So, welcome to another weekly e-charting analysis webinar. We're on the 20th of June 2016, and it's the final week before the big one, before the Brexit referendum. And obviously it's roiling markets. We saw a big rollover, as I assure you know, last week, but right in the final couple of days, we saw everything turn around. And that's getting extended today, big time, looking especially at the British pound, also the FTSE 100, seeing huge gains, biggest one-day gain in the British pound since 2008. So those who are predicting volatility, yep, you've got it right there. Calling direction, definitely slightly trickier, it's really turning on the flip of a coin at the moment. So that's our risk warnings out of the way. So I'm going to jump straight to the British pound. Apologies to anyone outside the UK, but I think probably no apologies due there. You know the Brexit is obviously front and centre in markets at the moment. This is a chart of the British pound. Now obviously this is a charting webinar, and I'm slightly biased towards discussing the charts and the benefits of looking at charts, but just look at this, look at how markets have reacted from our support here. Now I've gone through these two support levels here, down here, but really if you had your buy order in and around the low, then we've just spiked 200 pips in a single day from that support. Even if you'd just taken profit there it was a nice tidy little trade, but if you'd held on two days later we're now at the top of the range again, and we've basically paused for the moment, bang on the initial peak that we made back in February. So we're just trading right back and forth within the range here. And you can see that as the polls were, you may have seen from our weekly email that I sort of did a little chart narrating how the polls had moved sterling around. During this period of time the polls were mostly favoring remaining in the EU, and then it was during this sort of period that we saw a sharp downturn, big turn in volatility, obviously getting closer to the vote as well. Polls started to show favoring a Brexit, favoring a leave, and now just over the weekend we've had this big gap through the support zone here, I'd argue, to push us right up to the top of the range. So 144, kind of the middle of the range of this 140 to 147 that we've been in for quite a while now since we've had that first drop down. So I know this is obviously very counter to the current momentum, but my assumption still here would be that sometime between here, maybe not right here, it's possible, but sometime up towards the peak that we saw on the 3rd of May, right in this 147-60 type vicinity, there's a good chance that some sort of poll comes out in favor of leaving again and markets get rocked and we've rolled over again. So very tempting to go with the current momentum, but we are still in a trading range. Now that, obviously, any trades at the top of this range here could obviously go wrong, but the nice thing about a range trade, especially a nice wide range like 140 to 147, 700 pips, is that you can take trades at the top of the trading range with a low risk to reward ratio. So you have your trades near, your shorts near the highs, if they go wrong, you just have your stop above the high somewhere, obviously up to your decision as to how much above the high, the higher you have your stop, the less risk there is of getting stopped out, but obviously the bigger, greater financial risk you're picking yourself under. But if we did drop down towards the bottom of the range again, that's 700 pips on offer for risking a small amount. So that's the nice thing about a very well-defined trading range. Obviously, it may not go the whole 700 pips and you have to manage the trade according to your system, often with the benefit of hindsight, this trade at the bottom, best holding it to the top of the range, but sometimes that's not the case. We easily could have capped down here had the polls been different, so there we have it. We're in range trading conditions, also worth noting that we're back here, which hasn't worked too well in terms of here, but we broke down from this rioting trend line. We're back here testing the underside of this raising trend line, and we're back up above the 60 mark, and you can see here that about 65 has been resistance in the RSI recently, so maybe a little push up to 65 into this rising trend line on the RSI could be another layer of resistance on top of the top of the trading range and the 200-day moving average. Let's have a look at Euro sterling as well. Another good example of a good technical level. When we've obviously had this resistance sewn on for a while, the rather kind of, you know, and I said it at the time that the head and shoulders pattern was tricky because there really was no obvious neckline, so the height of the shoulders was very nice. So here we have left shoulder, right shoulder, but we're looking at this. Where did we have the neckline? So if you've taken this as the breakdown, maybe you've done OK here, but if you've taken this much 10th low as the neckline, pretty much faked out on the breakdown here, and we've gone all the way back up. And then we were talking last week extensively about these long tails beneath the 80 level, and eventually that selling pressure, which is over in here, where we tried to, you know, buy as a push the market up, the sellers have pushed it right back down again, having four days in a row. Eventually, we closed down here, that was the lowest of one, two, three days, and then we just broke down again on Friday, and then obviously gapped lower, matching that rally in the British pound today. So there isn't quite the equivalent in terms of support level as well defined as the 79 level, and it's perhaps on these sort of occasions where when the support is not so obvious, you know, I've obviously drawn in the 0.7650, which I think, you know, does seem to have acted a bit of a pivot since it was reversed strongly on that March 10th. That would be an area to consider, otherwise the tailing stopped. Forgot to mention before that any questions at all, obviously, feel free to send them through the Q&A or the chat box. So let's keep the focus Brexit here. So let's move to the FTSE 100. So again, quite, I mean, it's very choppy trading and it's certainly tricky and anyone who had been trading this head and shoulders pattern had a luck here. The objective of that head and shoulders was down below 5700. We obviously not met that whatsoever. We've run into this support zone in and around the 5900 and rallied pretty strongly from there. And it would appear that if we're maintaining, say, this is the support zone around the 5900 and this is the resistance zone around 6500. It now looks like we're going to push back towards the top of the trading zone. And trades in this area, which are basically in the middle of the trading range, have just a smaller probability of working out. Because we're not really in a world of fine trend here. You can certainly trade on us getting to the top of the range. But say if you do place a trade here around 6200, 6250, your downside to the bottom of the range is right down to 5900 again. But if you're assuming we stay in the range, the upside is only up to 6500. So your risk basically equals your reward. So you're more than likely to come out behind if you consistently take those kinds of trades. So that's UK markets. But if we look over to the US, which is also rallying alongside the general improvement in risk sentiment today, it's a little bit more bullish, a lot more bullish. This is the, we've had this on the chart for the last few, well, many, many webinars. This is the record peak up here on the 18, just above 18, 300. So we've had this nice bounce off the broken falling trend line. And now it looks like the market's going to have to challenge. I've got the 179.30 in there because that's been respected a few times. But really, 18,000 is the big one. We need to break back through 18,000. And then that opens the door to those record highs again. Still, at the moment, we are in a trading range. We made a lower low here. But we've obviously made a higher high. So we're kind of chopping left and right. There's not really a too well-defined upper barrier. I'd say it's just very choppy around the 18,000 mark, which is also the peaks from back in November. So quite a wide range in terms of risk since this is this peak up to the record high. But you could equally say there's another range from here down to 179.30. So in terms of quite where the breakout takes place, it's tricky when you have such a wide resistance zone. So I would say the most clear cut breakout to a new trend would be a close for the week above that record high, which is still a fair way from where we are now. And I think below there, still like the 5100, even though this chart looks more bullish, is for me, the fact that we're still in a range adds the bias to the downside. So just be aware that even if you're taking some short-term trades going along the market with the momentum at the moment, be aware that we're still in a range, in my opinion at least. And that adds risk to the downside in and around the 18,000 mark and above. Obviously, I haven't really mentioned any market events barring Brexit. So I should say that my opinion, I've been saying it all long, is that in the end, we vote to remain. It's not a personal opinion or anything. It's just how I see it playing out. And that's obviously being reflected in the market at the moment, but that can change quickly. There's a lot of people who share the opinion that the result will be remain. But if you've got a big position, you're a long investor, long the FTSE, long stocks in general. But you see an increasing risk of us exiting, which you consider a downside risk to market. So I think that's a fair assumption to make that the pound would drop, FTSE would probably drop if we did vote to exit.  You're basically doing some hedging trades. And so you're buying put options, or you can be on our platform, obviously, just selling the index, when maybe you have a portfolio of stocks and shares, but you're selling one of these index products, different ways of hedging. But obviously, as that poll came out over the weekend, saying that actually the remain camp was ahead again, a lot of people just peeling off some, if not all, of that protection that sterling in the FTSE may drop. So that all adds to the volatility here. I think one of the good things about this is very hard to call the reaction in markets in the lead up to the vote. But it will be very telling on how markets move after the vote. If we get some breakouts on that big news of the vote, that could have some good momentum behind it. So we could actually break out of some of these ranges that we've been talking about and open up some trending markets, which tends to be a little bit easier to trade. You know, when it's a trend, I mean, this is simple stuff, but always worth reminding yourself. When there's a trend, the direction is taken care of, you just have to time it. And a lot of evidence to say that really, you know, timing is not the main thing. When you're in a trend, you just have to get in there and ride it. Easier said than done, obviously. So let's just look at how Europe is reacting to all of this. Got the Germany 30. Again, in terms of, you know, very difficult to call exactly politically and economically how the Brexit will work out. But using the charts, working pretty beautifully right now in the Germany 30. We've had this support zone in since April with those two lows and it's bounced perfectly from there. So, you know, if you just had a simple buy, resting order at, you know, in and around this April 7th low, you know, that's worked out pretty beautifully at this point. And we're just running into a bit of potential resistance, I would say, from this broken rising trend line in around the round number of 10,000, obviously round numbers themselves, a bit of a cause for resistance. Bit above there, we've got the 200 day moving average. At about the 10,100 mark. So 10,100 in itself been a bit of a pivot in the past. So round number slash trend line resistance around 10,000 and then 10,100 has been a pivot in the past and the 200 day moving average. So definitely some strong resistance in those areas could mean, you know, depending on how polls work out, how sentiment changes, we could roll away from those areas or if we do get a breakout, you know, any short trades are not going to work out, but obviously that would be very telling that there is some inherent strength in the market and we could push a decent bit further from there up towards that 10,500 that's been the top of the year so far for the Germany 30. Should mention the other big event this week, Science and Brexit that actually comes before it is tomorrow Janet Yellen is going to be testifying before the Senate Banking Committee Congress on Wednesday and she's going to be summarizing the Fed's outlook on monetary policy. The fact that the Fed not only stayed on hold but also were fairly dovish in their statement has helped this return to risk sentiment this week. You know, things started to turn last week before we had any change in the polls. Now, may be debatable to some extent that the death of MP Joe Cox has somehow shifted the sentiment. Yeah, I'm sure it's, you know, if anything, it's a negative for the leave campaign. It does tarnish the image a bit but how negative is fairly debatable. You would hope that people are not basing their decisions on that, but you know, some will be, you know. So that certainly played a part but you can't get too tunnel visioned, as I mentioned here in my note here in the insights. Also central banks are still as important as ever. So since we've looked at a couple of currencies already, let's go back to a good example of what's, you know, of how to judge the risk on risk off nature markets at the moment. So Dolly Yen, a bit of a tricky one here because they're both being perceived as haven currency. So both the dollar and the yen. So we're getting a bit of a pickup off 104. We've not had a close below 104 yet. Trend is still very much down and we're below the level that we've been highlighting for a while, which is a 105 round number, but also the 105 30. We've, if you notice here, where we found the support, not a coincidence, I think, that it set this peak for May in 2013, which is where we've rebound a little bit from here. But the rebound's been fairly shallow, pretty weak. And again, it's because, yes, as the market rallies here, it's dumping yen, but it's also dumping dollars, particularly given that the more dovish Fed. Yeah, absolutely, feel free to ask questions anytime. So while we're at it, I just haven't had a look at Euro dollar yet. Still in this very choppy long-term range capped by about 115. Again, Euro a little bit tricky because it's, you know, we're trading Euro sterling, we're also trading Euro dollar. So sterling rallying here, but dollar is weakening. So Euro dollar, you know, sort of sitting in the middle, just based on the assumption that sterling is kind of driving FX volatility at the moment, means that Euro dollar is kind of sitting in the middle of nowhere. This recent range has been about 111 to 114 and change, and it's basically at 113 at the moment. So not far off the middle of that range. I think, again, it's range trading conditions. So 114, 60 to 15 opens up some better opportunities.  And you know, it's going to be very short-term traders with much shorter timeframes that are going to be making those kinds of trades, not for anyone with a slightly longer-term outlook. So let's switch gears to commodities here. I think my take on oil at the moment is that the fundamentals are slightly shifting. For a while, we've had slowing U.S. production, that's still the case, but we've had the rig count. So the number of oil drillers in the U.S. rise for a third week. So just a bit of an indication that maybe some U.S. shale is going to come back onto the market and add to that supply glut. But I think more than anything at the moment, oil is just moving in sync with risk-taking and stock markets. So along a lot of occasion, at least intraday, sometimes the closes vary. So you'll actually see that the correlation between the daily close on oil and the daily close on stocks, not that good, but intraday moving a lot together. And so oil rallying here, is it based on any news about the oil market? I would say not so much. It rallied when stock markets rallied on Friday. And I don't think you could say it's any news from the oil market that's causing oil to rally, and that's what's caused stocks to go. I think they're moving up in sync. So if you are trading oil, certainly still be aware of what's going on with Brexit and the other markets, they're all kind of correlated. Again, trading-wise, I to be fair, have not really been using this trend line that worked pretty nicely here. I've been using this channel which kind of drops through this low here, goes through this low. So that was slightly lower. It had a correlation, a sort of combination of this pivot area here at 46.20, which I was eyeing. We didn't quite get there. We basically used this one, two, three, this pink trend line, and we've rallied up from there with a bullish and golfing candlestick. So one thing to note here is that even if you missed the use of that trend line, once you get that bullish and golfing candlestick pattern, so reversal candlestick after a short down trend from a known support area, then that is still a by-signal itself at the end of that daily candle. Gold, obviously, going very much to the other direction today, again, it's being a haven. Gold is a safe haven as well. And so we're seeing gold fall away from 1300 again. Gradually pushing its way up there with slightly higher highs, trying to get above that 1300 barrier, not managed to close above there yet. And I think the risk here is after that sharp reversal that we got on Thursday, is if we 1275, if you could see on a sort of an hourly chart a lot better, has worked to support a number of times here. So if we get through 1275, then I think the momentum could quickly start sliding to the downside. We've obviously formed a lower high here. So through 1275 basically signifies us making a lower low as well. One quick thing I realized I wanted to mention just in terms of the yen and havens on all that business, the Japan 225 pretty much moves in sync with Dolly Yen. And we're seeing again a nice example of rallying of support in a range. So we've had this fairly well-defined range here in the Japan 225. Worth noting that I don't believe I had this trend line at any point. But obviously there's been a bit of a reaction there, a close below, a test, and a drop again. Trend lines like that oil one where there's only two touches often don't work. So you had the buy there on the Brent chart, that worked well. You had the buy here on this Japan 225 didn't work so well. So don't use the trend line by itself, try and keep it in the context of the kind of market we're trading in. This is clearly a range, whereas oil is clearly in an uptrend. So that uptrend line is gonna work better. So yeah, to finish things off, we've got a bit of a question going on here in terms of the long-term direction of US 30 and FTSE. A long-term direction of US 30 and FTSE. Yeah, well, certainly you've alluded to it in that, I'm not sure if anyone can see that if it's gone to the group or not. Basically, if the UK votes to stay in Europe, that obviously opens the door to the Fed raising rates and raising rates is obviously a big concern to markets, especially when growth is pretty lackluster. But if we do leave, the uncertainty would cause a sell-off as well, so yeah, two good reasons either way for why the market would sell off. I guess, I tend to agree that there's some definite strong resistance above us in markets as we currently stand and it's gonna take a fair bit to get through there and it's hard to really see what can be the catalyst to get us up through those new highs, but I'm not sure that Brexit is the only reason the Fed is, the Fed has held off raising rates in the June meeting. I'm more of the camp that I think they see some genuine risks to the global and the US economy. Things have started to slow down. That 35,000, whatever it was, non-farm payrolls, you can't blame that on Brexit. That's just the US job market slowing down. And that's been the main reason for wanting to hike. So even if things look okay in the US economy, has the Fed left it too late? And do they want to hike rates just as we're seeing the best we're gonna get? Even though if the economy would be fine going forward, if it's slowing, that's not normally the time in which the central bank tightens policy. Normally it's when it's accelerating, is when they tighten. So if the Fed holds back, then the economy kind of chugs along, then that you could argue is enough for markets to keep moving higher because the Fed at the end of the day is still backstopping us. So that would be the counter narrative I would say to those two scenarios. I hope that makes some sort of sense. So I think we'll call it a day there. Thank you very much for everyone attending. Good luck with trading this week. It's gonna be certainly an interesting one. And let's see how we vote in the referendum. And I'll certainly be talking to you about it in the weeks ahead. Okay, thanks a lot everyone. Jasper Lawler signing out.


Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining the current installment of the Monthly DataVersity Smart Data Webinar Series with Adrienne Bowles. Today, Adrienne will discuss machine learning from discovery to understanding. Just a couple of points to get us started. A digital large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via the Q&A in the bottom right hand corner of your screen or if you'd like to tweet, we encourage you to share or highlight your questions by Twitter using hashtag smartdata. If you'd like to chat with us and with each other, we certainly encourage you to do so. Just click the icon on the upper right hand corner for that feature. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now, let me introduce to you our series speaker for today, Adrienne Bowles. Adrienne is an industry analyst and recovering academic providing research and advisory services for buyers, sellers, and investors in emerging technology markets. His coverage areas include cognitive computing, big data analytics, and the Internet of Things and Cloud Computing. Adrienne co-authored Cognitive Computing and Big Data Analytics, Wiley 2015, and is currently writing a book on the business and societal impact of these emerging technologies. Adrienne earned his BA in Psychology and MS in Computer Science from SGU New York Binghamton and his PhD in Computer Science from Northwestern University. And with that, I will give the floor to Adrienne to get today's webinar started. Hello and welcome. Thank you, Shannon. As always, it's great to be here. And for those of you that were on a few minutes ago, it's just great to be out of Atlanta much as I love that city. Last week was a nightmare. So let's get right into it. No need to rehash that. Everybody's seen the news about the airlines. Today we're going to talk about machine learning and the subtext is from discovery to understanding. One of the things I struggle with a little bit in preparing the my notes and slides for today is just how much math we were going to include, frankly. And the possibility went all the way from no math to, you know, little linear algebra, little calculus to doing the whole thing in equations. And so normally I would take kind of the middle ground, but I've just tried it. Yeah, I can't talk. Decided to try something extreme. We're going to do this with virtually no math. And the idea is to help people get an appreciation, not like a music appreciation like, ooh, you know, that's a good deep learning algorithm, I can dance to it. But to get us to think in terms of what problems are we trying to solve, how does this relate to human problem solving? And not worry as much about the actual numerical underpinnings. And I'll point to some things. And if people want more detail, certainly as always we'll give you an email address. We'll make it easy to follow up. But what I wanted to do today was really look at this critical area of machine learning and see where we're going without getting too bogged down into some of the minutiae. So my agenda is pretty straightforward. I'm going to go over a few basic concepts, learning, reasoning, understanding, et cetera. And then I'm going to talk about the two fundamentally competing, if you will, approaches to artificial intelligence in general. Then we're going to go right into machine learning and deep learning basics, sort of a mini tutorial, and then talk about trends. So without further ado, let's put some context in here. The title, as I said, is machine learning from discovery to understanding. I think it's important that we start out by having a common understanding of what we mean by some of these terms. So when I think about understanding in the context of artificial intelligence and machine learning and systems, it's pretty straightforward conceptually. You understand something if you have an awareness of the meaning. But one of the limitations that we have when we do webinars is you can't see how often I do air quotes around understanding. So I'm going to tell you right now that as we get into this, we'll see that the machines will help us understand a lot of things, but don't really understand much at all themselves. So the key phrase is here. Learning is to acquire this understanding. Understanding is the awareness of meaning. But the systems that we're most concerned with throughout the webinar series are the ones that can reason, that can apply some logic or some process to create value, if you will, from the data. So with that in mind, I've used this slide in a couple of different contexts in the past. This is a diagram that I borrowed from loop AI labs. And basically, the reason I use this so frequently is it's a nice graphical representation of the output of the system that has analyzed a volume of text. It was actually from Al Jazeera in Arabic. And looked for concepts and relationships between them. And in this diagram, even if you know Arabic as I don't, but you're a traveler as I am, you may see that there's a theme here and start to get some idea of what it was picking up. We've got 727, 737, 767, which to those of us that spend too much time in airports are all going designations, A300, Airbus. And as it turns out, the Arabic phrase that's in the, almost in the lower left, the highlighted area, I would not have known what that was, except at one of our conferences. So we spoke up and said, yeah, that actually represents airplane. And all of these other words and phrases represent aspects of air travel or flight. And the reason I use this slide so frequently is this is a great example of a machine learning system that has now gone through text. The system itself has no awareness whatsoever of the meaning of the text. It's looking for statistical relationships. So there's no understanding there, but there is this discovery of relationships that somehow all of these things are statistically related to each other frequently enough that they're pulled out and shown to the user, kind of augmenting the user's intelligence, to say these are concepts that are related. I don't understand what they are or how they're related. You know, you can drill down here. But I use this because so much of what we do in machine learning may appear to be magical, but it's really more mathematical. It's looking for associations. It's representing data in a digital form. Obviously everything ultimately gets represented digitally. But then looking for relationships between pieces of data that can be mathematically modeled and perhaps with the assistance of a human if we're talking about augmented intelligence, maybe then we describe being to it, or maybe we attempt to take it to another level and place a label on these relationships based, again, on observation and analysis of the relationship between how things are used, but the system itself at this point doesn't understand. And that's important. So let me go from that into the way humans do things. And we're going to look at how today's machine learning is inspired by human cognition, but it doesn't really mimic it. It doesn't really model it the way we do it. Oddly enough, some people are now looking at human problem solving in terms of machine learning. So it's giving them a different perspective, but I don't want anybody to think that when we talk about something that's machine learning that it's doing things the way people do. The important part here is looking at the scope and the scale. So when we're having a conversation and we're dealing with perception, perhaps it's via text. You're looking at it through your eyes or glasses, but you've got photoreceptors with 120 million rod cells and 6 million cone cells. Perhaps you're just listening in case of a webinar and you've got all these cells that are now changing the analog signal that you're getting from my voice, which somewhere along the way was digitized in the middle, but you're looking at that. But to actually learn from what I'm saying and to understand what I'm saying, now we get into the neurons and synapses and at this point the scale just goes crazy. You've got 100 billion neurons, 100 to 500 trillion synapses. We're not going to build a system that does the same thing, but we're going to be inspired by this and start to look at how we can change the architecture, hardware and software to model the way small, relatively independent processing units within the brain start to communicate based on electrical signals. And that's where we are right now. So I'm going to take that background and say something that hopefully you'll remember that I've been thinking about for a long time. And sometimes it's a terracy to talk in these terms. I think this is probably the most important thing, which is that it's possible, maybe likely, that we're going to build a deep learning system pretty soon that knows everything, knows everything there is to know, but understands nothing. I think it's important. I think it's going to be useful. But it's really mostly the point is that you can't assume that a system that exhibits the behavior of a biological system that does understand actually understands anything. So let's look at the major approaches. There are two twins to the spectrum for building these systems. And we'll start with the knowledge-centric approach because throughout history, this is really where AI began. I think in the first webinar this year I talked about the artificial intelligence as a discipline roots going back to the 1950s. The approach that was taken early on was to try and leverage expertise to create a representation of the problem area that we were studying and use the human expertise that would be captured in a system to map between that representation and some problem domain. And this is something that obviously requires a lot of effort. It requires smart people who understand the problems that we're talking about, whether the problems are tax law or medical diagnosis or weather interpretation to be topical. So upfront, the people who are building the systems have to develop algorithms or they have to develop or capture the rules that experts are using to solve the problems without automation. The advantage of this type of a system, and this is certainly the basis of things like rule-based systems or knowledge-based systems or expert systems, is that even follow generally established practices from logic, first-order predicate logic. And by doing so, even though it takes more effort upfront to build a system like this, you're capturing all that knowledge from humans separate from the data that you're going to analyze. And then when you make a recommendation or you have an answer, if you will, from a question, you can completely trace back and have complete visibility into what assumptions were made along the way to get to that answer. There's no ambiguity. There may be some probabilities associated. You may have a system where, let's say you're looking at a patient and they present with certain symptoms and your system is going to go through and look at all the rules, how do other doctors handle this, how is it viewed, everything has been digitized, now I can look at all the records. And maybe it says, I think there's a high probability that you have condition X. You would be able to interrogate that system and say what part of the data led you to that conclusion? Where did you get that confidence? Everything is evidence-based and it's perfectly visible. We're going to contrast that with what we have today as sort of the leading approach, which is using data-centric systems building approach with deep learning. So in this case, rather than using the human expertise to do all of the mapping and create the representations, you're, depending on having more data available to you, as I say here, let the data drive the process rather than have the process interpret the data, the data actually creates the process around itself. And the danger here is that these systems fairly quickly turn into a black box. You can't then go back and say, well, how did you arrive at this conclusion? And in some cases that's okay, you know, as long as the answers are in general better than or certainly sufficient to make your decisions. Maybe you don't need to go back into it, but there are going to be disciplines like in medicine, like in financials, in accounting, if you're giving somebody advice and they make a bad trade. So what I want to do is look at kind of how we got here in terms of going from being completely focused on human knowledge to automating the process, if you will, with an approach that's more statistically based than knowledge based. It's more based on attributes of the data than attributes of the designer. And to do that, we need to spend a few minutes on the idea of representation. So here I've got a taxonomy that should be relatively familiar to anybody that's played 20 questions. It's loosely based on an early view of the hierarchy in the world of nature. So it's something animal, mineral, or vegetable. And the point of using this as a simple example is that a lot of what we have in terms of human knowledge can be represented in a graph in a hierarchical form. And so the thing to keep in mind here is if we think of this as capturing knowledge and capturing the relationship between concepts, then as you go from nature down to animal, down to mammals, down to primates, at each step, each time you're going lower, you're going from something that's more abstract to something that's less abstract until you get to something that's absolutely unique. If you will. So if we start just all through the red line, an animal is something in nature, it's distinct from mineral. It's very important here that in a real graph like this, you can't have something that's in more than one category at the same level. You can't be both an animal and a mineral, for example. So now with animals, we're going to look at mammals within mammals. We're going to look at primates, hominide, down to humans versus chimps. At each level, you have all of the attributes in common with your peers that you would hear from above, all the behaviors.  And then we get down into a specific example. And statistically speaking, somewhere on the call today, I haven't seen the list, we have someone named Bob and that's where you are. That's how we got there. So we're going from something that's very abstract down to something that's relatively concrete and something that's very specific and individual. If you think in terms of object-oriented programming, for example, we've got a class of objects called humans and we've got an instance of that object called Bob. Bob has uniquely identifiable characteristics. It's coming from the top down. Okay, so we can obviously represent this in a data structure. Don't need to do that today, but if you have any questions on that, you know, certainly follow up with me. But I'm just going to assume that we're putting this in there somewhere and we've got some storage for all of primates where we have all of their attributes. And that links to everything at the lower levels, links up and inherits. One of the talks I gave on natural language processing, I used an example like this. The reason I'm using here is, again, it's about representation. We can have the same words used in different contexts. And if we're trying to understand them as humans, we need to know how they're being used. And so obviously we also get into things like analogies and sarcasm. But let's for the moment just go straight through language. Words have meaning. Words have structure. So we have the syntax, which is the structure. We have the semantics, which is what they mean. So we're trying to build a system that can learn from usage how to either correct or complete when you're typing. Maybe you're typing a text and your cell phone is doing a look ahead and trying to predict what you're going to do next. That's based on a representation that has the words that you've used or the words that are known to the system. And the probability that based on what it has seen, what it's going to see next. And a lot of that is pretty well understood. But the issue here is that we don't have a standard way because we don't think about things that way. I don't think in my brain, where am I storing the word, boy versus buoy versus bye. But there is a neuro-synaptic, if you will, relationship. And so what we have to do in building these systems to learn is have a way of representing different concepts, different words, different phrases that may be logically related like the Al Jazeera example earlier. And having a mathematical representation that puts them closer in proximity in a way that we can describe as a mathematical function. So I can look at two words and see if they're close. Look at two words and see if, in the same context, one is, look at three words, let's say, and see if two are closer in terms of the probability that I should pick one and suggest that as a next step. So this is as close as I'm going to get to showing how we do this mathematically. But what I want to look at next is the trend in applying this type of reasoning to building smarter systems. And this is a diagram that was actually working on this in Atlanta.  The idea is that over time going from left to right, the amount of effort or investment that we've put into any given system for AI using machine learning. The relative importance of algorithms and rules versus data has been steadily shifting. It's certainly not linear, this simplification. But the idea is that in the beginning and the beginning being, you know, the 50s, the 60s, the 70s, the main focus was on getting the algorithms and the rules and this knowledge from the people and being able to operate and build systems with relatively little data. As we migrated and where I would say to the right of the line that splits data where we are today is that most of the systems, most of the investment in these machine learning systems is really taking advantage of the availability of very large data sets. And the architecture and infrastructure to process them, which we didn't have in the 60s, 70s, or even the 90s. This is a relatively recent phenomenon that we have huge data sets available to us and the raw power to process them, which allows us, excuse me, to start to build systems that can look at the data and make inferences from the data about how to process that data going forward. We had to do more planning, if you will, up front when we were dealing with much smaller data sets, much slower computers. And what you'll see if you kind of take a historical look at this is that a lot of the algorithms are being used today. I mean, there have certainly been some advances in talking about those. But a lot of what's been done in the last 10 or 15 years, the knowledge that we had for algorithms and how we wanted to process this data has been around for decades, but is only now practical be used. And so this is a, I don't want to say an inexorable trend, we'll see why I think we're reaching the limits of how much is going to be done based on data driving the decision versus knowledge, knowledge driving the interpretation. But this is definitely a trend that you can map out, we can look at products and where it goes. So with all of that as backdrop now, I'm going to do a quick walkthrough of learning machine learning terminology that we're going to take a little bit deeper look at deep learning and then look at how we put all this together to get into real discovery. So, when we talk about learning, and we talk about people, we think of learning as involving different types of reasoning inference deduction, etc. So for the learning that we do with machines and again this is sort of air quotes because it's not learning in the sense of understanding concepts at a level removed from fact, but we're looking at machine learning. So the the simplest way to look at that, and probably the most powerful way is to define performance on a specific task, maybe it's identifying. Maybe it's a system to do differential equations, maybe it's a system to make a recommendation on music choice. Based on what it knows about you and your previous purchases and circle of friends, but whatever that performance criteria is that you define. We say that a system learns if it can improve its performance based on experience with new data, but not reprogramming. The learning of the improvement has to be based on the system changing how it interprets data based on experience with the data rather than you run it and you find that it gives you, you know, it's generally off by 10% or whatever and then going in and changing the code. No code changes when we're talking about this aspect of machine learning. Within machine learning. Two major categories in a subcategory supervised learning versus unsupervised supervised. We teach the system to either detector match patterns based on some training data training data is an important key phrase here. We have to have data available to provide this training. And that's one of the things that has changed a lot since the beginning. If you're looking at systems today, well, typically do training with anywhere, you know, on the order of 5000 or more examples of what a system, how a system should respond to input. So that's kind of the minimum threshold you may get up at, you know, hundreds of thousands of training data sets, but that's supervised. You learn by example, you present to the system, you build the system so that it will evaluate these samples, and then try and generalize from the specific. Right. So you give it a set of stimulus response parents and say the case where this is equal to one value and why is equal to another value. This is the decision that we make. And the more of those examples the system sees the more accurate it's going to get reinforcement learning is part of the supervised sphere, if you will. And here the system learns or develops its strategies and strategies. Again, in mathematical terms, how it's going to operate are based on feedback that you give it based on its performance. So that's part of the supervision. It's like training someone to do a job. And you give them feedback and say, yes, this is good. This is bad. It's that's the reinforcement and it's exactly as it sounds from behavioral psychology. You reinforce by providing a positive input for things that are done properly. Negative input or negative stimulus, which will be punishment, if you will, in the laboratory for things that are incorrect on supervised learning. This is where many things get more interesting. The system has to discover patterns based on experience. And so it's always looking for something that's novel. And this is where the systems today really weren't possible at all. It's not just their orders of magnitude better or faster or more accurate. You couldn't do this if you didn't have enough data to allow the system to, I don't want to say think outside the box, but to experience enough different data points to discover a pattern. So that's the fundamentals. Just to feature those to kind of put it in context. So when we have supervised, if you've done a lot of experience, you've got the training set with the QA pairs. This is the kind of stuff where we've had some very good success early on in diagnosis because there's so many documented cases to look at the relationship between symptoms and conditions. If you were to take something with the diagnostic and statistical manual, excuse me, of the psychiatric association that shows you that's, you know, the desk reference, if you will, or the PDR, the decisions desk reference for medical rather than behavioral conditions. It's creating an environment where the system can learn from those associations. Reinforcement. The best use for reinforcement learning that I've seen is when you have a situation where there are just too many possibilities. If you're building an autonomous helicopter, for example, that has to operate in three dimensions under all sorts of conditions. It's easier, if you're doing this, let's say you're doing it as a simulation before you get off the ground, to provide feedback when the system does something that is inappropriate or that is going to cause a crash than it is to try and come up with all the rules in advance. And so, you know, each of these approaches has a class of domains where it's most appropriate. And unsupervised is generally in case where you don't know to begin with what the possibilities are. If we're dealing with, my example here, network intrusion, you know what every sensor along the system's edge, let's say we're talking about a power grid, has recorded over every period of time for some historical values. Now, if you find something that's an anomaly, that's something to get reported. That's where you get into augmented intelligence versus artificial intelligence. And now you want that feedback. So you have unsupervised can find the anomaly, something that's unexpected, something that hasn't been seen before. And then there is this combination is almost everything is a hybrid, so we said, now you can either act on it or not act on it, but if you act on it, or don't add on it, you can provide feedback in there, which actually makes this look more like reinforcement learning, because now you're providing that. So what we want to do is look at how these things come together and what the technologies are today that allow us to execute. And the last one for definition sake, deep learning, which is what we're going to look at in more detail right now. When we talk about deep learning, deep learning can be used for supervised reinforcement or unsupervised. But the common thread for deep learning is that the architecture, again, which may be entirely in software is biologically inspired in that the processing units or the processing elements are loosely based on the idea of neurons that fire and connect with each other based on some association. And whether we take it to the extreme and say that we're trying to model human information processing, for the most part today, that's not what we're doing. So we have processing elements and you can think of them as being, let's say your entire desk is covered by these tiny processing units that communicate with each other. They fire when they exhibit some electrical response when they're stimulated. And the paths that those responses take are based on maybe loosely on fundamental assumptions about how the human brain works, but there's no dependency there. So if we're dealing with a model or a neural network and we've got all these little processing elements, each one by itself is pretty simple.  We're not talking about a grid of personal computers. Each node, each element in the grid is the simplest possible computing platform, if you will. But it's only when we start to put them together and combine them and have them talk to each other that we get meaningful results. And when we get into deep learning, the key here, the separate deep learning from a simple neural network is that we're going to have multiple layers of these processing units that are successively dealing with problems. So let me give you a picture. In this one, we've got one neural network. That's the orange surface with a bunch of dots, and each of the dots represents a neural processor. And the idea is that these are each of the little dots are connected to each other. We get some input, and the input is in a mathematical form. It's a digital form. It's either a set of numbers that represent the state. If the input is, we're going to do mostly image processing today. It's a picture. Then each of these may be looking at a single pixel to begin with. It may be looking at a collection of pixels, but it's looking at some digital representation. And then based on the mathematical values that it sees, it's going to do a simple process and produce an output by itself. One layer of these looking at a picture on a typical frame from a TV screen today. If you've got 1920 by 1080 pixels, that's how many images you would have. And those could either be individually assigned to a processing unit, in which case you're going to need a lot of these processing units. Or maybe it does it by taking a group of 10 by 10 and looking to see what are the values. What colors do they represent? And every pixel you'll be able to, you can digitize and see what color it is. You can tell the RGB values or a hex value for the color. You can tell what intensity it is. And then you start to go, hmm, okay, by itself, just looking at it once like that, it's hard to get any meaning out of it. But the real meaning comes when we start to create multiple passes, if you will, through these things. So if we take this digital signal that's coming in, we do something with it and then we process the less abstract signal. That starts to get interesting.  The idea here was just say we're dealing with multiple levels. And in some cases we need to be able to preserve the state or information about what we had before us. So that each at each step, we're building on what we had in the past. So, if we're not doing this as an exercise in deep learning for the moment, let's say I give you a picture. And you have the picture in analog and digital form, you can take a look at it. And I ask you to start to just do discovery. Tell me what's in the picture. Well, you might think what are the things that I know of that I can recognize in the picture in the abstract. And maybe I'm going to look at the first level for a concept. Is it a animal, mineral or vegetable? What am I going to be looking for to make that determination? Maybe I'm looking for some shape. Maybe I'm looking for some shading. But if you think of this as a flowchart, I'm just taking the lines out for the moment. But you go from one step to the next to the next, you make decisions. Maybe you can evaluate the decision and maybe you have to look at more than one thing in parallel. But basically, you're stepping through a sequence of decisions at each point you're doing some processing. And at the end, you make a decision and you say, okay, what you showed me was a picture of a cat, because the cat seems to be the universal answer here. Well, how this relates to machine learning rather than having a person do it. If we're doing this in the knowledge-based approach versus the machine learning approach, then again, it's up to the designer, the creator of the system to have the knowledge put into the logic of the programming to look for specific things. And if you've ever tried to work with vision processing or audio processing or anything that's not really very highly structured, you realize that there's a lot of stuff to look at. There's a lot of stuff you have to discard. We do it very easily. As humans, I'm going to show you some real pictures and sort of see the difference. But for a machine, it's really hard. And so to give instructions to a machine, that's really hard. So the reason that a deep learning approach is so powerful is that we can take something like this flow chart and convert it into multiple levels of neural networks to communicate with each other. And at the top, we have what's called a visible layer. And that's a neural network that takes in, as input, the digital representation of the observable variables. It's a horribly cumbersome way of saying it, but basically you start out and you've got all the pictures. And if the purpose of the first layer is to just find the edges so that we can start to figure out what's on there, now it becomes a little bit simpler problem. Now we can say, okay, what are the different methods that we can use to find within a picture? What are the edges? And if you think about how you would draw out edges on a diagram, what you're looking for typically is a change in brightness or a change in contrast or a change in color between pixels and their surrounding area. And for the moment we'll just assume we're looking at still pictures. So now in this first step, if we just get rid of everything else and we'll look at it as the edges, we've made a lot of progress, but we still don't know what we're looking at. So the next layer in this example could be, okay, now I've got edges. Let me find shapes. And to find shapes, instead of looking at brightness and contrast, maybe now I'm looking at the rules of geometry. And I'm trying to figure out if this little doodle here is actually an ellipse. It's close enough for a circle. Maybe it's supposed to be a circle. Is it a face? Is it something like that? If you know what you're looking for, if you built this with the knowledge that you're looking for people, you're going to handle the instructions, if you will, to each of the layers differently. But the idea is whether you're doing discovery or detection here is pretty much the same or similar, that at each level through the architecture, you're trying to refine what you're looking for. So you can only have one visible layer. We can have any number of hidden layers. Obviously it gets more expensive. It gets more complicated. And then the last hidden layer is the output layer where you say, okay, this is a human being. And then if we get into facial recognition using the same type of thing, you would just have something deeper. And so this is kind of flip the world on its head. It's going from the very concrete, the very specific, the bit level representation of the pixels to the abstract, find me a person in this diagram. And that's how it steps through. I'm going to take this diagram and just try to declutter a little bit and say, well, if you think about that, once you've found the edges, and again, that's generally pretty simple, how do you, what sort of things do you look for to determine the gender? Can you figure out regional origin? Where does somebody come from? Can you figure out their emotional state? And that's the kind of thing that we've been talking about recently in some of the webinars in terms of looking for gestures. And that's a lot harder to figure out what a person is trying to express when you've figured out that they are a person. There's an example that I was reading about recently that I thought was kind of interesting, using a deep learning system. And they were training it to find barbells, gym equipment. And the unexpected result was it couldn't find barbell without also having a hand or a forearm. It thought that those were inseparable because all the training examples were that. So there's a lot of things that get into it. And what I always think is interesting as you go through this is that unlike the flow chart where you can trace back, once you start to have the system create its own models and figure out how it's going to do this, you get a lot more power, you get the power from the data, but you lose visibility into what was used for decision making. So now I'm going to give you two examples. These are highly personal. So looking for features. Here's a picture of three adorable kids. I used to have kids that look like this. And the issue that the question is, which one is not like the others? The downside of a webinar is that we can't have immediate feedback. But usually when I show this picture and we talk to people, they say, okay, I can see where the edges are, the lines. I can figure out that it's three people. And what's different? In this case, you would pick generally the center person because of the dark hair. Otherwise, they're all pretty similar. So you're looking for what are the distinguishing features. Now, same three kids. Different setting. It's all about the context. Which ones are different? Now you can't see enough of the hair to know which one has dark hair. But if you happen to be a sports fan, you would know that there's an issue here because we have two of one kind and one of another. If you were to put this in as a discovery and just say, find the one that's different, this would be a much, much more difficult problem for a machine vision system using deep learning to figure it out. They all have hats. They're all similar skin tones. They all have similar features. What is it about it that anyone on the streets in northeast U.S. but immediately recognize how to factor these? So the context is the most important thing. I just put this one in for Shannon's benefit. When we're looking at pictures and saying, okay, if you have a lot of noise, it makes it more difficult. If you're looking at this and saying, find the tree, find the ferris wheel. You have a higher probability of success than if you're just saying, from discovery, what can we learn from this picture? So it's so much context driven. I'm going to quickly go through. So the reason I wanted to do this topic this month is because there's a lot of things going on. Here's an article that came from the MIT technology review just this week. The dark secret at the heart of AI and it's good article. But the, and a couple of slides from now, I'll give you the full reference. The issue here is that once we get into deep learning, as I said, as you go from one layer to the next, because the system itself is learning from the data and the person that's trying to understand the results from the system can't possibly read the data based on the volume. We get to the point that the results that are being recommended by these large, high volume data sets through multiple layers of deep learning systems. It's really changed the definition, if you will, of what is an algorithm. I mean, an algorithm, when you're learning to write programs, you've got an algorithm that's a sequence of steps to achieve some goal. And you develop that before you apply it to the data. So it's algorithm driven. If you're creating the algorithm of all intents and purposes on the fly from the data, it's in response to what it's seeing, then the typical system isn't tracking that. You have no historical record. You have no audit trail to go back and figure it out. And so you can do a recommendation. And unlike one of the knowledge-based systems, you can't just say, how did you arrive at that? So there's a lot of work being done today in an area on the explainability, if you will, for lack of a better term, of systems. It should be auditability or visibility, but that's the term you're using. So this is one that I recommend. And the question of whether that's important, obviously I think if you get a system that's using deep learning to make a medical diagnosis and it's trying to inform a physician, but it can't show how the conclusion was raised. That's an issue. I don't think it would be approved as a medical instrument. And here's one from Finance. This is last year's financial statement from JP Morgan Chase talking to their investors about how much they're using machine learning now to predict outcomes to actually help them make investment decisions. And I've been involved with Wall Street firms off and on for a couple of decades. I've seen systems like this that were installed, not with deep learning, but using very high-level programming images like small talk in the object-oriented world, where you could create something that a trader could quickly create a financial instrument, run it, make a buy-sell decision based on it. But if that system, if that state of the system wasn't preserved, then when the auditors come back, you know, three days, three months, three years later and try and deconstruct the logic that was the basis for a recommendation, it's just not there. And that's the issue that we have right now with the deep learning systems that can't be audited. So I'm going to wrap it today with another look at the trends and map them to this diagram. So where we are today is that the availability of big data from things like social media, from things like the IOT, this is just going to continue to increase from groups that are putting behavioral data in the public domain as open source. We covered this last year in one of the webinars, Yahoo is putting a lot of behavioral data. It remains to be seen what will happen as a result of the recent congressional decision on allowing ISPs to sell personal behavioral data. But no matter how that works out, we're going to see more and more data available. And the horsepower to process it, so we're going to continue to see this. Deep learning is now practical. The success that we've had with deep learning, particularly in recognition, the stuff that Google has done with Google Translate when it switched over to a deep learning approach from a more knowledge-based approach. I think that was about a year and a half ago. Just phenomenal increases in performance. IBM just announced a dramatic improvement in recognition in natural language processing based on deep learning. At some point you have to say, well, we may not be able to understand how a decision was arrived at, but we're going to live with it based on performance. So all of the success is spurring more and more innovation and investment. So the investment is now leading us to look for newer types of domains to apply this to. And that's where, in the title talking about going from discovery, now we're seeing it being applied sometimes in a hybrid mode with algorithms and rules, to looking for things that would be unsupervised and trying to put those in context. So there's certainly a caution when the transparency is critical. Sometimes it's regulated that it be critical and sometimes it's not regulated, but it's still common sense that you need to know how decisions were made. So now there's a movement in this explainability research. And I think that for the next few years, as we're getting to the point where we know where a system knows everything but understands nothing, we're really going to be focused on some better hybrid solutions to augment intelligence for these critical applications. And with that, I'm always running to the last five minutes and say, well, I have a few more things that I would rather hear some questions. So Shannon, can we open it up to questions? Absolutely. We have a few great questions coming in. And to answer the most commonly asked question, just a reminder, I will be sending out a follow-up email by end of day Monday with links to the slides, the recording, and anything else requested. So diving right into it, Adrienne. So when we say a machine will not understand, do we have adequate understanding of, quote, unquote, understand to make that statement? I've also heard that machines produce conclusions that humans don't understand. Are these statements in conflict with each other? Can I throw up my hands and say I didn't understand that? Could you repeat that? Because I think I have an answer to it. I want to make sure that I do. Do we have an understanding of, quote, unquote, understand to say that machines will not understand? And to add to that, I've also heard that machines will produce conclusions that humans don't understand. And are those two statements in conflict with each other? I don't think they're in conflict at all. And let me tell you why. The first part was do we have enough understanding of understanding to say that a machine can't do it? And I would say that I believe that it's absolutely true. If you look at the, when you talk to people and you try and understand, it's hard to have this conversation about overusing the word understanding. But if you try and determine somehow objectively whether someone understands the way we do that in school, which may be antiquated, but we give them a test, right?  So if you start to look at kind of an interview process and how you assess out what someone really does know, it's generally by asking them to put data or information in a different context and use things like analogies. Now, we, we've talked in this series and we will again in a couple of months about natural language generation and the difference between natural language understanding again in your quotes and generation. I think that what we know that disqualifies systems from the level of understanding that I would attribute to humans is that ability to use something in a novel context. The issue is, for the second part of the question, if a system can make a recommendation or, I think that's how it was worded, that a human can't understand. Well, absolutely. And that's really what I'm getting at in one of the issues. The system may make a perfectly logical or valid recommendation. It may also make a mistake. And we are at this point, unless the system can explain, as you would with a, if you had a knowledge based system, an expert system, you could say which rules fired, you know, go backwards through the chaining of the rules and say, oh, okay, well, you know, if I'm doing underwriting for a bank's loan system, and it says, okay, I'm going to make a recommendation for this person based on the fact that I read a Facebook comment that they made and a tweet that they made and all that. And I have determined their emotional state and it's a bad loan. As long as you can go back and explain what data was used and how it was weighted. And that's something that we didn't get into. Like, and every time you have data that's going from one layer down into another layer down into another layer in deep learning, when you're going from the concrete to the abstract. There are ways of doing like backward chaining and changing the weight and how things are interpreted that as you're training the system. Without that, I don't have any problem saying yes, a system is going to be able to create a good recommendation that I will not understand. I just, it comes down to trust and transparency. But on the first part, I would love to have that conversation with the person, you know, in terms of whether they can understand it, because honestly, I took the exact opposite position with someone well known in the field, who was very upset that IBM was running Watson ads saying that Watson had read all the lyrics of Bob Dylan and understood them and came up with some conceptual themes based on reading the lyrics. And the person was upset that, well, no, it was all about protest. It was all about war. And just because that wasn't in the lyrics system could never figure that out. And my point was, well, it performed as well as a college freshman who also didn't know that Bob Dylan was writing about those things. So it can create the illusion of understanding. And for many purposes, that may be good enough to let it slide in terms of whether or not it understands and it may understand the associations or the relationships. Well enough that for all practical purposes, you will trust if there's transparency. I don't want people to think I'm saying, oh, this stuff is worthless because it doesn't understand the way it understands doesn't have to be the same as the way a human understands the way we have concepts stored in our gray matter based on relationships and reinforced neural paths can be completely irrelevant to the way a natural language system or an AI system using deep learning stores and finds those relationships. If those relationships are useful to produce answers and results, then call it learning. I'm okay with that. Sure. And that was quite an in-depth question, too. So I'm afraid that is all we have time for. But if you have more questions, go ahead and submit them. Adrian, if you don't mind, I'll get you the questions that we didn't have time to get to, and then maybe we can get that out in the fall-up on Monday as well. I'll be happy to. Thank you, Adrian, for another great presentation and really appreciate it, especially after travel woes getting back from the conference. We're glad you're home and safe and thanks to all of our attendees for being so engaged in everything we do. We really appreciate your insights and questions coming in. And I hope everyone has a great day. Thanks, Adrian. Thanks, Shannon. Thanks, everyone.


Johnny dollar New Orleans, Louisiana. That's right. Still down here, New Orleans. How are you Don? I'm just fine Johnny. Boxing? Yes Seems to me I won a fast 20 bucks from you that night on an Italian kid who called himself touchy Tarantino That's right. That boy was a fighter with a real future Not anymore Johnny. This is why you covered your losses by selling him a $25,000 policy What was that crack about his future? Now what's that supposed to mean? What happened to him? Nothing Johnny. Not yet. But you think something will? Like I said, well which and why? Okay, Don. I'm on my way CBS radio network brings you Bob Reddick in the exciting adventures of the man with the action-packed expense account America's fabulous freelance insurance investigator yours truly Johnny dollar Expense accounts submitted by special investigator Johnny dollar to the surety mutual insurance company, New Orleans office Following is an account of expenses incurred during my investigation of the ring of death matter Expense account item one four seventy five for a cab to Bradley Field Item two eighty seven forty for a flight to New Orleans It was early evening by the time the plane sat down at Moisand and National Airport and item three is 550 for a cab into the Roosevelt Hotel From there knowing that Don Pinkley would be long gone from his office. I set out to explore the town Most interesting city in the United States. It's called and with good reason The famous old French quarter is one of the most beautiful spots that I know I'll have to dock the deafening din from the nightclubs along Bourbon Street home of the real original Dixieland jazz item for if I can get away with it is $15 for Well, call it refreshment or maybe midnight lunch after a haul I did put away a sandwich at the old absent house on the corner of Bourbon and the end row and Then at 9 30 the next morning I barged in on Don Pinkley at his office on St. Charles Avenue. Sure Don, whatever you say Sit down, sir. Thanks Here for a little drink at this hour of the morning most of you northern boys seem to expect it when you get down this part of the Country. No, thanks. Okay I'll get right to the point touchy Tarantino Only his first name is really Tony Antonio Tarantino, and you've ensured him for $25,000. So what goes with him Johnny? When you were down here before He was one of our most promising young fighters. We certainly looked at when I saw him. Yes, and he married a nice little girl named Angie She's his beneficiary. I see. Yes, Tony did very well for himself Except financially. What do you mean? I mean his manager a Crooked promoter by the name of Raul Martinez So touchy took all the wallops and Martinez took all the dough That's right. They're not in Los Angeles a couple of weeks ago. Tony fell out of the ring and busted his head That's too bad. Did Martinez stick around helping with his hospital bill and so on he did not he left him flat But now Don and what happens as soon as Tony and his wife are able to work their way back home I don't know what happens Martinez has latched on to him again and set up a fight for him and He'll be murdered Johnny. You mean that bang on the head. Of course The doctor out in LA told Angie that for him to step into the ring again Would be nothing shorter suicide one hard blow above his left ear and he's dead Madonna for the takes of the word of a doctor to keep him from fighting somewhere in Mexico Mexico and the fight is tomorrow night Where in Mexico? Well, that's the trouble. I have the least idea But if you can find out Well Johnny I Can try Finding out where Martinez had taken Tony. He wasn't nearly as hard as I'd expected My pal Johnny Ortiz a police headquarters gave me the name of a stooley in the Mexican section whose name was Miguel Andrade Andrade for 50 bucks. That's item five Gave me the name of a small town not far across the Texas border in Mexico And I'm not naming that town say for diplomatic reasons Item six is five dollars for a cab to the airport and then 43 oh five plane fare to Brownsville, Texas Item seven is $21 for a taxi to that portable town in Mexico. I Arrived about 9 a.m. The town consisted of a main street mostly full of souvenir shops and lined with tourist cars from the US At one end was a small sports arena Closed but there were billboards all over the place advertising the fights including Tony's with poncho Gutierrez So I tried the one so-called hotel there along the main street and in a set of rooms on the second floor Found his wife. Yes. Yes. So I found out to the way you did mr. Dollar and I followed them down here I found out that they were staying here, but what can I do Angie? So please if you can keep my Antonio from fighting tonight. Where is he Angie is he here? No, this Raul Martinez has him locked up in the dressing room in the in what they call the Coliseum where the fight will be Okay, because per carita. He knows that I would stop him that I would not let my Antonio go into that ring tonight So what can I do? What do you mean? What can you do? You know the Tony might be killed to be fights? I know I know But since the accident to his head my Antonio is not the same He believes anything this Martinez he tells him He tells him that he can beat this Pancho Gutierrez and Antonio believes now listen Angie I have seen Tony fight and I've seen Pancho Gutierrez fight I've seen him in Boston, New York all over the place And what do you see even if Tony were in shape and I don't see how he can be after what happened to him in Los Angeles Well, I would hate to bet on him, but they tell me that all of these what you call the odds are on on my Antonio Oh, so that's it That's why Martinez brought him down here where they don't know what happened to Tony Sure the odds are on him, but Martinez money is on Pancho but You mean that Martinez he he bets against his own boy my my Antonio knowing what he does about him Why else would he let him fight? Then he wants him kill all right Angie come on we're going over to the Coliseum, but I can't why not because of Jose Who's Jose because that Martinez it Because I am not alone here That is right, sir.  That's a corny line. Jose, please So you would keep Tarantino from fighting tonight. I'd keep him from getting killed and There's juice and your me. What do you mean me if you do not wish to be killed? You will forget Tarantino and you will go back to their state. Sorry, mr. No Oh put that thing down Like this Repeat after me, please What do you want when you need brand what do you want when you need brand reliability? Now what do you get in Kellogg's all brand Right hi, this is Dennis James to explain why Kellogg's way is the reliable way to get the effectiveness You want from brand with just half a cup a day See Kellogg's all brand is the real Battle Creek formula the one that millions of people depend on and They depend on it because Kellogg's all brand contains more vital brand bulk to help you keep regular It's low in calories and it's mighty pleasant eating to Kellogg's all brand comes in crisp toasted shreds that have a wholesome brand muffin taste I think you like it So be sure you remember for the effectiveness you want from brand get reliable Kellogg's all brand Martina's little pal Jose, whatever his name was Had suggested there in that hotel room that I mind my own business go back to the States And that's exactly where I found myself when I came to On top of a rubber sheep somewhere on the outskirts of Brownsville, Texas minus my gun, of course How he smuggled me back across the border. I'll never know but at this point I didn't care Item eight is another 21 bucks for another cab that little unnamed Mexican town And this time I went straight to the so-called Coliseum a little sports arena Five bucks American got me in through the janitor's entrance into Tarantino's dressing room And I suddenly realized what a blow on the head had done to this Formerly clean alert ambitious young fighter. What do you mean? You'll stop the fight dollar What's the matter with you, Tony? Don't you realize that if Pancho Gutierrez hands you one over that left ear You'll never get up out the canvas alive.  Oh, I don't know You think he ain't fixed it up so I don't get hit that way kid.  Don't you talk like that? I'm all right Martinez says I'm all right now. Listen to me Tony Martinez doesn't care how you get hit And he doesn't care what happens to you when you do you think that crook is betting on you tonight No, I don't think for one minute that What was that? So the fire is fixed So I take a dive in the port so everything's okay You throw a fight why not so I'll clean up keep that word clean out of it What do you mean?  You would have slugged it. So what? So it's now I got to think of my wife.  You're a clean fighter That's why Angie marriage isn't it? You'll keep her out. Listen to me, Tony So what if I can't fight a genie anymore and up in the States? I throw a fight for Martinez like this one here in this town where nobody cares He'll give me enough dough to get back on my feet Maybe start a business something like that believe that why not? Why didn't he give you an honest share of the purses that you won for him before because I was only starting out. Oh sure fine That old line or why didn't he help you after you busted your head in LA? Well, why didn't he I? Don't know I Don't care. He's helping me now. He's helping you get killed What's the matter with you Tony when you got back from LA you went to Martinez because you didn't know where else to go, right? So I was broke And I told him after the way that he left me there He had to help me had to get you more fights that he had to help me is all So he said he would and now we you dumb ox. I don't you talk like that to talk And when you came begging to him all he saw was that if he didn't get you out of his hair He'd have you tagging after him the rest of his life So why not down here in a place?   You think he fixed the fight you think he'd tell me take a dive Yeah Yeah, maybe that's it Tony maybe watch it. Do you know for sure that this fight is fixed sure?   Do you mean she's down here? Well, I'm not quite sure how I'm gonna do it, but when I bring her to the arena tonight.  I'll have the police stop this fight.  Yeah.  Oh say Martinez he wouldn't like this dollar and so he should know about it. Oh, no, you don't This time I was ready for when I ducked in his roundhouse sweat over my head I leaned into his knees and then rose and slammed the edge of my hand across the back of his neck and again some strange reason Suddenly gave up He was dead to the world when I hauled him into a cleaning closet at the far end of the corridor and gagged and tied him down with some Old rags rolled up lengthwise to make him plenty strong That accomplished I started back to Tony's dressing room But the door that was a jar and by edging up close I could hear someone talking And you believe in me Tony this one is to this Johnny dollar Don't be silly boy. I see I don't know If you come around here you walk in here again Ken sabe who knows You see this. Hey, you You've got a silent son at gun see So nobody knows if I use it on him, eh?   What if you do not take that dive tonight. Oh, no, listen Not worry boy. Don't worry In the fourth you take the dive that's all The cleaner the tobacco the better it tastes and in commander Vacuum clean Try new king size Philip Morris commanders made on a new machine the mark eight that takes rich full-flavored tobacco and Gently vacuum cleans it and the cleaner the tobacco the better it tastes noticeably better the cleaner the tobacco The better it tastes and in commander Back with vacuum clean commander Welcome aboard. I Tore on over to the tiny police headquarters and talk to the fat cellar-looking chief Who sat there in his rumple uniform sipping in a glass of tequila? But every time I mentioned the name of Martinez he shrugged as though he didn't understand English and stared blankly out the window And I tried to get tough with him. He spoke in order to a couple of his men who promptly tossed me out into the dusty streets It looked as though Tony was right Somehow Martinez did have this dirty little town under his thumb I went on over to the hotel again to look for Angie a suite of rooms was empty a Stupidly grinning clerk at the desk could tell me nothing except that she'd left a while before with Jose By the time I got through knocking on doors all over town finding no sign of her it was time for the fights I Knew there was no chance of getting a Tony in his dressing room again, so I bought myself a ticket and went in Every seat in that scrubby little stadium was taken people were crouching in the aisles But then I saw Angie standing at the rail in back of the last row of seats Angie Excuse me. Let me through here Angie Could you stop I don't know Angie. I don't know I do Feel this here against your side And so your father if you try anything anything at all I will pull this trigger Now shall we watch the fight? And so we stood there the three of us watching the final dull preliminary and then Tony's fight They look pretty good despite his lack of training physically that is mentally I Wondered if I didn't wonder about Martinez God was there against my ribs all the time Don't worry dollar you cannot last If he does not drop in the fourth bunch of right to the side of his head It will be all over In the first round Circling slowly in a half-prouch playing it safe and then backing away to make poncho lead Watching it watching doing all he could to keep the left side of his head away Couple of times he leaned in and flicked a hard left to poncho's mouth Poncho was plenty cagey to knowing that Tony would tire along before he would By the end of the second Tony was tired The Mexican boy was too fresh too fast for him and Tony's punch lacked the power it used to have In the third the lack of training really began to show it was obvious now that poncho was aiming deliberately aiming for only one place For a spot over Tony's left here In the fourth poncho danced easily around him trying to set him up. He drove a couple of right hook-back in the Turn him again With a half-rolling clinch along the ropes Tony's spine and slammed at the side of poncho's head with a looping overhand right But it left his own body wide open for jabs and almost broke his ribs And then in a series of clinches Tony lasted out the fatal round beside me Martinez began to mutter angrily And in the fifth poncho knew that Tony wasn't going to take that dive then or ever And when he tried for Tony's head over the left ear He telegraphed it every time and Tony was ready for him But Tony was tiring badly now and hanging on too much After an awkward fate he tried a looper to the head that missed by a mile Controletted breeze on by and then threw a short hard right to the tin as Tony sighed against the ropes the bell saved him But he was badly hurt. It was all too obvious that his seconds weren't giving him much help How he ever got up on his feet for the six I'll never know but somehow he did and then when poncho tore into him again Aiming for his head Tony turned almost as though he were falling But he came up all the way up from the canvas with everything he had So the right that caught poncho on the button that lifted him clear off the floor Poncho fell Tony crumpled his way almost blind me to a neutral corner and managed to stay on his feet only long enough to Hear the full count but the crowd went wild And then I felt a certain movement beside me and I saw that Martinez had raised the gun to lean against the post so that he couldn't miss Even at that range and his face was living I swung up with my left with everything I had and the gun whirling as he struck it against the post went off in his face It's all right Yes Martinez was dead and of course the police moved in And when they realized he was definitely finally out of the way I'm sure they all breathe the deep sincere sigh of relief And believe it or not Angie and Tony and I had a formal escort back to the border that was fit for royalty Expense account total including all the incidentals I could think of 389 dollars even yours truly Johnny dollar Now here is our star to tell you about next week story next week a trip to South Jersey and a murder To stop a murder Join us. Won't you yours truly? Johnny dollar fully Johnny dollar starring Bob Reddick is written by Jack Johnstone Produced and directed by Bruno Zerato Jr. musical supervision by Ethel Huber Her to now a cast were Robert Dryden as touchy Joan Loring as Angie Ralph Camargo as Martinez Mandel Kramer as Don Pringle and Danny Akko as Jose Be sure to join us next week same time and station for another exciting story of yours truly Johnny dollar. This is Art Hanna speaking