 Good day to all of you. I am going to give you a 15 minute talk on the topic of how to be smart. In fact, how to be smarter? Because I am assuming that all of you are smart already and I am going to position this after understanding or thinking about what it means to be smart. So, for example, if you go to the net and do a Google search on the word smart, you will see these adjectives or attributes – clever, bright, intelligent, energetic, swift, lively, sharp, bitter and so on. So, you can understand that sharp has a number of different dimensions to the term. And so, let us take one example. Suppose I call an auditorium as smart and I am going to use this example in the rest of my talk to illustrate what is it that computer scientists can do to make things smarter and in the process also understand how humans can become smarter by doing the same sort of things that we provide smart artifacts to make them smarter. So, let us understand what a smart auditorium might do and use that as example as a way to illustrate other smart devices, smart artifacts including humans. So, I am going to talk to you about the smart auditorium as an example of artifacts which have or can have the attribute of smartness. So, a smart auditorium might tell people who are in the back of the auditorium to move to the front rows, why would it do that? In that case, people in the back do not need light because there is nobody there and the air conditioning for the auditorium can be adjusted so that the energy can be saved. And it might conclude by looking at the number of people in the auditorium that if they all moved forward 30 percent energy could be saved. How did it come up with the term 30 percent, the number 30 percent by doing analysis? Or it might say that current energy prices at this time of the day will contribute to a reduction of 600 rupees in the energy bill for the auditorium. Or it might say that the energy now is coming from fossil fuel and other resources which are required to produce that energy will contribute to the loss of 2.5 trees. So, all these are equivalent in some sense, but giving you different dimensions to the savings. So, now all of this requires you to know what the power consumption is, how many people are there, what is the outside temperature, what is the limit of the maximum power available to use in the auditorium. So, with all these questions answered with analysis, with data from sensors, we can respond by asking people to move forward. So, you have a sense followed by respond. In fact, there is something more and that is that we have dependence on whole bunch of sensors. For example, here we are going to use smart electricity meters to tell us how much energy is going to be consumed in the process of cooling the auditorium. Another example would be a smart door. A smart door will allow people who have only those people who have access to the room that you enter through the door. This could be possible by having somebody's capabilities in terms of entry and exit stored in their smart phones. And this is read by the door and then it allows only those people who have access privileges. So, in this picture, I am showing you a smart cheap door. In fact, all it does is counting. We need something more. For example, we need to identify the people so that only those people whose identifications or whose identities are stored in a database are allowed entry or exit through the door. Let us start with counting first. What we will do is to look at those two lasers which have cut in one direction, one after the other will mean entry and the other direction will mean exit. And when you know that somebody has entered, you add to the occupancy count of the area into which you have access through the door and vice versa for the exit. Now, if I want to know who is coming in, then I have to also know their identity. But before we talk about this, I want to just mention that a good decision, for example, decision about whether to increase the temperature or decrease the temperature inside the auditorium or whether to ask people to come forward is based on knowledge and not simply number. The knowledge that we are talking about here is identity. This identity information can be derived only through analysis. For example, suppose I know that certain people who are inside the room have a combination of height and weight which makes them unique, then I can use that information to determine who is inside the auditorium, inside the lab, inside the office as the case may be. So, in general, you can answer these questions through analysis. How many people are there? Where are they seated? For example, if I take a picture of the camera in front of the auditorium, then I know where they are seated. I can do temperature sensing to figure out what the variations across the space are for the temperature values. I can look at outside temperature. I can look at the power structure, power price structure and all these analysis capabilities will let me respond in a meaningful way. Also, I want to know what I need to sense at any point in time so that I can focus on certain sense values to make me analysis more meaningful also. So, I need to be sensing meaningfully, analyzing meaningfully and responding meaningfully to answer questions such as how many people are there. And secondly, the response has to be timely. If I do the analysis now and give the response to this question of where should people be seated tomorrow after the whole event is over, then the response is no meaning. So, I need to sense meaningfully, analyze and respond in a timely way. So, now if you look at the blue colored letters, you see the word smart emerging out of it. Sense meaningfully, analyze and respond timely. So, if somebody asked you whether an artifact is smart, ask the question is the sensing its environment in a meaningful fashion? Is it doing analysis with the sense of information and is it responding timely? And all of this has to happen in a feedback driven fashion because as we all know, we remember and analyze the current situation in the context of what we remember from the past. So, there is a feedback loop here as you can see from sensing to analysis to response back to sensing. So, now you see that everything comes together. We sense meaningfully to understand the, to get the responses to the questions what, when, how, where, which, things have to be sensed. Analyze to produce a right kind of response and what do we analyze? We analyze archival data, real time data, aggregated or disaggregated. And response will be in terms of who should be responding, when should we respond, where should we respond? All of that should be done in a timely fashion. And one more item I want you to think about is remembering the response. Now, if each one of us were to do analysis, every time we have to face a situation, that will take a long time. So, humans have a way to remember. Memory serves the purpose of remembering and if you are taking the context of a computer, it will have to be put in its memory in a way that the memory will survive failures and so on. So, when you come back to situation after having gone to sleep, you still remember that situation the next day. And this whole thing happens in a loop, in a feedback driven fashion. And another thing to remember, build smartness into the whole auditorium, not only during run time, that is to say when I am going to be using it for a certain performance or an event, but also during initialization time. For example, in the morning when I come in, I might set the temperature to start with at a certain value, depending on what events are going to happen that day. Or I can do the deployment time, for example, this might be something where I have envisaged the use of the space as an auditorium, that is the deployment of the space as an auditorium. And I might make some decisions then as to where to position the vents for the airflow and so forth. Or I might do it at design time, when I ask the question how should this whole area be designed to make it possible for it to be used as an auditorium also. So, smartness will be applied to all of these times and that means they ask questions relevant to that particular time, design, deployment, initialization or run time and make whole artifacts smart. So, what I have shown you so far is one aspect of smart energy solutions. What we have done here is to understand which devices which vents can be closed, which lights can be turned off to avoid or reduce consumption. Similarly, I can make the air conditioners or the vents be open in a staggered mode. So, as to optimize the level of consumption across large time span. So, this is demand supply matching, demand made by the appliances like ACs for energy. Supply is the amount of energy available. So, you want to make these two match. Flat profiles are important because peaks contribute to large difference between normal and maximum requirement and those are bad for pricing for example. Renewable resources are very important because they are better from the environment point of view and of course, you want to store energy when plentiful energy is available and use them when the amount of energy required is much larger than what is available. So, all of these require smart approaches which means what? Which means we have to sense, we have to analyze and we would like to respond in a timely fashion. So, let me close my short description of smart things with some thoughts. Anytime we answer a question or make a decision just like the AC did or the auditorium did, the responses reflect our smartness and smartness can be applied at all stages design time, deployment time, initialization time, run time is continuous feedback driven resource and time constraint. And we there is an observation that people are made that all humans become smarter as time progresses. This is true for individuals and also generations of humans also become smarter and smarter as time goes by. The reason for that is because they are learning from the previous generation or the previous day if it is an individual. Then of course, issues of introduction, privacy, security that come in whenever you sense and analyze and respond, we then get into that today. The other thing is that sensing cost money, analysis cost time and response cost infrastructure which means that the high tech solution that we are proposing here for being smart does have cost implications. So, we will have to be worried about the payback and computer science is a major role in terms of optimizing these costs in such a way that the benefits are more than the costs involved. So, finally, I would like to find you that if you think about all of the components of being smart, all topics in computer science and electrical engineering come together, databases, operating systems, autonomic computing, embedded systems, AI is on and on. And so, any aspect of computer science, any topic in computer science has a role to play in building smart things. And that I will close and thank you very much. And also thanks to all my collaborators through whom I have gotten the insight about being smart. So, hopefully you not only know what smart artifacts are, but also know how to use the attributes of smartness for your own future. Thank you very much.