done and we did an analysis in the financials and we can talk about that. But the thing I want to talk to you about here is VMware's AI play, we're going to talk about that. They kicked off the event with all the CEOs from all around Silicon Valley. Who'd they have on there? They had Adam Solipsky, they had Silicon Valley extended. Satya, Satya was on there or not? No, Satya and Satya, TK, Thomas Kurian. Who else did we see on there? The head of R&D, group CTO I think at Hugging Face. At Hugging Face you had, and of course you had Jensen on stage, so they sort of had some good AI action. But what we said was we're going to hear more about cross-cloud services, Tanzu and Aria, which they're basically bringing together and developer extensions. Then we said incremental enhancements to the core. vSphere, vSan, and SX. And we're going to talk about that quite a bit. We said, I kind of had a question mark around it. I took it out, but around Project Monterey, our intent was, haven't heard much about that lately and we still haven't heard much, and I don't think we will hear much. And then the ecosystem showcase, which we can also talk about. But let's start with the core. The core incremental enhancements, vSphere, vSphere Plus, vSan Max, NSX Plus, let's start with vSan because that's where VMware started. Yeah, and I think with vSan, I think it is more than an incremental, I guess you could say announcement that they are doing this disaggregated storage play where they're allowing you to grow storage separately from CPU and memory. I think this is a direct shot at some of the other hyper-converged infrastructure players, HCI players that are out there. I think it's also a good way to be able to move between being, you feel like you're stuck on a single box and be able to move your VMs very easily using storage vMotion. That being said, I think, funny enough, I was at HPE, feels like decades ago and it was almost a decade ago that we did HCI using store virtual underneath the hood and did disaggregated where you could have a separate box for storage and you could have vSphere on top of it. So I think a big piece of this is how really innovative is that, and I think it's good. I think it's a little late to the market. I think also it doesn't go far enough for the multi-cloud vision that they have. So hopefully we're going to see some of that $2 billion that was talked about going into, hey, let's make this a true platform that can compete with an S3 or an EBS or an FSX type platform. So Rob, I remember when HPE, and it was HPE at the time, initiated that disaggregation of storage, it was at a big data show, where the term big data was in vogue. This is how long ago it was. And the guys came to me and said, we know you've been talking about this problem of separating compute and storage. We're working on it. We have this skunkworks and they wanted to pick my brain about it. This was like ancient history, to your point. And I looked at that announcement and I was like, wow, it's about time. I'm pretty sure I was on the cube in 2015 talking about it. Maybe in Barcelona, at VMworld Barcelona or something like that. I'll have to go back into the archives and look. But I think, again, it's definitely something that's needed. I think their customers will get great value out of it. I think it's a smart move on their part, but it's incremental. And I don't think it's earth-shattering. It's still not file for everybody, although you could theoretically use it. Object is through a partnership with somebody like MinIO. And really, the block is only for vSphere. And again, you start to look at it and how they've closed off their ecosystem and how the API set in the storage is really closed off. And it has been, since the EMC days, it started to close, they started to close the gaps, signing IO paths. I think the one thing, and we heard about this and we just mentioned it before on our other keynote analysis is they are good at IO. They can get high IO. And I think that does play well for AI and what their private AI strategy is, but there's a gap between the IO and the actual application. And there's that data layer. So, speaking of IO, when VMware in the early part of the last decade, so kind of 2010, 2011, even before that, 20, 2008, 2009, 10, when they were really on the upswing and IO was the big problem, right? And it was really complicated if something went wrong and you had to bring in guys with lab coats to figure out what went wrong with your VMware environment. And so VMware initiated and it did a really, actually really good job working with the storage companies. I think originally it was the VMware API for storage and then the Vasa API came out and they pretty much released the SDK to all the majors anyway. Majors being, obviously EMC got it because they owned them, but HP, NetApp, CertiDell, FreePar, they got it, they got the SDK and they all worked really hard and they became really good at solving those IO problems. It goes back to what Paul Moritz said back in, I want to say 2009, when he said we're going to build a software mainframe. The mainframe was really good at IO, it was really good at minimizing the impacts of virtualization on performance and that's essentially what VMware has done. Now, having said all this, I would argue that during the EMC ownership years, when you talk to the original folks that created VSAN, they felt that they were handcuffed a little bit, that they couldn't just go off and attack the market opportunity because their owner wanted to preserve some of the revenue for themselves. So they sort of neutered VSAN and said, all right, keep it low end, make sure it doesn't scale too much, don't encroach on our block business and now I feel like the handcuffs are off. Yeah, and I think for the last couple of years, that's why this is a first salvo I think for them into competing potentially in software defined arrays and being able to be on clouds with VSAN because right now the only way you can get VSAN really is on premise for that most part. Even they talked about there's ways to get some of the storage aspects of VSAN in cloud but at the same time, it's under the hood of the different services. It's not there front and center. VSAN Max will not be in the cloud during this first release and I think that's telling to potentially where the roadmap will take them. Yeah, and then the other two big core announcements, NSX Plus, which essentially is a networking super cloud where they're abstracting the underlying primitives and eventually they'll go to all the public clouds and they're going to take care of all that complexity. So there's an allure there but NSX adoption needs to increase as we know and they've got a lot of work to do there and of course VSphere Plus is, I mean, that's the core. Yeah, and I think the NSX Plus is a place where I'm very positive on that direction. I see it as one of the hardest places for these cloud architects to play. The networking is still really tough to go and do and I think, especially if you talk about multi-cloud or cross-cloud, even if you're say in an equinex using VMware there and using VMware in your, say VMware on cloud, the VMC in AWS, it's still hard to make the VPCs and all of that work together even with the physical links being there from equinex. Yeah, that multi-cloud VPC. I mean, basically the core problem they're trying to solve and they talked about this in their little breakouts yesterday. You need a common cloud operating model, that's super cloud and DevOps wants to move fast. So those are both true. And so you've got to evolve NSX to NSX Plus where it's got these kind of multi-cloud standardization. And I think it's moving it beyond just being a product to being a platform and a platform as a service and I think they look at what the clouds have done really well is simplifying it down to a couple clicks in the UI in their console and going and doing that. Now, how does that play if I'm going between one console and another console? Are you going to have consoles of consoles and how do you do all of this? Are they going to automate all of that through the Azure APIs and the Google APIs and the AWS APIs? They kind of have to, they have to. If they don't then what's the real value? It's just the manager of managers. They got to go beyond kind of policy management. They got to really, really create that true multi-cloud abstraction layer. And then there's a data lake which is providing intelligence which is very important to have the metrics. Let's talk a little bit about the AI play. Jensen's on stage, they're betting a lot of their AI on NVIDIA. Like a lot of companies, you know, okay, that's a good bet, smart to be there. But it's a safe bet. But it's a safe bet and you have to be there. It's almost like table stakes. It's not going to bring a ton of differentiation, but you got to have it. And then their database is Postgres, and I guess Green Plum, but Postgres is where they're starting. It's very functional and great Oracle alternative and relational capabilities and fairly mature, very mature actually. And then they've got Huggingface for their repo and that's where ostensibly they're going to get access to customers will get access to multiple LLMs. I would say this, and we talked about this again in breaking analysis. The cloud vendors, and we've got, you know, anecdotal evidence from some of the developers that we talked to, you in particular did this research where they're saying, hey, I'm actually quite impressed with what I'm getting out of AWS. I got LLM optionality, Lovin' Bedrock. I'm playing with all these different LLMs. They're fenced off, the LLM vendors are fenced off from getting access to my customer's data. So that's a plus, plus I got encryption at rest, encryption in motion. I got all the cloud security and I'm moving fast. It's making me productive. I'm putting out product. We're actually going beyond MVP. So very, very high marks. What you hear from the on-prem crowd, HPE will have LLMs as a service in fourth quarter. Dell, we got Project Helix and we got some reference architectures.  We didn't hear LLMs as a service. But so the point I'm trying to make here, Rob, is that the cloud has the advantage of innovation and speed. The one advantage, ostensibly, that the on-prem guys have is, and VMware trotted out its general counsel today and said, we've got the fud of fear, fear uncertainty and doubt of legal and compliance risk. So you better do this stuff on-prem, so wait for us. But the market's not going to wait for them too long. No, I think it's a private versus in-the-cloud type of, and I think it's private in-cloud or private on-prem, and that's going to be some of the big choices that people have to make. I think that VMware did a good job of talking, even in one of the slides, had Dolly, Falcon, Lama. They talk about, I think it was Platypus or one of the Platypus two from Boston University, Go Terriers, but I think it's one of these things that when you start to look at how they're bringing it together, it's still a 60-page reference architecture. And I think it's going to have to be simpler to be a platform as a service like Bedrock is if they really want to achieve where they're going in this market, they got to make it simpler. It just has to, it can't just be, yes, we use Nvidia's workbench. You go in here and this is how you're going to keep it secure and here, by the way, we encrypt it all, all the way through and confidential computing comes in there and how they're doing all of their encryption. So they do have a good story there that they can build upon and good infrastructure. I think it becomes a, can they make it simple enough to achieve that, to get there fast enough? I mean, the big concern I have is what is their differentiation in AI? Obviously they got great infrastructure and they can bring that infrastructure, evolve VMware infrastructure so that it's capable of handling AI. I don't question that, but what the gap to me is the data play. I'm going to bring my data to the cloud, I'm going to bring my AI to the cloud because AI is there and B, my data is there. I'm going to bring my AI to Snowflake, which is also in the cloud, by the way, because my data is there. I'm going to bring my AI to Databricks, whether it's in the cloud or on-prem, because my data is there. Now, VMware doesn't have a strong data play. Obviously there's a lot of data moving around the VMware infrastructure, but it's not in a cohesive way. There's no data platform play there and so I think they can build that, but it's got to be more than just, hey, we're doing Postgres. They've got to have tighter relationships with the data platform players and that means database, that means governance, that means privacy, that means data cleansing, data management, all that sort of ecosystem that exists that you see when you walk around Databricks and Snowflake shows and AWS and other, Google will see it next week, that's lacking here. Yeah, I think you're dead on and I think they don't even have the story about how you can get at it with SQL or how you work in these other tool sets and use those to get at the data in VSAN. Because I think it was, the play is very similar that they have VSAN, now VSAN Max, it's going to be disaggregated. What if you put these interfaces on top of it? SQL, for instance, and you allow people to do that. You have file already. Other file, Databricks uses a file system of their own that stretches over S3 or over file and actually runs really fast on file so faster than it does on objects. So if you start to look at how things could be built, that's a path forward for them that we're just not seeing that vision out of them this week which is a little surprising because you had even just a couple of weeks ago when we were with VAST, they were talking about this and same thing with IBM, talking about it the week before that. So you have Dell, IBM, VAST, HPE, with Esmerell all talking about how you bring the data to the storage and make the data lake be the storage and not separating those pieces out. I think the difference is, and it's true, virtually every technology company has been playing with AI or doing AI prior to the open AI announcement of ChatGPT. The difference is there's a certain sector of companies that were basically born to do AI and provide AI services to customers versus those that are embedding AI to make their infrastructure run better or maybe do some chat botting, whatever. But that's I think the difference. Look at VAST, but VAST basically has a platform that is a strong infrastructure for AI, at least ostensibly. IBM obviously has a long heritage in AI even though they sort of fumbled Watson, but they have a lot of experience there so they were ready to take it. To the point about Snowflake, they can kind of bolt on AI because they got the data play. Databricks has always been deep in AI. And they bought MomentSake ML so even give it another layer up. So you have these companies that are, have truly been thinking about how do we deliver AI services to customers? The cloud guys very clearly, really it's the three cloud guys and Databricks are the big ones. And then now open AI is shot to the lead as well just in terms of presence in the market and awareness. And then you had a lot of other companies, Anthropic and Huggingface and Cohere and on and on a C3 AI and DataRobot and all these specialists that are also in a pretty good position. Yeah, I mean all of them, yeah. Absolutely and so they've got good businesses but to our power law you're going to have the giants that and there's going to be a few of those very large language models and then the long tail is going to be very highly specific, maybe industry specific models and there are going to be a lot more of those but they're going to be a lot smaller. Yeah, I talked to three CIOs so far this week just in the last two days about this and what they were talking about is they're going to be very specific about how they use AI and how they build their models and their LLMs or specialized or segmented language models for doing specific things. So you're going to have to your point a long tail of very small models that don't need 10,000 GPUs to go and train. They need a couple thousand or not even a thousand or maybe tens of GPUs, not hundreds of thousands of GPUs. Yeah and Jensen up on stage today was giving away some GPUs that were signed by Jensen and Raghu. I do think because Raghu's a technical visionary, I think Jensen respects that. They got the Silicon Valley bromance going and so that was kind of cool. They've got five of them, they're giving away or raffling them away, presumably to partners or customers. But Rob, great analysis. Thanks for spending some time on theCUBE. More to come, wall to wall coverage this week from VMware Explorer. We're live, theCUBE's coverage 2023 VMware Explorer. We're right back right after this short break.


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The 2019 novel coronavirus, 2019-NCoV, was found in Wuhan, Hubei Province, and was named as 2019-NCoV by the World Health Organization, the WHO, on 12 January 2020. For its infection ability widely and quickly attracted the world's attention, but without treatment and control manual, we drafted this rapid advice guideline. This guideline is useful for the frontline clinicians and nurses, managers of hospitals and healthcare sections, community residents, public health persons, relevant researchers, and all person, who are interested in the 2019-NCoV. This article was authored by Yinghui Jin, Lin CAI, Junchun Cheng, and others.


Now the federal government has directed all ministries, departments and agencies, MDAs to close by 1 p.m. today ahead of a second leg clash between the super egos and the black stars of Ghana.