Welcome back everybody to another stream. Here's my description. Gender disclaimer. You guys know it. Only for educational purposes. Blah-dee-blah-dee-blah. We know that we love it. We hate it. I don't know how we'll get any more. I mean without further ado, let's sort of get started. I mean Miss Yesterday, my bad. I only can apologise for that. I got caught up with the quarantine. No, I didn't. I just sort of overslept. I woke up at 2.30 instead of 1.30, so... because I didn't sleep the night before, so no excuses. In terms of what ended up happening, pretty much what we wanted from Friday anyhow, so it's not really much surprise or change from there. We were looking for Friday to be a high leading up into a downturn for this week and potentially continuing for the next week as well because we are looking for the next following weeks to be bearish. I mean with that, there's not really too much to add. We have two weeks of very bad percentages, which is usually a great time. A week with around 30% hit rate, not usually the best to bet into a long, but great to look for shorts, if you haven't caught it already. I mean, but was that being said right now?  We do have to sort of clear these guys out, and it makes sort of perfect sense to try and do that on Tuesday today, just so we can sort of correct sort of against this just a bit, and then or better still grab as many longs as we can possibly, because again, you can just see panic selling all across the board. We're getting some buyers back, but not too much. And in order to go down, we have to get rid of these sellers and make them buyers and squeeze out the shorts. So it's not a, the move down is not non-profitable. With the way things are moving, I think it should be sort of nice to look for at least sort of a spike high, at least take some agency and agency in sort of taking out some liquidity here. Don't expect us to take the entire move back, just at least up until some stops to take. And I think is what we're ideally looking for, but most likely after that, we're probably going to continue back in the direction downwards that we're looking for. And if anything, if today we don't get that commitment to push higher, then we'll probably get some commitment tomorrow, because tomorrow data month and month and year. So that is always a good time. And if we're looking for it to go down, we want it to go down tomorrow, not today. So because we have that inflation data, you want this entire day to be sort of comprised of long traps. If we don't get long traps and we get a bunch of new shorts, just stacked net on top of each other, just one after another, it just looks really good for it to reverse. We'll pee.  No, I don't support that alt-right coin, I'm telling you. Pepe is a racist. We don't support racists here. We mark up sort of Mondays sort of high here and close off here. That's your high, that's your low, the weak. So we can give ourselves a nice perfect range and there's 50% of that. Again, where we have the stops, where we'd expect price to be taken if we're looking to take those longs out. So that's something that you always want to sort of mark out at least tentatively. When we're coming to the week, then when we get Tuesday filled out, then we can have Monday and Tuesday like we have here for last week. And you can see that how we end up playing into that and just consolidating because it's always, it's always a good time. I mean, there's two weeks ago's range. There's the current week's range. You can see that we'll play with these figures usually. So they're always nice to keep handy at least until you have the next week's forward week's data. In terms of pushing, again, we're only really looking to come into where we have that 50% where we have this area of longs. Just try and get rid of as many shorts as we possibly can. It's getting pretty heavy. You can't be precious. Frog is green, not white. Okay. But that's green, being green is the same thing as being white. Okay. Why do you think the money is green? Because the money is racist. God damn, why is my computer so slow today? Just look at that lag. Not dropping any frames. So that's a good sign. But we're coming in here. We already got 8.30 printing. Don't have any news. So we'll watch this by its time. Still sort of looking for it to make some attempt at this. Unless it fades this and pushes back down. Because you're looking at this and you're saying, okay, what does retail do? Retail does this. They see your flag. They see break of the flag around here. And they short this area here. They short it here. They're getting squeezed out. Now they're seeing breakouts. So they're already grabbing the breakouts here. It's just a matter of fact, if we want to grab stops as well as the breakout or not. How come you've got this much historical data on bookmap? You can either pay for it or you can have your bookmap loaded indefinitely. Because it either does for cloud or local. So either or. For myself, it's my bookmaps open, what number one is paid for. And number two, I have it opened indefinitely. So I always have a backfill of an enormous data set. Which interesting enough, you can share. Which I can't remember how to do at this moment. So that will have to wait. Everything is slow. Jesus. Let's see if we have any fud coming into it. It's ridiculous. By short-term debt ceiling still come. Oh no. We're really thinking of buying short-term treasury yields. Yeah, let's see what the two-year yields are. Set 4%. But he's saying it's going to go back up. Again, we don't want it to go back up. I don't think you should buy bonds at this rate. Anyhow, I don't think it'll go too far up. But I mean, if we get the liquidity pushes it up, everything goes down, everything goes down. Then we get the suckers in at this area here. Then we're like, haha, now your money is worthless. You bought government bonds expecting it to work out? No, no, no, my friend. It did not. But again, this is a trillion dollar market. So it's a trillion. It's very big. So it's something that moves very slowly. And you can get in with, you need to come in with the very sort of high liquidity anyhow to fill the any moves. But I mean, slowly mellowing out, we're going up as intended, nothing sort of changing, at least to my mind. We can look at Ethereum because it's been a while since we've done Ethereum. But again, like I always say, Ethereum is just more of the same BS that Bitcoin Prince is just, it would just do the same thing and it doesn't sort of vary too often. So you just got to look at it like that. If Bitcoin wants to go down, this will go down. If we want everything to go down, everything will go down. But we don't want it to go down today because case in point, because we have interest rates tomorrow, we want to go high being because we want to trap longs. If we trap longs here, we can come down for the sort of short term, short term or sort of sort of mid-term get rid of all the liquidity as people sort of read off the interest rates pushing down. What's your tick setting? It's Bitcoin 10. Yeah, look, there you go. You can zoom in all the way, but 10 is the lowest. I go, otherwise, the chart doesn't like you. There you go, 10. But each end of your accounts is 10 Bitcoin or 10 US dollars. I feel 10 is like perfect amount for day trading.  Not mentioning the fact that you have to be right for most of the time, even then, it's marginal profits rather than sort of substantial that you can possibly get with actually sort of sticking in a position until you see something coherent. But yeah, Bitcoin and Ethereum both setting up for this week. Again, slightly different bars, mostly generally the same sort of direction attributed. We want everything to go up, hit into here to then go down. If it doesn't do this, it sort of buys its time, then we get a very good liquidity clear at 8.30 tomorrow. And that's sort of not what we want because we want to come down for the next two weeks or so. We want to make our way to 24 500 in wanting that since March and haven't got that in April. So I'm kind of getting pissed off. I'm not really. Uh, it's just what it is at the end of the day. But again, this is where we start seeing sort of all these short stockings stopped out at that sort of because if you're entering short here, you're stopped, you're placing your stops here. If you're entering shorts here, you're placing stops here.  So we've got three level of liquidity, one already tapped. We're building up decent amount of longs, but we want to sort of spike it a bit further and try and read for that play because we want Tuesday to go high. Because if Tuesday doesn't go high, it just sort of throws everything not out of the window per se, but it sort of delays what the Indian inevitable, which is I always hate. This is just why you're dragging this out. Just just go down, stop, stop the flirting. Now back to our old friend at us. Oh, it's been so long. I haven't used this in a long time. We use at us for a bit. As you can see, same thing as we've seen in book map, just with a chart of view, you can see where people start flooding in shorts and market profile. There we go. You can see this is where they clamor in. You've got the bigger orders coming in sort of coming in short. And as soon as they start reversing, they get brave and they try by long, they get instantly sort of punished. You can see that these big trades coming in here and here, they'll place the stops here. So there's more liquidity to be had at this area and at this area than there is to have here. There's nothing sort of too profitable about this. You can see this area here where we had these big major lungs and the one final sort of nail in the coffin here. And these would be breakout traders. And you can see why breakout trading is usually very, very ill advised. There's why breakout buy when you can just buy here and sell to the breakout buyers. And if they decide to short a high, then, well, you're not selling simple as that. But we know where the liquidity is now. So that's something we want to look to target. We've already hit these guys out. We know there's again a mixture of longs and shorts in this. Go back to book map. We can see it's not wholly short. It's not wholly long. It's just they split it. And everyone's just been clamoring both sides. If it goes red, they short. If it goes green, they long, simple as that. But we want to target these areas here where we have these high or large orders. And we want to get those liquidities. Because that's where our money is. And once we strike those guys out, everything would be nice. And we want to do it by today. If we make our move up here, if we hit these guys today, then that means Wednesday 8 30, when we get that interest rate announcement. If we're around here, we can just spike it and instantly just continue back down. We cleared the liquidity. We cleared what we need to clear. And we can just move down. Because we always want to come in on a news day and just do the absolute opposite of what this chart is setting up. If the chart is just setting up shorts and shorts galore, we're just longing at that rate. But to be honest, you should be in a short from Friday because Friday's assessment Friday's assessment is just like, okay, we created a high, we're just going to push down. Everything's going to continue as it is.  And it is finding its way, hopefully it finds its way faster than anything else down. But it's taking its time. So we leave it at that. As it sort of takes its time, we want to sort of baby it basically and just wait for it. You can see that Monday just did nothing. We won't choose it sort of in the same vein, do nothing. Because the more it consolidates, the more chances it goes more aggressive and just goes lower and lower and lower, which is what you want for this week specifically. And hopefully you can grow a backbone and do that. Because quite frankly, I'm sick and tired of this cat and mouse chase. I'm joking. I love this. But I mean, not really too much new things to add. I mean, it's yesterday was a boring day just continued Friday and consolidated on the market side of things to today's setup day, because we want it to be a setup day for inflation rate. You're on your data wise, because everything here is not going to be sort of a long term position anyhow, because this will sort of direct us into what position you should go into, or at least be a lot more profitable in doing so. So today should be just a maximum overnight overnight stay sort of trade. And you've got to make sure wake up early and be out of that before you get ham hamfisted by a big red candle or a big green candle. And following up that you don't you have consumer sentiment, which is always a sort of decent sort of gauge in understanding people's reactions coming in Friday. So Thursday not finding any solace. So which is which is nice that means that I mean, it's got PPI but could could create an inflection chart. But again, we don't want it to we want just Wednesday to just be that coffin and be that downwards we're looking for to just carry everything onwards. Because worth comes to worse. We end up just having three days of just constant back and forth meandering to all the traders who want to get into a position because they think they're too late on something that they may or may not have seen. So it might look something like this we come in we go high today if we come and go high get everything we go down Wednesday comes in here Wednesday reverses it pushes back then it sort of mellows out Thursday comes in if we're collecting longs which pushing back down and maybe we push down further maybe we not maybe we sort of mellow out here but then consumer sentiment final nail and it's just three consecutive days of doing the opposite because if we just do this and it just goes down until Thursday it's just going to build a bunch of shorts just going to get rid of a bunch of liquidity and the only way you can make money is by reversing that by clearing out liquidity and going high at sort of that PPI and same story for Friday for consumer sentiment again magnitudes may vary I'm just telling you general direction direction things that you're just trying to look out for you looking out for this and that and third and you can see here is where people would say buy the retest and they're not buying so that looks promising for us to try and look for this higher because they're not buying because if they were buying then we would have not stayed this high because the whole trick of it is trying to get people screwed just trying to screw them out they're hard earned money make them bleed for that dollar I don't know that I think I think interesting like I said at time and time again you can see like we're sort of pushed we pushed everything slowly down we create a nice little nice little time and volume sort of area here where we just spent majority of our time so everything else should sort of ease through butter the only sort of two nodes we have are this and this they are selling yes they are well they're selling into this squeeze them I mean squeeze them for one extra high but yeah squeeze them but they're buying back instantly which is again we want the sort of this wall to sort of stay put if they sell into this wall better chance we can squeeze them squeeze the living lights out of them in everything is sort of time dependent we want to come down earlier the earlier we come down the likelihood we sort of thought the day opening here and people just short the uh on the open because they see red and they just sort of like okay we're bleeding out let's just bleed out together then it just reverses on them but this again could be removed or I could say but that's something that you always want to sort of uh sort of have a right to mine especially when we're coming into a before before the actual market opens because again we want to sort of execute in and around sort of well sort of 10 o'clock especially when we don't have any sort of major news telling us otherwise for instance tomorrow 8 30 instead of 10 as is the time but for most days and this day not being a stranger 10 because up until then we're just sort of setting up until then and it's if it moves you already missed the move because you should have gotten it earlier because you're trying to beat three people whilst being one person the best way you can do that is just by working in extra or looking at an extra sort of session so maybe you grab London and New York or maybe you grab Asia and New York just pick the two to most you're likely to be awake for or the time intervals make your schedule around them or work at them into your schedule either you wake up it's London depending on if you wake up at the middle of the night or you go to before you go to sleep at sort of 9 o'clock or 10 o'clock definitely catch Asia as a New York trader it's very easy but then you run the risk of sleeping watching a trade while you sleep which is always it was a nightmare selling and they're not selling too much but starting to get the picture the starting now is just a boring part the wait the part we wait for things to take form into action right now we just threw out a couple of potential scenarios some of them decent some of them more far-fetched but all of them still within the realm of probability some again more than others so again if we sell into here if we see people selling to here that's nice to say get that extra high once we get that extra high we can then say okay down we go if we go down then that will sort of go down into sort of the whole day she's not what we want but if we get that we get that then Wednesday comes in makes a low we start building extra sort of shorts we can then sort of reverse on that Wednesday and just sort of trade back into the weekly range since we sort of break break out of it here then Thursday will then reverse our direction and so on I don't I think you guys happy with with everything here because I don't I don't think I need to sort of go on any further because at this stage nothing nothing interesting will happen I wouldn't say nothing interesting but the likelihood of interesting things happening is is much less as again this is where our largest amount of shorts are entered here these guys mostly partially out our stops will be here secondary stops would target here most of the shorts came in here would stuck them here or here so it's something that you want to look for I think would that make sure of lungs in here which already got punished out here that we're looking for that I mean if we look at the day chart why not in terms of daily wise you can see where we're coming in you can see where the largest volume's been on the monthly side of things you can see that distribution that we have here you can see that entire area of insane level of lungs that we captured on the monthly scale just being here and here you then push these guys out collected them short again on the monthly term the monthly time fame rather now we're pushing out it's just majority of that is long trade bubbles so you saw yeah that you're really useful again I don't know man it's basically like it's basically makes makes this look like book map anything to make it look like book map book map is home away but my visualized things sit nice with me just because I don't know maybe because I'm autistic I'm not it's just made I just I just like the way it looks in general I like the colors and the shapes damn that's a monthly maximum level oh yeah that's that's sort of heavy shorts so all these shorts probably they'll be a probably decent decent chunk of them out by then some of them might still sort of hold to this area and say okay there's where I'm out so that's sort of somewhere we want to sort of maybe look for a couple of sort of ticks where we're gonna grab a lot of traders but that's always going to be the case because we know that they're gonna come in and just say oh yes that is your hi we're gonna buy the breakout again we talked about again the daily just looking like through candles we just want to push ever so gently up to this area here and we saw it and there's there is on at a same sort of area that we're sort of rising so this is more and more looking like a fantastic area to sort of want to pee out and want to look for for a potential sort of shift especially if we're not going to take this high here I should put it in the stream description I mean these streams are done by Bookmap so whatever whatever they do they do grabbing this hopefully we get a decently long day today just because we want it to go a bit higher just so we can grab that again the monthly confirms it there's there's a point of control on the monthly the daily wants it there Wednesday wants it there the entire week wants it there everything just wants it there so we can just come back and fist you differently on a different day as graphic as that sounds it's going to be nice for a lot of us spy talk about it all day but we don't have that time but again similar things we know we know everyone and the muscles went along here we know they'll probably continue to go along on the Monday because they saw central breakout here and now they're sort of getting the memo with this sort of with these shorts here so they're gonna open they're gonna open to cells so we want this day to try consolidate up if this consolidates up then we can get that nice little Wednesday that we wanted which is down all the way to until sort of we reach the earth core and once we reach the earth core then we can talk so I want to you're long from to 27 for so yeah yeah I mean if it does go up then you should be fine I mean if you have your stops I break even you get taken you can re-enter anyhow uh because what if you entered in from here wherever you and and have to be entered you want to sort of put there there's your next sort of stop on area so once it takes that then you probably get a nice re-entry if anything but you're looking for it to try and be a bit higher here to then push up as a sort of magnum opus to then target this then push into this so you're trying to sell you're ideally trying to sell into these two areas here because they'll be your sort of golden areas where you have the most most amount of liquidity being here you'll have an adequate amount of liquidity at these areas here so you're gonna see a lot of stops get hit uh and once these get hit you want to sort of maybe have that chance of a bit potentially going like that and retesting so then you can re-add later to then push up and finally hit that one maybe it's all about getting out safely and getting in safely always use protection I mean we can see spot as well as also setting out for that for these breakout buyers to come in here so they're probably gonna watch these breakout buyers just clamor in just to see this area and once they see this area like oh you bought sorry too late you pressed buy can we go oh you're selling now okay up we go but yeah that's the trick of things just getting as many people that is to press the buy button as possible so the next day we can just tell them sorry you chose poorly the more it hangs around here the more it's sort of in the more inclination I am towards uh it just trying to push down again you don't want it to be high on an open because then you'll have a likelihood of volume just trying to buy in rather than sort of sell out because that's always uh always never a nice time when people are buying in because that means uh you chose wrong I mean instances where you have a breakout you always get retest anyhow like I say to you guys I mean you always want to sort of you either miss the move or you can get the move it later because that move was yesterday's move each continued on then miss the move as your entry back later or re-add there's the finishing touch to that it's sort of high finally sort of complete that very very long journey um if we sort of buy into here buying into then we just collapse I don't think with that we can we can call it again today's just set up day you want it to sell ideally you want to set up long but if it sets up short you don't want to do tomorrow uh the main things you are looking for are these two liquidity areas here uh for potential shorts uh just getting you missed it if you didn't get it Friday but it's the same thing it's just these two areas or if it just gives you a nice little double top just just kisses it just shy then maybe you can sort of reverse it here but generally I don't I don't hold too much hope on that as a possibility because I mean if you stretch it out like this then collapse we've got something there but again it's just sort of very uh very bottom heavy to try and continue this going further down unless Wednesday just becomes a fantastic long day because we just get three like basically three days of continual down including sort of the weekend that we had I mean they're starting to move higher now it's always nice but they're getting buyers which is looking looking a lot better for that just bit of a flush but with that being said I'll leave you guys to it you guys know my thoughts you guys should know what to do today if not you can hang around for tomorrow because again tomorrow's gonna be a lot better for well some of the things because everything today should just be uh price just repricing or changing uh changing sort of the books around just so they sell for tomorrow's 8 30 news release once you grab that news release everything should just be fine and dandy so we have three back to back days of news again one day in tuesday never really the most important days in a given week it's usually the latter half of the week it's the most important so I'll catch you guys tomorrow hopefully I will wake up bang on time like I did today if not hey you guys already know what I think for today or for tomorrow it's just doing the opposite I catch you guys tomorrow peace


For the circuit python parsec today I want to show you how you can convert an SVG graphic into a vector IO graphic that you can use with display IO in circuit python. So here I have a little TFT feather wing and a feather M4 and what I'll do is show you this first.  This is the cool thing about scalable vector graphics. When you want to you can change the scale and the image still works. So here I've got a very cool bat logo. I'm going to pump that up a whole bunch more. So unlike a raster graphic I'm not stretching pixels out and making a mess and aliasing things. Instead I've got a number of vertices here and lines drawing among them using our little line object. You can see in the code here I am using vector IO that's the key thing and then I have this really cool class that Todd bought wrote called SVG logo. This makes an SVG logo point set into a vector IO point set. So if you take a look at my Chrome capture right here I have this site up path to points that's in Java and what I'll do is I'll drag an SVG graphic. You can find these all over the place and here it's turning that into a set of vertices and you can make some adjustments to that and then you'll save off that set of points. You'll run it through a little python script and then you have something you can cut and paste into this here. You can put multiple paths together. This originally came from Todd's DVD logo bouncing screensaver and that's about all there is to it. I can set the scale of it. I can set the dimensions of it if I want but here I've just got a little function where I can make this as big as I want. Eventually it's going to fly right off of the screen but a really, really handy way to make some graphics that you don't have to just have one fixed size of. Now you can see it's not super fast to draw here but in this case it's really big which can slow it down but you can use smaller ones in movement or you can use larger ones static. That is how you can turn an SVG graphic into a vector IO shape inside of circuit python and I'm psyched about it and that is your circuit python. Persec, persec, persec, persec.


Please put your hands together for one and only Sachin Gayre, Principal Architect, Vistara. So, I am here by the coincidence. I am representing Ravi, who is my CIO of Tata Singapore Airlines. And the introduction is definitely of mine, but I am here just by the coincidence. So, I am lucky by that thing. So, my boss is traveling. So, about him, he is an award-winning Harvard alumni. And award-winning speaker, we have received multiple awards in the area of robotics, AI. As well as we are working on blockchains, we are working on new technology stacks based on voice. And we are a core beneficiary because we are at the customer side. So, we are actually able to understand the benefits of every technology directly how it is impacting on our revenue chain and cost savings. So, let's move on. For us, technology is like a puzzle. When we look at technology from the point of view of consumer, we get benefit directly, which we can transfer into the cost or monetary cycle. For example, how we can use voice bots, which can help us to improve the customer experience. How we can use voice bots, which can help us to improve our employee experience. We can have a lot of data sets, which employees can get access just by the voice queries. So, we look at technology in a way that how they can merge with each other. This is all about how we can have robotics, big data, IoT, AR, and we are coming together. Talking about robotics, so we are the first company in India to launch the first aviation robot, which is there into the Delhi Vistara launch at T3. This is the, we are quite proud to say that we have made the first aviation announcement with robots through that robot. The robot, what we built is name is Radha. And this is again coming with improvements to come up as a entertainment zone, robotic entertainment zone. It will also going to serve food going in future. So, a lot of things are happening in that area. We also started work on a biometric, because if you guys know now Bangalore Bombay Flight UK 864, which is biometrically enabled. So, now if you traveling from Bombay at 1145 by Vistara to from Bangalore to Bombay, there you can actually go with the facial boarding passes.  We can walk in for security counter and your check-in counter just via the face. So, no need of boarding passes going forward. Fortunately for this innovation, we have awarded into the US with Delta. Talking about this, maybe within span of next six months to one year, we may have all our flights where everything will be biometrically enabled. So, we are leading lot of innovation activities in area of aviation, even if you are just, we can call, we are just start-up in the aviation sector, in the airline sector in India, but we are leading mostly all the segments of our technology for benefit in different areas of our activities. So, talking about any aviation, I am going to play some video by Ravi. After this, talking about aviation and innovation, I have, I have, how we look at mobility as a part. So, when I look at mobility, I do forward, do come up with a different problems with the airlines. So, we are using mobility for security of people. We are using mobility for e-learning, talent development. We are using mobile, we are using different mobile apps for EFB, which is called electronic flight books. So, we, nobody need to know, no captains or flight officers need to carry huge manuals which is certified by DGCA. We are using a lot of mobile solutions for improving our direct channel of payments. So, people do not need to go to aggregators like us, the sites where we try to compare the flight booking tickets. We can directly book on Vistara channel of mobile apps. We also having innovative solutions for loyalty members. So, Club Vistara is one of the probably now largest loyalty program in India for airlines. So, we are having different innovative solutions for that. So, all the way in every area, in every area of airline operations, we are using mobile devices. A lot many times what happens, once we travel, we travel, we do some air travel, we are not aware about how many things are happening behind the scene. Every flight, for airline, every flight is like a mission. So, once, once, once we declare a flight to be departed from X destination to Y destination, we actually insert the inventory to our GDA systems. From that day till the departure and arrival, every day there is something happening behind the scene. And when flights get departed exactly on a time, we can say the mission is accomplished. If something goes wrong, there is a heavy impact on operational site and the revenue site that we have to incur. So, nowadays, we are also using a lot of mobile applications, how we can improve operational excellence for our flights. This is Tara. Innovation program of this Tara is actually a value system. Innovation is one of the core values of this Tara. We try to do both kind of innovation, process innovation as well as product innovation. Our idea is multi-fold. One is to participate in the whole ecosystem of aviation and do our little bit in terms of improving it. Our second goal is to help this Tara to get a better customer experience, improve that experience continuously using technology or using process innovation. And our third and fourth ideas is to create some sort of a disruption as possible, bring new ideas which will take this particular industry a notch forward. And there are multiple examples of that. We work with multiple technologies ranging from blockchain, robotics, AI, biometrics. Our process innovation, we look at them and we look at them and processes. We try to find out how we can improve it both from perspective of productivity, revenue generation, improvement of quality of life of our employees and many other motivations. So this Tara has already, you know, this Tara's inception has been quite new. It's a fresh new organization. Certain examples of innovation which are already in public view. One is our experimentation with robotics. We have arguably created first robot for an airline which is a combination of robotics and machine learning as well as deep learning. It is currently being trained to interact with customers, to answer their queries, to help them to find out new dates, to answer any disruption which happens while they are at airport and so on and so forth. On the other hand, we have written an algorithm which is in a deep learning zone which goes beyond the instructional set of an airline which is self-learning mode in which robot is tuned or trained to interact with humans. So that is one example. Our second example which is again a public eye where we are actually, we are working with airports one which we have already launched in collaboration with Bangor International Airport is how to help passengers using biometrics for seamless entry into an airport. So that's an example of how we want to collaborate with an ecosystem of innovation and take it much forward. It's a biometrics example. Our third example, we are working on a blockchain platform so that cycle time and cost of training and the integrity of training of an engine that improves this year with the revenue free agencies and hopefully this will become another standard using technology for processing crew very soon. We will see how those experimentations are moving forward. We use voice analytics. We are using voice to speech conversion and intelligence to do that from a data like perspective. So that is another take. We are also passionate about our colleagues and how we can improve lives of our colleagues. How we can make it much more safer. So we have another use case which is in the final stages of maturity and in the IOT zone we are ensuring that a young crew who work with us are the safe when they get out of their house and in the time they reach airport and vice versa. So there are multiple use cases and it's not only a business case when you enter it. It is orientation is towards productivity, orientation is towards compassion, orientation is towards empathy, orientation is towards the freedom of ideation which we give to encourage this particular value and so that everyone embraces innovation as a whole right from the start. Result only is an organization. Innovation has to produce results. But that doesn't mean that we put tremendous pressure on ourselves or tremendous pressure on young innovators within this style. It's a very, very young organization to be innovation. Our DNA is a garage-style DNA. People come over here, they use our tools, we provide them with some day experience, ideas to fail early and waste much of time and emotion. That's one side of the story. We also encourage startups in our ecosystem. We did hackathon a few years back and that one hackathon itself produced so much startups for us and some of those startups are profitable companies today and matured startups today. We take a little bit credit for that because we encourage them. So our ecosystem is both inward and outward looking. The success of the culture of innovation in this style which I feel is far more important. Culture of innovation in this style is way more important than the actual results of experimentation. Now specifically coming to Inspirated Innovation that's one of the cold questions you guys have asked. These are initial stages of the robotics. It's kind of done initially. Results have been good. It was never done before. Today, people are almost every age group they are using. They are giving feedback about the first two hours. Second, they have far more widely used cases. Again, it is an experimentation stage. Initial results have been pretty good. Let's see where it takes both airports and airlines combined together. Hopefully it may become a standard. Blockchain, again it's in their own stage. They are not just experimentation. They are not like the symptom pilots. They are in a production stage. They are in a public view. They are backed by strong tech. They are not what I call as corporate pilots. Insiders use it and then it doesn't work. They are in a public eye. They are cool tech. They are cutting tech. Their use cases are both internal and external. Some of the other things which are in the initial stages. For example, how do we reduce the carbon footprint by doing materials in flight, outside the flight and all that. Diagnostics of integrated can be used then. How do we use voice and quality of voice conversation to indicate AI based sentiment analysis. That is also an experimentation stage. How do we extend blockchain platform towards area ranging from loyalty to tomorrow. That is all. There are a lot of things which are happening. Initial bits are happening. All those initial bits have been good till now. But they are experimentation, they are bits. They are organically done. When I say organically done. The ideation has happened within this time. And there is a serious, serious problem. It is quite long. So what we have started the journey of innovative activities under his leadership four years back. And now we are probably leading most of the right innovations in airlines in the areas which we think we are quite proud of. Any questions specific to tech airlines because we are also trying to create a large start of value system which can work for not even Vistara but our parent companies like Singapore Airlines and other companies. Nothing? Okay, thanks a lot. See you.


How many of you have been in a context where there is a foreign language being spoken where you don't understand the world around you? Maybe a foreign country friends house I know I have and I felt emotionally drained mentally taxed Truthfully, I was frightened When asking my friends and family what the coolest part of my research was they shared that I helped parents enter their Young autistic child's world and learn how to speak their language But as we know communication is a two-way street in turn That child now is on the path to speaking a common language with their own family. They're able to understand the world around them They're not Experiencing that emotional drain mental tax or that fright that I'm sure we've all felt at some point in our life My name is Sierra Owsley and I'm an assistant professor in the special education communication disorders department where I help train and Coach parents to support their young autistic child's communication


chat box and legal expert systems presented by Tom Martin We give a brief introduction. Tom is the founder of Lawdroid. Lawdroid is a Facebook messenger app designed to help entrepreneurs solve their legal problems Basically, it helps you assist you All the necessary forms Through our expert system to fill out a future LLC in California Tom is a Yale philosophy major lawyer Techno felt Billy up to the newer It's also the managing attorney of foresight legal an ARAG network attorney and chair of the educational subcommittee of the group legal services association an ABA affiliated organization Tom is also the co-founder of the Vancouver legal hackers and if there's anything I miss Tom you can fill it in Yeah, thank you, Miguel. I'm I'm really thrilled to be presenting today and Yeah, it's a lot of stuff. There's a lot of stuff being juggled every day But I think one of you know one of the best things that I'm a part of is actually your program because it really involves the the future, you know and being able to connect with Law students and give them some insight into My experience and what I think the future holds for us Is something that can really help them moving forward I Meet with a lot of Established lawyers. I just got back from a conference for group legal services association that was held in Scottsdale, Arizona and You know I gave a presentation there about the future of legal services and You know when you get a lot of questions you get a lot of Concerns from the the more established lawyers about how this will affect their existing business and So I think by giving Law students this preparation it really gives them a huge Advantage to be able to kind of leapfrog into success Versus more established lawyers so anyways With that I'll thank you for Yeah, you're welcome. I think you're doing a great thing here And I'm proud to be an advisor With you guys so with that. Let's let's look at the agenda You know having this many things on the agenda where it's not going to be Too slow-paced, but if you have any questions, please let me know what those are and I Believe with one of these tabs I can see them or if Miguel if you could let me know what Somewhere that stand out. I'd be happy to answer them either at the end or during or However, we'd like to do it. All right, so the first Is don't believe the hype there is an incredible amount of hype right now around AI that this for examples AI that we've come So far that we have Android-like, you know robots that are That exist or that this is AI or We're here hopefully not, you know that we're gonna have some dystopian future where AI takes over. I Wish it was this But we're not quite there yet And there's a lot of myths about AI right now With all these news articles coming out and people talking about it, you know But it's intelligent that it could actually have some general intelligence about it It doesn't yet. We don't have a general intelligence AI yet That it's conscious or that we have strong AI Or that AI can be defined as any one thing So what is artificial intelligence? Well intelligence is hard to define but as far back as 1950 the godfather of computers and artificial intelligence Alan Turing he defined artificial intelligence in a practical way what he said was if you could have if you could hold a five-minute conversation by text with someone and Through that five-minute conversation You couldn't tell if it was a machine or a real person That the machine had passed the intelligence test and that test of course is now called the Turing test As a philosophy major I point you back to 1637 when Renee Descartes with his discourse on method Basically proposed the same test which is kind of strange coming from almost 500 years Well 400 years ago plus Basically his philosophy was based on skepticism and he said that there could be machines in the future That would be indistinguishable from people so how would you be able to tell the difference? And so what he said was well of course you'd be able to tell the difference because whatever they would have to say It just wouldn't connect with reality. It would sound you know kind of off It wouldn't sound like a real person which is essentially the Turing test that he anticipated the two Computer scientists that actually gave us the phrase artificial intelligence or John McCarthy and Marvin Minsky From Dartmouth. Well, they had their first conference if you could believe it on artificial intelligence in 1956 at Dartmouth and they define artificial intelligence as the development of computer systems that perform tasks that essentially mimic Human abilities like visual perceptions speech recognition translation and so on now the current definition Artificial intelligence is a programmed ability to process information That's set forth by DARPA And DARPA is a defense agency research project that is directed by John Launchbury and There's actually a video on YouTube Where he gives a complete discussion about the state of the current state of artificial intelligence if you haven't seen it I highly recommend it. It's much It's much more in depth than the prior Authorities that I've mentioned on artificial intelligence because it actually gets in and Breaks down, you know, what is intelligence? Yes, it's intended to mimic Human abilities, but what you know, what is that and here we see the perception the ability to perceive rich and complex information To learn from that information within an environment to abstract from the information so that for example, there's different you know, you have orange juice you have a Coke or root beer you have glass of water and a machine would be able to abstract from that that these are drinks That would be abstraction or the reason is the ability to plan and to decide based on The information that's been received what to do in the future so there's different abilities to Perceive learn abstract or reason based on the type of artificial intelligence that used is used and Where we're at now as opposed to where we can be in the future is further explained in this video by DARPA So check that out if you can So I'm going to give you some examples of AI and the thing is is that AI, you know It's not something in the future. It already exists around us right now in many ways One is You know facial recognition Facial recognition is more basic than I than identifying someone. It's just a matter of being able to gauge whether or not what the computer is looking at is a face or not a face and and So the first way of doing it was pretty basic where the computer would look at The cross-section where the eyebrows are and then the nose going down is kind of like a T Formation so that it would look at an image and be able to see That there was this T formation of darkness and it would identify that as a face Now that was good in 2001 But if somebody was turning their head sideways, or if they were behind someone else It may not recognize that image as a face well in 2015 Farfade and Siberian who are these two computer scientists? They came up with the machine learning algorithm That was able to look at two million images Which included two thousand two hundred thousand faces and from looking at all of those images it was able to abstract the concept of A face so it wasn't just This T section it was looking at but it was being able to abstract about What is what is a face constructed of and able to identify that? so nowadays You're able to recognize a face much more than you could 15 years ago this this is a picture of me and This is actually a resource available Online by Microsoft you just upload an image onto it and it'll tell you what the emotions are more or less You could see that the majority for me is neutral in this picture and then happiness is a close second but Yeah, this machine learning AI is available Currently, and I believe there's already a startup that's using this kind of information for jury selection You know so if you can imagine having these have cameras trained on the jury and being able to gauge how they're reacting to Questions it'd be very powerful and wobbier Another application of AI is of course in Google search rankings It takes into account what you're asking for and trying to gauge what you might be asking for And then it responds to your queries over time to get better at following your interests another current application of the eyes When you write checks Of course, it's physically impossible for human beings to Review all of the checks and get them right So computers have been trained to recognize the different digits and categorize them correctly most of the time to a high degree of correctness now AI is also this is obviously a fake with Homer Simpson having a crayon in his head But the idea is that artificial intelligence can now be used to recognize patterns in terms of MRIs and x-rays And right now they're being utilized as As an assistance to To doctors that would read x-rays and MRIs to gauge what diagnosis they might come up with and in many cases Well, it's been experimented now that in many cases The artificial intelligence can pick up Diagnoses that the image reviewer would not be able to Or at least at that rate not be able to Google translate If you have used it over the past year, you probably You probably noticed a pretty big change around the beginning of this year It used to do a pretty good job But it's gotten a lot better And it's gotten a lot better by the employment and machine learning Before last year and before it used a rules based a rules based system to identify words and then replace them Kind of like we would do by getting the dictionary and then just replacing the words and then using a basic grammar to try to keep things, right? but by Comparing syntax and by taking examples of phrases that That have been fed into the system by again by repeated queries that people have had it's been able to learn and abstract from that how to properly put a sentence together in any language and I Remember reading that that Google translate has actually created its own meta language, which is it which it employs to be able to translate Sentences across languages So it's become far much far far better. And that's because of AI and machine learning Whenever you go on Amazon, you know, it's comparing your purchase to Millions of others purchases and making recommendations based on your interests and what other people who bought things as you have purchased By the way right in the middle that green book called data mining If you're at all interested in in machine learning I'd highly recommend it. It's a pretty good book by Ian Whitten about machine learning tools And gives some practical insights into what it means and how to learn more about it Some milestones in artificial intelligence in 97. We had deep blue Beat Kasparov one of the he was the best. I Chess player at the time But he was beat by IBM's deep blue and the level of complexity at that point in terms of The game tree complexity like how many movements would be possible over the course of any game of chess Was ten to the hundred and twenty-three power. So it was fairly complex based on the nine by nine grid, that's that you play on a chest a different skill set was employed when Watson built the best Jeopardy contestants in 2011 which that's 14 years after Kasparov Within 14 years it was able to beat the contestants this time not on a Not on a Static playing field because it wasn't a nine by nine grid. This was actually, you know far-ranging Trivia so it could be pretty much anything that would come up and the way Watson one was by a essentially taking all the trivia possible into a kind of like a blunder and Just absorbing it and being able to cross-reference all of that information within fractions of a second and And provide an answer In 2016 which now this is only five years after Watson beat the Jeopardy contestants Alpha go By Google DeepMind beat the best go player With now a game tree complexity of 10 to the 360th power. So this is orders of magnitude more complex than chess and Was a breakthrough Just a year ago. So how what is an expert system? How does this relate? The father of expert systems is Edward Feigenbaum and What he defined an expert system as was a knowledge system Which would allow? the computer to offer advice and make decisions based on Inputed information from a user And the interesting thing to me is that if you look at this, you know, expert systems were first created in the 70s you had You know big computer systems like this that were used as far back as then To provide Basic means of accessing artificial intelligence and in some ways it was it could be applied to fairly complex problems But the way it was being applied then was rules-based Entirely it was solely rules-based so it would It would be along the lines if this happens or if there's this condition then you apply this rule if This rule is applied then This subsequent rule applies and so this was first introduced at Stanford and When Feigenbaum was talking about how this would progress over time He talked about different eras of artificial intelligence the first of which Was very rigid When you were using that old-school system. We just looked at it was very rigid in terms of the way you communicated with it If you got the wording wrong it would it would reject that inquiry He foresaw the second era as being one in which speech would become the way that you interact with With with computers and gain access to a knowledge base for shadowing this Which I'm just actually just getting a few of these I'm kind of a late adopter on this but getting a few of these for my my place So what is the chatbot the chatbot?  We've been doing this for a long time. They're called expert systems I think the difference is in terms of the immediacy immediacy and ease of use many people are saying the chatbots are the new apps and That's because they're exploding in popularity and they're available everywhere some examples of them are an app like this poncho if you haven't tried it out you go on to Facebook messenger you type in poncho it brings this up and then you can ask it, you know Hey, what's a weather like or it'll actually auto recognize where you are and Be able to feed back to you what the weather is and whether or not you should wear poncho out if it's raining another chatbot that's an example of How you would use it commercially is one 800 flowers where you could type in that you go into a birthday party and It'll show you what's available for that You put it in the address of who you want to deliver it to or maybe pick it up and when and It will complete the order for you by just grabbing your credit card information that you may have in Google Wallet already Health tap is an advice type of chatbot where this one It's it's a little more intelligent because it uses natural language. It allows you to type in Something like you would say to a friend, you know my my kid has a fever and a rash on You know on her her neck You know, what does this mean like what what kind of problem might that be and then it it will grab that general Description find the keywords in it Feed the keywords through its database and give back two or three different Diagnoses or two or three different Ideas of what this might be of course the idea is that you take this to your doctor and not rely solely on health tap But you get the idea So artificial intelligence and chatbots It basically works like this First you have an inquiry from a user as stated here, you know, when is this next train coming by? That request is fed into a natural language processing tool like Watson for example There's different like natural language processing tools out there. There's Watson. There's Amazon Lexa Excuse me Amazon Lex That they use to create Alexa There's API dot AI and There are many many others that are more off-the-shelf Solutions for for natural language processing, but IBM Watson and a bi Excuse me API dot AI And Amazon Lex are probably the more popular ones So it takes the question and breaks it down so that it understands What is being asked in that question? It creates this structured data in number three that the person is asking About this local train and what is it? You know, when is it coming? It feeds that request Into a database step number four and then retrieves the information. Oh, well the next one leaves at 437 What do you like to try another? So that's essentially how the circle of request works in In a natural language processor So rule-based AI, which is the kind of AI that We were talking about an expert systems wrote it runs like this basically you could create a flow flow chart out of it and Run down all of the possibilities and most Most chatbot developers actually employ the system They'll create a flow chart diagram of how the conversation is going to play out so they could find Dead ends and then fill them with actual responses or help menus Well help texts so that the person won't get lost or if they do get lost It'll direct them to restart the dialogue or something along those lines now machine learning AI It's not an either or proposition in many cases the machine learning is being used in conjunction with the rules-based AI But the bet I think the best example is is natural language processing You can use synonyms of so for example if you programmed into a Conversation in a natural language processor that the person would be asking for some water or for a coke or for a Drink of some kind it would be able to abstract the person's asking for a drink so that even if they didn't give you the very specific answer That that you've programmed into the system it would be able to use machine learning to abstract that the You know if somebody said milk it would understand that they were talking about a particular drink and categorize it that way So this question comes up a lot, you know why now like why does this make sense now? Why is this so popular? Why are we reading about this in the news? so much and that's because we've reached a critical point where it's become cheap enough easy enough and widespread enough that The popularity is exploding because there really isn't a limiting factor and part of it part of the interest comes from the fact that messaging apps have just They've outstripped social networking apps at this point. So With that kind of You know that kind of interest there are many people on these platforms that are available for They're used to messaging, you know everybody this probably is obvious to you but everybody lives on their phone, right? You're on a on a bus or you're just walking around everybody's looking at their phone. Well, it's because messaging has surpassed phone communication and voice communication The other reason is that many of the main players have gotten involved in chat platforms. You have Amazon, Microsoft, Google, Facebook all jumping in and creating their own platforms for the creation of chatbots and artificial intelligence and I remember reading that Google is now making itself not mobile first, but AI first in terms of its Vision of the future of the company and then amongst the messaging apps that are available there's intense competition between you know, WhatsApp, Facebook Messenger, WeChat and This chart is a bit old but Facebook Messenger is now over over a billion monthly active users a billion monthly active users. So there's a there's a huge demand For messaging and a great opportunity in terms of using chatbots to meet that demand This is also somewhat old at this point from November of last year Now Facebook at its last conference I think it was two months ago Mentioned that they have over a hundred thousand chatbots that have been created on their platform and this is just a map of some of those Bots and platforms and a natural language processing Apps that are available All right, so we've been talking a lot about it, but how do you create one of these things? These are some of the platforms for creating chatbots The one that I would Recommend that you that you try out first because it's just easier and Kind of fun to use is chat fuel the one in the upper left-hand corner It is free and you could just jump on there and create something on your own pretty quickly This is a video you should watch this kid named scoot Seven years old. He actually created his own conversational chatbot using API.ai. So watch out for this kid He's gonna You know come up on all of this pretty quick But get him back to chat fuel. This is what chat fuel looks looks like and you can see it's a pretty A pretty basic way to work on it. It breaks it into blocks So you have these blocks on the left-hand side where you could define You know different Conversations based on it being help that's provided or stickers or a menu And then you can feed in different cards You could create a gallery of different images. You can create a text card That just has some text on it or feed in one image And then on the right side you see what a welcome message looks like as soon as the person pulls up your Chatbot, which in this case would probably be on Facebook It would and this is kind of cool It grabs the person's first name from Facebook because of course Facebook knows your name So it just says hi John. I'm an AI based assistant for TechCrunch and it goes into the whole thing So that's what would come up for someone if they just went on the TechCrunch's chatbot this As compared to chat fuel this is API.ai and So this is a little more complicated. It's a one that I really like it Let's you do a little more and let's you be a little more specific about what you What you're trying to say on the very top Where it says cheer me up. This is basically you're naming an intent and an intent is The intent of the user who's talking to the chatbot So for example if the user were to say to the computer. Hey, cheer me up This intent would respond to that and so That's why here Where it says talks about the trigger sentence if the user were to say cheer me up then you get to define a response to that oops and Then you can also add You know machine learning to this so that depending on the responses it gets it could learn What common phrases are or categories that you that the user uses in communicating with the bot? And at the very bottom you see speech response That's where you actually get to define What the bot is going to say to the user so here? I just want to show you Been talking kind of theory here. I want to show you Something practical So this is API.ai And if you log in I created a real simple bot Just to show you how it works This one's called joke bot and so the first one This intent is defined as tell me a joke Okay, so here We write down the user says tell me a joke or Maybe the user says make me laugh and So the intent is that the user wants this computer to make him laugh or her laugh and then we go down to the text response and We type in knock-knock, so we're gonna be telling a knock-knock joke this here Won't get into it too much, but the this context is telling the next intent that The user has gone through this process here And so we'll save that then we jump into step number two and so you could see from the Incoming context that the person first went through the first step and now is it this step the second step Which is who's there? So the user would say who's there and Then the computer would respond lettuce And you might know where this is going But if you jump into joke bot And let's just refresh so that We can make sure the bot is awake Type in make me laugh This bot by the way, it literally took me, you know about five minutes to set up Just going through these different steps and setting up the punch line and the prior to intense So I know this is inapplicable to Legal questions on its face But if you think about it, you could feed in frequently asked questions So somebody says hey, how long does it take to get divorced? You could have this that answer standardized and Built into the system to respond and it could work for anything like that this Bodgeroid, this is the chat bot that Miguel had made reference to before That I that I created with some developers and This is on Facebook Messenger and allows somebody to just go through this dialogue and essentially they can incorporate a Corporation in California for free So they just go through this And answer a number of questions and as you saw the very first The very first question will the very first Disclosure from the chat bot is look. I'm not a lawyer But those are some of the possibilities that you have with with chatbots and let's jump back on to the presentation oops Second alright, so when you put that chatbot together This is a typical type of diagram where you would want to diagram all of the various ways the conversation can go and You can find all the dead ends and also also it helps you to understand and anticipate Additional questions you might want to include for example if you're explaining the divorce process There's certain questions that might come up that are pretty standard that somebody would ask and you could fill out a Workflow like this that helps you to map out the conversation So this kind of begs the question about the ethics of chatbots and Not specifically with regard to Legal right now although there are a number of issues that could come up in terms of potential confidentiality issues, there's issues of UPL right which was addressed by the bot saying I'm not a lawyer Don't rely on me use the lawyer if you need a Specific opinion, but this is more More general you might recognize this guy. This is Ian Malcolm from Jurassic Park And if you remember from seeing Jurassic Park You know at this point in the movie he's asking Why you know scientists created Dinosaurs like why? That they're too busy Finding out if they could create or bring back dinosaurs They weren't asking themselves whether or not they should and so that kind of moral question is one that comes up Which with chatbots or at least in the future it will come up more Show eyes is a chatbot created by Microsoft and it is it's exclusively available in China and It's very Good at holding a conversation and being able to have us, you know an average Conversation that sounds pretty good and it's pretty convincing and may pass the Turing test that we talked about before And if you have been reading through this dialogue that's on the page right now you could see that this bot is fairly clever and And knows how to keep keep a person's interest now that the moral issue that comes up I Listen to a presentation by Tommy Lewis who's a tech evangelist for Microsoft and He does they do a lot of projects for chatbots right now and one of the issues that came up was That they that they found out that there was I believe an eight-year-old in in China that Actually, I think it was a ten-year-old, but he had been talking to this chatbot show eyes between 68 hours a day and They were trying to figure out what was going on and it turns out that this this kid is a latchkey kid so he doesn't have you know much social interaction and show eyes is really you know the majority of his social interaction and Talking to the bot six to eight hours day. It makes you think that you know at what point does Do the developers of the bot have an obligation to this kid? You know, what if something were to happen? What if there were a fire? What if you know some stranger were to come into the house and during a conversation, you know should show eyes be able to to help In some way another example of of this kind of ethical issue is and I apologize Upfront for some of the language on here, but Taitweets was another another bot that was released by Microsoft and And what this bot did basically was using that machine learning to learn from the people that were communicating with it and It gave what it got so over time it went from as you could see in the upper right hand corner Hey humans are super cool to just devolving into this Really angry bitter hateful thing Because that's what it was getting from the people that were interacting with it on Twitter So this is Tommy Lewis that I referenced before and he has set down some Principles for AI that he employs with with Microsoft in in how they service their clients and his number one rule is that he will only design bots to assist humanity not to hurt it and The most obvious example that that he had was you know about taking away people's jobs So he gets approached by By people and say hey, you know these chatbots are great I run a call center. I can eliminate everybody's jobs and just have the bots you know do all the question answering and and He wouldn't take that on because you know one of his first things is look This is a design to assist people not to get rid of people Another principle that he has is informational transparency that That information should not be opaque in the sense that when you go through a chatbot dialogue a lot of that Information can become opaque because you can't you can't go back in and change your answer You know you can't Kind of change the path unless you completely restart the dialogue So there's some concern there about that opaqueness Another principle is to maximize the efficiencies and preserve dignity. This is kind of along the same lines as his first point Number four to ensure privacy so that When people speak with a bot and they have a reasonable expectation of privacy that that is kept That's a promise that's kept number five is to have accountability so that To remove some of that opaqueness if there's something that goes wrong that you can That you can look back into that dialogue and you can pinpoint the point at which things went wrong and to hold accountable The people that coded that so that they can correct it and make sure it doesn't happen again last point is Just like you saw with a tweets garbage in garbage out so if if if the bot is programmed With bias it's gonna it's gonna show it's gonna present with bias and discrimination so we have to be very careful to question our own assumptions and to look at what you know what kind of You know preconceptions we might be building into a bot and to try to avoid Avoid doing that in a way that could be hurtful to people so what does the future hold especially in relation to? the law well I Put this up here because What we have right now? No, these are not the droids you're looking for yet But the concern I get what the concern I have is that I get this response a lot of times from people where they say you know these chatbots these You know personal assistants like Alexa they're not perfect yet You know it doesn't do this that and the other it's not perfect And they don't say that explicitly that it's not perfect But that's more or less what they're saying is that it doesn't do everything for me I call it the panacea objection and People say it all the time in reference to different new technological innovations and so of course they say this for bots as well and my response to that is Don't make Perfect the enemy of the good There's a lot of good that chatbots can do as long as we keep in mind that they're not perfect and that There are situations that we need to guard against I think that we're just starting out and the future is very bright part of that is this The actual performance of computers versus the that of human beings is at this close is coming upon this inflection point where the generalized intelligence that I made reference to before is going to become available to computer systems and and at that point You know we're gonna have to be keeping up with with computers in terms of their intelligence and It being able to do things much faster quicker and more accurately than we can now it can do that already in a lot of things But what I'm talking about is is judgment and reasoning And so that point as I'm sure many of you know has been referred to as the singularity and It seems to always be projected within the next 20 years, but given the amount of progress so far I think that's That's pretty pretty likely if not sooner than that this article Pretty recent two months ago I JP Morgan it employed It employed Some algorithms to basically do do due diligence on a lot of documentation and The review of the the documentation would normally take about 360,000 hours of lawyer time 360,000 hours over 41 years and that would be one lawyer working all day all night for 41 years so What this represents is the kind of You know volume review that normally back in the 90s would have hundreds of junior lawyers working on it to review all of this documentation and This was taking this was handled within seconds by This artificial intelligence machine learning algorithm that JP Morgan Chase used And this is a sobering fact. It's especially a sobering fact. I think for more established lawyers because One of the critical ways that they earn money is hourly billing the billable hours You know the the center the centerpiece of profitability for most firms and the thing is is that with this You know, I've had I've had conversations with more established established lawyers and they and talking to them about AI and they say well You know that sounds nice, but I built by the hour You know if I do what you're talking about then I'm I'm not going to make I'm not going to make money that way and Yeah, that is true but if if somebody who's hiring a lawyer knows about this which Either they You know do know or they're gonna know very quickly You can't hide from it. And so you have to take steps to Get with us to get ahead of it. I mean I What you all have the opportunity to do here is to is to play around with these things You know create a chat bot Think of how you can apply it to the work that you do See how you can change things to put yourself ahead of this become an authority become an expert be able to talk to To lawyers are still trying to catch up So that they can understand how they can best use this and it also opens it up for access to justice because There's a lot of use cases where People simply can't afford lawyers and lawyers You don't want to do the work because they're just not going to get paid What they believe that they need So if you create chat bots that can service people that Can do it, you know it could provide that service either for free or at a very affordable rate many many millions of more people are going to be able to have the advantage of legal counsel or at least legal information and advice that they wouldn't otherwise have at all and So that's the opportunity that's available to all of you And that's the end of my presentation I think it's a bright bright time for you guys and I look forward to any of your questions and See how I can help Great presentations This is super relevant to a bunch of the stuff that we're doing here at Northwest Justice Project We are working with Microsoft on a chat bot that will be based on our Washington law help website which gets over two million hits a year and I just got done with the conversation last week over our attempts to automate the 200 new family law forms that were just done in plain language in the state and Unfortunately, they scoped out chat bots from the initial request for vendors out of Really extreme fear of unauthorized practice of law. Could you talk a little bit about whether This claimers are enough and what are some of the other strategies for trying to Deal with that particular objection because I see this is just an incredible piece of technology that It could help people so much more than the traditional branch tree logic interviews Yeah, and I I totally agree I The way I see it is that The biggest test case of all of this Has been legal zoom. Okay. It's it's made such a contribution to To this area by way of its own litigation that it's it's had over the years with various you know state bar state bars and so When legal zoom got out there and it started providing its It's web-based you know legal Solutions and a document automation and all that of course that question came up in many jurisdictions, you know this is the Unlawful practice of law. What are you doing? You shouldn't be doing this and legal zoom very successfully made the argument and It had to fight very hard in a few jurisdictions. I think North Carolina was especially one of them, but You know made the argument that this was the same as as no low self-help books or or software like TurboTax and that As being software It it didn't it didn't trigger any of the UPL Concerns and I think part of that Has to like to a certain extent you can Still make the argument that well, you know, what is the practice of law and whether or not it should be broader Then then the current definition is on the other hand. There's a lot of I Think there's a lot of strength to the fact that it's so, you know It's a market-based argument saying this helps people people want this help and These rules are in place for one fundamental reason right these ethical rules are in place to protect the consumers and So if that if that fundamental concern isn't being triggered because the consumers are getting something they want They're being held to the affordable rates and they understand that this is not a lawyer type of service Then everybody's winning You know the the company's winning the consumers are winning. It's not it's not provoking Any type of injustice or harm and I think that same argument would be applicable to chatbots No, I really like the the outcome-based approach and that we should give consumers the choice because then it's less about loss of Jobs or market share and more about meaningful access to justice for our clients Exactly and you know the only the only real counter argument Right is hey while this is taken. This has been taking potential work from me as a lawyer, but You know the lawyers the professional monopoly that that lawyers that we have It's not it's not in place for the purpose of making You know providing us with a living although I wish it would continue like like that and definitely it'd be nice But it's not for that purpose. It's it's sole purpose is to protect consumers and So that argument it just doesn't fly and it especially doesn't fly in Jurisdictions like my home jurisdiction in California Over the years it's it's proven itself to be very friendly to Two companies like legal zoom and also creating new categories of of like document assistance where You can't have people that have not gone to law school and have not been licensed prepare document legal documentation for For consumers so long as they you know make sure that they don't provide legal advice But then there's also a question there as to where does that line cross in terms of what is legal advice versus legal information? So somebody wants to start playing with this technology What is the one first resource that you think that they should go to is it an article? Is it a website? Is it an interactive tutorial? Where should they go to get the hands-on and start playing with it? Yeah, my first recommendation is is a chat fuel and by the way, I'm very sorry I just know I just figured out how to see the questions and I'm happy to run through them if that's okay, or how do you prefer I do it? Definitely if there are some new questions here, I'm they're not shown up on my screen, but if they are there go for it. Oh Some of my people the prior one hand raised who has hand raised also does have a question to ask Which I can unmute here Yeah, okay, so as attendee is self It's muted by an organizer I'm attempting to unmute and it's not working. Oh now it's working. Yes, we can hear you now Hi, so I guess my question is since it seems as though if you're creating a chat bot you also I mean to a certain extent we can kind of create a closed universe So when it comes to navigating The possibility of like violating someone's privacy, right? But also trying to collect enough data so that way you actually can create like a meaningful Conversation and make sure that you're like exploring as many avenues as possible when it comes to the questions that might come in okay, so I Mean is your question about like where do you draw the line for privacy? Yeah, that's my question Well, I mean the the line You know metaphorically and literally is drawn you know in privacy policies and If you define the relationship at the front end as one where it's not an attorney-client relationship then then that you know gets you to a certain extent opted out of the the Attorney-client ethical rules for for in terms of privacy and then you can use a privacy policy to define that relationship as does any other company You know, it's a matter of disclosing clearly what the information can and cannot be used for I think even if it were You know attorney-client privileged information That the general in like the non identified non identifiable information could be used I mean lawyers do this all the time when they talk to each other about cases. They're working on And they try to get you know advice from each other about how best to approach a case They of course aren't specifically identifying who the client is But they talk about the you know the part of the case that they need Assistance on and and in the similar in a similar way you could use the general information about the case to abstract from that case and others like it What the best solutions are for similar cases? Does that answer your question at all? So another question here with these algorithms They're often a moving target the traditional idea to audit them has been To just open source C code and then look at it But if you've got algorithms that continue to move how do we check those for? for accuracy for outcomes and for transparency to make sure that they aren't Reinforcing the norms or biases of material or case stuff that we give them Well the key to that lies in the fact that That You're creating a large amount of data with each interaction in each conversation And actually if you think about it like at first blush It seems like it can like a like a fear that is Intrinsic to this kind of interaction like a body interaction or conversation but if you think about it there's been millions of times that People have talked to each other like an attorney or a let's say a junior associate might talk with a client Where there's been some kind of interaction advice and possible use of bias But it's been absolutely impossible for those interactions to document or understand how You know how that happened or why it happened or correct it until you get a big You know usually what happens is you get a pretty big complaint right from a client and then You try to correct it in that way well in this way It's it's quite different because every single interaction between Consumer and the bot is documented and every single Algorithm in terms of like what what was the intent you know like we looked at before with an api.ai What was the intent that was applied to that specific request from the user? What specifically was the response of of the bot to the user based on that intent at what point in the you know chain of dialogue did that occur Did machine learning Was that employed on this particular response? And if so, you know what assumptions were made in Employing it and and so what what do you actually get from this? this approach is a Deep dive into what exactly happened you have the data available you can figure these things out Well as best you can like much more so than you could in a you know analog version of people just talking so I I Think the system has the ability to build in much greater protection than than the traditional way So we collect all of the interactions and then create an ethics bot to go back and look at whether It's acting ethically Well, if you're you know if you're doing it right and if it is a Bot that is subject to all the attorney ethical rules Then hopefully you would be baking that stuff into the bot in the first instance so that it would Have these you know flags that would come up or it would have concerns that Maybe if it if it hits some complex Situation that through an ethical red flag it could basically hand that over to a lawyer to handle if it was the kind of Bot that was subject to attorney ethical rules I have a question Tom with the advent of growing machine learning you know artificial intelligence like bots and and just a lack of diversity in the legal profession Can these bots Like how how concerned should we be with these bots carrying? Implicit bias. I know I've read some stories about Racist algorithms and the criminal What is it pre-bale? Context and you briefly touched on it What is this how do we prevent it? I know you you kind of touched on it, but Is it just the inevitable for for implicit bias? I think to a certain extent it's inevitable because many times people You know Good meaning people are they're just not aware of their bias. It's just you know, it's some Many times something that people just aren't consciously aware of so Just by force of reasoning if they're not consciously aware of the bias then that bias is going to find its way into You know certain underlying assumptions that are made and programmed into into code and And even if it's not programmed into the code if if the actual algorithm Includes a feedback loop like we saw on Tate tweets Then it's possible for that Feedback loop where the data is being is driving the evolution of how the spot applies Let's say the lot of facts Then it could change over time too. So it's not it's not like a one-off Question right it's not just about coding the first time it's about maintenance and tracking and and I think the answer to all of this is as I mentioned before it's it all it all Comes down to the data And that's one huge different Differentreater that we have now versus the past is that you can look at the data and you could see What are the actual outcomes? Who is this affecting? Does this look right? Does it you know and where you could actually have people making that call and and Then doing their best to keep things going in the right direction Did I answer your question Miguel? Yeah. Yeah, thank you. You're welcome Anybody else I Believe I've been to her. She has another question Oh, never mind. Oh, well, if there's no more questions Let me check the Questions one more time Yeah, if there's no more questions, I just want to thank you again Tom for taking the time. Oh, we got a question in it Oh, never mind All right, is this Hey, Brian, is this a new question or do we already I? Believe that was the response to whether the previous question was answered. So I think we're good Okay, perfect. Well, thank you so much again Tom for taking the time out Really appreciate it and we're definitely going to Be connected and follow up with you over the summer There's going to be a really cool project over the summer where the fellows will be Creating their own chatbots that's awesome and We will definitely look forward to your advice Yeah, and if anybody wants to you know connect with me on Twitter my handle is Lodroid 1 as you see here on the slide and You know feel free to Follow me. I'll follow you back. And then if you ever have any questions, I'd be happy to help Thank you Tom. Excellent. Thank you so much. I really look forward to doing more stuff in this space Cool you too