Who of you is using Facebook, Twitter, diaspora, and all of the data you enter there gets to a server, gets into the hand of somebody who's using it, and the next talk is especially about that, because there's also intelligent machines and intelligent algorithms that try to make something out of that data. So the postdoc researcher Jennifer Halsby of the University of Chicago, which works in this intersection between policy and technology, will now ask you the question to whom would we give that power? Thanks. Okay, so today I'm going to do a brief tour of intelligent systems and how they're currently used, and then we're going to look at some examples with respect to the properties that we might care about these systems having, and I'll talk a little bit about some of the work that's been done in academia on these topics, and then we'll talk about some promising paths forward. So I want to start with this, Kransberg's first law of technology. So it's not good or bad, but it also isn't neutral. Technology shapes our world and it can act as a liberating force or an oppressive and controlling force. So in this talk, I'm going to go towards some of the aspects of intelligent systems that might be more controlling in nature. So as we all know, because of the rapidly decreasing cost of storage and computation, along with the rise of new sensor technologies, data collection devices are being pushed into every aspect of our lives, in our homes, our cars, in our pockets, our wrists, and data collection systems act as intermediaries for a huge amount of human communication, and much of this data sits in government and corporate databases. So in order to make use of this data, we need to be able to make some inferences. So one way of approaching this is I can hire a lot of humans, and I can have these humans manually examine the data, and they can acquire expert knowledge of the domain, and then perhaps they can make some decisions or at least some recommendations based on it. However, there's some problems with this. One is that it's slow and that's expensive. It's also biased. We know that humans have all sorts of biases, both conscious and unconscious, and it would be nice to have a system that did not have these inaccuracies. It's also not very transparent. I might not really know the factors that led to some decisions being made. Even humans themselves often don't really understand why they came to a given decision because of their being emotional in nature, and thus, these human decision-making systems are often difficult to audit. So another way to proceed is maybe instead I study the system and the data carefully, and I write down the best rules for making a decision, or I can have a machine dynamically figure out the best rules as a machine learning. So maybe this is a better approach. It's certainly fast and thus cheap, and maybe I can construct the system in such a way that it doesn't have the biases that are inherent in human decision-making, and since I've written these rules down, or a computer has learned these rules, then I can just show them to somebody, right, and then they can audit it. So more and more decision-making is being done in this way. And so in this model, we take data, we make an inference based on that data using these algorithms, and then we can take actions, and when we take this more scientific approach to making decisions and optimizing for a desired outcome, we can take an experimental approach, so we can determine which actions are most effective in achieving a desired outcome. Maybe there are some types of communication styles that are most effective with certain people. I can perhaps deploy some individualized incentives to get to the outcome that I desire, and even if I carefully design and experiment with the environment in which people make these decisions, perhaps even very small changes can introduce significant changes in a people's behavior. So through these mechanisms and this experimental approach, I can maximize the probability that humans do what I want. So algorithmic decision-making is being used in industry and is used in lots of other areas, from astrophysics to medicine, and is now moving into new domains, including government applications. So we have recommendation engines like Netflix, Yelp SoundCloud that direct our attention to what we should watch and listen to. Since 2009, Google uses personalized search results, including if you're not logged into your Google account. And we also have algorithmic curation and filtering, as in the case of Facebook News Feed, Google News, Yahoo News, which shows you what news articles, for example, you should be looking at. And this is important because a lot of people get news from these media. We even have algorithmic journalists, so automatic systems, or generate articles about weather, traffic, or sports instead of a human. And another application that's more recent is the use of predictive systems in political campaigns. So political campaigns also now take this approach to predict on an individual basis which candidate voters are likely to vote for, and then they can target on an individual basis those that can be persuaded otherwise. And finally, in the public sector, we're starting to use predictive systems in areas from policing to health, to education and energy. So there are some advantages to this. So one thing is that we can automate aspects of our lives that we consider to be mundane using systems that are intelligent and adaptive enough. We can make use of all the data and really get the pieces of information we really care about. We can spend money in the most effective way. And we can do this with this experimental approach to optimize actions to produce desired outcomes. So we can embed intelligence into all of these mundane objects and enable them to make decisions for us. And so that's what we're doing more and more. And we can have an object that decides for us what temperature we should set our house, what we should be doing, etc. So there might be some implications here. We want these systems that do work on this data to increase the opportunities available to us. But it might be that there are some implications that we have not carefully thought through.  In some cases, there may now be the options available to people. And this approach subjects people to suggestive messaging intended to nudge them to a desired outcome. Some people may have a problem with that. The values we care about are not going to be baked into these systems by default. It's also the case that some algorithmic systems facilitate work that we do not like. For example, in the case of mass surveillance. And even the same systems used by different people or organizations have very different consequences. For example, if I can predict with high accuracy based on, say, search queries who's going to be admitted to hospital, some people would be interested in knowing that. You might be interested in having your doctor know that. But that same predictive model in the hands of an insurance company has a very different implication. So the point here is that these systems structure and influence how humans interact with each other, how they interact with society, and how they interact with government. And if they constrain what people can do, then we should really care about this. So now I'm going to go to sort of an extreme case just as an example. And that's this Chinese social credit system. And so this is probably one of the more ambitious uses of data that is used to rank each citizen based on their behavior in China. So right now, there are various pilot systems deployed by various companies doing this in China. And they're currently voluntary. And by 2020, this system is going to be decided on a combination of the systems that is going to be mandatory for everyone. And so in the system, there are some citizens and a huge range of data sources are used. So some of the data sources are your financial data, your criminal history, how many points you have on your driver's license, medical information. For example, if you take birth control pills, that's incorporated. Your purchase history, for example, if you purchase games, you're downranked in the system. Some of the systems, not all of them, incorporate social media monitoring, which makes sense if you're a state like China. You probably want to know about political statements that people are saying on social media. And one of the more interesting parts is social network analysis. So looking at the relationships between people. So if you have a close relationship with somebody and they have a low credit score, that can have implications on your credit score. So the way that these scores are generated is secret. And according to the call for these systems put out by the government, the goal is to carry forward the sincerity and traditional virtues and establish the idea of a sincerity culture. So wait, it gets better. So there's a portal that enables citizens to look up the citizen score of anyone. And many people like this system. They think it's a fun game. They boast about it on social media. They put their score in their dating profile because if you're ranked highly, you're part of an exclusive club. You can get VIP treatment at hotels and other companies. But the downside is that if you're excluded from that club, your weak score may have other implications like being unable to get access to credit, housing, jobs. There are some, the some reporting saying that even travel visas might be restricted if your score is particularly low. So a system like this for a state is really the optimal solution to the problem of the public. It constitutes a very subtle and insidious mechanism of social control. You don't need to spend a lot of money on police or prisons. If you can set up a system where people discourage one another from antisocial acts like political action in exchange for a coupon for free Uber ride. So there are a lot of legitimate questions here. What protections does user data have in this scheme? Do any safeguards exist to prevent tampering? What mechanism, if any, is there to prevent false input data from creating erroneous inferences? Is there any way that people can fix their score once they're ranked poorly? Or does it end up becoming a self-fulfilling prophecy? Your weak score means you have less access to jobs and credit and now you will have limited access to opportunity. So let's take a step back. So what do we want? So we probably don't want that. But as advocates, we really want to understand what questions we should be asking of these systems right now. There's very little oversight. And we want to make sure that we don't sort of sleepwalk our way to a situation where we've lost even more power to these centralized systems of control. And if you're an implementer, we want to understand what can we be doing better? Are there better ways that we can be implementing these systems? Are there values that as humans, we care about that we should make sure these systems have? So the first thing that most people in the room might think about is privacy, which is, of course, of the utmost importance. We need privacy and there is a good discussion on the importance of protecting user data where possible. So in this talk, I'm going to focus on the other aspects of algorithmic decision-making that I think have got less attention because it's not just privacy that we need to worry about here. We also want systems that are fair and equitable. We want transparent systems. We don't want opaque decisions to be made about us. Decisions that might have serious impacts in our lives. And we need some accountability mechanisms. So for the rest of this talk, we're going to go through each one of these things and look at some examples. So the first thing is fairness. So as I said in the beginning, this is one area where there might be an advantage to making decisions by machine, especially in areas where there have historically been fairness issues with decision-making, such as law enforcement. So this is one way that police departments use predictive models. So the idea here is police would like to allocate resources in a more effective way, and they would also like to enable proactive policing. So if you can predict where crimes are going to occur or who is going to commit crimes, then you can put cops in those places or perhaps following these people, and then the crimes will not occur. So it's sort of the pre-crime approach. So there are a few ways of going about this. So one way is doing this individual level of prediction. So you take each citizen and estimate the risk that each citizen will participate, say in violence, based on some data. And then you can flag those people that are considered particularly violent. So this is currently done. This is done in the US. It's done in Chicago by the Chicago Police Department. And they maintain a heat list of individuals that are considered most likely to commit or be the victim of violence. And this is done using data that the police maintain. So the features that are used in this predictive model include things like, well, things that are derived from individuals' criminal history. So, for example, have they been involved in gun violence in the past? Do they have narcotics arrests and so on? But another thing that's incorporated in the Chicago Police Department model is information derived from social network analysis. So who you interact with as noted in police data. So, for example, your co-arrestees when officers conduct field interviews, who are people interacting with, and then this is all incorporated into this risk score. So another way to proceed, which is the method that most companies that sell products like this to the police have taken, is instead predicting which areas are likely to have crimes committed in them. So I take my city, I put a grid down, and then I use crime statistics and maybe some ancillary data sources to determine which areas have the highest risk of crimes occurring in them. And I can flag those areas and send police officers to them. So now let's look at some of the tools that are used for this geographic level prediction. So here are three companies that sell these geographic level predictive policing systems. So PredPol has a system that uses primarily crime statistics, only the time, place, and type of crime to predict where crimes will occur. HunchLab uses a wider range of data sources, including, for example, weather, and then Hitachi is a newer system that has a predictive crime analytics tool that also incorporates social media, the first one to my knowledge to do so. And these systems are in use in 50-plus cities in the US. So why do police departments buy this? So some police departments are interested in buying systems like this because they're marketed as impartial systems. So it's a way to police in an unbiased way. And so these companies make statements like this, by the way, the references will all be at the end and they'll be on the slides. So for example, their predictive crime analytics from Hitachi claims that the system is anonymous because it shows you an area. It doesn't show you to look for a particular person. And PredPol reassures people that it eliminates any liberties or profiling concerns. And HunchLab notes that the system fairly represents priorities for public safety and is unbiased by race or ethnicity, for example. So let's take a minute to describe in more detail what we mean when we talk about fairness. So when we talk about fairness, we mean a few things. So one is fairness with respect to individuals. So if I'm very similar to somebody and we go through some process and there is two very different outcomes to that process, we would consider that to be unfair. So we want similar people to be treated in a similar way. But there are certain protected attributes that we wouldn't want someone to discriminate based on. And so this is other property, group fairness. So we can look at the statistical parity between groups based on gender, race, et cetera and see if they're treated in a similar way. And we might not expect that in some cases. For example, if the base rates in each group are very different. And then there's also fairness and errors. So all predictive systems are going to make errors. And if the errors are concentrated, then that may also represent unfairness. And so this concern arose recently with Facebook because people with Native American names had their profiles flagged as forgery and far more often than those with white American names. So these are the sorts of things that we worry about and each of these are metrics. And if you're interested more, you should check those two papers out. So how can potential issues with predictive policing have implications for these principles? So one problem is the training data that's used. So some of these systems only use crime statistics. Other systems, all of them use crime statistics in some way. So one problem is that crime databases contain only crimes that have been detected, right? So the police are only going to detect crimes that they know are happening, either through patrol and their own investigation or because they've been alerted to crime, for example, by a citizen calling the police. So a citizen has to feel like they can call the police, like that's a good idea. So some crimes suffer from this problem less than others. For example, gun violence is much easier to detect. Relative to fraud, for example, which is very difficult to detect. Now, the racial profiling aspect of this might come in because of biased policing in the past. So for example, for marijuana arrests, black people are arrested in the U.S. at rates four times that of white people, even though there is statistical parity with these two groups to within a few percent. So this is where problems can arise. So let's go back to this geographic level of predictive policing. So the danger here is that unless this system is very carefully constructed, this sort of crime area ranking might again become a self-fulfilling prophecy. If you send police officers to these areas, you further scrutinize them, and then again, you're only detecting a subset of crimes and the cycle continues. So one obvious issue is that this statement about geographic-based crime prediction being anonymous is not true because race and location are very strongly correlated in the U.S. And this is something that machine learning systems can potentially learn. Another issue is that, for example, for individual fairness, one of my homes sits within one of these boxes. Some of these boxes in these systems are very small. For example, PredPol, it's 500 feet by 500 feet. So it's maybe only a few houses. So the implications of the system are that you have police officers maybe sitting in a police cruiser outside your home and a few doors down. Someone may not be within that box and doesn't have this. So that may represent unfairness. So there are real questions here, especially because there's no opt-out. There's no way to opt out of this system. If you live in a city that has this, then you have to deal with them. So it's quite difficult to find out what's really going on because the algorithm is secret. And in most cases, we don't know the full details of the inputs. We have some idea about what features are used, but that's about it. We also don't know the output. That would be knowing police allocation, police strategies. And in order to nail down what's really going on here, in order to verify the validity of these companies' claims, it may be necessary to have a third party come in, examine the inputs and outputs of the system and say concretely what's going on. And if everything is fine and dandy, then there shouldn't be a problem. So that's potentially one role that advocates can play. Maybe we should start pushing for audits of systems that are used in this way. These could have serious implications for people's lives. So we'll return to this idea a little bit later, but for now this leads us nicely to transparency. So we want to know what these systems are doing. But it's very hard for the reason described earlier, but even in the case of something like trying to understand Google search algorithm, it's difficult because it's personalized. So by construction, each user is only seeing one endpoint. So it's a very isolating system. What do other people see? And one reason it's difficult to make some of these systems transparent is because of simply the complexity of the algorithms. So an algorithm can become so complex that it's difficult to comprehend even for the designer of the system or the implementer of the system. You might know that this algorithm maximizes some metric, say, accuracy, but they may not always have a solid understanding of what the algorithm is doing for all inputs, certainly with respect to fairness. So in some cases, it might not be appropriate to use an extremely complex model. It might be better to use a simpler system with human interpretable features. Another issue that arises from the opacity of these systems in the centralized control is that it makes them very influential and thus an excellent target for manipulation or tampering. So this might be tampering that is done from an organization that controls the system or an insider of one of the organizations or anyone who's able to compromise their security. So this is an interesting academic work that looked at the possibility of slightly modifying search rankings to shift people's political views. So since people are most likely to click on the top search results, so 90% of clicks go to the first page of search results, then perhaps by reshuffling things a little bit or maybe dropping some search results, you can influence people's views in a coherent way, and maybe you can make it so subtle that no one is able to notice. So in this academic study, they did an experiment in the 2014 Indian election, so they used real voters and they kept the size of the experiment small enough that it was not going to influence the outcome of the election. So the researchers took people, they determined their political leaning, and they segmented them into control and treatment groups, where the treatment was manipulation of the search ranking results, and then they had these people browse the web. And what they found is that this mechanism is very effective at shifting people's voter preferences. So they, in this study experiment, were able to introduce a 20% shift in voter preferences. Even alerting users to the fact that this was going to be done, so telling them we are going to manipulate your search results, really pay attention, they were totally unable to decrease the magnitude of the effect. So the margins of error in many elections is incredibly small, and the authors estimate that this shift could change the outcome of about 25% of elections worldwide if this were done. So, and the bias is so small that no one can tell. So all humans, no matter how sort of smart and resistant to manipulation we think we are, all of us are subject to this sort of manipulation, and we really can't tell. So I'm not saying that this is occurring, but right now there is no regulation to stop this. There is no way that we could reliably detect this. So there's a huge amount of power here. So something to think about. But it's not only corporations that are interested in this sort of behavioral manipulation. So in 2010, UK Prime Minister David Cameron created this UK behavioral insights team, which is informally called the Nudge Unit. And so what they do is they use behavioral science and this predictive analytics approach with experimentation to have people make better decisions for themselves in society as determined by the UK government. And as of a few months ago, after an executive order signed by Obama in September, the United States now has its own Nudge Unit. So to be clear, I don't think that this is some sort of malicious plot. I think that there can be huge value in these sorts of initiatives in positively impacting people's lives, but when this sort of behavioral manipulation is being done, in part openly, oversight is pretty important. And we really need to consider what these systems are optimizing for. And that's something that we might not always know or at least understand. So, for example, for industry, we do have a pretty good understanding there. Industry cares about optimizing for the time spent on the website.  They want you to click on ads, click on newsfeed items. They want you to like things and fundamentally profit. So already this has some pretty serious implications and has had pretty serious implications in the last 10 years in media, for example. The optimizing for click-through rate in journalism has produced a rate to the bottom in terms of quality. And another issue is that optimizing for what people like might not always be the best approach. So Facebook officials have said publicly about how Facebook's goal is to make you happy. They want you to open that newsfeed and just feel great. But there's an issue there, right? Because people get their news, you know, like 40% of people, according to Pew Research, get their news from Facebook. So if people don't want to, you know, people don't want to see war and corpses because it makes them feel sad. So this is not a system that is going to optimize for an informed population. It's not going to produce a population that is ready to engage in civic life. It's going to produce an amused population whose time is occupied by cat pictures. So in politics, we have a similar optimization problem that's occurring. So these political campaigns that use these predictive systems are optimizing for votes for the desired candidate, of course. So instead of a political campaign being... Well, maybe this is a naive view, but being an open discussion of the issues facing the country, it becomes this micro-targeted persuasion game.  Those are the people that are going to get attention from political candidates. So in policy, as with these Nudge units, they're being used to enable better use of government services. So there are some good projects that have come out of this, increasing voter registration, improving health outcomes, improving education outcomes. But some of these predictive systems that we're starting to see in government are optimizing for compliance, as is the case with predictive policing. So this is something that we need to watch carefully. So I think this is a nice quote that sort of describes the problem. You know, in some ways, we might be narrowing our horizon, and the danger is that these tools are separating people. And this is particularly bad for political action because political action requires people to have shared experience and thus are able to collectively act to exert pressure to fix problems. So finally, accountability. So we need some oversight mechanisms. So for example, in the case of errors, this is particularly important for civil or bureaucratic systems. So when an algorithm produces some decision, we don't always want humans to just defer to the machine, and that might represent one of the problems. So there are starting to be some cases of computer algorithms yielding a decision and then humans being unable to correct an obvious error. So there's this case in Georgia in the United States where two young people went to the Department of Motor Vehicles, they're twins, and they went to get their driver's license. However, they were both flagged by a fraud algorithm that uses facial recognition to look for similar faces, and I guess the people that designed the system didn't think of the possibility of twins. Yeah. So this is one, so they just left without the driver's licenses. The people in the Department of Motor Vehicles were unable to correct this. So this is one implication. It's like something out of Kafka. But there are also cases of errors being made and people not noticing until after actions have been taken, some of them very serious, because people simply deferred to the machine. So this is an example from San Francisco. So an ALPR, an automated license plate reader, is a device that uses image recognition to detect and read license plates, and usually to compare license plates with a known list of plates of interest. And so San Francisco uses these and then mounted on police cars. So in this case, San Francisco ALPR got a hit on a car, and it was the car of a 47-year-old woman with no criminal history. And so it was a false hit because it was a blurry image and it matched erroneously with one of the plates of interest that happened to be a stolen vehicle. So they conduct a traffic stop on her and they take her out of the vehicle, they search her in the vehicle, she gets a pat down, and they have her kneel at gunpoint in the street. So how much oversight should be present depends sort of on the implications of the system. It's certainly the case that for some of these decision-making systems an error might not be that important, it could be relatively harmless, but in this case, an error in this algorithmic decision led to this totally innocent person literally having a gunpoint at that error. So that brings us to, you know, we need some way of getting some information about what is going on here. We don't want to have to wait for these events before we are able to determine some information about the system. So auditing is one option to independently verify the statements of companies in situations where we have inputs and outputs. So, for example, this could be done with Google, Facebook. If you have the inputs of a system, say you have test accounts or real accounts, maybe you can collect people's information together. So that was something that was done during the 2012 Obama campaign by pro-publica. People noticed that they were getting different emails from the Obama campaign and were interested to see based on what factors the emails were changing. And so I think about 200 people submitted emails and they were able to determine some information about what the emails were being varied based on. So there have been some, like, successful attempts at this. So compare inputs and then look at why one item was shown to one user and not another and see if there's any statistical differences. So there's, you know, some potential legal issues depending, you know, with the test accounts, so that's something to think about. I'm not a lawyer. So, for example, if you want to examine ad targeting algorithms, one way to proceed is to construct a browsing profile and then examine what ads are served back to you. And so this is something that academic researchers have looked at because at the time, at least, you didn't need to make an account to do this. So this was a study that was presented at Privacy Enhancing Technologies last year. And in this study, the researchers generate some browsing profiles that differ only by one characteristic. So they're basically identical in every way except for one thing. And that is denoted by treatment one and two. So this is a randomized controlled trial, but I left out the randomization part for a simplicity. So in one study, they applied a treatment of gender. So they had the browsing profiles in treatment one be male browsing profiles and the browsing profiles in treatment two be female. And they wanted to see, is there any difference in the way that ads are targeted if browsing profiles are effectively identical except for gender? So it turns out that there was. So a third party site was showing Google ads for senior executive positions at a rate six times higher to the fake men than for the fake women in this study. So this sort of auditing is not going to be able to determine everything that algorithms are doing, but they can sometimes uncover interesting, at least statistical differences. So this leads us to the fundamental issue. Right now, we're really not in control of some of these systems and we really need these predictive systems to be controlled by us in order for them not to be used as a system of control. So there are some technologies that I'd like to point you all to that we need tools in the digital commons that can help address some of these concerns. So the first thing is that, of course, we know that minimizing the amount of data available can help in some contexts, which we can do by making systems that are private by design and by default. Another thing is that these audit tools might be useful. And so these two nice examples in academia, so the ad experiment that I just showed was done using adfisher. So these are two toolkits that you can use to start doing this sort of auditing. Another technology that is generally useful, but particularly in the case of prediction, it's useful to maintain access to as many sites as possible through anonymity systems like Tor, because it's impossible to personalize when everyone looks the same. So this is a very important technology. Something that doesn't really exist, but that I think is pretty important, is having some tool to view the landscape. So as we know from these few studies that have been done, different people are not seeing the Internet in the same way. So this is one reason why we don't like censorship. But rich and poor people from academic research, we know that there is widespread price discrimination on the Internet. So rich and poor people see a different view of the Internet. Men and women see a different view of the Internet. We want to know how different people see the same site. And this could be the beginning of a defense system for the sort of manipulation tampering that I showed earlier. Another interesting approach is obfuscation, injecting noise into the system. So there's an interesting browser extension called AdNauseam, that's for Firefox, which clicks on every single ad you're served to inject noise. So that's, I think, an interesting approach that people haven't looked at too much. So in terms of policy, Facebook and Google, these Internet giants have billions of users, and sometimes they like to call themselves new public utilities, and if that's the case, then it might be necessary to subject them to additional regulation. Another problem that's come up, for example, with some of the studies that Facebook has done, is sometimes a lack of ethics review. So for example, in academia, if you're going to do research involving humans, there's an institutional review board that you go to that verifies that you're doing things in an ethical manner. And some companies do have internal review processes like this, but it might be important to have an independent ethics board that does this sort of thing. And we really need third-party auditing. So for example, some companies don't want auditing to be done because of IP concerns. And if that's the concern, maybe having a set of people that are not paid by the company to check how some of these systems are being implemented could help give us confidence that things are being done in a reasonable way. So in closing, algorithmic decision-making is here, and it's barreling forward at a very fast rate, and we need to figure out what the guide rails should be and how to install them to handle some of the potential threats. There's a huge amount of power here. We need more openness in these systems. And right now, with the intelligent systems that do exist, we don't know what's occurring really, and we need to watch carefully where and how these systems are being used. And I think this community has an important role to play in this fight to study what's being done, to show people what's being done, to raise the debate and advocate, and where necessary to resist. Thanks. So let's have a question and answer. Microphone 2, please. Hi there. Thanks for the talk. Since these pre-crime softwares also arrived here in Germany with the start of the so-called COPWAT system in southern Germany and Bavaria and Nuremberg, especially where they tried to predict burdened crime using that criminal record geographical analysis, like you explained, leads me to a two-fold question. First, have you heard of any research that measures the effectiveness of such measures at all? And second, what do you think of the game theory if the thieves or the bad guys know of that system and when they game the system and they will probably win since one police officer in an interview said the system is used to reduce the personal costs of policing so they just send the guys where the red flags are and the others take the day off? Yeah. So with respect to testing the effectiveness of predictive policing, so the companies, some of them do randomized controlled trials and claim a reduction in policing. The best sort of independent study that I've seen is by this RAND Corporation that did a study in I think Shreveport, Louisiana, and in their report they claim that there was no statistically significant difference and they didn't find any reduction and it was specifically looking at property crime, which I think you mentioned. So I think right now there's sort of conflicting reports between the independent auditors and these company claims, so there definitely needs to be more study. Yeah, and then the second thing, sorry, remind me what was the second thing. What about the guys gaming the system? Oh, yeah, I mean, yeah, I think it's a legitimate concern. Like if all the outputs were just immediately public, then yes, everyone knows the location of all police officers and I imagine that people would have a problem with that. Yeah. Microphone number four, please. Yeah, that's actually not a question, but just a comment. I've enjoyed your talk very much in particular after watching the talk in hall one early in the afternoon, they say hi to your new boss about algorithms that are trained with big data and finally make decisions. And I think these two talks kind of are complementary and if people are interested in that topic, they might want to check out the other talk and watch it later because these fit very well together. Yeah, that was a great talk. Microphone number two, please. Yeah, well, you mentioned the need to have some kind of third party auditing or some kind of way to peek into these algorithms and to see what they're doing and to see if they're being fair. I mean, can you talk a little bit more about that? Like going forward, I mean, some kind of regulatory structures would probably have to emerge to, you know, analyze and to look at these black boxes that are just sort of popping up everywhere and controlling more and more of the things in our lives, important decisions and so just, I mean, what kind of discussions are there for that and what kind of possibilities are there for that and I'm sure that companies would be very, very resistant to any kind of attempt to look into their algorithms and to, yeah, just... I mean, definitely companies would be very resistant to having people look into their algorithms so if you want to do a very rigorous audit of what's going on then it's probably necessary to have, you know, a few people come in and sign NDAs and then look through the systems. So that's one way to proceed but another way to proceed that, you know, so these academic researchers have done a few experiments and found some interesting things and that's sort of all the attempts at auditing that we've seen, you know, there's one attempt in 2012 for the Obama campaign but there's really not been any sort of systematic attempts, you know, like in censorship we see a systematic attempt to do measurement as often as possible, check what's going on and that itself, you know, can act as an oversight mechanism but right now, you know, I think many of these companies realize no one's watching so there's no real push to have people verify, are you being fair when you implement this system because no one's really checking? Do you think that at some point it would be like an FDA or an SECM to give some American examples? An actual government regulatory agency that has the power and the ability to, you know, not just sort of look and try to reverse engineer some of these algorithms but actually peek in there and make sure that things are fair because it seems like it's so important now that it's, again, it could be, you know, the difference between the life and death between getting a job, not getting a job, being pulled over, not being pulled over, being racially profiled, not racially profiled, things like that. Is it moving in that direction? Or is it way too early for it? I mean, so some people have, so like someone has called for like a federal search commission or like a federal algorithms commission that would do this sort of oversight work but I mean it's in such early stages right now that there's no sort of real push for that. But I think it's a good idea. And again, number two, please. Thank you again for your talk. I was just curious if you can point to any examples of either current producers or consumers of these algorithmic systems who are actively and publicly trying to do so in a responsible manner by describing what they're trying to do and how they're going about it. So, yeah. So there are some companies, for example like DataKind, that try to do, deploy algorithmic systems in as responsible way as possible for like public policy. Like I actually also implement system for public policy in a transparent way, like all the codes in Goetheb, et cetera. And it is also the case to give credit to Google and these giants. They're trying to implement transparency systems that help you understand, you know, this has been done with respect to how your data is being collected.  You can see this item was recommended to me so you know that prediction is being used in this case and it will say why prediction is being used because you purchased some item. And Google has a similar thing. If you go to like Google ad settings, you can even turn off personalization of ads if you want. And you can also see some of the inferences that have been learned about you, a subset of the inferences that have been learned about you. So like what interests? A question from the internet please. Yes, BilletQ is asking how do you avoid biases in machine learning? I assume analysis, for example, could be biased against women and minorities if used for hiring decisions based on known data. Yeah, so one thing is to just explicitly check. So you can check to see how positive outcomes are being distributed among those protected classes. You could also incorporate these sort of fairness constraints in the function that you optimize when you train the system. And so if you're interested in reading more about this, the two papers, so let me get our references, there's a good paper called Fairness Through Awareness that describes how to go about doing this. So I recommend this person read that. It's good. Thank you. Microphone 2, please. Thanks again for your talk. Hi. Hello. Okay. I see, of course, a problem with all the black boxes that you described with regards for the crime systems, but when we look at the advertising systems, in many cases, they're very networked. There are many different systems collaborating in exchanging data via open APIs, RESTful APIs, and various demand site platforms and audience exchange platforms and everything. So can that help to at least increase awareness on where targeting personalization might be happening? I mean, I'm looking at systems like Build With, that surface, what kind of JavaScript libraries are used elsewhere. So is that something that could help at least to give them better awareness and listing all the points where you might be targeted? I mean, so like with respect to advertising, the fact that there is behind the scenes this like complicated auction process that's occurring just makes things a lot more complicated. So for example, you know, I said briefly that, oh, they found that there's this statistical difference between how men and women are treated, but it doesn't necessarily mean that, oh, the algorithm is definitely biased. It could be because of this auction process, it could be that women are considered more valuable when it comes to advertising, and so these executive ads are getting outbid by some other ads, and so there's a lot of potential causes for that. So I think it just makes things a lot more complicated. I don't know if it helps with the bias at all. Well, the question was more in direction, can it help to surface and make people aware of that fact? I mean, I can talk to my kids probably, they will probably understand, but I can't explain that to my grandma, who is also looking at an iPad. So the fact that the systems are, I don't know if I understand. Okay, I think the main problem is that we are behind the industry efforts to being targeted at, and many people do know, but a lot more people don't know, and making them aware of the fact that they are target in a way is something that it can only be shown by third parties that dispose that data, and make audits in a way, maybe in an automated way. Right. Yeah, I think it certainly could help with advocacy, if that's the point, yeah. Another question from the Internet, please. Yes, on IRC they are asking if we know that prediction in some cases provides an influence that cannot be controlled, so R4 v5 would like to know from you if there are some cases or areas where machine learning simply shouldn't go. So I think the one I mean, yes, I think it is the case that in some cases machine learning might not be appropriate, for example, if you use machine learning to decide who should be searched. I don't think it should be the case that machine learning algorithm should ever be used to determine probable cause or something like that. So if it's just one piece of evidence that you consider and this human oversight always, maybe it's fine, but we should be very suspicious and hesitant in certain contexts where the ramifications are very serious, like the no fly list and so on. And number two again. A second question that just occurred to me if you don't mind. Until the advent of algorithmic systems, when there have been cases of serious harm that's been resulted in individuals or groups and it's been demonstrated that it's occurred because of an individual or a system of people being systematically biased, then often one of the actions it's taken as pressure is applied and then people are required to change, be held responsible and then change the way that they do things to try to remove bias from that system. What's the current thinking about how we can go about doing that when the systems that are doing that are algorithmic? Is it just going to be human oversight and the humans are going to have to be held responsible for the oversight? So in terms of bias, I mean, if we're concerned about bias towards particular types of people that's something that we can optimize for. So we can train systems that are unbiased in this way. So that's one way to deal with it. But there's always going to be errors. So that's sort of a separate issue from the bias and in the case where there are errors, there must be oversight. So one way that one could improve the way that this is done is by making sure that you're keeping track of confidence of decisions. So if you have a low confidence prediction, then maybe a human should come in and check things. So that might be one way to proceed. So there's no more question. I close this talk now. Thank you very much. And a big applause to Jennifer Halstead.


Hi, everyone. Welcome back to the last half of our day two event. So I think the next session is going to be on soft skills that make a great software developer. So I think this is something which is really gentle and it's kind of with respect to your technical track. We'll all be interested to hear. And I'm really happy to welcome Wilson Anandraj, who is with me, who is joining us as a speaker for the day. So he's a vice president at software services factory and he's currently working at Infinite Computer Solutions. And there's a lot more to talk about him. He has around 22 years of experience in R&D and which includes global delivery, PCA, solution architecture. I must tell you the India leader at Alcatel Lusant India. And he's also head in India IPTC in Alcatel Lusant. And he's expertise in operations management and transformation. And he has been successful in creating offshore teams and leading people. And he's proficient in resource management, analytical skills, mentoring skills, presentation, what not, learning leadership and budgeting and interpersonal skills. So I'm really happy to welcome you, Wilson. So this is all yours. We're excited to hear you. Thank you, Pavitra. Good afternoon, friends. I'm really, really happy to be part of this event. And it gives me an immense pleasure to be here to talk to you about some of my experiences, some of the learnings that I have got in the software industry. And I know this is a Saturday and it's around two o'clock. People are wanting to go for lunch. What I'll do is I'll go through the slides. I love you to ask questions whenever it is possible, either through Slack or through Pavitra. And we'll interact as much as possible even after the session. The reason why I chose this topic is, guys, I had been very, very passionate about training people, mentoring people. I have brought in lots and lots of good, passionate software developers. And I know this industry ever since in 1992 when I took my first GW Basic class where I started writing code.  And from that time onwards, this has been a fascinating industry. And what India is today is because there has been millions of software developers who have gone around the world, have proved that India is the best place for software development. So what I'll do is I'll quickly go through this agenda. I'll tell you what is software development all about, which probably you all know, and the way soft skill fits into that and the importance of it. And what are the few important soft skills that you and I as a software development community should have? And then we'll finish it with a QA. Let me start with a very, very generic software engineering kind of a definition. Guys, I tell you this is taken from Carnegie Mellon University where they say software engineering is a branch of computer science that creates practical cost effective solutions to computing and information processing problems, preferably by applying scientific knowledge developing software systems in the servers of mankind. If I need to put it in a different way, my own Wilson's own definition of software industry, software engineering, I would call that as art. It's an art of making the silicon chips do what you want them to do for you, right? It's a communication between your brain and the and the silicon chips, which have no animate life, which do not have any life in themselves, but they are programmed, they are controlled in such way that there is a beautiful output that comes out of it, which is used for the service of mankind, right? Remember, again, the words of my own mentor who said computers do not do anything, anything. You know, you talk about ML, you talk about AI, you talk about orchestration, all these words today, but computers and networks, the systems do not do anything other than what you tell them to do. That is the power of software engineering and that is the power of software developers. We have created this industry and this industry has seen a massive growth from 1990s, from 1980s onwards, ever since the first computer was invented from there. And today what we see, it is all the brain of the developers which has gone in. It is all the skills of the developers which has gone in and computers are just boxes of silicon chips. In all this, what we have done is, remember guys, this is something that we need to remember. We as superior human being have gone down below our intelligence. We have gone way below our intelligence to talk to the computer to make it understand, to make it work the call flow that we want that to work and then have been very, very successful in making it work. And that is the tremendous change that you are seeing in the world today. Computers are doing all that now we say and for which the computers which has got no intelligence, we have made it intelligent not by bringing up the computers, but we going below with our soft skills to make computer understand what we want them to do. And if that is the case, what is that we miss in soft skills? If we are able to talk to inanimate things and make a powerful industry out of these inanimate things, what are we talking about lack of soft skills in software engineering? Let's go to the evolution of software engineering. Guys, if you remember the Pressman book on the software development lifecycle software engineering, we had seen waterfall as a methodology for developing software. Remember in software development process in waterfall methodology, we used to talk to the customers, understand what they wanted to do and develop software. And it was a pretty successful model. There were lots of computer systems and computer applications which we had created using waterfall model. There was absolutely no issue. We had seen multiple ways in which we talked to the customer, the Big Bang approach. There were some shortcomings in that. But important soft skills that we displayed in waterfall model includes when the customer was not able to articulate what he wanted to bring up in the system. We used methods like observations, interviews, focus groups, all the soft skills that we used to understand what the customer wanted and then we built systems and it was a pretty successful model. There were some features which were not used when we were building a Big Bang approach. There were issues that were coming up and then we said, hey, let's improve our soft skills better. Let's talk to the customer better. Let's understand the requirements of the customer better. Then we moved on to agile. Again, it is soft skills. Bringing in the customer into our premises. Are we going into the premises of the customer having an iterative way of development, creating a prototype showing to the customer and saying, hey, customer, is this what you like? Is there a chain that you want? We spoke, we understood. We understood what he wanted, what he had in mind. It is all about soft skills. So waterfall agile was a natural evolution. There were lots of good things that started happening on agile. We created bigger agile, safe agile. There were bigger frameworks which were created for software development. Then we still saw that there are some more soft skills that can be brought in. What is that? The requirements are collected from customers. As software engineers, we understood. We had that skills to understand, develop the product. There was software, but software connected the person who created the software engineer and the end customer. The end user of that. Between these two people, we wanted to have less of gaps. We wanted to bridge them more than it was a natural evolution on DevOps. If you see all these three models, and today we have DevOps, we have other methodologies that might come in, but all that we are seeing today is the methodology in which we started writing the software is based on the soft skills, based on identification of gaps that we had with users. There are technologies. I'm not talking about technologies and tools and frameworks which have been there, but it is all bridging gaps in terms of our soft skills between the creators of the software, which is software development community and the users of the software. That's what these evolutions have taught us. Just one point about DevOps that I liked. I took this definition again. DevOps is a combination of software developers and operations. Look again, we are talking about not about frameworks. We are not talking about the tools. We are talking about the combination of two people. There is a middleware in between. We call that as a software, but it is the creators and the users. It is defined as a software engineering methodology, which aims to integrate the work of software development and software operations team by facilitating a culture of collaboration. Take a note of this word. Collaboration is a soft skill that we are talking about. Shared responsibility. Accountability is what we are talking about. Methodologies might be different, but ultimately the creator of the software and the user of the software should have kind of an understanding. When we talk about soft skills, there are around 65 soft skills that a person can have. If you have read a book called For Your Improvement, it talks about the skills that you need to have as a leader. There are 64 skills that they talk about, and one of them is collaboration. It is a soft skill which makes you very successful in whatever you do. Accountability responsibility, which tells you what kind of maturity we have as a human being. DevOps is all about collaboration of people. Collaboration is a soft skill. Shared responsibility is a soft skill. Look at what are the goals of DevOps. It represents a change in mindset. Of course, there are changes in frameworks. We have got better way of storing the repository. We have better way of integrating continuous integration. But all that is okay, but it has created a change in mindset. The change in mindset is a culture. That is a soft skill that I am talking about. In building on top of the agile and lean practices, which have given a way to new frameworks and new tools, DevOps focuses on incremental development and rapid delivery of the software so that the end user can use it. The success relies on the ability to create a culture of accountability. Just take a note of this. If you want to be a great developer, if you want to be a world-class developer, if you want to be recognized as the best person in your company, in your industry, one thing that we need to cultivate is accountability. Success of DevOps itself relies on the person's accountability on how we are able to collaborate with a team. It is not one single person. We are not creating software for a person living in an island. Improved collaboration. Empathy is again a soft skill. Empathy is not written anywhere. Empathy is understanding what the end user wants to do and then creating a code, sitting with the computer, creating algorithms, creating this code in such a way that we understand the ultimate goal of using that software. Empathy and joint responsibility for business outcomes. So that is definition. It tells you how important it is for us to have soft skills of accountability, of collaboration, of empathy. We might be a fantastic coder. We might be a fantastic person in terms of our Python skills or Java skills. But unless we understand the reason why customer has come to us, why the customer has paid us to create that code, we wouldn't be able to be a good software developer. So therefore, the reason why software, soft skills are important for a software developer like us is when people work together on a software project, other skills are necessary to implement. Of course, there are techniques and tools and frameworks that we need to use. But apart from that, our ability to understand people, our ability to communicate what we have in our brain, our ability to interact with the team, each person has got an ego. Everybody in this world who is born has got an ego. That's what Sigmund Freud tells us. So interacting, communicating with multiple egos, with multiple other team members, other stakeholders of the project, be it customer, end user, or be it vendors, or be it the policy makers, ability to manage time, ability to prioritize what we want to present, and ability to report, presenting the progress of the project, not the Big Bang approach, not doing everything that we can do, but not communicating, presenting the progress of the project, negotiating with the customers in terms of what feature needs to go on priority, what feature can be held, what are the things that we can improve in the product? So this is negotiating skills with the customers. Solving problems for which we are paid, we are respected in this world as problem solver. Every software developer is a problem solver. Every customer comes to you with a problem statement. And what we do is we create programs to solve problems. Making decisions, decision making is an important skill among others. So that is the importance of soft skills. So guys, there are, as I told you, there are 64 skills that you need to have as a software developer, as a software engineer. And there is a big list of, probably the soft skills are getting increased these days because of the technology that we are working on and so on. But allow me to talk about few skills that I had seen as most important for software development community today. Number one, to survive in the industry. Number two, to excel in the industry. Number three, to be the best in the industry. And these are the important skills that I talk about. Number one, our ability to pay attention. That soft skill that we need to inculcate. Number two, our ability to speak and transfer the message that we have in our brain to another person, communication. Number three, ability to live this life within 24 hours a day. We have only 24 hours a day and how do we live our life? We have family, we have our own personal priorities. All that we do is we work so that we can have a good life.  How do we manage time, management of time and organization of activities? Collaboration, getting that mindset of collaboration, teamwork, adaptability, self-learning and accountability. These are some of the important skills that I prioritize for software developers. So let's move on to paying attention. Guys, in this era where we have millennials coming in, there are researchers happening on how long a person can pay attention to anything. The span of attention itself is shrinking. But as software developers, we need to remember that we are creators. We are creators of programs. We are creators of solutions. So therefore, to be on top of this profession, we need to pay attention to detail. Just take a note of this. Paying attention to details will put you on the right track to your growth. We need to pay attention to problems. There is lots of times that we assume certain things. Assumption has become a big problem these days. It has become a big challenge while training people, while coaching people. Paying attention to details, maybe it is in the customer problem. The problems that we are approached with, paying attention to solutions and seeing where the solutions can break, where the solutions need to be integrated with other solutions, paying attention to the solution that we build, paying attention to risks that we have not seen, but might approach us at any point of time. Risk is an event that can happen at any point of time. Paying attention to risk, paying attention to vulnerabilities, these are details that we need to pay attention to. Pay attention to your customers. This is to people. People have got emotions. People have got body language. People have got tones. We need to pay attention to what customers want. We need to pay attention to how our peers feel. We need to pay attention to what our supervisors in the organizations are looking for from this project. And we need to look deeply into the weak links in our team and how do we support them. So pay attention to customers, peers, people within the organization. Pay attention to where the team can break the weak links in the team. Pay attention to the environment. We all work in a political environment. The environment in which the project is executed, if you see the PIMBOC, the project management body of knowledge, it talks about environment consists of multiple things, external environment, internal environments and political environments. We need to pay attention to that. Look at the changes. Look at the changes that might happen in the project. Look at the political surroundings. Look at the technical factors. Pay attention to the environment. And pay attention to this is my favorite statement that I tell in all my team meetings. Pay attention to what is not spoken. That is the ultimate soft skill that one can develop. Somebody might come and say, yes, I will do this, but that yes may not be yes. If you have looked at the language, if you have looked at the tone, probably he's saying he's not interested, but he's being compelled. Yes can be interpreted into a different way. So therefore, as developers, as we are growing in our career in software industry, what we need to inculcate, what we need to adapt is to listen to words which are not spoken, to read lines which are not written. I always keep saying that when you get a mail, anybody can read English in that mail. My own daughter who is in third standard can understand the English of that. But the difference between her and I is, I understand the meaning behind the email, the intention of sending that email. When you're talking, observe body languages, observe the tones. When you start paying attention to details, to people, to environment, and start focusing on what is not said, what is not written, then we become the superior software developers that the world is looking for. And this will automatically push our career ahead of others who may not understand the importance of paying attention. Paying attention is one of the great skills that software development community needs to have. Communication is another soft skill, very, very important. For some reason, I believe that instead of increasing our maturity in communication, because of the hurry, the urgency in which we are living, because of the busy world that we are living, communication has deteriorated. Remember today, all of us who are working from India today on some software projects is not because of your credit. It's not because of our credit. In 1980s, when the software engineering brand started opening up in the world, Indians around the world went to US, went to Europe, started telling, started proving that India has got the best of brains and that's how Indian industry, today Indians of the industry has grown and lots of credit to people who have done this in 1980s, 1990s, right? And they were able to communicate. And today, what we think is we have selective listening. We don't listen to the entire statement. We don't listen to the core message. We are all interested. People have misunderstood communication as speaking English. People have misunderstood communication as in a group meeting, group discussion, you need to be vociferous. You need to be loud so that people think that you are communicating. Communicating is not that. Communicating is ability to put what you have in your brain to the other person. What communication is all about. Animals communicate, babies communicate without language. It doesn't need words for communicating, but when we are in this industry where the vocabulary is big, the grammar is big, we need to speak with clarity, understand, we need to know what we are speaking, speak with conviction, speak with confidence. People will trust you only when you appear trustful. Keep yourself composed, be polite, respect the other person. Listening is equally important as part of your communication. Remember that communication includes non-verbal, only 7% of communication happens over words and there are body languages and tones which you need to pay attention to. Communication will take our career much faster in our growth. Understand time management, guys, when you have lot to do, knowing how to understand time is very crucial. You need to have the perfection versus what is required for delivery. We tend to spend lots of time trying to perfect what we are trying to deliver and then we miss our timelines as well. How much time do you spend in planning? How much time do you spend in actually writing the code? How much time do you spend working with your team to come up with new ideas? Managing your own time efficiently allows you to focus on what is most important in your task. Some of the time wastes, if you had seen there are evens that will waste your time and that is what we need to pay attention to.  So paying attention to unneeded meetings, spam mails, social media that can give you lots of hours. If you cut down one hour in any of these activities, probably you are getting a lot more into your life.  Collaboration and team work very important as I said for those of you who have read the seven habits of highly effective people by Stephen Arcavi he says synergy. Habit number five is synergy. If you have read 13 principles of success by Napoleon Hill he talks about principle number two which is mastermind. When you are by yourself, the amount of things that you can achieve is lot lesser than when you have collaboration with somebody else. So whenever you get a chance to talk to your customer take him as your collaborator. He is part of your team, a collaboration. When you are working with your team collaborate with your team. It is not easy because as I told you every human being have got their own understanding they are seasoned with the experience that they have got. You might have difficult people to work with but that's where our intelligence our emotional intelligence should come in and that's where we need to make conscious decisions saying that everybody has got their own value system. I have open mind I will accept the other person as it is. I tell you guys collaboration and teamwork the best way to increase that is teach to somebody something on everyday basis. Learn something by yourself go to the team, teach something be it as small as a poem that you read from Shakespeare. Go teach to them. When you teach to them, when you mentor them when you become a go giver, collaboration the rest of the world universe, rest of the people will start reacting to you in a positive way that's how we can handle teams that's how we can get better collaboration with teams kind of last but one slide adaptability and self-learning a skill which is required very soft skills when I was in my college days I used to learn a programming language called prologue which was an artificial intelligence language mobile. I used to learn fox pro I used to learn VB 4.0 all this is dead all this is not used anywhere given this nature of technological sector in which we are in the envious position that we hold in the industry developer with an adaptable and self-learning attitude has an edge today I need to learn Kubernetes I need to learn open shift and that's a mandate of this technical sector we have to learn as long as we work in this industry the day you decide to stop learning is the day you are heading for path of you become obsolete very very quickly do not be afraid of learning something do not be afraid of experimenting with some new technologies new frameworks new tools create code on a daily basis even if you have become a manager even if you have become a director put your hands dirty make your hands dirty by coding I know my own manager called Adrian who was working with me in one of the earlier companies used to code he was writing code for augmented reality when augmented reality was not even in scenes and today augmented reality is everywhere being able to keep with times and work on upskilling oneself to adapt to the current scenario is one of the best soft skills that we require in this specific industry we are not driving MTC buses for 40 years the steering remains the same for 40 years the brake remains the same whereas here the language is changed the frameworks change the tools change and if I do not learn on a daily basis I become obsolete I will be thrown out and how do we increase our value by being with the with the right technology accountability my last slide guys I know I am running short of time accountability is what distinguishes software developer from a programmer take accountability the code that I write is my own code the code that I write is going to be used in an airplane the code that I write is going to be used in an elevator do I want to get elevator struck do I want to have a mishap in any of my software take accountability to grow as a software developer you must accept and take ownership of your mistakes of your learnings accountability is all about first step towards our leadership in industry I am not talking about positional leadership I am talking about you as a software developer leadership personal accountability is the first step and as the saying goes let's stop working today let's start enjoying our hobby our hobby is to create code our hobby is to solve problems and therefore I love what I am doing therefore I am not tired of working I am not going to office to work I am going to office to play my hobby which is writing of code and solving of problems let's take that accountability and with that guys I thank you very much for your presence and over to you Pavitra Thanks a lot I think it was a really great session you have consolidated so many things which everybody everyone of us needs to invite from college students and professors so I think everybody would have really enjoyed a lot of key takeaways I should say and it's all kind of life lessons more than the technical stuff it is all life lessons and all of us should definitely know and verify so it was really great having you on our event so thank you so much I think people are so excited I could see a lot of you know this might be coming up so thank you so much thanks for the session Thank you everybody bye


Hello and welcome to the session. Let us understand the following question which says, if x and y are connected parametrically by the equation given, without eliminating the parameter find d by by dx, the given equations are x is equal to 280 square and y is equal to 8 multiplied by t to the power 4. Now let us proceed on to the solution. We have to find dy by dx. First given to us is x is equal to 280 square and y is equal to 8 t to the power 4. dx by dt is equal to 480 and dy by dt is equal to 480 cube. Therefore, dy by dx is equal to dy by dt divided by dx by dt which is equal to dy by dt is equal to 480 cube and dx by dt is equal to 480. 4 and 4 gets cancelled, a and a gets cancelled, t gets cancelled with one t of t cube, so we are left with t square. Hence, the required value of dy by dx is t square. If you understood this question, that's all for the session. Bye and have a nice day.


This is TfNN the tiger financial news network Headline news update Tom O'Brien Welcome folks. We have the Dow industries down 336 you get the Nasdaq off 80 S&P's down 31 That's a drop of 1.3 percent in the Dow industrials 1 percent in the Nasdaq as well as the NDX 100 gold contract gold contract up $4.60 trading at 12 81 announced we got a silver contract up 11 cents $14.43 light sweet crude down the 39 cents now light sweet crude came back quite a bit It was down a buck 75 with 58 75 notes and bonds higher price lower yield In a big way you get the 10-year note up by seven ticks trading 125 19 the 30 a bond up 22 at 1 152 23 and we take a look at this 10-year yield folks right now. We are at 2.23 Bottom line is that that baby is on its way down to 2% has been a fast and furious move King dollar King dollar up 203 ticks trading 98045 the euros at 11 to 1 US dollar the yen is at 109 45 and the pound is at 126 22 Well, you know when we take a look at these futures what you're looking at inside the S&P futures folks is that you are down 34 bucks you're at 2770 and before we Get into this clothes this wants to go after this is a low the lower today There's the 2766 it is of high volume low and I expect you're gonna basically go after the next few minutes out here And the the S&P itself is in a much larger ABC structure on the way down This isn't the front the size of break out here today You know your next leg down is somewhere around 2726 right now you're at the 2770 2776 gets you down to that March 6th level if you do go look at the Dow industrials You see the Dow itself broke and That type of break in the Dow like we just broke from February 5th Everything above it and when you jump the creek and that's what we did out here We jumped the creek and we did it with velocity So bottom line is that what you have out here on a basis, you know, whether it's three four or five weeks Is that low of December 24th? That's that's up for Getting tested gold contract. What do we have with the gold contract gold contract still needs a bid Bottom line you get a little juice in it 182,000 contracts you need more than that stay right there folks next 60 minutes trading coming up And you can expect the volatility to continue right into the close out here today. Dow industrials down 365 Nazzak off 91 S&P's down 35 come right back


In this study, ground glass powder and crushed waste glass were used to replace coarse and fine aggregates. The results showed that the use of waste glass powder provided better bonding with cement due to its pozzolanic effect, resulting in increased compressive strength. However, the presence of large glass pieces resulted in voids being formed in the samples, which had a negative impact on the strength. Simple equations were developed based on existing data in the literature and the results obtained from the current study to predict mechanical properties of the concrete with recycled glass for practical applications. This article was authored by Ali Aysin-Selik, Yesin Anyarapa's Kilik, Azair Zeebek, and others.


Demetian Idris is officially adding producer to his list of talents, FX has announced that the Snowfall actor will be producing episodes of the hit show's forthcoming fifth season. In doing so, he joins the critically acclaimed creative hub established by the late grain John Singleton, Eric Amedio and Dave Andron. Set to premiere in 2022, the next installment of the series will also see the 29-year-old reprise his lead role of Franklin will continue to navigate the 80s cocaine boom, the 80s cocaine boom. It's one of our favourite shows here at TGP and Demetian is undeniably an incredible talent. So could us to him for getting his on the front and back end, so said by the producer.


Hi, my name is Berkika and I'm an actress based out of California and today I want to tell you what I think you should do. These are just my opinions, what I have learned. I'm going to tell you how long I think you should wait before you get an agent and what your general actor package should look like before you even try to submit to agencies. This is probably like the 3,000th time I've said that I only have representation in the San Francisco Bay Area because it's true I'm doing a lot of work on getting representation in other places but as of right now I'm represented by Marla Dell Talent in San Francisco. They only represent me for the San Francisco Bay Area. Hopefully LA representation very, very soon and I will show you guys how I got it when I got it. So basically I'm waiting to get representation before I show you guys but today I wanted to tell you what I think your resume should look like before you get an agent, what your general actor package should look like before you get an agent and just have a quick little chat about agents, what they do, do you pay them, like all of that. If you're a new actor and you think, okay if I have an agent going into it then that's how I'm going to have the best chances because agents of course get you auditions. Well, yes and no because agents want to represent you. Agents want to represent you when they know that you're going to do a good job just like any other employer of course when they're interviewing you they want to know that you're knowledgeable about something or that you can easily develop and learn more so they have to see some kind of potential.   It depends on your location as well. So if you live in a different country that's not the US or if you live in a really small town, small estate, whatever that doesn't have a large acting market then it's going to be a lot harder. So let's start with what your actor package should look like. You should definitely have a professional looking headshot. It doesn't have to be professional but it has to be professional looking because of course there's a lot of really great cameras, a lot of phones that have great cameras. You can potentially get a really great shot of yourself or somebody in your family, your friends can get a great headshot. You need a great headshot period. One that at least looks like you because that's the most important part of a headshot that it looks like you because you want to know what they're calling who they're asking to do a soft tape or who they're calling in for a meeting.  If you don't have anything on your resume, go do something. Go sign up for some kind of online reading or an online class. Just get something on your resume. Create a resume if you are brand new and have absolutely nothing to put on it then make something to put on it. You can create your own work and put it on there for now. Once you do get more work that has been published, then you can add that on to your resume. A reel, of course, so you can show them your acting or some kind of acting clip. Your reel traditionally should be your work, some kind of work that you have been in in the past, but especially right now with 2020, a lot of people are really open to seeing work that you recorded yourself at home or something that happened online. Some kind of reading or play that you were on through Zoom or any kind of platform of broadcasting. That shows them your acting abilities. If you don't have acting abilities and you say, well, I want to be an actor, but I'm not a good actor, then I would highly suggest to, of course, take an acting class that come on. You knew I was going to say that, like take an acting class. If you want to learn how to do something, take a class for it, practice it, read about it, do it. Let's just say for argument's sake that you want to go and get an agent today. Of course, you're not going to be able to get a bunch of training today for acting. I suggest you get a commercial agent. In general, if you are starting out and you are struggling getting representation, focus your attention on trying to get commercial representation because for commercials, often you won't speak at all. It's all about your looks or you will speak a little bit, but it'll be natural. They'll ask you to just be natural, act as if you were cooking dinner with your friends. In general, for commercial acting, especially in the beginner level, it's going to be much easier to get an agent if you fit a look, if you are super beautiful or if you are very specific of something that's trendy right now, something that's used a lot, anything like that, anything that a specific market is needing a lot of. For example, in the San Francisco Bay Area, there's a lot of tech. So if you fit into the tech look, whatever that might be for that specific agency, then you can go and submit for that agency. A lot of agents allow you to submit online. So if you research agencies, which the best way to research them is to go to IMDB Pro, see what other people are represented by, and then you can start your research from there. There's a lot of states that require, I believe it's most states that require this. I know California for sure requires agents to be registered through the state. So you can look them up and make sure they're registered. There are a lot of SAG after affiliated agents or agent talent agencies. So you can also see it on the SAG after website if they are SAG affiliated. So there are a few ways that you can do your research yourself and go online and see if they are registered appropriately. Even if they are legit and are registered correctly, you still don't know if they're good agents. And what you need to do is again, IMDB Pro, see who their clients are, see who's represented by them, and then you can contact that actor directly and through social media or whatever and ask them, hey, what's your actual opinion? What has been your experience? And you have to understand that everybody's experience is going to be different. I'm sure there's people that have been with MDT who absolutely hated them, who hated their experience. And then there's a lot of people as well that love their experience with MDT, which is the agency I'm with. I have liked being with them. And when you are doing research for agents, make sure they are at the very most three hours, driving distance away. I say that as a general rule of thumb because I say that all of the time. Of course, it depends on so many things, but there are some developmental agents that will allow you to live in other places and help you a little bit distance wise, self-tape auditions, stuff like that, but it gets really complicated and it's very specific to each one. So again, you simply have to do your own research and figure out which one it's going to be for you. If you don't live somewhere like San Francisco or LA or New York, places like that, that you do have access to them very closely. Those production companies are filming in those cities and that's why they want actors in those cities because they don't want to deal with actors coming from somewhere else. Time is another huge factor in getting an agent because a lot of people have a second job or another primary job other than acting. And it's very likely that they're going to ask you during your meeting, if you get a meeting with an agency, how much time you are going to dedicate to this? Like, do you have a full-time job? What is your schedule like? Is it flexible? Some agencies will want you to be a full-time actor. And some of them will understand in markets like San Francisco that you're most likely going to have another job because acting simply can't, you know, you can't make enough money from acting sometimes. So a lot of agents are very open to that, to you having another job, but they still want to know that you have time for it. I would advise that when you actually have 10 to 20 hours a week to dedicate for acting, whether it be training or classes, auditions, gigs, 10 to 20 hours a week that you can dedicate to that. Do you have flexibility or you don't have anything else going on? Because time's going to be a huge thing. If you don't have time for it and you do get booked by an agent, let's say you're a full-time tech employee and you're working from nine to five every single day and they're sending you auditions Monday through Friday. And most of the time they need it within 24 to 48 hours. And you're not sending in those auditions. You're obviously not going to get any jobs. Your agents aren't going to be happy. And your agents dropping you is very likely, if you are brand new to that agency and they see that you're just not putting in that work, of at least sending in the auditions. Because if you're sending in the auditions and not getting booked, like that's fine.  But if you're not even submitting the auditions, like they're not going to want to represent you because it's going to be a waste of time. You're wasting their time if they're having to send you these things and remind you of all of these things. Like they could just be sending it to somebody else that might actually book the job and, you know, make them money. Because ultimately, that's what agents do. They get your jobs and they get paid from that. So that's one of the questions I have seen. Quite a bit in the comments is how much do you pay an agent? And as far as I know, agents in California, they have to work off of commission. And really, if somebody's telling you like you have to pay them a monthly fee or a yearly fee to get represented by them, I would say keep doing more research and get represented by somebody who will simply take a commission from you. Usually 10 to 20% depending on the type of agent. It will be 10 to 20% of commission of whatever jobs they get you. My advice on if an agent wants to charge you money is don't do it. Do it only on commission. Make sure to read your contract. Of course, make sure that you're not trapped in a contract that's forever. Something that you are able to get out of. Most acting contracts will have some kind of verbiage in it that says the agents can drop you and you can drop them at any time. Usually they want it in writing.  But always make sure to leave it open, especially because you are an artist and people can take advantage of you if you somehow make it really big and they in your under a contract where you're forced to give them 10% of the $10 million you did that year or whatever, which is very uncommon. Let's be realistic, but you should always protect yourself and always read your contracts. And if they're not providing a copy of the contracts that you are signing at a gig or for an agency, make sure you take photos of them because they will come in handy. There are some registered legit agents that charge money, but I simply don't see the reason to I don't see why they wouldn't do what pretty much everybody else is doing. I'm not saying it doesn't happen that people ask for money to be the representation, but just don't do it. Just continue to do your research and find something that's a little bit of a better fit. For me, in my opinion, what I think is a better fit is if they're taking a commission. Of course, your talent level and how much experience you have is important to when you are approaching an agency, but there's so many different kinds of talents agencies that are boutique ones or just really big ones that represent America Ferrera and huge stars like that. But there are all kinds of agents or developmental agents or agents that will not even look at you unless you've been at least had at least five co-star roles. There's all kinds of agents. So you simply have to do your research. And like I said, commercial agents are a lot easier to get than let's say voiceover agents or theatrical agents. But from my experience, let me tell you that I went to college for theater and I had some experience in theater and I had a little bit of work here and there of commercials. And when I submitted to agencies, I actually only to submit it to two agencies in San Francisco. I was treating it as a, oh, this is practice. This is to see what it is like because I didn't get taught in college what that was like at all. So I submitted to those two. I went to my meetings. I brought what they asked me to bring. And they both wanted to sign me. And I was like, oh, shoot, I should have done this as soon as I moved to the Bay Area when I was 18. And then I could have so much more experience because they would have been getting me jobs throughout college and I could have paid for college with some of that money. But go for it. Even if you apply now and you maybe aren't the most developed as you can be, look for the agencies that are willing to take people that aren't as developed, look for commercial agencies and just go for it. Because if they say no now, it can be that you are just not developed enough. You need a little bit more training, more experience, or they already have too many people that fit your same look, your same type. And maybe in the future, they will have need for you. And in the future, you can apply to be represented by them again. They often will have on the website, like how long you should wait before you reapply. I know I talked a lot in this video, but I just, I wanted to talk to you about agents in general, because I've been getting so many questions about it. You can be an actor without an agent. Of course, an agent will help you develop your career and you will get much, much better opportunities through agents. There's a ton of videos that I have made about casting websites, because that's one of the ways that you can find your own work by self submitting. Let me know if you have any other questions about agents, because of course, I didn't cover like everything. I just covered the surface of a lot of things. So I will, of course, make another video like this about agents soon. Thank you so much for watching. At the end of every video, I feature another channel. This is today's feature. If you would like to be featured on my next video, make sure you're subscribed, like this video and leave me a comment.