The following is a presentation of TFNN. The Trader's Edge with Steve Rhodes. Toll free at 1-877-927-6648 or internationally at<PHONE_NUMBER>. The Trader's Edge. Now, Steve Rhodes. Good afternoon from TFNN. Welcome to the September 11th, the wonderful Wednesday edition of today's Trader's Edge show. I'm your host, Steve. Perseverance Rhodes, who absolutely knows that each of us should always be pioneers of our future versus prisoners of our past. Hope everyone out there is having a great day. Hey, let's have an extraordinary one. And the easiest way to do that is to always remember that life is happening for us, not to us. All right, when you and I make that one little two-by-four shift, it means we can find the gift in every set of circumstances that life is going to toss at us. Now, today, you and I, we're going to go check on the circumstance of these markets. We're going to go figure out what those bulls and bears, what those buyers and sellers are communicating to you and I just passed one o'clock in the afternoon. I want you to know that I'm absolutely grateful for your presence here, but way more important than that. During this next hour, I'm here to serve you. So feel free to pick up that phone. You can dial on it at<PHONE_NUMBER>. If you can't dial in, we've got you covered. Just send me an email. Steve at tfn.com inside the subject heading. Please put radio show question, of course, in our Tigers then. Well, any ping will do. So let's go ahead and get this show started on wonderful Wednesday. Of course, this is Tiger Financial News Network. I'm Steve Rhodes. Welcome to a last show right now. We've got the Dow trading up 109 points, about a half a percent, a little bit less than half a percent to the upside. Half a percent of the upside is the S&P. That's 15 points. NASDAQ 100 up 9 tenths of a percent or 67 points. Russell's up one and three tenths percent, 21 points to the upside. Another nice day as is as semiconductors are also having their 20 bucks or one and a quarter percent. So it's meaning green across the board as it should be, as it's to be expected as you and I have discussed here for days now. Of course, part of that is because of that spot volatility next, which continues to trade lower, should continue to trade lower down into the 13 points. It's at $14.70 and if that is what unfolds, well, what that means is you should anticipate and expect the markets to continue to move higher. You've got gold, which is basically flat. It's up a buck, 16. No big deal there. It's right now at 1500.80. It is below the bottom of its daily profile that says expect and anticipate a change in trend. You've got silver down three pennies. She's trading at 18.15. Light sweet crude is off the buck 50. That's down two and six tenths percent. So we've got plenty to look at. Of course, I want to look at what you want to look at. Lead the charge, by the way, to the upside this afternoon is booking holdings. That's up 22 bucks. Intuitive surgical is up 19. Google is up 15. Metler Toledo International is up $15. Zscaler, ZS is a ticker symbol down 21 percent or 12 bucks in change. Fair Isaac Corp. down seven bucks, nearly a little over 2 percent. Lenox International off six and Guardian Hell off a 5 percent out there. So the first question that came in came in earlier this morning, I mean like 4 a.m. or so, was a request to go take a look at light sweet crude. So to do that, we do a couple of different things out here. One, we take a look at four different timeframes, our 60 minute, our 120 minute, our five hour, which is 300 minute, and then our daily timeframe. Now in the daily chart, we have both the daily and the weekly profiles out here. Of course, when price is above the top of a box, we'll see a green bar. It tells you that price above resistance. When it is below the bottom of box, it tells you you have broken through support. Now, in the case of the day, we can see on the intraday timeframes, we've seen quite a move lower over the course of the last several hours, four hours or so. Let's take a look at the daily timeframe chart, get a feel for what it's communicating to you and I. Now, what we know about light sweet crude is that it had tried, it attempted to break a descending trend line. It closed above that level on the ninth, and then yesterday, it tested and rejected that area. And we don't know where the light sweet crude is going to close today, but if it does close below 5635, it's back inside the daily box. It will have been a false breakout of that trend line, and that would say to expect lower price. Now, expect lower price to where? We go right to the bottom of that profile. It would be in the 51-34 range between 51-34 and 52-34. That's what light sweet crude is telling us based upon its daily and its weekly profiles, as well as that trend line. If I had to pull over, if I were to, which we will do, pull over Stevie's white background charts by Ninja Trader charts with all my other proprietary tools out here, you can see the price is pushing right down into Stevie's red line. That's the oscillator and change line. So it is pushing into a support level right here. That's why the end of the day will tell you what light sweet crude is really going to do. If you had a hankering to be bullish, then right now would be the time that you would buy. I'm not suggesting that at all based upon the chart patterns that I don't see out here. I don't see something that tells me we had a significant bottom back here on August the 7th, and based upon the trend line reversal that we took a look at, well, the only way that I could give you a buy signal right now would be to look at an inter-day time frame chart. So let's go do that. Let's go take a look at the 30-minute time frame chart for light sweet crude. Of course, we're taking a look at the October contract, and the only pattern that we have out here at the moment is A to B equals CD to the downside. The A point out here at the most recent high, that would have been it looks like at about 11 o'clock in the morning. That was yesterday. Yeah, that was yesterday. So you've got the A to B equals CD. We can see prices made the 1 to 1.618 price projection area. That's at 55.76. We're trading at 55.83 right now, but that doesn't mean just because we hit that level that this is a bottom, in fact, from an inter-day standpoint, that being 30-minute time frame chart. I could not give you the signal to go ahead and fire away based upon the fact that price is sitting at Stevie's red line on the daily time frame. You need to see some type of bullish reversal candle formation. It is not present. With it being not present, it says maybe price moves down to the next extension area of that A to B leg, and that would take us to about the 55.18 area. So we're just simply watching here from an inter-day standpoint for some type of bullish reversal signal to suggest you should at least see some type of bounce or counter-trend move out there. So LightSuite crew, that's what I see. I don't see a whole lot of reason to play in that area. I just, there's other vehicles that have generated better bottoming signals and so on and so forth. So hope that helps you out. I believe that was Vicki that wrote in earlier. Now let's go take a look at some other questions that I see at the moment. So let's go take a look at the equity markets. And it's interesting where we're at. In fact, let's go take a look at the cash indices. Some of you may have got this morning's 9 o'clock show. I was doing Larry's show this morning, as I have the last couple of days. Won't be doing it tomorrow or Friday, but I believe I'm back doing a show on Monday and Tuesday out there. Don't quote me on that, but I believe that that is the case. So what's interesting about the equity indices charts out here, let's take a look at the S&P as an example. Today is going to be or appears that it's going to be day number nine or bar number nine of the TD set up nine count. Now, what you and I know is that on bars eight, nine to the bar following nine, as long as eight, nine or 10 are making the highest high inside that pattern, that can be a reversal area. So it says we need to be on guard today. We need to be on guard tomorrow out here inside the S&P 500. Now, the S&P 500 has not done thus far. After Stevie's red line turned green about four trading sessions ago, typically you will see that level and price catch up to each other. That level right now being 2946. It's not my call just yet. I say it's not my call. I'm not making that call. I'm not suggesting you go short the market. I'm suggesting that we need to be paying attention today and tomorrow specifically for a potential for some type of retracement to begin, at least back to Stevie's green line. We'll take a look at the other cash indices as soon as we get back from this break. Steve Rogent here. If you're not currently using the TAS Profile Scanner when looking at setting up your trading opportunities, then your arsenal is short a mighty weapon. The TAS Profile Scanner is a standalone piece of software that instantly filters over 2,500 global financial markets such as stocks, ETFs, commodity futures, and forex. Heated by Steve Dahl, TAS understands that in today's technological world, the use of top flight software applications and technical analysis expertise is essential to successful trading in today's market. You also gain access to the webinar that Steve Dahl and Tom O'Brien just hosted, the best way to use the TAS Profile Scanner to profit. 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Call us today. Many of our new listeners have heard about The Tiger's Den. The Tiger's Den is a lively community where professional traders and investors can meet, exchange ideas and information in a comfortable, moderated atmosphere. Hear all of the TFNN shows. Plus see all the charts as they happen live and have access to archives of all of those charts. You can test drive The Tiger's Den absolutely free for 30 days and greatly enrich your knowledge of these markets and how to make your money work for you. Details on The Tiger's Den are on the front page of TFNN.com. Check out Tiger TV live in high definition or just accessing your newsletter subscriptions. We even have new pricing in six months and yearly options. Check out the new TFNN.com now and experience all the upgrades. TFNN.com, educating investors. The Tiger's Den says, for those who missed a big part of this upturn, do you see another pullback soon? And if so, where do you see strong support in the Dow in a good area to buy? So, look, we just looked at the S&B 500. Let's pull over the Dow Jones chart out here. And you'll see all the indices right now today. It looks like all of them, yeah, all of them are doing the same thing by the same thing. Well, some are in day eight, some are in day nine. Looks like no, it looks like we're all in day nine here. Nine count of the nine count bar out here. So you'll see yesterday was day number eight. Kevin, inside the daily chart for the Dow, we know on bars eight, nine, or the bar following nine. So that could be Thursday. There could be some type of high. Now, specifically in the Dow, first of all, I want you to sell tops and buy bottoms. And when this pattern shows up, that's what I want you to really focus on and pay attention to. Because you're asking the question, where's a good area to buy? How does the market communicate to you what its intent is? For example, when the Dow went ahead and formed a bottom down here on June 3rd, June 4th. And I remember everything looked like all heck was gonna break loose. The world was coming to an end. Of course, you and I knew that that wasn't the case. We just knew that the Dow was being stretched to the downside unreasonably. So we're still in it with less energy, even though it was making a lower low. That's how bottoms are formed. And then that's what creates that, so the rose momentum indicator pattern out there. You then get the bullish reversal signal on June 4th. That was your sign that, okay, the buyers were out there in force. They didn't tell us whether they were gonna win the battle or not. But we knew that the cavalry had arrived on shore. And that is very important. And that says, okay, the bulls now should have the ball. And you're gonna go ahead and take that move on the bulls out here. Price was above Stevie's red line and green line. Really remained above that for its entirety until we got into the July 24th, July 25th time frame. July 25th, by the way, was a key reversal session. That is a bearish reversal candle. And that was confirming the rose momentum indicator top that had generated July 23rd. Then when the bottom forms out here, and this bottom forms, we don't know exactly when it's gonna have, we can monitor it to watch for this. But certainly on August 15th and the 16th, there was a 16th when the Dow generated that bottom signal. Again, the same pattern. It just is like rinse, repeat, do it over again. We don't have that pattern right now. We are not going to see rose momentum indicator top tomorrow. Not much I can guarantee you. We're nowhere near that. But what we do have is we do have that TD set up nine count to the downside where 27, 281, 65 is resistance. Typically when a nine count pattern forms and it's below a prior resistance level, you expect to see a turn in the market. Now, the turn in the market here for the Dow could simply take it out into Stevie's Greenland. It's 26504 right now. Two days from now, that number will be different than the number I give you right now. But it will be in the general vicinity, so to speak. Now, this is gonna be hard for us to call. This top should have formed. It's going to be hard for us to call. And I'll show you some of the reasons why. But what I want you to do is now is not the time to enter. That's for sure. You're not gonna enter when you're close to the potential of a topping signal out here in the markets. And all the markets are doing the same thing. That was the Dow. Here, let me pull over the New York Stock Exchange. Here you'll see the New York Stock Exchange. A higher high today. Bar number nine of the TD set up a nine count pattern out here. Let's take a look at, let's look at the semis. They're having a nice strong day out here. What are the semis doing? The semis have recycled, if you will, to wave number seven. That's letter H on my screen. We know there is no such thing as wave number eight. So we'll go ahead and get rid of that. We'll go ahead and change it out to blue out there. It's wave number D, wave number four. But really what it is is bar number nine today of that TD set up nine count. As price comes back into a prior high out there. Now look, these patterns don't always identify the top or the bottom. But when they do show up, we do pay attention. If you take a look at the bottom that formed back in the Maytime frame for the semiconductor, it was with the TD set up nine count. That was the signal. That was the pattern that is out there. That's the reason that we're focused and paying attention today, tomorrow. What do you do if you're long? Just make sure that you've adjusted your stops. Take a look at the transports out here. What are the transports? They are in bar number nine as well. Now in the case of the transports, Stevie's red line turned green. Typically the phenomena is at price in that level. We'll catch up to each other. That could be the buy point into the Dow or the transports out there. We just have to take things one step at a time. So it's impossible for me to give you an accurate forecast. I'd rather wait to see what patterns are playing out to then make that determination. If we take a look at the Wilshire 5000, where is it at? Bar number nine. So everything is at bar number nine. It's done it in unison out here, whether it's the Russell 2000, whether it's the NDX, whether it's a NASDAQ composite, it doesn't really matter. They all are doing the same thing right now. So why is it that Stevie is saying that this is going to be difficult in essence to really call a top out here? And I'd say the real reason is because the spot volatility index is not done doing what it needs to do out here. Yeah, it's that simple out here. What do you mean it's not done doing what it needs to do, Stevo? Well, what it really needs to do, it needs to get down to this yellow line on the very bottom of my screen. Yesterday was priced at 1310 or 1309. Today it's 1311. The spot volatility index is 1469 right now. Now, maybe it gets down there into that 1311 area. We see the markets move higher again tomorrow. Bar number 10, that's normal. And then the whole combination might come together. In fact, what we might see is we might just see the advanced client oscillator reading stall at 150. It's at 149.21 right now. I do not know what the end of day reading is, but there are plenty of what I call plus 150 failures out here. Last time we saw plus 150 failure, I really took place on June the 20th. We saw a bit of a move lower. Then a move higher again. That move higher generated a topping signal for the New York Stock Exchange in a way that prices moving higher, but its advanced client oscillator was not making a higher high. It was making a lower high. That too was a signal. That eventually led to a consolidation and a final breakdown. Of course, we know that the bottom that formed in the NYSE was the exact opposite pattern. The advanced client oscillator reading was making higher lows in the face of lower lows with regard to price. We don't have one of those patterns that is present right now, Kevin, inside the New York Stock Exchange, but we do watch the plus 150 area, but we certainly watch that spot volatility index. It really does need to push its way down to the bottom of that Bollinger Band, where tops can form in the marketplace out there. That would also say, hey, we could go take a look at shorter-term timeframes. On the shorter-term timeframes, are there any signals out there? That's an excellent question. To answer that question, we just pull over Stevie's market analyzer. Now, I know that the data here is going to be a little bit small, but you can see some of the futures contracts that we take a look at. You've got the equity futures. You've got gold, silver, light-sweet crude, natural gas, and the 30-year treasury. In this case here, you've got your 30-minute, your one-hour, your two-hour, your five-hour timeframes out here. So what I have listed is what is the market condition. You'll see on the 30-minute chart, everything is in a breakout bull mode in the equity futures contract. Same thing in the one hour. Same thing in the two hours. Same thing in the five-hour timeframe. What does that mean? That means that prices above all levels of resistance out there. And in doing so, what we don't see on a 30-minute timeframe, a one-hour, a two-hour, with the exception being the Dow. We could take a look at that. Or the five-hour, we don't see a Rhodes momentum topping signal. So the short-term timeframes, which is where you would see a top form first, doesn't have it out there. Maybe this time the TD set up nine count is not going to show us a top, but where a new level of support would be. Steve Rhodes with TF and that. It's not going to be easy to call this next top. Not too easy. We'll be right back. I'm certain you are, or strive to be, one of the best of the best at everything you do in life. It's the most common trait that we Tigers and Tigers share. If you're looking to become the best of the best when it comes to managing your money, let me teach you to do what most wealth managers tell you can't be done, which is how to time the markets. I'm Steve Rhodes, author of Mastering Probability. And for the last 12 months, Timer Digest has been tracking my newsletter signals, which have earned me the ranking as their number one market timer in the nation for the S&P 500 for the last 12, 6, and 3 months. 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This segment is brought to you by Think or Swim. For more information, just click the Think or Swim banner on the front page of TFNN.com. Welcome back, folks. So, well, one of our donors asked to take a look at the market breadth for the NASDAQ. And what we do have here, Coda, is the market breadth for the NDX100. And that comes to us via our folks over at TAS Market Profiles. And so here what this is helping you and I understand for those 108 or nine constituents that are inside here, this is the weekly timeframe, by the way, that we're looking at first here. We've got 50, or it has 50 issues trading above the top of its box and only 18 below the bottom of its box. So percentage-wise, nearly 50% above the top and 17.5% below the bottom. This is really strong market breadth. This is a weekly timeframe. So the market breadth inside, and this flipped on September 1st, I believe, was when it switched back to bullish out here. In between that, we had a bit of a little consolidation. If we take a look at the daily timeframe, out here you're going to see that this punched through to the bullish side back on August 29th with its crossover. Here you've got 62%, 61%, I apologize, of the constituents trading above the top of the daily and only 20 below the bottom.  The market breadth was just too strong out there. And so there were no cracks in the armor, so to speak, out here. The pullback that we saw in the NDX100, that was normal natural because TV's red line had turned green. We'll go ahead and pull that NQ chart up there again so you can take a look at it. So what was unfolding was normal and natural. What you and I talked about for the cash indices and our expectation that that will also unfold. But we've got to really navigate this one step at a time out here. Here take a look at the four-hour timeframe chart out here at Coderwood. It's going to flip back and forth a little bit more often, but here as of yesterday, 12 noon, this thing is in bullish mode out here. And if you go to the shorter term one hour duration, this shows us again yesterday at 2 p.m., everything went back to bullish. So market breadth for the NASDAQ100 is very strong. And this is based upon our reading of the TAS market breadth system out here. If we take a look at the S&P 500, give me a moment, we'll pull up that chart, that tool out here. You're going to see, you'll see it if I can actually grab it. Here we go. So here we're going to take a look at it. Just let's do the same thing. Hopefully that's okay with you. If we take a look at market breadth, again, market breadth, when you are above resistance, that's what this is doing, or below support. Above resistance, it's bullish. It's in a breakout mode. Below resistance, it's bearish. It's indirectly speaking, it is in a breakdown mode. You've got of the, you've got 48%, 48% of the issues inside the S&P 500 on a weekly timeframe are above resistance and 11.13% below the bottom on a weekly basis. This is super strong market breadth. It could get stronger certainly, but it's super strong market breadth. The weekly daily timeframe, you've got 64% above the top of a daily box and 16 below the bottom. It's why any dips are being bought out here. It's just super strong market breadth. Somebody is telling you that the market breadth is weak out there. I don't know what it is that they're looking at. Well, they're not looking at this. One thing we can be certain of. They're certainly not looking at the bullish crossovers for the S&P and for the NDX100. And yes, this is not going to call the top and it's not going to call the bottom. But assuming that you've missed those two things in the middle of the sandwich, this tells you it's pretty meaty out here. Take a look at the four-hour timeframe chart. Again, bullish market breadth, the one-hour timeframe chart. It's been bullish market breadth in the S&P since September the third out here. So market breadth is very strong. Market breadth in the New York Stock Exchange, let me close that out here, is also very, very strong. That advance decline also that are getting up to the plus 150. It's plus 151.26. The fact is a close above 150, and that's possible today. Any close above 150, the plus 150 level tells us that we have higher prices to come. Doesn't mean tomorrow. But whenever you see a reading above 150, and you go back and you research this, you will see that we have seen higher prices. Now, of course, somebody could say, man, that's fairly easy to make that decision, Steve. Oh my goodness, we're only 1% away from the all-time highs out here. I know, but still go back and take a look at it. Now, I don't know if I have on this chart here the advanced decline line. Let me see if I do. It may be on another chart out there. It may be during a break. No, I do have it here. So let's go ahead and put that up. So where are we on the advanced decline line? We're at a new all-time high today.  And that's what it's telling us. This is also the advanced decline line, the lack of any kind of divergence. Divergence would be the advanced decline is not making higher highs in the face of price making higher highs. That pattern doesn't exist out here right now. So we're very market breadth positive out here. It's why we take a look at the seasonal cycle, but we don't use the seasonal cycle as religion. We use chart patterns. We use chart patterns, simple tools. That took me a long time to figure out and learn. That's for sure. But listening to the show and me sharing with you what it is that I share with you, it means you don't have to invest those thousands of hours. You can just pick up the work where I've got it, figure out ways to improve upon it. And trust me, I'm certain that this can be improved upon. It's always about improving things out there. Always. But right now, all the signals to all of the listeners out there, to you, Kota and the Dan, who was asking about it. I don't see anything but strong market breadth everywhere. Again, market breadth is not going to help us to identify a top. The advanced decline I said reading at the plus 150, that's a different story because that has a history for us to go back and say, okay, this is the time where the sphincter muscle, the bulls needs to tighten up just a tad. Tighten up just simply means make sure you put stops in place. I can't know for certain what the exact next move is going to be. I don't know what sweet might come out. By the way, the tweets typically come out between noon and two. And they typically kind of, I guess historically somebody went back and did a study on this. I think it was Bloomberg or something. I read an article somewhere over the past week or so. And one o'clock seems to be the average tweet time out there. So I don't know what might impact the markets next, but with the market breadth so strong out there, taking short trades is just really not the signal that's being present out here. Doesn't mean we're going to ignore those topping signals. It's just a signal as we speak out here. That's why I said calling the top of this time right now specifically is not going to be an easy thing to do. We've just gone through this little checklist here. I'd get a little more comfortable and maybe try to identify top if that spot ball utility index was down in the 1311 area at the same time that we're getting to a TD set up nine count. I'd certainly feel much better with calling the top or bottom if the roads meant the indicator signal was present, but it's not and we just have to go with what we've got out here. If we take a look at the ES mini, this says to you and I, it's headed to 3014, 3030. If we take a look at the NQ, picking close above 7881, it's going to make another run for its all-time highs and the Dow equity futures contract is already headed back to its all-time highs. Steve Rhodes with TFNN. We'll be right back. If you're in the CD market and looking for a secure investment, the Tiger First mortgage program may work for you. The security for these first mortgages are building lots in the tax opportunity zone in St. Petersburg, Florida. The Tax Act of 2018 set up tax-free zones across the country where you can build and hold for 10 years and pay no tax on the profits, which makes these lots valuable. The investment is anywhere from $30,000 to $75,000. The interest paid is 7% yearly paid on a monthly basis. According to bankrate.com, the best rate for four-year CD in the country as of February 20th is 3.1%. A $50,000 investment at a normal four-year CD rate of 3.1% would give you income of $1,550 per year or $6,200 over the four-year period. 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The funds are designed to be utilized only by sophisticated investors such as traders and active investors. Distributor, Foreside Fund Services, LLC. Don't forget, you can listen to TFNN live on your mobile device 24 hours per day. Go to TFNN.com and hit Watch Tiger TV. That's TFNN.com and hit Watch Tiger TV for the latest market information. Code, I know you were showing the NYS, the NASDAQ composite out there. So the only thing I can pull up here for you for the NASDAQ composite is it's advanced decline oscillator reading out here. I do have a way to get the advanced decline date. I couldn't do it during the commercial out here. But I did see that you had posted something with regard to that. But here what we can see about the advanced decline oscillator, it too is rising out here. It's almost back to a high from back in January of 2018. So again, this is market breath very positive here inside the NASDAQ composite, but that doesn't mean that this thing is not also possibly making a top. We have to just simply pay attention to those TDES set up a nine count out there. And where we will see some type of if there is a change in trend, we'll see it typically, we'll see it show up for example on the intraday time periods. So as one example out here, let's just go to the NQ as that example and let's stay here. So for the NQ what you need to start seeing are the levels of support being broken. These are breakout areas of support. So inside the NQ as an example, that level is going to be 7805 to 7798. The 7798 was tested earlier this morning. That was a breakout area was tested, it was rejected in price and screamed to the upside. Of course, we had already known that the NASDAQ so the NQ, here's where things get a little bit tricky. So in the case of the NQ, what we can say is that yesterday and Stevie's green line, when that line changes color, red to green out here tells us of an impending move in price. It tells us of an impending connection between price and Stevie's green line at this stage here. Now the NQ itself has not generated a TD setup a nine count pattern. So maybe the futures will provide us with better information out here. But it's only the NQ, the ES I can see has taken off this screen here to my right for my other screens, the Dow has and the Russell has, but the NQ itself hasn't. So here's the divergence so to speak. What the NQ has done that the others haven't done is come back and pull back and in essence tested that level. When it tested and rejected that level yesterday that was a bullish test says that price wants to be higher and what we do is we put that together with what else was going on inside the markets yesterday inside the NQ specifically which was the NQ broke above its consolidation on September 5th. On September this 9th a few days ago on Monday price pulled back to test that level when you break above a resistance area consolidation top of the consolidation would be resistance you love to see price come back and test it and reject it to tell you it wants to move higher. That is exactly what took place yesterday during the day most certainly price was below that area but by the end of the trading session that's what's important price had rejected the top of the consolidation. We had another test of that area this morning price has rejected it. This says to us that if the NQ is going to do what normal breaks of consolidations do this is where things get tricky here then what we should see unfold is a measured move up to 80 to 35 I don't make these rules up I just share with you the rules that exist out there and they work really good. So what's that nine count malarkey about out there this is where we're going to have to be watching like those inter-day time frame charts to be looking for signals out there early signals in the case of the Dow it's broken through that consolidation appears to want to head to 27 548 the measured move it could go beyond that because measured because consolidation projections price projections are equal to or greater than the consolidation of only put in the equal to consolidation and inside the Russell 2000 who knew how it was going to happen but it has happened it's at 1567 it's just got another 30 points to get up to the 1605 level now that would be ideal I'm not saying this is what's going to happen because it would be one heck of a move out there but that would be ideal complete the consolidation form TD set up the nine count pattern do it tomorrow on bar 10 get the spot volatility next down to the 13 ish range out there then that could be the perfect setup for the next decline out there but right now everything continues to point to higher highs and let's not get caught up in the fact that the NQ hasn't made a higher high meaning taking out the September 8th or 9th levels out here it already did its bullish tests yesterday and the day before and once again this morning so that little puppy could easily go ahead and power itself up at least get to the top of its daily profile at 80 3175 but right now I think well that I mean that is a resistance area it's really 7951 to 8031 out there so that's that's the markets that that's really the take on the message of the markets it says okay we just need to be careful be observant because of the patterns that are in place here there's nothing that says exit there is most certainly nothing that says go to the short side of the market out here and just says be cautious and just simply continue to adjust your stops okay so what else is it that we want to take a look at there's no other questions that we have I take that back December hogs Ruby wanted to take the look at December hogs and see if we can get that up on our screen out here so give me a moment to get down to the December hogs and so you say December I happen to have up here November but we're going to change that out for you so let's go take a look at the December hogs let's take a look at the daily time frame chart look for its daily profiles and what we have out here so it's possible Ruby that that lean hogs have found a bottom very possible now it's although it's hard to read on my chart the bottom of the daily profile is 6066 the bottom of the weekly profile is 6076 so you got a 10 cent difference there but here's what you also have today now I happen to I'll see if I can pull up on my other charts see if I I don't know if I've got December I'm going to hope that I do give me a moment here just well let me just try it this way H well one night I'll know momentarily no it did not like that so with that being the case how else is Stevie and so here's here's what I want to do I share with you a Ruby today is a key reversal bar it appears to be a key reversal bar it depends on the close you see the open was 6177 we're trading at 6177 in order for it to be a key reversal it has to close higher by at least one tick the high of yesterday and the low of yesterday have been exceeded but in order for it to be a key reversal bar to at least get back in the range and say the price is going to go test the top of the daily profile or center of the weekly at 6716 you'd like to see some type of bullish candle that bullish right now it's just a doji candle it's a true out doji the open in the and where prices trading right now are exactly the same so doji the doji is at the market is tired whether it's at the upside or at the downside now where that where dojis have the largest impact this coming from Stevie is coming from from my years of knowledge and studying the doji the doji candle out here they work better at support and resistance for identifying turns out here the middle Schmiddle they don't mean a thing so we're close we're close Steve Rhodes with TFNN right back since 1984 Bazel Chapman has been using the Chapman wave methodology to advise traders of his expert market opinion while originally hand drawing charts from the late 1970s into the 1980s Bazel noticed that prices under most circumstances virtually always had a certain number of likes to the upside before declining sharply Bazel found that computer software which included the standard market technical indicators enhance the degree of accuracy in calling price turns as well as market trend calls thus was born the Chapman wave sequence using the Chapman wave methodology along with other indicators Bazel Chapman advises his subscribers of his expert market opinion each market day with his opening call newsletter right now you can get a two week free trial to the opening call Bazel's daily trading newsletter by visiting the front page of TFNN.com cancel it anytime during that trial and pay absolutely nothing get your two week free trial to Bazel's newsletter the opening call today by visiting TFNN.com if you're a trader in the market looking for exposure to gold or gold mining equities then now is a perfect time to sign up for Tom O'Brien's gold report the summer is over gold is trading back above $1,500 and the 10 year treasury is hovering at around 1.5% Tom O'Brien has been writing his weekly gold report for almost 18 years there's no one that knows more about how the gold market trades and how gold mining equities react new subscribers get a 30 day money back guarantee so you have nothing to lose every Monday morning Tom publishes his weekly gold report with coverage of gold, silver, bonds, the XAU HUI, GDX, the dollar as well as more than 30 different mining equities as of September 3rd gold report subscribers have 5 active open positions with an average unrealized profit of almost 38% for each position to see for yourself the types of profitable trades that are recommended within the gold report sign up today by visiting TFNN.com you know what's cool taking something that's good for you something specifically formulated to help with weight loss better sleep stress reduction and the need to detox Nicar hunter and gatherer ancestors found all their nutritional requirements for health in their wild environment but today no longer contain the vitamins, minerals and nutrients our bodies need to stay healthy and strong that's why we need primal edge daily nutrition it includes a special blend of ionic, soil based vitamins minerals, baddie and amino acids and an easy to use liquid form primal edge is powered by highly concentrated folic and humic acids nature's preferred delivery system they've been called miracle molecules because like sunlight air and water life cannot exist without them that's right page they ensure we receive all the nutrition we need to be healthy and thrive we take it every morning primal edge formulated and approved by Niko and Paige of living a primal lifestyle buy it today for just $89 click on the primal edge banner on the front page of TFNN.com this is David White stay tuned because coming up next is the power trading hour right here on TFNN welcome back folks so we're taking a look at cosan limited out here for ruby and the tiger stand ruby price is running into a possible resistance level the resistance level being the top of its weekly profile $14.99 now it's only Wednesday but as prices pushed into the prior swing point which was from the week that began July 29 that had 3.7 million shares you're at 1.7 as we speak right now from a daily standpoint that high is again $14.99 August the first volume of $7.87 10 of you are pushing into it $409,000 doesn't mean it can't push through it and above that level it's just you'd like to see it do it with some volume out there this chart here is not updating it just is grabbing through yesterday so my apology for that but as we take a look at it knowing that it's moved to a higher high today is going to be day number 8 day number 8 of that TD set up 9 counters we also have a topping pattern at a prior high with lighter volume so not saying to short this but does have an indication that we might see a retracement or a pullback into the 14 40-ish type area out there maybe 14.05 the top of the daily profile but here with regard to COSAN Limited it's up against support the final issue we'll take a look at out here for a set is a ticker symbol what is it? SMAR let's go take a look at SMAR the question is can I buy a few shares here so what you're looking for is some type of bottoming pattern to have taken place this too is not picking up today's action only yesterday's and so I don't have a bottoming signal here just yet so let's do this oh I take that back yesterday was wave number 7 letter G so if you are so it's pretty simple you go ahead you take the trade you take a few shares and you put your stop below yesterday's low do the proper position sizing and you're going long because of SMARTsheet singing in the key of G folks thanks much for being here stay tuned David White's up next your favorite polar bear Tom O'Brien 3-5 I'll be back with you tomorrow wonderful Wednesday and a moment of silence as always on this day take care


Vasilis asks, will there ever be a time when BTC can be used as everyday currency, and what does it take for this to happen? Is it worth investing in such business solutions? Vasilis, I think yes, I think that BTC can be used as an everyday currency. But in order to do that, two things need to happen. One, volatility needs to be reduced to a level that allows people to plan, spend, and not be freaked out by sudden changes in the price of Bitcoin, or in the exchange rate or value of Bitcoin. For that, it takes more liquidity, more adoption, more volume of transactions. The bigger Bitcoin gets, the lower the volatility gets. The more traders, the more users, the more merchants are participating, the more volume of transactions, the more liquidity the lower the volatility gets. The second thing, of course, is very scaling solutions to make it possible to do transactions at lower fee rates. At the moment, the fees that are present in the Bitcoin network make it very difficult to use for small retail transactions, and mostly suitable to use for large value transactions. There are a number of possible solutions that are in play right now, including increasing the block size, which we saw with Bitcoin Cash, second-layer networks. The implementation of segregated witness opens the door for a lot of alternative solutions, as second-layers, sidechains, etc. Lightning network is in the final interoperability testing, and it should be a pair in wallet form, usable on the mainnet, at least for beta testing pretty soon. Is it worth investing in business solutions around retail everyday currency use of Bitcoin? Honestly, not yet. We have seen a number of companies that have tried to base their business plan on enabling small amounts of retail transactions for Bitcoin, and are struggling with that business model. In some cases, they are demanding a block size increase to make their business model work, even if that comes at the cost of decentralizing more centralization of Bitcoin nodes. At the moment, it is really not possible to do the types of small retail transactions. If you base a business around that, you will have a lot of difficulty making that work. I think there are a lot more promising opportunities in building basic infrastructure, and that means on-ramps and off-ramps, payment systems, exchanges, preferably decentralized mechanisms for buying and selling Bitcoin, and trading it with other cryptocurrencies, as well as with fiat, various grass-root-type exchanges, where you can exchange Bitcoin for cash, person-to-person, things like Bitcoin ATMs that allow people to access Bitcoin easily, better wallets that do better fee management and fee estimation, and have the capability to adjust fees with things like replaced by fee and child-paste for parents. All of these kinds of technologies, really, the boring but necessary infrastructure stuff is more important at this point than retail sales. Drew asks, in Bitcoin, in 2013, it was banking the unbanked, the other $6 billion. Now, transaction fees are much higher than the average daily wage of these people. Is this vision dead? Absolutely not. It is not dead. It will be achieved through a combination of multiple technologies, but it will take time. We are still in the very early stages of this technology, and in the very early stages, the financial conditions have not yet achieved the types of economies of scale that we expect to see over time. We are not yet in the optimization phase. We are still in the exploration phase. Think of this a bit like saying in the 1980s, I think one day, cellular telephones will change the lives of a farmer in Kenya.    At that point, the cost of a phone call was so expensive. It brought tears to my eyes every time I used a cellular telephone, which I did in 1990. It was a giant brick, and it didn't have any battery life. It cost a ridiculous amount of money to make a phone call. In fact, I hoped no one would call me because I had to pay for incoming calls, too, at a ridiculous rate. Does that mean that the vision of everyone having a cell phone was dead? Absolutely not. We saw all of the years, the economy of scale kicks in, the software gets better, the hardware gets better, the protocols get better, the scale gets better, the optimizations get better. Now you have a $5-$10 phone that can be charged off a solar panel that costs almost nothing to operate and also has other functionality. We are going to get to systems that are more efficient than today's banking system, more efficient than Visa, that can do more transactions, orders of magnitude more transactions, at a fraction of the cost. The marginal cost of a transaction will trend towards zero. We can't do that on the base blockchain, but we can do it with a combination of scaling on the base blockchain, second-layer solutions, sidechains, and various other things, maybe some of which we haven't even come up with yet. In fact, most of the innovations that are going to be required haven't yet been invented, but I wouldn't count them out. Which stage of grief are the banks at these days bargaining? They are flip-flopping from, this is stupid internet money that doesn't mean anything, to Bitcoin will destroy the planet right after it crashes the world economy, and can only be used not just by criminals, but by terrorists trying to destabilize the very foundation of our moral and effective society, bargaining, anger, somewhere between there. At the same time, they're touting blockchain, they're pointing at more benign, less offensive versions of more centralized versions, like, we hate Bitcoin because it's full of terrorists, but we love Ripple, great, that's bargaining.


from Las Vegas. It's theCUBE. Covering Nextwork, 2019 Americas. Brought to you by Juniper Networks. Welcome back everybody. Jeff Frick here with theCUBE. We're in Las Vegas at Caesars at the Juniper Nextwork event. About a thousand people kind of going over a lot of new cool things. 400 gigs, who knew that was coming? New information for me. But that's not what we're here today. We're here for the fourth installment of Around the Cube unpacking AI. And we're happy to have all the winners of the three previous rounds here at the same place. We didn't have to do it over the phone. So we're happy to have them and let's jump into it. So winner of Round One was Bob Friday. He is the VP and CTO at Mist, the Juniper company. Bob, great to see you. Yeah, good to be back. Absolutely, all the way from Seattle. Sharna Parky, she's a VP applied scientist at Textio. Good to see Sharna. Good to see you. And from Google, we know a lot of AI happens at Google, Rajan Sheth. He is the VP AI product management at Google. Welcome. Thank you. Great to be here. All right, so let's jump into it. So just to warm everybody up and we'll start with you, Bob. What are some, when you're talking to someone at a cocktail party Friday night, talking to your mom, and they say, what is AI? What do you give them as an example of where AI is impacting our lives today? Well, I mean, I think we all know the examples of the self-driving car, you know, AI starting to help our healthcare industry being diagnosed cancer. For me personally, I had kind of a weird experience last week at a retail technology event where basically we had these new digital mirrors doing facial recognition, right? And basically you start to have digital mirrors that are going to basically start guessing, hey, you have a beard, you have some glasses, and they start calling me old. So this is kind of very personal when I have AI.   No, no, no, no, no, no, no, no, no, no, no. So they started giving me some advertisements that were like, okay, guys, this is a little bit over the top. All right, Sharna, what about you? What's your favorite example to share with people? Yeah, I think one of my favorite examples of AI is kind of accessible and on your phone where the photos you take on an iPhone, the photos you put in Google Photos, they're automatically detecting the faces and they're labeling them for you. They're like, here's selfies, here's your family, here's your children, and that's the most accessible one. The ones that I think people don't really think about a lot are things like getting loan applications, right? We actually have AI deciding whether or not we get loans and that one is probably the most interesting one to me right now. Rajan, so I think the photos example is probably my favorite as well and what's interesting to me is that really, AI is actually not about the AI, it's about the user experience that you can create as a result of AI. What's cool about Google Photos is that my entire family uses Google Photos and they don't even know actually that the underlying is some of the most powerful AI in the world but what they know is they can find every picture of our kids on the beach whenever they want to or we had a great example where with our kids, every time they like something in the store, we take a picture of it and we can look up toy and actually find everything that they've taken picture of. It's interesting because I think most people don't even know the power that they have because if you search for beach in your Google Photos I was looking for an old bug picture from my high school there, it came right up so until you kind of explore, it's pretty tricky. So Rajan, I think a lot of conversation about AI, they don't always focus on general purpose, general purpose, general purpose machines and robots and computers but people don't really talk about the applied AI that's happening all around us. Why do you think that's so? It's a good question. There's a lot more talk about general purpose but the reality of where this has an impact right now are those specific use cases and so for example, things like personalizing customer interaction or spotting trends that you wouldn't have spotted before or turning unstructured data like documents into structured data, that's where AI is actually having an impact right now and I think it really boils down to getting to the right use cases where AI can add value. Sharan, I wanted to ask you, there's a lot of conversation always is AI replace people or is it an augmentation for people and we had Gary Kasparov on a couple years ago and he talked about, it was the combination of he plus the computer made the best chess player but that quickly went away. Now the computer is actually better than Gary Kasparov plus the computer. How should people think about AI as an augmentation tool versus a replacement tool and is it just going to be specific to the application? How do you kind of think about those? Yeah, I would say that any application where you're making life and death decisions, where you're making financial decisions that disadvantage people, anything where you've got UAVs and you're deciding whether or not to actually drop the bomb like you need a human in a loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and AI doesn't know that if it's always seen sheep in a field that when the sheep aren't there that that isn't a sheep. Like it doesn't have that kind of recognition to it. So anything where we're making decisions about parole or financial, anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. Great. So shift gears the team, or did you have something to pop? No, no, I didn't. Okay. The team reminded me I've been delinquent on my bell so I'll be more active on the bell, sorry about that. So everyone's even, we're starting at zero again. So I want to shift gears and talk about data sets. Bob, you're up on stage demoing some of your technology, the missed technology, and really it's an interesting combination of data sets. AI in its current form needs a lot of data. Again, kind of the classic Chihuahua and blueberry and photos you got to run a lot of them through. How do you think about data sets in terms of having the right data and a complete data set to drive an algorithm? Yeah, I think we all know data sets was one of the tipping points for AI to become more real, right? Along with cloud computing storage. But data is really one of the key points of making AI real, right? And my example on stage was wine, right? Great wine starts with great grapes, AI starts with great data. For us personally, LSTM is an example in our networking space where we have data for the last three months from our customers, and we're really using the last 30 days to really train these LSTM algorithms to really get this anomaly detection to a point where we don't have false positives. How much of the training is done once you've gone through the data a couple of times and adjust versus when you first start and you're not really sure how it's going to shake out in the algorithm? Yeah, so in our case right now, right training happens every night. So every night we're basically retraining those models basically to be able to predict if there's going to be an anomaly or network. You know, and this is really an example where you look at all these other cat image things. This is where these neural networks are really one of the transformational things that really moved AI into the reality column. And it's starting to impact all our different energy, whether it's techs, imaging. And in the networking world is an example where even AI and deep learning is really starting to impact our networking customers. Sharon, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of Chihuahuas and blackberries and I want to apply some machine intelligence to the problem. I mean, so you need to have the right data set. You know, big is a relative term and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares and then you're trying to detect some anomaly, right? If you train in AI what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there. So you actually need to over sample. You have to create a population of data that allows you to detect images. You can't say, oh, I'm going to reflect in my data set the percentage of black women in Seattle which is something below 6% and say it's fair. It's not, right? You have to be able to over sample things that you need and in some ways you can get this through surveys, you can get it through actually going to different sources but you have to bootstrap it in some way and then you have to refresh it because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time and so you may have been able to find the one yesterday but today it's a completely different ball game. Prozinc to you, which comes first? The chicken or the egg? You start with the data and I say this is the right opportunity to apply some AI or do you have some AI objectives that you want to achieve and now you got to go out and find the data? So actually I think where it starts is the business problem you're trying to solve and then from there you need to have the right data. What's interesting about this is that you can actually have starting points and so for example there's techniques around transfer learning where you're able to take an algorithm that's already been trained on a bunch of data and train it a little bit further with your data and so we've seen that such that people that may have for example only 100 images of something but they can use a model that's trained on millions of images and only use those 100 to create something that's actually quite accurate. So that's a great segue. Give you a ring on that one. It's a great segue into talking about applying one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Are you risking all kinds of interesting problems by taking that and applying it here especially in light of when people are going to go to algorithm marketplaces because I've got a data scientist I can go get one in a marketplace and apply it to my data. How should people be careful not to make a bad decision based on that? So I think it really depends and it depends on the type of machine learning that you're doing and what type of data you're talking about. So for example with images there are well-known techniques to be able to do this but with other things there aren't really and so it really depends but then the other really important thing is that no matter what at the end you need to test and iterate based on your data sets and based on sample data to see if it's accurate or not and then that's going to guide everything ultimately. Sharan, I was going to go to you. You brought up something in the preliminary rounds about open AI and kind of this we can't have this black box where stuff goes into the algorithm and stuff comes out and we're not sure what the result was. Sounds really important. Is that even plausible? Is it feasible?  You talked about the business objective that someone's trying to achieve. I go to the data scientist here by data you're telling me this is the output. How, kind of where's the line between the layman and the business person and the hardcore data science to bring together the knowledge of here's what's making the algorithm say this. Yeah, there's a lot of names for this whether it's explainable AI or interpretable AI or opening the black box, things like that. The algorithms that you use determine whether or not they're inspectable and the deeper your neural network gets the harder it is to inspect actually, right? So to your point, every time you take an AI and you use it in a different scenario than what it was built for, for example, there was a police precinct in New York that had a facial recognition software and a victim said, oh, it looked like this actor. This person looked like, I don't know, Bill Cosby or something like that. And you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So to Rajan's point, yes, like you can transfer learning to other AIs but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Even if your AI is explainable and somebody tries to use it in a way that it was never intended to be used, the risk is much higher. Now, I think maybe I'd add, you know, if you look at Marvis, kind of what we're building for the networking community, a good example is when Marvis tries to do, estimate your throughput, right? Your internet throughput, that's what we use what we call decision tree algorithm. And that's a very interpretive algorithm. And when we predict low throughput, we know how we got to that answer, right? We know what features got us there. But when we're doing something like anomaly detection, that's a neural network.  But that's a case where we actually use neural networks to actually find the anomaly. And then we're using something else to find the root cause. So it really depends on the use case and whether or not you're going to use an interpretive model or a neural network, which is more of a black box model to tell you you've got a cat or you've got a problem somewhere. So Bob, that's really interesting. So can you not unpack a neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the separate kind of process to get to the root cause? Yeah, you know, it's always hard to say never, but inherently, yes, neural networks are a very complicated set of weights, right? It's basically usually a supervised training model and we're feeding a bunch of data and trying to train it to detect a certain feature, a certain output. But that is where they're powerful, right? And that's why they basically are doing such a good job because they are minicking the brain, right? That neural network is a very complex thing. It's kind of like your brain, right? We really don't understand how your brain works right now. When you have a problem, it's really trial and error when we try to figure out what's going on. So I want to stay with you, Bob, for a minute. So what about when you change what you're optimizing for? So you just said you're optimizing for throughput of the network, you're looking for problems. Now let's just say it's into the quarter. So some other reason we're now you're changing, you're changing what you're optimizing for. Do you have to write a separate algorithm? Can you have dynamic movement inside that algorithm? How do you approach that problems? Because you're not always optimizing for the same things depending on the market conditions. Yeah, I mean, I think a good example, again with Marvis is really with what we call reinforcement learning, right? And reinforcement learning is a model we use for like regular resource management. And there we're really trying to optimize for the user experience and trying to balance the reward. The model's trying to reward whether or not we have a good balance between the network and the user, right? That reward can be changed. So that algorithm is basically reinforcement. You can fundamentally change how that algorithm works by changing the reward you give the algorithm. Great. Rajan, back to you. Couple of huge things that have come into play in the marketplace. Get your take. One is open source. You know, kind of what's the impact of open source generally on the availability to use I and more applications. And then two, cloud and soon to be edge. You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of AI? Yeah, I think open source is really important because I think one thing that's interesting about AI is that it's a very nascent feel. And the more that there's open source, the more that people can build on top of each other and be able to utilize what others have done. It's similar to how we've seen open source impact operating systems, the internet, things like that. With cloud, I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to create these networks. And so the capacity for data and the capacity for compute is much higher. Edge is going to be a very important thing, especially going into the next few years. You're seeing more things incorporated on the edge and one exciting development is around federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make edge even more powerful. But it's got to be so dynamic, right? Because the fundamental problem used to always be to move the compute to the data or the data to compute. Well now you've got on these edge devices you've got ton of data, right? Sensor data, all kinds of machine data. You've got potentially nasty hostile conditions. You're not in a nice pristine data center where the environmental conditions are and the connectivity issues. So when you think about that problem, yet there's still great information there. You've got latency issues. Some might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break up the problem to give you a step up? What we see a lot of customers do is they do a lot of training on the cloud but then inference on the edge. And so that way they're able to create the model that they want but then they get fast response time by moving the model to the edge. The other thing is that like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud but the other way to do it is to filter and to try to figure out what data you want to send to the cloud so that you can create the next datasets. Sharuna back to you. Let's shift gears into ethics. This pesky issue that's not a technological issue at all but we see it often, especially in tech just because you can doesn't mean that you should. So and this is not a STEM issue, right? There's a lot of different things that happen. So how should people be thinking about ethics? How should they incorporate ethics? How should they make sure that they've got a standard kind of overlooking kind of what they're doing and the decisions are being made? Yeah, one of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change a behavior. We need to ask questions like what is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem. And this can be true of the intention goal gap or the intention action gap. We can see when we regulated for cigarettes. We can't just make it look cool without telling them what the cigarettes are doing to them, right? So we can apply these same principles moving forward and they're pretty accessible without having to know, oh, this philosopher and that philosopher and this ethicist said these things. It can be pretty human. The challenge with this is that most people building these algorithms are not, they're not trained in this way of thinking. And especially when you're working at a startup, right? You don't have access to massive teams of people to guide you down this journey. So you need to build it in from the beginning and you need to be able to then based upon principles. And it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem. You need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to build that product for a wider audience. It seems like that we're on such a razor sharp edge, right? Coca-Cola wants you to buy Coca-Cola and they show as for Coca-Cola and they appeal to your, let's all sing together on the hillside and be one, right? But it feels like with AI that now you can cheat, right? Now you can use behavioral biases that are hardwired into my brain as a biological creature against me. And so where is the fine line between just trying to get you to buy Coke, which somewhat argues probably just as bad as Jewel because you get diabetes and all these other issues. But that's acceptable, but cigarettes are not and now we're seeing this stuff on Facebook where they're coming right at you. So we see taxes on a soda now, though, right? So we know that this is bad. And Coke isn't just selling Coke anymore, they're also selling vitamin water. So their play isn't to have a single product that you can purchase, but it is to have a suite of products that if you want that Coke, you can buy it. But if you want that vitamin water, you can have that too. I think if vitamin water and a smile, that only comes with the Coke, though. Bob, you ought to jump in. Yeah, I think we're going to see ethics really break into two different discussions, right?  Doing bad behavior like discriminatory hiring, training that behavior into AI is going to be wrong. It's wrong in the human world, it's going to be wrong in the AI world. I think the other component to this ethics discussion is really around privacy of data. It's like that mirror example, right? Who gave that mirror the right to basically tell me I'm old and actually do something with that data, right? Is that my data or is that the mirror's data that basically recognized me and basically did something with it, right? That's the Facebook example when I get the email telling me, look at that picture, and someone's tagged me in the pictures, like, where did that come from? Right, but I'm curious, Bob, to follow up on that, as social norms change, we talked about it a little bit before we turned the cameras on, right?  Not that long ago, right, in the 60s. So if someone had built an algorithm then that would have incorporated probably that social norm, but social norms change. So how should we kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, ooh, that's no longer acceptable. We need to tweak this thing. I would have said in that example, that was wrong 50 years ago. Yep. It was not okay 50 years ago. It was wrong, but if you asked somebody in Alabama, you know, at the University of Alabama math department who've been born red and born red in that culture is, well, they probably would have not necessarily agreed. But so generally though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying forward things that are no longer the right thing to do? Well, I think, as I said, I think, what we know is wrong is going to be wrong in the AI world. I think the more subtle thing is when we start relying on these AI to make decisions like shouldn't my car hit the pedestrian or save my life. Those are tough decisions to let a machine take off or your bomb decision, right? When we start letting the machines or, is it okay for Marvis to give this VIP's preference over other people, right? Those type of decisions are kind of the ethical decision. You know where they're right and wrong in the human world. I think the same thing will apply in the AI world. I do think we'll start to see more regulation. Just like we see regulation happen in our hiring, that regulation is going to be applied into our AI solutions. We're going to come back to regulation in a minute, but Rajan, I want to follow up with you. In your earlier session, you made an interesting comment. You said 10% is clearly good. 10% is clearly bad, but it's a soft squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people kind of make judgments in this big gray area in the middle? Yeah, and I think that is the toughest part. And so the approach that we've taken is to set out the set of AI principles. And what we did is actually wrote down seven things that we think AI should do and four things that we should not do, that we will not do. And we now have to actually look at everything that we're doing against those AI principles. And so part of that is coming up with that governance process, because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what you should do. And so that governance process is something we're doing, but I think it's something that every company is going to need to do. Sharana, I want to come back to you. So we're going to shift gears to talk a little bit about law. And we've all seen Zuckerberg, unfortunately, for him has been stuck in these congressional hearings over and over and over again, a little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. Like, you know, he stumbled into some really big, hairy, nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen unfortunately in a lot of those hearings is a lot of our elected officials are way, way, way behind. They're still printing their emails, right? So what is the role of the law? How should we think about it? What should we invite from the law to help sort some of this stuff out? Yeah, I think as an individual, right, I would like for each company not to make up their own set of principles, I would like to have a shared set of principles that we're following. The challenge, right, is that with between governments, that's impossible. China is never going to come up with the same regulations that we will. They have a different privacy standards than we do. But we are seeing locally, like the state of Washington has created a future of work task force and they are coming into the private sector and asking companies like Textio and like Google and Microsoft to actually advise them on what should we be regulating? We don't know, we're not the technologists, but they know how to regulate and they know how to move policies through the government. What we'll find is we don't advise regulators on what we should be regulating. They're going to regulate it in some way, just like they regulated the tobacco industry, just like they regulated sort of monopolies that tech is big enough now. There is enough money in it now that it will be regulated. And so we need to start advising them on what we should regulate because just like Mark, he said, well, everyone else was doing it. My competitors were doing it. So if you don't want me to do it, make us all stop. I think, can I do a negative bell on that one? Not for you, but for Mark's response to me, that's crazy. So Bob, old man at the mall, it's actually a little bit more codified, right? There's GDPR, which came through May of last year. Now the noon is to California, make sure I get to write California Consumer Protection Act, which goes into effect January one. And it's interesting, is that the hardest part of the implementation of that, I think I haven't implemented it, is the right to be forgotten. Because as we all know, computers are really good at recording information in cloud, it's recorded everywhere. There's no, they're there. So when these types of regulations, how does that impact AI? Because if I've got an algorithm built on a data set and person item number 472 decides they want to be forgotten, how the heck do I deal with that? Well, I mean, I think with Facebook, I kind of see that as, I suspect Mark knows what's right and wrong.  Please tell me what to do. I see AI as kind of like any other new technology. It can be abused and used in the wrong ways. I think legally, we have a constitution that protects our rights. And I think we're going to see a lawyer street AI, just like any other constitutional things. And people who are building products using AI, just like when you build medical products or other products and potentially harmful people, you're going to have to make sure that your AI product does not harm people. Your AI product does not include and promote discriminatory results. So I think we're going to see, our constitutional thing is going to apply to AI just like we've seen other technologies in work. And it's going to create jobs because of that, right? Yeah, so it'll be a whole new set of lawyers. A whole new set of lawyers and testers even, because otherwise if an individual company is saying, oh, we tested it, it works, trust us. Like, how are you going to get the independent third party verification of that? So we're going to start to see a whole proliferation of that type of field that never had to exist before. Yeah, one of my favorite, Dr. Reuben Chowdhury from Accenture. If you don't follow her on Twitter, follow her, she's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And ramen up on the keynote this morning, talked about mist and really this kind of shifting workload of fixing things into an autonomous setup where the system now is finding problems, diagnosing problems, fixing problems up too. I think he said even generating return authorization for broken gear, which is amazing. But autonomy opens up all kinds of crazy scary things. Robert Gates, we interviewed, said the only guns that are autonomous in the entire US military are the ones on the border of North Korea. Every single other one has to run through a person. So when you think about autonomy, and when you can actually grant this AI, the autonomy, the agency to act, what are some of the things to think about and what are the things to keep from just doing something bad really, really fast and efficiently? Yeah, I mean, I think this is what we discussed, right? I mean, I think for our practical purposes, there is a tipping point. I think eventually we will get to the CP30 Terminator Day where we actually build something on par with a human. But for the real purposes right now, we're really looking at tools that are going to help businesses, doctors, self-driving cars. And those tools are going to be used by our customers to basically allow them to do more productive things with their time. Whether it's a doctor that's using a tool to actually use AI to help make better predictions, they're still going to be a human involved. And what Rami talked about this morning, networking is really allowing our IT customers focus more on their business problems where they don't have to spend their time finding bad hardware, bad software, and making better experiences for the people they're actually trying to serve. Right, trying to get your take on autonomy. Because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own volition. Yeah, there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read, there's a book that just came out. Oh, what's the title? You look like a thing and I love you. It was a book named by an AI. If you want to learn a lot about AI and you don't know much about it, get it. It's really funny. So in there, there is in China a factory where the AI is optimizing output of cockroaches. Now, they want more cockroaches. Now, why do they want that? They want to grind them up and put them in like a lotion. It's one of their secret ingredients. Now, it depends on what parameters you allow that AI to change, right? If you decide to let the AI flood the container and then the cockroaches get out through the vents and then they get to the kitchen to get food and then they reproduce, the parameters in which you let them be autonomous over is the challenge. So when we're working with very narrow AI, when you tell the AI you can change these three things and you can't just change anything, then it's a lot easier to make that autonomous decision. And then the last part of it is that you want to know what is the results of a negative outcome, right? What's the result of a positive outcome and are those results something that we can take actually? Right, right. Rajan, I'll give you the last word on autonomy because kind of the next order of step is where the machines actually write their own algorithms, right? They start to write their own code. So they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in this space. You have huge data sets. You got great technology. You got TensorFlow. When will the machines start writing their own algorithms? Well, and actually it's already starting there. That, you know, for example, we have a product called Google Cloud AutoML which basically takes in a data set and then we find the best model to be able to match that data set. And so things like that are there already, but it's still very nascent. There's a lot more that can happen. And I think ultimately with how it's used, I think part of it is you have to start to always look at the downside of automation and what is the downside of a bad decision? Whether it's the wrong algorithm that you create or a bad decision in that model. And so if the downside is really big, that's where you need to start to apply a human in the loop. And so for example, in medicine, AI can do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need to have that in place in many situations to make sure that it's being applied well. But is that just today or is that tomorrow? Because with exponential growth and as fast as these things are growing, I mean, will there be a day where you don't necessarily need to maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and kind of pros and cons of treatment options that are more emotional than necessarily mechanical. Because it seems like eventually that doctor has a role, but it isn't necessarily inaccurately diagnosing a problem. I think for some things, absolutely. Over time, the algorithms will get better and better and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence, that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where that is. So for example, self-driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is going to be much lower than what's there with humans today. And so that will get there, but it will take time. And there will be a day when it'll be illegal for you to drive, if you have manslaughter, right? I believe absolutely there will be. And I don't think it's that far off actually. And I'm waiting for the day when I have my car take me up to Northern California with me sleeping. And it's the day, I live that long. That's right, and work while you're sleeping. All right, well, I want to thank everybody a ton for being on this panel. This has been super fun and these are really big issues. So I want to give you the final word. We'll just give everyone kind of a final say. And I just want to throw out there a Mars law. I mean, people talk about Moore's law all the time, but a Mars law, which Gartner stole and made into the hype cycle, is that we tend to overestimate in the short term, which is why you get the hype cycle. And we tend to underestimate in the long term the impacts of technology. So I just want to, as you look forward in the future, we won't put a year number on it. Kind of how do you see this rolling out? What are you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. Yeah, you know, for me, you know, the day of the Terminator C-Pythero, I don't know if it's a hundred years or a thousand years that day is coming. You know, we will eventually build something that's in part of the human. I think they mentioned about the book, you know, you look like a thing and I love you type of thing. That was written by someone who tried to train AI to basically pick up lines, right? Cheesy pick up lines. I'm not for sure I'm going to trust AI to help me with my pick up lines yet.   I love you, I don't know, it may work. It's kind of cute. Yeah, but who would have guessed online dating is what it is if you had asked, you know, 15 years ago. But I think, yeah, I think overall, yes, we will see the Terminator C-Pythero. It's probably not in our lifetime, but it's in the future somewhere. AI is definitely going to be on par with the internet, cell phone, radio. It's going to be a technology that's going to be accelerating. If you look at where technology's been over the last, is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah. And this curve of technology accelerations, this is amazing. It's a bit into curves, Sharuna. Yeah, I think the thing I'm most excited about for AI right now is the addition of creativity to a lot of our jobs. So a lot of, we build an augmented writing product and what we do is we look at the words that have happened in the world and their outcomes and we tell you what words have impacted people in the past.  They get to use language that have never been used before to communicate an idea. You can do this with any field. You can do it with composition of music. You can, if you can have access as an individual to the data of a bunch of cultures, the way that we evolve can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building to give AI to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the internet to build a model on a data set and I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not. And so it's an interesting time because we're in that middle area. And- It's getting loud. It's getting loud. It's getting loud. All right, Roger, we'll just throw it to you before we have to cut out or we're not going to be able to hear anything in a few minutes. So I actually start every presentation out with a picture of the Mosaic browser because what's interesting is I think that's where AI is today, compared to kind of when the internet was around 1994. We're just starting to see how AI can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the companies I talk to, the first question is, why should I be using this and what benefit does it give me? Why? 70% asks you why. Yeah, and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long run and we underestimate the long run, I think that every company out there and every product will be fundamentally transformed by AI over the course of the next decade. And it's actually going to have a bigger impact than the internet itself. And so that's really what we have to look forward to. All right, again, thank you everybody for participating. That was a ton of fun.  As I look at the score sheet here, we've got Bob coming in in the bronze at 15 points. Rajen at 17 and our gold medal winner for the silver bell. And Sharna at 20 points again. Thank you. Thank you so much. And look forward to our next conversation then. Me too. All right, Jeff Frick signing out from Caesars. Juniper next work, unpacking AI. Thanks for watching.