So our next talk goes in a little bit different direction. It's about causal inference Hi, yeah, how are you? Hi, my turn? Hi, everyone From where are you streaming from? From London London. Okay. Great. Yeah So and you are talking about causal inference now Yep Okay Great. Thanks a lot So hi everyone as I said, my name is Ayal. I'm speaking from London I am a senior data scientist in a health tech company named Babylon and I'm really excited to talk about causal inference and for Most of you who this is an introductory to I'm very glad to talk about this just before I start I want to just emphasize that these are my opinions not those of my employer and Martin can I just ask how many people are attending the talk? Do you happen to know that? In any case Unfortunately, I can't I can't tell you because I can't see the number at the moment, but I can't I can't tell you after the talk Okay, great. So in any case I recommend whoever likes taking notes. That's great But I also put up all the slides on Google On Google slides So feel free to use that if they happen to be more than 100 people on the talk then It'll be good courtesy to make a copy of your own because they do have that limit Okay, great. So I'm ready to start We put a lot of trust in data So much so that we value the opinion of experts for example researchers to tell us what is healthy and not healthy to put in our bodies We have so much trust in data that we're willing to provide it to non-sentient beings like machines to Analyze it learn from it and make suggestions for us for what actions to take a procedure that's commonly known as machine learning We're all beneficiary beneficiaries of this for example We live healthier lives in previous generations and we're also walking around with smartphones with really powerful apps That make great suggestions. My personal favorite is Google Maps I love the fact that I have access to so much data so I can better navigate the city so One way to put it is that we have a lot of confidence in data and the decisions that we can make based on it But we all know that sometimes researchers might misinterpret the data That will be a big part of this talk of how Data might be misinterpreted and how we overcome that We also know that the algorithms that machine learning use sometimes they result in ridiculous conclusions for example This image recognition result totally failed to recognize the stop sign It has a lot of confidence in calling it a speed limit sign And so these raised the questions How much trust can we put in the data the reason being is that the researcher or the machine learning developer? They're kind of sometimes they're apologetic in which let's say well We don't really truly understand the causation because correlation does not imply causation And so then we really have to ask the question. How much trust are we going to put data on data on its own? As a researcher my experience tells me that data is a great first step But what we really want is an understanding of the data in order to trust it What I've been following recently is Judah Peril who's considered the godfather of causal inference him and his researchers for the past 30 years have really been Propagating what they call the causal revolution and so we can truly get beyond correlations and really make decisions based on Causality meaning understanding the impact of one parameter on another So just one way of paraphrasing a lot of his research is within the sentence is Which is the main takeaway from this talk is that the story behind the data is as important as the data itself So throughout this talk, I'll emphasize this point time and time again Where what we'll start with is misinterpretation of data how by understanding the on the story behind the data We can avoid misinterpretations I will also introduce an awesome tool called graphic models Which help us visualize the story behind the data and this is a very useful addition for any analyst toolbox Those are the main two points. I'm imagining for most of the audience Another takeaway for those who are really keen and want to take things further is I will suggest first steps to learn The topic that I tell were useful for me. There's no expected background in terms of statistics It's very minimal Target audience that is in mind is anybody who makes data-driven decisions So analysts, of course will benefit from this but not limited to developers or managers also they all make data-driven decisions and What you'll get out of this talk is going beyond the common usage of correlations and better to understand What is required to make those better decisions using causality? I have over 15 years of experience of data analysis. I start my career as an academic Throughout my career. I used Different data sets for example, I started off analyzing astronomical data to learn about things like the expansion rate of the universe Then in 2014 I transitioned to the private sector. I worked in consulting for a while So there I worked for example with restaurant chains or bus operator data then I went back into the sciences in which I worked in a In a biotech lab in which I worked with protein engineers So in DNA data and now I work with health data For me causal interference is a real emotional roller coaster as I described over here If I'm if I'm successful today, you will learn a few things if I'm really successful You'll have a better appreciation for us as as as homo sapiens and how we managed to really critically think and manage to Quantified impacts of intervention But I do have to give your heads up that there will be stages today of complete confusion And if I'm good at what I'm doing, hopefully you'll be able to overcome that In order to overcome confusion what I found useful is normally I prototype within Jupyter notebooks But in order to really understand statistics, I like to visualize and play around use toggles and for that I'm really really very appreciative for For people that work in streamlets in which they really Enable me to go from prototype to a nice UI Web app that I can host both locally and they're hosting for me and I'll present I'll give you access to apps that help me understand this topic of causal interference better So what we're going to do you can imagine that we're gonna we have this What you know how it causes causal ladder and what we're going to do is I might describe climbing of these rungs We're right now. Most of us are right over here When we're dealing with statistics and machine learning they give us really amazing results but at the end of the day, it's dealing with correlations and It's you know, we're robots and maybe more primitive sentience are and as well and today I'll kind of bring you towards this second run called doing in which we're able to do causal Inference for some populations And then I'll just mention for those who are keen to learn more about how to get to the third run in which you'll be able To do causal inference not for a group at large But rather for each individual each individual, which is a really powerful thing and that will be in the summer So this will be our Our trajectory for today. I'll start talking about the limitations of correlations I'll give a lot of examples and I'll talk about Simpson's paradox. What it is. It's a situation In which data might be misinterpreted So I'll describe what it is. You learn how to identify how to resolve for it. And this is where That's said app will come into play and you'll be able to play around with that for yourself Then I'll introduce something that's missing in the In common vocabulary in mainstream statistics, which is graph models Which enables us to visualize the story behind the data so we can have true trust in understanding what we're doing with the data And we'll use it also to do Very important step in causality, which is controlling for what's called confounders. It's okay. If you don't know what that is I'll describe it later. I love the way that Randall from XKCD describes various Various things in math and computer science for example here very nicely articulates like correlation versus causation in which you see at the end of this conversation that She basically asked was this interventional successful and he is inconclusive and that's what happens a lot of times with correlation All right at the end of the day no matter how much effort put into it We just don't know we're inconclusive about the result and The reason for that can be seen as like this example over here in which if we might have two data sets There are totally they might appear to be totally unrelated and they happen to correlate Right, if you just do linear regression or you give it to a machine learning model Right, it's just two columns of data and it might find something potentially interesting But you really need somebody who actually understands what this data means in order to see this up That we can actually take action on is the marriage rate in kataki anywhere related to the amount of people who drowned after falling from a fishing boat So what's the story behind the data? We'll continue with the with the theme of drowning But from a different aspect Let's imagine for example that you work in a city council that has a beach and you want to figure out when you should put life Cards in order to reduce the amount of drownings in the city So what time of year would you put it or what time of day in order to answer that question? Well, what sort of data do you need to answer that challenging question? Well, if you if you take this sort of data and you compare it to a lot of other and you just look for correlations Then your linear aggressor will just just hey you can compare this to ice cream sales Obviously if if a company that produces a lot of ice cream suddenly sells more because of a promotion Well, you know, that's not gonna cause many drownings So most of you in the back of your mind, you already know the solution to this and I'll get to this in the next slide But first let's look at two more useful examples. So what has to do with fire alarms, right? So let's say that you're working in a company. That's engineering for home safety devices and you want to improve the fire alarm So what sort of data would you collect in order to test to see if you're really doing any improvement? Should you test for like fire data like changing the distance or the heat of it? For example, or is there something else some other data that might be useful to better learn about this device that you're developing Nowadays we know that smoking causes lung cancer, but back in the 1960s That was still controversial like we had data on smoking. We had data on lung cancer There was a correlation, but was there causation? For example, the tobacco industry made the argument that well, maybe there's a causal a common component called DNA that might be our genes might be impacting both which is not present in the data and that's very hard to analyze So what do we see here is a problem, right? We have a lot of problems that we want to solve and we have one that we have one data set like drowning What other data would be useful in order to answer for this question? And so in order to resolve this we need to know the story behind the data How were the drownings generated? How where the ice cream cells come from? So most of you probably guessed that it has to do with weather Right the hotter it is the more drownings and the more ice cream you're going to sell Okay, so this is a common factor between them.  It's detecting its mediator, which is called smoke Which is the smoke, right? You can imagine for example a room full of fire But there's like a huge breeze towards the window and no smoke gets to the alarm and so the alarm won't detect the fire So this mediator smoke in this case is what you really want to test in order to Figure out how the quality of your smoke alarm and the conclusion of the Of smoking causing lung cancer without was resolved due to the fact that Researchers realized that the cigarette itself is not causing pure poor health. It's actually the tar that's being Deposited within the lungs and that does not depend on the DNA And so this is another mediator in which that resolved the question of causality And so that means that they understood the story behind the data and they realized Oh, we should be collecting data on the star in order to prove causality So that summarizes the first step in which we appreciate now the limitation of just correlation And why we need the story behind the data now We'll talk about an interesting artifact called simpsons paradox, which is a situation in which data might be misinterpreted Just before I start I want to emphasize that all the data here is made up But it is plausible. You might have situations in your own datasets that you're currently analyzing or making decisions on And for simplicity, you might see small numbers, but we're not concerned with statistical significance Just if you are concerned with a small number just multiply times the thousand or a million that is the same conclusions So we're going to go through this imaginary study and I'm going to present Um So Imagine that you are in the board meeting in a pharmaceutical company You've been waiting for months for results of a study and you have to make a very expensive decision If to continue to develop a drug for market or should you actually stop it and go with another drug? So the study contains 2006 patients where it's equally distributed to control group Which means they have a placebo versus treatment where they actually get the drug And so you're listening to the analyst and they say well The result from the treatment group is that 58 percent recovered and the result from the control group Actually 72 percent recovered So the immediate conclusion from that is 58 is smaller than 72 and by 14 percent And so that means the drug is dilaterious It has a harmful effect and that suggests that we should stop this very Very expensive project But of course You know, you don't want to make those decisions. Hey, so you want to really understand the data? And so the main stakeholder asked the analyst Well, let's look a little bit deeper. Maybe it's beneficial for males and not females And so they they go into um, oh just beforehand. Um, it's very useful to look at visuals of distributions. So Before males and females here, you can see the control the difference between control and treatment is 14 percent Okay, so now the analyst looks and they say well, I see actually two interesting things First of all the females they recover much more frequently than the males. It's 90 percent versus 50 percent Uh in treatment and 80 percent versus 40 percent in the control But also when we compare treatment versus control, we see that the treatment For both males and females recover 10 percent more than the um Then than the control and that suggests an alternative solution that um or result that the Drug is actually beneficial by an absolute 10 10 percent So it's the same data and we have two alternative conclusions So this might appear to be some sort of analytical illusion It actually has it has a name. It's called simpsons paradox And what we'll see the next few slides what it is at the end of the day. It's a misinterpretation of the data It's named after Edward Simpson who developed it in the 1950s It was actually mentioned in the early 20th century by you and one interesting aspect of him is that Before that during world war two He actually worked about one hour train ride from where I'm sitting at What's called bludgeoning park in which him and the father of computer science Alan Turing and other statisticians They managed to break the german uh enigma encoder in order to help the allies win world war Two and uh, so if you're looking for something off of the beaten track here in london I highly recommend visiting that museum All right back to our case study. So all above is the same thing. We have the two separate conclusions What i'm adding here is the uh full data of the cohorts Um, like I said, you have these slides. You can sit on these later If you're not seeing it right now, but basically all the numbers add up Where the end result the treatment minus the control calculation for the total population is minus 14 for the males It's 10 percent A positive 10 percent from females. It's also a positive 10 percent So this is the confusion that arises. This is the symptoms paradox and here. I'm just visualizing it Here we're doing treatment minus control population minus 14 and then each cohort has 10 percent So how do we resolve this? It's very useful as you can see by now that visuals Are key in order to resolve to understand these problems and to solve for them So what i'm highlighting here in in red is the actual population results. So here you have control and here you have treatment um In which uh, you can see this difference that I mentioned minus 14 percent Which is here on the horizontal axis should also emphasize the vertical axis. This is the number of participants So you see 1 000 participants in each um And which these red lines are actually factors of these gray ones It's a weighting on them in which you can see that here. We have a group with a With with a recovery rate of 80 percent versus and that's 800 a number and this is 40 percent at 200 And so this is the average of them and we have same thing about the opposite over here So how to resolve this? Well, um What we can do is actually can look at the cohorts themselves So here you can see here the females and here the males and the 10 percent is what I'm displaying over here Here you can see both males and females have Uh a beneficial effect By the way, I want to give credit to mark class who came up with this design of presentation And uh in the comments, I have a link to his blog post in which he first showed this so What is the mission? What was the source of the misinterpretation? Well, probably a lot of you have figured out by now that what's happening is that we have an uneven distribution of the Um of the cohorts So even though we have a thousand males and a thousand females Actually, you can see the males are only 200 of the control 800 of the treatment and vice versa for the females And so what that means is that both the recovery and the group variables They both depend on gender and that's what we call a confounder And the problem the the fact that we have minus 14 here and 10 here and 10 here is because it's symmetric that I call Uh treatment minus control does not account for this confounder And so it's it gives it this this weird biased result. What should we trust? Well, we know if something's beneficial is both for males and for females Well, it's going to be beneficial for the population. That's just common sense And so how do we result how can we create a metric that's actually more meaningful for the population? Well, before we get to that, let's formally define Um, so instead of calling just treatment minus control what it is It's called a risk difference and here I'll kind of formalize it a bit more So the risk difference is the difference in the recovery rate between the Uh control group. Sorry the treatment group to the control group If you're wondering what this pipe here, that means just conditioned on that means here We're focusing just on the treatment group and here focusing just on the control group and we're uh subtracting So it's basically this 58 percent minus 72 or 50 minus 40. That's all we're doing over here and here. I'm just courting the results So, um, this is a good step except for the population We should not always find the dope for the males and females But not for the population for the population What we want to do is you want to take the same risk difference But we want to weight it by the population itself and that's a way for us to control for that confound What we're doing is we're doing a weight adjustment Uh, and so here I'm just um here. I'm just rolling it out You can see the risk difference of the males 10 percent times the 50 percent of the female of the males in the population at large That's so that's the way plus the same for the females and that gives me this average causal effect Or ace for short of 10 percent Uh, so that's the solution to this and here's just a visual where I showed the problem before and here's the solution We see it at 10 percent Now I'll be the first to admit that this is still confusing.  I had to visualize and try to make sense of this And what I found very useful for me is to just create an app and that's what I'll show you over here Um, you actually have access to it. So you can go online and play around with it Um, and a lot of thanks to streamlin again for for their api and for hosting Uh, so here, I'll just show you a quick run of of of the demo Which is interactive. Um, so here I'm just showing the data frame of this Of this, uh, made up data And so here's the problem with this visual. Uh, here's the mark plus visual Showing the same minus 14 percent And here I'm showing the solution in which what we want actually instead of this risk difference of the population we want to use the ace and here is this visual which you can see that this Plays well with both the males and females. You can see the treatment is better than group But let's take it. Let's look at the interactive stage of it So there are a few parameters that go into this and the one of of interest to result That causes simpson's paradox is the fact that the males and females didn't have the same Distribution within the treatment. So here it was at 20 percent if I bring it higher to like 35 percent You can see that these are not impacted But this one which doesn't take into account is confirmed the factor It's actually, you know, gives us another like meaningless results. Okay Um, and here you can see the heights are nearly equal Um, to really bring it home. What we'll do is we'll bring this to 50 percent And this is mean that we have even males and even females And so what that shows over here is that the risk difference of the population is as you'd expect from, um, from from, um And this is the in this is the case of the Uh Of what we'll get to later as random control trial um I think there are quite a few people on the app. So I did prepare Another app I'll just hold and post it locally because I do want to show one more thing, but feel free to play around with it um Yeah, so another example. I want to run is um here. I'll just put this Uh and here, um, so let's say That Yes, so you notice here how um, I'll just show this I'll be in solution mode. You can see how here the males are 80 percent here and 70 percent here This is another parameter. I can play around with And so here's the 80 percent and 70 percent and now let's make these even. Let's see what happens So here I bring it down to 70 percent and here you can see now this and this are even And what do we see here? Well here we see that the males. There's no difference, right? That's this fact that these are aligned And the females are the same the same 10 percent difference between this and this Okay, and what um here it shows us something noncicle. It doesn't Take it to account the confounding factor What does the ace do? Well, the ace does exactly what we expect the average between zero to 10 percent We get this five percent There's a lot of other examples that I found useful playing around with this app So feel free to um to use it in order to understand this complicated topic of simpson's paradox Again, it's really not intuitive and take it from homer What I suggest is that You know take your time sit on the numbers play around the app at the end of the day You'll figure it out Visuals are very useful and I love this visual that I pulled from info We trust in which it introduces the continuous version of simpson's paradox simpson's paradox as I described it Is uh with discrete data Um Right it was categorical recovery no recovery genders Um treatment control but lord's paradox deals with the situation which you have continuous data for example medicine and milligrams or For example health score and what you see beautifully here illustrated Is the simpson's paradox or lord's paradox here imagine that you didn't know The cohort of each right each simpson here is a cohort So if this was all like great dots then basically what you have here is um You have an apparent uh correlation But once you've got to get the information on the cohorts men boys girls and female and and and women You actually see that there's a deleterious effect, right? It's going in the opposite direction So this is a very good useful visual to Understand simpson's and lord's paradox So that concludes the first part of this talk We talked about the limitations of correlations and then we introduced simpson's paradox as a situation in which the result of a population Might be in total conflict of that of its cohorts And so that's a concern because you might misinterpret your data and get to wrong conclusions and make wrong decisions How do we resolve for this? Well, we appreciate the fact that we have a confounding factor that we have to control for Those who are really interested in understanding causal interference I suggest mastering this topic of simpson's paradox. So then you can really get into deeper levels of understanding of causal interference For most of the next part next part of this talk I will focus on this issue of confounding factors and how we control for them when we should when we shouldn't And what tools we have in order to do so but beforehand I have to address random control trials, right? That is what most people understand as to be the golden standard of causal interference in the sense that we use We do the scientific trials in which we We we try our best to control for as many parameters as possible except for the treatment And the output parameters we hold everything fixed We hope for the best and then we can say ah, we have causality But there are a lot of limitations to this first of all later on I'll talk about the fact that we shouldn't confound for everything. It should be actually justified beforehand But even regardless to that there are many practical issues. For example, random control trials are very Can be very expensive and the logistics logistics behind them, right equalizing between the genders where you can do age demographics, etc So that's like can be a logistical nightmare Second things might not be physical or ethical. For example, if you want tests for smoking habits Well, you can't force people to smoke for 10 years, right? And also a lot of times you want to investigate populations in their natural habitat not in something artificial And so all these are arguments for What can come in place of random control trials, which is much more accessible That's what's called observational data. Basically, that's the data that we have with our own work environments or Just free online that we can use and so This causal revolution is helping is building has been building tools for the past 30 years in order to Do causality on these much more accessible data where the main idea is understanding the story behind the data And because that's important as the data itself So I talked about the topic of controlling for parameters I'd love to listen to podcasts like Freakonomics in which you have talk experts and researcher talking about their About their topics of research and a lot of times you might hear them talking about controlling for many factors like age, gender, uh income, uh, social social status, etc And just like a shopping list of parameters. The question is is that true should we should we control for everything? And the answer to that is we should not do this blindly but rather every time we control for a factor It should be justified. Well, how do we justify this? Well, we have to use some sort of common sense And this is where graph models introducing it to our vocabulary and adding it to our Toolcase of analysis. It's a really powerful way for us to tell the story behind the data in order to make Better educated judgments on what we should control for and what we shouldn't and that will be the focus of the next Of the last part of the talk in which I'll introduce graph models Why it helps us visualize the story behind the data and how we use it to justify controlling for confounders So first let's start with the definition of a graphical model So just imagine that you have variables and you just graphically you put them into nodes or vertices That's what these are and if I want to relate between two parameters Then I just draw an edge between them. So for example, c and a have some sort of correlation between them Where c and b are independent from each other Changing one does not impact the other That's a graphical model Then we have something called a directed graph, which is taking the next step right want to go beyond correlations We want to go to causation And so by instead of replacing adding to the edges in an arrow So that says which parameter is listening to who and what I mean by that for example is changing a will change Cause some sort of change in d but not vice versa Here we happen to have a cyclical relationship between d and c changing d will change c and changing c will change d In cause and cause of interference most of the time what we deal with is actually something Even simpler than that. It's called a direct and a cyclic graph or that in which we make sure that we don't have that Sort of feedback. So what that means in practice is starting from any parameter. For example a following the arrows You'll never get back to a that's what a dive is essentially So why graphical models useful? Well, they tell the story behind the data For example, they enable us to tell how the data was collected We can put that information in the graph and like I said before we can say which parameter is listening to which So what can we actually do with that sort of information? Well, first we can Design better experiments. We can be more cost efficient. It's beyond the topic of this talk But for example, you can figure out which parameters are worthwhile collecting information from in which you actually don't need to To tell the story and of course the most important thing is with these graphs, we can draw causal conclusions and not Be stuck just with correlations So let's actually look at a graph model that will simplify our understanding of simpson's paradox So I know it was kind of a burden trying to understand it and it's still confusing But that's where graphs are great. They help us understand what's actually going on and here's the story that Here's exactly the story that we were telling before if you remember. So gender is independent. Nothing's impacting it But the group Was a function. It wasn't independent. It depended on the gender, right? We had that uneven split And that's what we're learning from this arrow and recovery rates depended on both So this is presenting the simpson's paradox problem in order to solve for it. What do we do? Well, we controlled for The confounding factor the gender and so effectively what we did is we did what's called graphical surgery and we took Off this arrow. So now both group and gender are independent Where recovery rates still depends on both. So what does this graph represent? Well, it either represents what we're actually doing in a random control trial where we Control for the gender distribution, but even with observational data, right? Without without controlling beforehand, we can still do the waiting adjustment I mentioned before what we call the ace metric. So it represents both basically So I have to address the topic of subjectivity Because probably in the mind of many people listening the fact that I'm applying my opinion on the relationships Between the parameters a lot a lot of people think that researchers statisticians And anything else with science Are are really objective and because that's what we see read textbooks But in everyday research what we really what we really know is that there have to be subjective decisions made along the way And the challenge is really to make sound judgments that reflect reality I'll just give you a few examples Any developer knows that if you have this distribution And you want to quantify that somehow. Well, if it's an if it's a nice belt Curve shape, then it's pretty simple. You have the mean and you have the variance, but if it's kind of skewed Well, what's more meaningful? Is it maybe the median or if there's like a few modes to it? Do I choose the highest mode? So that's a decision another decision that a lot of times we make is Binning of histograms or how we present numbers it can be on a linear scale Or if there's a few orders of magnitude, we might do a log scale So all of these are subjective decisions that we do on our daily lives as analysts and decision makers But the challenge again is to make sound decisions. So yeah, so graphs Are based on subjectivity, but beyond the scope of this talk is the fact that there is Methods in which we can challenge our opinion Use the data to challenge our opinion about different regions within a graph I'm just showing three nodes over here, but imagine the graph with many nodes and we can actually Challenge our notions with the data. But again, that's the subject for what was ever really interested in taking their studies further I should also mention that Causality isn't always possible. There's a list of assumptions that are required to pass for completeness I'm showing them here. These are each of these are talked within their own But I'll actually the purpose of this section is talking about confounders This is what's called ignorability. It's the fact that all confounders have been treated And so that's this last part over here. Let's see how we can Good and bad practices of controlling for parameters and how we use graphs in order to justify this So this slide is the same as before but just to emphasize the fact that graph models are actually our vocabulary to Indicate which parameters we want to control for in which we should it And now I'll talk about this in practice when we should it when we should it based on these types of graph flows over here So here we have A relationship between x and y in which it has this fork in which they have this common cause Variable called z as opposed to this inverted fork in which there's this collider called c So you can see the relationships are totally different And what I'll show you the next few slides is that in the case of forks We want to actually control for this Common factor in order to resolve for spurious correlations, but actually in inverted forks We do not want to control for this collider because it will generate spurious correlations All right, so let's go back to this example of drowning in ice. Well, we know that there's this common cause of the Weather right if the sun is out or not. So what happens if we don't control for the common cause? So we get something like this. We have this spurious correlation, right in which We let's say we know nothing about the weather in these data points And so our eye and our regression In our regression algorithms will point out. Hey, here is a Correlation and of course this is spurious Why because this this common cause is a confounding factor and we should control for it Just before we control for it. Just to formalize this a bit We say that x and y are dependent when we do not condition on a so even though we these are not That that these don't impact each other the fact that we did not control for for the weather means that They are mathematically. They are dependent when we didn't Control for it. So how do we resolve for this? Well, of course here this shaded color means that we control for it And here we clearly can see if we analyze for example the sunny days Then there's no real correlation between the two and the same for the chilly days. Okay, so by Controlling for the common cause we result spurious correlation So that's an argument a justification for when we want to do that and just to formalize this again We say that x is independent from y conditioned that we when we condition on the common cause a So that's quite intuitive. The graph makes a lot of sense Now I'll show you something that's a bit more challenging So this is an inverted form and I'm going to use switches on the wall in a light bulb as an example So here you can see that the bulbs on the wall they Bulb on wall b It depends both on switch one and switch two and the switches them and it's called a collider the switches themselves. There are independent and So here you can see by that I mean that one's one's up and the other's down You when one's up or down and that's one you don't know what s2 was happening with s2 I didn't talk about the impact on b is and it could be anything It could be an or relationship For example, it turns on as long as one of them is down or an and relationship Or takes both of them to be down in order to go on or I'll actually talk about this other another logic Called xor in which it's on if they're actually in opposite situations So let's look at this and the conclusions are relevant for any resulting bow But it's useful to demonstrate on this So when I'm not controlling for the for the bulb itself Then these are independent as we saw as we started off But the interesting thing is that if we control for example for when the bulb is off You can see that by knowing s1. You know what s2 is they are correlated either up or down Right, and if we control for on they're actually anti-correlation ones up the others down and so you see by by controlling for this For this collider we're actually introducing spurious correlations that weren't there before This is such a brief summary if you want to go over the slides later in which I'm presenting all the cases But I think that this example is enough to understand This situation and so again, why am I talking about this? I'm talking about this because There's I'm talking about the importance of spurious correlations Okay, thanks for that mentioned martin. I see it. So I have until 10 minutes past 2. I see correct um So yeah, so here I'm just summarizing the fact that we wanted to justify when we can When we can control for parameters and when we can't so here you can see for example in forks We justified it what we want to resolve these correlations, but when inverting forks We we do not want to control these colliders most of the time because we'll just be introducing these spurious correlations and so That's the main takeaway for most of the audience. I'll just mention those who're really interested in in causal inference and take it The next step. This is a big part of causal inference Just identifying these colliders these common causes or if you remember early. I mentioned mediators just When we should confound for them in which we when we shouldn't that is a big part in causal inference Uh, just for completeness. I'm mentioning that I talked about two types of flows within daggers I talked about forks the inverted forks and there's another one which is just called the chains and here I'm just providing a cheat sheet in which you can learn about the dependencies when you are Controlling for parameter a in each case We're nearly at the end. Um, so here I just like to um summarize Um, what we've learned so far and those are interested how we can how you can move forward Learning the topic. So I made the argument that at the beginning. We're at this bottom room is seeing in which correlations is like seeing because Because seeing may be deceiving And I made the argument that just by using correlations. We might misinterpret results I demonstrated that both in Simpson's paradox and I also talked about spurious correlations With observational data, we can actually resolve this Um by doing action called doing as sentence. We actually learn right ever since we're babies by actually doing things And our mathematical equivalent of doing is controlling for the confounders, right? We can do random control trials, for example All right, or we have this asymmetric And with that what we can do is we can learn causality on populations, right? We learned about For example, uh drug tests on genders, for example For those interested, there's this whole topic called do algebra, which actually gets into the fine details of of this What's very much beyond this talk is this next one here, which is imagining And so what imagining is is we can do what's called what if conditional hypothetical conditioning and what the beautiful thing about this is that we'll be able to make Understand the impact interventions not on a group, but rather on an individual for example, we'll be able to answer questions like how much would one's salary increase By another two years of college or we'll taking this medicine Cure my headache given past results So you can really do it on an individual level as opposed to a large swath of of populations And so the topic this is called counterfactuals So here I'll be a bit cheeky and I'll take william demings. He's a statistician I'm just going to paraphrase his quote and not only It's not enough to bring data But you have to actually bring the story behind it as a data scientist I have many examples in which I got data and I didn't really trust until I really understood the story behind How it was collected and then things actually made sense for me The mantra of this talk was that The story behind the data is as important as the data itself And the reason for that is that it's crucial to understand how the data was collected and to understand the causal relationships between the parameters and only in that way we can actually make causal inference So the way I demonstrated the importance of the story behind the data Is we talked about how data might be misinterpreted if it's Simpson's paradox if it's spurious correlations I introduced an awesome tool to add to your toolbox called graph models in which you can visualize the story behind the data And that enables us to see the causal relationships between the parameters and we can justify actions like confounding for variables For those interested in taking this further, I suggest mastering Simpson's paradox Again, I recommend using this calculator. Any feedback you might have on it. That would be great Also, there's this topic of dual algebra and counterfactuals in which you can really assess causal impact on cohorts or individuals For those interested I I refer you to a lone nears talk. He's an excellent speaker He'll give you a few different angles about causal inference tomorrow and how to slide your way into that I'll just end here with my resource pages things that I found useful in my learning curve In which I do highlight two books by judo peril. One is a popular science book Um, which really gives you a good intuition for the challenges and how to solve for them And those more mathematically minded. I do suggest this textbook Which has only like four chapters in it and you can really learn a lot from it So with that, I think yeah, I'm glad to take questions at this stage Thank you very much wonderful talk. You've got some comments like this is a brilliant talk loving it by the way, so That's always Great. There are some questions and we we do have a little bit time maximum five minutes We are already into the coffee break, but I think that's fine So Would you recommend repositories catalogs of curated graph models? Repositories catalogs of curated graph models. I'm not sure what that means, but I can I can't get my opinion About repositories. Uh, here. I suggested two repositories. Um, I'm not that familiar with them I suggest anybody who uses it try to, you know, understand what happens behind them Uh, I know the intuition of wanting to use a package as a black box A lot of us Kind of do it sometimes with uh scikit learning things like that But truly you want to understand for example if you use a random forest You want to understand the intuition what the hyperparameters actually mean In order to build, um in order to build, uh Like graphs I highly suggest this, uh, excuse me for the blasphemy. There's no solution for this in python yet So this is an r in which you can actually Create your own graphs here. Um, you can change parameters and really once you learn causal inference You'll actually understand what all this is what I was talking about like, um independence between a and b A and d are independent condition that we Control for e that's what I was talking about. They have a whole lot of examples For example, this is actual research. He's interested in the relationship between tooth loss and mortality a lot of amazing things online So, um, I have a resource page here. You'll have access to I will add also a repository Um in the future alone alone near and I we're building a repository, um to um in order to um To provide the sort of tools and places that you can further learn the topic Okay, next question beside medicine. Can you cite examples of a causal interference? Inference has allowed to establish causal relationships or interpret data correctly the question continues Are there such examples in economics? For example, proving that raising certain tax has a certain effect on unemployment Sure, that's an excellent question. And of course, um, yeah, so I can tell you from my personal experience that That causal appearance helped me structured my everyday work Like my current hobby is learning a system and quickly like creating one of these graph models like in the stagety app And understanding the relationship between the parameters and then I can first convince myself that What i'm seeing is actual um causality or vice versa at work We have conversations that um how to avoid simpson's paradox and certain decisions that um That our stakeholders might be making so in I can on my own experience I could say definitely when it comes to impact on on the journal public Um, I am not that that that first I do recommend There are a lot actually there are a lot of examples within the book of why so there you can really learn about example He has a whole chapter dedicated to the resolution of the Of the issue of the impact of smoking on um Uh The impact of smoking on lung cancer. So that is a huge triumph. Um, oh you said besides health and economics Uh, I don't remember on the top of my head, but I would kind of refer you to the book of why that will explain that That will give you like examples So sorry about the dog No, no worries And I also want to add the fact that yeah, um a lot of a lot of time causality is actually it is hard And you sometimes have limitations of parameters that you feel that you need But it's just very hard to get uh to to obtain and of course, it's always the topic of subjectivity. So Um, you know, so put all that together Hopefully within your work, you'll be able to implement cause of fear and for what the immediate things that you try to account for I think you have time for one last question. It's about the common sense Why ice cream sales can't cause drownings and sunshine because we use some kind Common sense to exclude that and not a question real problems for different people Each have different common sense Sure, but that remember that that's what steers are left, right? We make decisions based on our interpretation of the world Um, that's me getting a bit philosophical. I can't address like the ice cream sales, for example Well, just do an experiment like we'll ask the question How much money would you be willing to put in? You know, let's say that that you're running an ice cream company and you want to really test if I sell more ice cream Will I see more people drowning at the beach? I don't think any Right-minded person will do that experiment and I'm pretty sure that if they do They'll fail because We know how you know, you do want to consult with world experts and us as world experts of You know of weather like the impact of weather We know that more people go to the beach when it's sunny outside, right? We see that We go to the beach. We see it ourselves or if we read it in the newspapers So that's why you know at the end of the day like, you know Any developer knows that you have to put your common intuition into things and he can't think that oh everything's we have to be Uber objective. No, we do have to apply domain knowledge Into our mechanism. Those are familiar with, you know, bayesian versus Frequentness inference. You know that Bayesian tends to give more reasonable results when dealing with smaller sample sizes because you can introduce your belief Where of course the disclaimer is you have to be still cautious about that Yeah, so I think I kind of digressed here Okay, thank you very much again. Wonderful talk Great Q&A session So we have five minutes time for a coffee break and the next will be the keynote


I was later to learn, and I certainly didn't know that day, that the woman that my client killed was John's wife. Yep. I think about it, I mean, anytime you take a case, you know that, and I've got, at any one time, I've got cases where my client is accused of killing somebody else, either negligently, intentionally, carelessly, in some form or fashion. And as the person's handling the case, I have extreme empathy and sympathy for the other side, but I've got to compartmentalize that and look at it from the standpoint of I've got a client to represent and try to get them out of this mess and make sure that they're never in that again. So at this point, you didn't know John personally? I had never met John. I didn't know John. I didn't know his name. I knew his wife's name because her name is on the arrest advisory. That means when I got the case, I looked at the search warrant, I looked at the filing documents. I knew that the woman who had died, I knew her name, but I didn't know John, I didn't know his son, I didn't know his family, I didn't know the story, I didn't know the history, I didn't know anything other than I got a client who they want to incarcerate and put him in jail for 15 years.


Hi, this is your host subtle Bhartya today. We have with us Adam the boss be VP of simulation and robotics at Robotech AI Adam is great to have you on the show. Thank you It's great to be here before we get started and talk more about open 3d engine Which is going to be the theme of today's topic. First of all, let's talk about Robotech.ai What do you folks do? I mean, it's in the name. So it's robotics and artificial intelligence What we mostly do we work with software. So Working on the robotics stack around perception and reasoning And we focus a lot on validation tools and chiefly simulation so how to Do R&D with your robots how to validate that they work correctly How to continuously improve and also assess productivity of your robotic use cases with the Benefits of simulation. Excellent. Thank you. And you folks are also associated with Linux foundations Open 3d engine. They have their own foundation focused on open 3d engine talk a bit about that 3d engine talk about the label of your association and When we talk about you know talk about the engine because sometimes when we talk about these the 3d engine most people think about just gaming But the fact is that it goes beyond that as you also talk of modeling and also Soon Apple will come up with the vision pro which means that you know VR 3d rendering a lot of things are happening in this space. So let's talk about number one open 3d engine Your association with the foundation open 3d engine Is the the chief project under the open 3d foundation? is For us a base platform for our simulations that we create for customers We focus on robot operating system, which is a framework a standard framework for robotics And we are making sure that open 3d engine is a very attractive solution for robotics developers who want to use ROS so robot operating system in their projects and We are a member company of the Linux foundation open 3d foundation and our focus is in the special interest group Simulation which I also chair. So as you mentioned game engines What we typically see as game engines and other examples being unity and unreal They are just More in more general terms 3d content creation tools to build simulated virtual worlds. So they are Often equally applicable for simulations. What kind of industries actually if you look at it almost an automotive robots You're I mean everybody's but what kind of industries because I just want to understand or explain to our viewers The scope of these 3d engines. They are not just limited to a niche. So robotics is a very broad field For example, as you mentioned automotive Includes robotics or self-driving cars This is a type of a robot Similarly, we see automation in fields such as mining where we have trucks or construction vehicles. So very heavy Vehicles very are typical in comparing to classical robots That also need to be Simulated because they are building operations automation of such sites Other areas include agriculture. Again, we are talking about larger areas and Machines But then when we move to things like warehouse automation, we are talking about mostly fleets of smaller robots that carry loads between different points and Then we get other fields such as of course healthcare customer robots such as automated vacuum vacuums And and there is many fields like that many application types such as inspection and maintenance where we have both airborne robots drones and ground robots Including robotic dogs Used for this kind of tasks So this creates a very diverse field it's Full of challenges and different applications, of course when we want to Provide tooling for them such as simulation have different challenges. There are different Key points to to address For example, one of the great benefits of our game engines Is the performance of simulating for simulating large areas multiple robots This is especially Clear when we compare these engines with classical Simulators that came from academia and are not coping with such tasks Let's just specifically since we are talking to someone from Robotech talk about the Scope usefulness of you know, open 3d engine of just 3d in general for robots robotics How does because robots when we look at this is something physical, but we are talking about rendering what we want to do when we simulate robots Is we want to take the software stack of the actual robot? Which we want to validate which we want to develop. That's the subject of our work, right and We want to keep it but replace the robot's body and the robots environment with virtual ones So that includes also providing sensor informations from this virtual world that look very much like real data Here of course one can consider things like photo realism of the engine So how good does the data look does it look like the real world can it simulate? similar effects of the sensor such as camera lens flare and distortions All kinds of noise that that you see in the real data. So these are very important aspects and What's important in simulation is of course you can Go very deep into realism. So what's the? Physical reality between the thing you want to simulate but from engineering point of view It's also very important to only simulate what is really needed to Validate that the robot works. Can we also talk a bit our distinction? And of course, you know apologies if I am going out of the scope of the description is that when we do talk about you know simulation Like for example, most of us don't know the internal working of you know, the modern robots that we talk about we Most we can think of the movie terminator back in those days are we look at Tesla Which is called so you can call Tesla also robots on you know, we'll because it is making a lot of decisions You know just it's our legs like Boston Dynamics. It has wheels So one is that the way we simulate how robots may look like and one could be the view that The word robots see because they have to make a lot of real-time decisions so when we look at these three engines is it for The internal workings of robots or is more about how people perceive them There is an argument to be made for each of course the primary function of the simulation is typically to validate The robot so it's important what the robot sees the There can be kind of user view in the simulation so that people understand what's happening was being simulated that things Work properly, but it's mostly about the sensor data. So cameras that are mounted on the robot It's LiDARs. So laser sensors and it's GNSS so global navigation positioning system such as GPS In all these sensor data that goes into the robot's reasoning system and allows it to make decision to perform work In this simulated environment much like it will in the real one, but also for humans If you are a company that wants to present For example to your customer how your robots will be deployed in their facility It's very useful to capture it in into a visual story. And of course then you can show The entire view of you know of the operation much like a game You would present to someone and they can really understand how this deployment will look like they can view it from different angles, you know and See the productivity gains through this Engine as a visualization tool. No, let's talk about, you know The technologies that you folks use, you know for this simulation and why you chose open 3d engine Why you chose to also become a member of Linux Foundation through this project? That's a very good question. Thank you So we have a field of simulators. So there are many simulators It's often called a simulator zoo The space so there is a lot of tooling to choose from and they have different strengths and weaknesses The type of use cases for robots that we as a company specialize in involve large areas a Lot of throughput in sensor data. So high fidelity sensor Automotive is a good example LiDARs and cameras produce a lot of data So for our company Performance was crucial. We wanted to simulate large worlds high fidelity sensors and multiple robots at once a Lot of classical simulators classic robotic simulators Instantly go out of Consideration because of that they just can't cope with this kind of requirements but game engines They were optimized for this kind of applications right for large worlds multiple characters telling good visual stories. So This this was the area that where we looked we started with unity 3d and Built our automotive simulations and mining simulations there, but we quickly found out, you know, there are limitations to that approach There is quite some technical details on why These engines weren't a great match Part of that is C sharp versus C++ which we prefer for robotic stacks due to robot operating system The technology choice the second part of course was the limited control on the in the engine code As well because it's not open source, right? We can't Do everything that we would like to we are limited to working with modules with assets building this this kind of scripting And so open 3d engine as an open source fully open source solution C++ engine Was a perfect match for us to build the kind of solution that we really wanted for our use cases to keep our customers happy And what is the reason for joining the Linux foundation? You can just consume their project. It's an open source community much commu much of it. This is community driven. So being able to take part in this community discussions in in the process of open source Content creation and choosing what kind of implementation to go with so that it's good for many companies so many users ensures that you know someone can take Over the parts of this code and contribute which for us, of course Is it's a kind of free value that we are getting and more and more companies are building their? Proprietary solutions on open source because of that There is a lot of value to be had from the community from other companies. It's like pooling resources Which allows you to compete with the biggest players there that build their own close source tags So to fully embrace this approach you need to engage within community and becoming a member of course grants you a certain privileges as well and You can be a part of Where the engine is going. Can you also talk about? How are you contributing to this project and foundation? We are Contributing quite a lot. So our main contribution is Enabling robotic simulation in open 3d engine We built So-called Ross to jam so robot operating system jam Jam is a kind of module that extends the engine. It's a highly modular engine so You can use it with a limited set of modules and you can add your functionalities to this kind of system so we built a Jam that includes all the necessities for a robotic simulation It's not just a connector in terms of to communicate with this robotic stack, but it provides a full kit to Implement sensors such as cameras, LiDARs, IMUs all this kind of the most popular robot sensors and also control methods, so Robot operating system typically has standard interfaces to make the robot go somewhere, right? We Implement not only the interfacing, but also the components in open 3d engine that realize that movement and it's both for the kind of like vacuum robots and Vehicle type of robots. It's a different steering, right? So we have built this Simulation gem Well, even a set of simulation gems because there's more than just that that allows you to Really quickly start with open 3d engine as a simulator Without that, you know, you would have to build all this yourself. So I think it's quite sizable contribution and We are continuing Work there and building on it further I'll go back to Robotech for a bit before we go back to talking about the foundation and the open-source product is number one is that When we talk about this simulation, can you also talk about the importance and rule of data? APIs and machine learning Absolutely. So one of the chief uses of simulation and increasingly so because of how Embodied AI is Rising as a topic with the new language models and people are looking for their applications it's only more important to Be able to provide a lot of data for the robots learning training data So that these robots can encounter plenty of situations and learn how to deal with them and this is a field Which is called synthetic data. So producing synthetic data from the simulation typically in large volumes much more than you could produce in reality because of you know Limitations of the real world you have to transport the robot. You have to actually You are limited by the number of robots that you physically have well in simulation you can parallelize you can Run many and once you can run faster than real-time if needed so that synthetic data is immensely attractive for the field of machine learning and Increasingly so not only visual data But the importance of additional modalities such as point cloud data For example a tactile so the sense of touch and recording this kind of data and using it to Build better AI models for robotics is crucial. So Of course having Good sensors so sensor simulation. This is the source of our data It's crucial because we need to capture what's important in This Sensor how they operate or what kind of data they produce to be close enough to reality So that it's useful for learning and then Being able to run the simulations at scale So integrations with all kind of scaling tools cloud solutions Being able to run headless and so and so on These are integrations that we also Already use our building and will continue building in in this year This is increasingly important as a company. We are embracing this wave of embodied AI. Can you also talk about? What kind of business model what kind of you know like because we talked a lot about the project that a technology side of Let's talk about the business side of Rope Tech and also How opens would because sometimes there's a misconception that open source versus corporate or business But the fact is that first of all open source is kind of driving the word today Number two is that if you look at Linux foundation organization with how it businesses open source will not succeed It will not be sustainable. So talk about that. Yes, that's a very good question. And I think that it's important especially for sales people who are kind of grew up or got experience a while ago To update themselves on the value of open source business models right now We as a company embrace it fully. So we We participate in several open source initiative Another one another example is Otoware foundation, which aims to build an open source Rost based Stack for self-driving cars. So that's an example of another initiative That we are part of Through this cooperation We not only built a kind of network of customers Where you know, there's always something custom that needs to be done But these are just services. We also built our products based on this open source engines open source modules where we have Open open core approach for some but we are also transitioning to providing services We foresee like like you mentioned the synthetic data Simulation at scale this will be using Increasingly using and they already are in automotive for example, this is already prevalent approach to use cloud computing To run many many scenarios to you know to increase the amount of simulation hours that you can Take out for you know for every kind of Software that you produce So so we'll be Operating also increasingly in terms of selling services But right now we already have our IP so our proprietary modules which extend what is Available in open source for example for sensors It's mostly about noise about simulating them in a more realistic manner While in open source, we have kind of perfect sensors, which are very useful that they are like they have interfaces for developers to build on But then the noise models are very specific to Devices often so we keep them as parts of IP Let's go back to and talk about the foundation of project a bit. We are in 2024 now Just share kind of the things that are in your pipeline the kind of road map that when we look at this project So for open 3d engine We are building three base platforms We are building a platform for mining For agriculture and for warehouse logistics. These are specific types of assets that are needed like 3d models and randomization tools for example to be able to You know run a lot of simulation in a useful way So we create these three platforms As products aimed at different application fields mining agriculture and warehouse logistics So that's already on the way and we Build functionalities for generative AI Such as large language models. We want these large language models to help our customers quickly create contents in the simulation such as Variants of the same environment For example, if you would like your robot to be able to clean Cantina what you would like to ensure is that it tries to do it for a million different containers with different layouts and different kind of problems and Generating these environments not just randomly but in a smart way Requires understanding of what the problem is This is an example of application of LLMs for simulation. We have several more Not going to talk about each of them But this is a really powerful wave of new technology that we want to Incorporate in into our products Adam Thank you so much for taking time out today and talk about normally the company But also this foundation that project. Thank you so much for all those great insights and I would love to chat with you again Thank you. Thank you. It's been a pleasure and also. Thank you for listening to the audience


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I'm very happy to contribute to those discussions about Scotland's long-term ambitions and bring my own perspectives, ideas and challenges. Many years ago, when I was a student at Murray House, one of my placement supervisors said to me, Cathy, you tread where angels fear to tiptoe. When I gently told the mafia-like community centre management committee that they couldn't ban young people from being in the building at the same time as the tapestry group. So, you can imagine how thrilling it was then to be asked to be deliberately provocative tonight. So, first what you said you wanted to know. How is Scotland doing and what do children tell us? The children's parliament's been working with and listening to children for over 20 years. We have a huge bank of evidence on our websites through films, reports and consultations, and it's all freely available and easy to access, so please do have a look at it. I don't have time to go into too much detail, but generally children feel that Scotland's doing well in relation to how it thinks about and delivers services to children. We have a great policy and legislative framework that's attracting international interest. Children find adults in their communities to be generally friendly and lots of children feel loved and cared for. They love the landscape and the beauty of Scotland and they're excited by our history. So that's the general picture. When we drill down, we get a more complex picture. 75% of the children that we work with come with additional support needs. Some have very chaotic family lives, many struggle with anger and emotional self-regulation, some are extremely shy or timid and others have difficulty with relationships and engaging positively in school and in out of school opportunities. All the children on our referred programmes come into this category. This morning, four children from our Streets Ahead project in East Lothian and three children from our Seen and Heard group for Loot After Children in Fife attended the second annual cabinet meeting along with seven MSYPs and Madeleine was one of them, who was at the cabinet table this morning. They met the First Minister, Deputy First Minister and other members of the cabinet. There was retolds at the back, she was also sitting at the table with the children and young people. Our children raised issues that are important to them and other children across Scotland. So the first issue was around children's relationships with their teachers. And while the children said that they have some great teachers, they said they feel that there's too much shouting, which makes them feel scared and unable to learn properly. They feel teachers don't always treat boys and girls equally and the East Lothian children had done an experiment to find out what happens to boys and girls who talk when the class has been told no talking. What happened was the boys got a row and the girls didn't. The children raised the issue of bullying again and they said that while adults always tell children they should tell an adult if they're getting bullied, when they do, often adults will say just ignore them or they'll actually make matters worse. And children feel adults should stop and think before they call behaviour bullying and they think there's a difference between an unkind remark, someone having an off day and consistent bullying behaviour. Children who are care experienced said they wanted to be defined by the whole person that they are and not by their personal circumstances and they asked that when social workers visit them in school could they please not wear their official badge because then everyone knows they've got a social worker. This makes them feel different and they can feel embarrassed and feel a loss of privacy. So far I've presented a bit of a mixed picture and we're getting it right for some kids and others feel that there's a lot to be done. First we're not very provocative. So let's move on to something that I believe is a potential to make a difference in the lives of all children but even more so in the lives of children who most need our support. If it's said that we have evidence in Scotland that a particular approach sees significant improvements in children's lives and forms the basis of a culture change that will have a legacy lasting till 2030 and beyond I expect you'd be keen to hear what the outcomes were and to learn more about it. The outcomes that we see for children are improved emotional well-being and resilience improved relationships with peers and adults increased commitment to school and learning. Would you still want to know more about it if I told you that it's the implementation of a children's human rights approach that achieves these outcomes? With such a consistently negative narrative it's understandable but nevertheless disappointing that human rights has such a negative reaction from so many. A children's human rights approach transforms cultures and encourages the development of mutually respectful relationships between children and adults. From this foundation we give children the very best opportunities to develop the skills and the values that they need to thrive. One of the barriers to implementing a children's human rights approach is that folk think that it's A, a means by which children get to do and say whatever they want B, they think it's wish you was, you do gooders who think that children shouldn't take responsibility for their actions or C, that adults lose control. Nothing could be further from the truth. A few years ago children's parliament ran a programme for children aged 8 to 12 who were on pathways to offending. The funding required us to work solely with children in this category so there was no opportunity for us to create the protective mix of children that we prefer to do. All the children came from very dysfunctional family situations that had significant issues engaging positively in school and in out of school activities and were clearly on the path to adolescent intervention programmes and some of them inevitably and eventually prison. We worked with the children over an intense of 8 day period and it was tough. The children's behaviour was challenging. Children communicate how they're feeling through their behaviour. At school generally behaviour that's considered challenging is usually dealt with through punishment. If we're to realise the potential of all our citizens and create true equality and aspiration and opportunity then we need to pay attention to how we're supporting those who face the most adversity in their lives. James was 10 when he was referred to children's parliament because of his significant anger issues. He was getting into fights in the playground on his way home from school and when he was out playing with his friends on the east end of Glasgow. At the end of one of our sessions he sat down beside me and he said a boy heeded me at football the other night when we were playing in the park because I got the ball off him and I didn't even fight him and I can fight. I asked him why he didn't fight him and he said because children's parliament taught me to feel proud of myself and I asked him how that felt and he said brilliant. Now if you think about James's life, his inability to self-regulate his inability to stay at fights and being in constant trouble at school what happened to him over the course of 8 days is really pretty remarkable. We didn't administer any medicine. We didn't put curfews on him. We didn't give him a row when he made mistakes. We gave him unconditional positive regard and that sits very uneasily for many people. But they say he was naughty. He needs to learn the consequences of his behaviour. He needs to be punished. Yes, he needs to learn the consequences of his behaviour. But what would we achieve by punishing him? Six years of school with its ethos of reward and punishment hadn't equipped the children to operate as confident responsible people. What would more of that achieve? The definition of insanity we know is to keep doing the same things over and over again and expecting different outcomes. So let's do something different. We have an opportunity now to break the cycle working with children and the adults around them in their homes schools and communities to build trust, mutual respect and therefore stronger, safer, more resilient and cohesive communities that will serve us now and in the future. Children's parliaments working with government and others at the moment to create a coaching and mentoring programme so that the mystery of a children's rights approach is removed and its potential realised. One individual, one group, one community at a time. James didn't not get into a fight in the park because he was scared an adult would see him and give him a row. He didn't get into a fight because he understood deep within himself that he had the power to choose to behave differently. He had the power and the resilience in him to make a positive choice not to get into a fight. This learning and experience will lead James into new patterns as long as he is supported on that journey and not punished for the times that he gets it wrong. He now knows for the first time that he can do it. That's the legacy of putting children's rights to respect for their human dignity at the heart of what we do. It has benefits for the whole family. When James's family came to the celebration event and we put a lot of effort into making sure that families get along to these things I went over to his dad and I said, are you James's dad? At which point his dad's face went chalk white and I could tell that he was expecting me to tell him all the things that James had done wrong because that's what normally happened. When I said he's been brilliant, he's worked so hard, he's been so helpful with all the other children, you must be really proud of him. It's moving to see the reactions that parents have. For many it's the first time that they've had a positive interaction about their child and this then gives us an opportunity to engage the parents and the wider family in the work and often it gives teachers a fresh opportunity to talk about the child from a positive place. Jack Ma of the World Economic Forum said recently education is a big challenge now. If we do not change the way we teach 30 years from now our children will all be in trouble. The way we teach, the things we teach our kids are the things from the past 200 years. We need to move from knowledge to soft skills, values, belief in themselves, independent thinking, teamwork, care for others, knowledge will not teach that. How do we move towards a shift in culture that will support these aspirations? How do we all get on the same page? I used to think that as long as adults are treating children kindly and meeting their needs, why is it important that we push for work to be named rights based, a children's human rights approach? The answer to that is because unless we do, adults retain all the power and children are completely dependent on adults creating the culture, ethos and values of kindness, trust, empathy and human dignity that children see they need and which evidence supports as the cornerstone of a healthy, happy and safe childhood. Placing knowledge in the hands of children helps children take a level of responsibility for their own safety and well-being. If a child doesn't know that adults are not allowed to do whatever they want to a child, why would they tell us when an adult is doing something that makes them feel worried or scared? Think of the Rotherham girls. Think of the footballers recently outside the Supreme Court saying we're passing on the guilt and the shame that we should never have had to carry. Why didn't someone speak up? Why didn't they speak up when there were children? Because they didn't know that adults are not allowed to do whatever they want. Because they didn't think adults would believe them because they thought it was their fault. It's in all our short, medium and long-term interests to think about how we create cultures around children that respect their human rights. This is our long-term security for healthy, happy and safe children and communities. In 2030, I hope children's experiences reflect respect for their human rights and that they feel valued and loved members of society. I hope they feel able to speak up for themselves when things are going wrong for them, knowing that adults will take them seriously and involve them in making things better. The United Nations Convention on the Rights of the Child gives us a set of minimum standards against which we can measure how we're doing and below which we mustn't fall. It's the golden thread that each and every one of us personally and professionally can hold onto to guide us towards best practice and ensure that our children grow up with respect for their own and others' human dignity. If we get this right now, we can look forward to 2030 with hope and confidence that we've built strong, capable and engaged communities where people of all ages feel valued and able to play their part in shaping Scotland's future. One of our early infuerties was Kate Gilmore, UN Deputy High Commissioner for Human Rights. She was addressing the UN Committee on the Rights of the Child recently and she said, every individual child amidst all of their and our diversity is entitled by affirmation of this convention on the rights of the child that you hold in trust to a common human dignity. In these, its children, the world has both hope and duty. The largest generation of adults that the world has ever known gives us unprecedented opportunity for investment and precious responsibility for protection. This isn't a niche issue for people who work with children and young people. It's the responsibility of all of us to build a country that values our youngest citizens. I was talking to a friend the other day about this presentation and she told me that after the heavy snowfall last week she answered a knock on the door and two boys from her street who she knows to have been living in very difficult circumstances were there and their behaviour isn't always what we might consider socially responsible. The boys were grinning up at her and they said, we've cleared your path, do you want us to do your pavement now? The boys hadn't done anybody else's path. She felt that they were acknowledging that she had never judged them. She'd always given them a smile, a kind word, a question about their day. What she had done was recognise their right to be treated with respect for the fact that their children, their human, and we adults, all of us are the duty bearers to support children on whatever journey they find themselves on. She was being an unfairty. I hope you'll consider being an unfairty if you aren't one already to sign up for and standing alongside children and young people in Scotland. I didn't actually work out how to use these slides beforehand, I realised halfway through. So at some point there's going to be, Rob's going to make a slide up here which has a big lovely drawing on it called the Unfairty. Last year was our 21st birthday and we launched our Unfairty's campaign because we wanted to bring together adults who feel committed to doing their best for children in Scotland. It doesn't mean that you know everything about children's rights, it doesn't mean that you don't make mistakes. We had a discussion at our team meeting recently about the need for us not to set ourselves up as experts. We're doing our best on this journey too. Unfairty's are adults, or in fact children have demanded to be unfairty's now so we have children unfairty's who stand up alongside and speak up for children. So it's very easy to sign up on our website, go to our homepage, you'll see Unfairty's and I think the children at the cabinet meeting this morning managed to persuade several MSPs and senior officials to sign up so it's really difficult. You get a really nice badge. And that's all I have to say. Thank you very much.


And since the earth is part of the solar system, by studying the solar system, we hope that we will learn more about how the earth got to where it is today and where it's going. We're not disappointed. This mission is exciting. Watch the behavior of these clouds the way they interact with one another. This is the most dynamic and active geologic surface in the entire solar system. Volcanic lava flows. Each of these satellites is unique. It can fit approximately 760 earths inside of Saturn. They are made up of individual small particles, perhaps something the size of a basketball.  You have to catch it just right when the outside freezes and the inside is still liquid. Saturn's large moon Titan has an atmosphere probably two to three times as dense as the atmosphere here on earth. An atmosphere predominantly nitrogen but with a trace of methane. Last night we had these frames come back from two billion miles away. It's difficult to explain. We're very sensitive to any dust in the rings. Expectations. Here we see a cliff 16 miles high on this fault. If you were standing here you could flip a rock off it would take nine minutes to fall to the bottom. When you generate a magnetic field of a planet briefly you do it by having an electrical current flow inside the planet. Micro seconds plus or minus one microsecond. The surprise to me was that there was anything to see at all. That's motion. That's how you study motion. Motion is the key to meteorology. It looks like we got pounded with about 300 hits per second of tiny little dust particles. And the science is pushed in in an amazing fashion. It's like fitting a jigsaw puzzle together. One of the interesting things that's kind of bizarre is the terminator region of Triton. It's gorgeous farewell to us and it's our farewell to you.


Baxter Drive and Baxter will look a little different come the summer of 2018. However, only half of the street will be worked on as the city owns half of the road. The city of Baxter will completely revamp Baxter Drive but only between Highway 210 and the southwest corner of the Westgate Mall. The stretch of the road from the corner of the mall to Excelsior Drive is privately owned. The renovated portion will include additional pedestrian walkways and green space alongside the road. Baxter Drive in 1996 was vacated the north half in order to accommodate development. City of Baxter is obligated on the south portion out to Highway 210 and we're looking at doing a full depth reclamation project in 2018. The estimated cost of the project is half a million dollars. If you've enjoyed this segment of Lakeland News please consider making a tax deductible contribution to Lakeland Public Television.


Hi, and how are you all today? Let us proceed on with the question that's given to us. It says, let A and B be sets. If A intersection X is equal to B intersection X is equal to 5 and A union X is equal to B union X for some set X, show that A is equal to B. So let us proceed on with the solution and let us make use of the things that are given to us. It's given that A intersection X is equal to B intersection X is equal to 5 and A union X is equal to B union X for some set X. Let's start with our proof. Now, we can write A, set A as A union 5, right? We can write it down like this. Now, we can say A is equal to A union. Now in place of 5, we can write B intersection X because it is given that 5 is equal to B intersection X. It is given to us in the question. We are just making use of it. B intersection X, proceeding on. We can have using distributive law. We can say that A union B, A is equal to A union B intersection A union X further. Now in place of A union X, we can use B union X that is given to us in the question that A union X is equal to B. A in this, that is given to us that A union X is equal to B union X. So in place of A union X, we can write B union X. Again, by using distributive law we can write it as B union A intersection X. And as we know that A intersection X is given to us in the question as 5, we can substitute the value of B union. A intersection X now can be written as 5, which will give us that A is equal to B. So this is what we were supposed to prove and hence we have shown the required thing. So I hope you enjoyed the session. Do remember all the properties and laws that you started before proceeding on with the solution. Bye for now.


Gary Cole not only seemed effective tonight, but unbelievably efficient. What was the biggest difference in that this evening? Just on the attack, you know, in a couple of times, even behind in the count, you know, he got some pop outs for an easy out and just never had that real long inning. I thought he was really good. I thought he was really effective. I thought he improved even as the night went on. I thought he did a good job of incorporating his change up tonight. But the curveball and the slider seem to continue to get better. And the fastball was there for him. I thought it was a real complete outing for him. What did you think of your offense tonight? Yeah, really good. Um, you know, good to see a lot of different guys having a hand in it. You know, we load the bases there in the first and didn't break through.  I know Glibertura's had a couple walks the other night, but a very solid night at the plate. What do you think allowed him to really turn the corner in the better? Yeah, I think he's just getting a little more patient. Um, you know, not necessarily chasing that result, you know, letting the game kind of come to him. And I feel like you're starting to see him get into better counts. And then when he is in some better counts, he's not just, you know, in swing mode, wanting to do damage. You know, he's letting them come to him a little bit. And when he controls his own like that, um, you know, he's, he's obviously trouble. We ask you so much about Gary Sanchez, both offensively and defensively tonight, just a reminder of the damage he can do when he gets a hold of one. Yeah. And again, I've, you know, he's not all the way there yet. But I thought you saw another night where he's close, you know, that first bat where he hits the ball to center. I thought it was a really good at bat. He just missed clipping that one. Um, you know, just the way I'd put it, he's in the fight a lot more. I feel like his balance is getting better. Um, and obviously, I mean, that's put an absolute charge into that ball. Uh, especially tonight where the ball wasn't really carrying, you know, uh, I think that wind was blowing in from kind of left center. So to hit that ball where he did, that's quite a shot. Thank you. Marla Rivera, you have the next question. Um, hi, Aaron. Before the game, you talked about having lost two MVP type players to injury in Aaron and Giancarlo. How important was it after that to see what Sanchez and Torres did tonight? Um, look, nothing, nothing changes for us and for our guys. And you know, I'm looking at our lineup card right now. And that's a that that's a force right there. Those guys are capable of going out and having a really strong night like they did tonight. Um, you know, you know, we we got to pick up the slack a little bit for those two guys while they're down. But um, this is a lineup that, you know, nothing changes as far as expectations. We expect to go out and be, uh, an offensive force with the guys we're running out there right now. And Aaron, is there a degree of frustration in a short season and with not that many off days, having to use a big arm like Audobino in a big game that you were leading by seven runs?  But you know, that said, you know, there's going to be times in a game where, you know, to in a lot of ways that that was their opportunity to get back in the game and Otto came in and forwarded it. So, you know, it's not always going to be, you know, roll it out perfect exactly like you want, even on a night where you kind of did a lot of good things. Thank you, Aaron. Eric Bollin, we can take your next question. Aaron, how would you describe the kind of run that Cole is on right now, not having lost since last May 22nd? I mean, best picture in the game and that's what you're seeing. You know, you're seeing a guy that's, um, graded his craft with elite stuff and, uh, the ability to command it. And I feel like more and more is getting settled into, um, you know, the season and, and, and all the weirdness that this has been, um, you know, he's, he's dealt with it great and he's, I feel like gotten a little bit better. It seems like each and every time, but he's, I feel like really settling into that groove where when you have the kind of weapons he does and the kind of command and the kind of detail that he, he does, you know, these kind of things are, are, are possible. Thank you. Brian Hope, go ahead and unmute. Hey, Aaron, uh, just checking, did Glaber come out, um, for rest or? Yeah, just rest. Just rest. Okay. And then, uh, to follow up on what Eric asked about Cole, did you sense any extra energy from him tonight, uh, getting to face the Red Sox for the first time as Yankee? Uh, not necessarily no.  I thought he was so under control and calm and, um, uh, so no, I didn't, I didn't necessarily sense that. Um, I just thought he was very sharp.