The following is a presentation of TFNN. The TFNN Bull Bear Training Hour. Every training day, live at 10 a.m. Eastern. Call now, toll free at<PHONE_NUMBER> or internationally at<PHONE_NUMBER>. The TFNN Bull Bear Training Hour. Now, Tom and Tommy O'Brien. Welcome folks, we have the Dow Industries down 32. NASDAQ up 26, S&P's are flat and that's after, bottom line, the markets themselves folks have already reached another all-time high today. Gold contract, gold contract on $4.80 straight and it's $14.55 an ounce. We have silver down 10 cents, $16.94 an ounce, light sweet crude flat, $58.42 a barrel, notes and bonds. You get the 10 year down 7 ticks, 30 off 11 ticks and good ol' Kingdala. Kingdala up 118 ticks, trading 98, 372, euros at 110, yen is at 109 and the pound is out here at 128 to one U.S. dollar. Tom O'Brien, what's going on besides all-time highs? All right, we're gonna get a recording going every day, man, all-time highs, all three indices, right? Quite a print. We got overnight 31.50 and we were, like you said, I think that was an intraday high that we had at the open, I believe. Yes, and folks, come over to our website at TFNN. You're gonna see right on the front page, Tom is now doing an update after the market close in the morning. Check it out right on the front page. You're gonna go, let me pull this right here. So as you're getting into TFNN folks, you're gonna see it right under featured content. Bottom line is that, check it out. Great update, Tom. Thanks, man. Yeah, there was some, you know, it's not that it's easy, but man, we've had a lot going on for a short holiday week where I pull up to see what's going on and it is full. As we're talking to our man, Kevin Hinks yesterday, he's talking about the number of economic numbers coming out this morning. Yes. We got the GDP number at 830, 2.1% for the third quarter. 1.9 was expected. We got durable goods as well. We get, we're still in earnings, right? You talked about that Dell was earning, Dell was moving this morning. John Deere came out and disappointed as well. They're down, let's see where they're at right now. It's just a plethora, man. There's a plethora of things. John Deere down 3.3% on their earnings this morning. But you know what my favorite story is? What? Of that morning report is if you go all the way to the bottom, man, I snuck in a little sports in there because Duke basketball last night lost to Steven F. Austin. And it's always fun rooting for the underdog, right? Especially against a franchise like Duke, number one ranked, Steven F. Austin took him to overtime. And folks, if you haven't seen the end of the game, I put the link in there for the ESPN article because you gotta find the end of it, man. Duke comes down the court, tied, okay? So they got the ball with like eight seconds left. Steven F. Austin steals the ball, runs down the length of the court, and lays it up as time expires to beat the number one ranked team in the nation. And that's the first time that Duke has lost a non-conference home game in 19 years, 150 straight games.  And it was the biggest upset in 15 years because Duke was favored by 27.5 points, which in college basketball is an enormous number because college sports, college basketball scores are not as high as the NBA scores. Yes. Many times you can have a 70 to 50 type game. So, I mean, just all around, check it out. The end of the game, you know, congrats to the players at Steven F. Austin because, whew, man, it's just, it's pretty cool. That's amazing, man. It is. You gotta go watch the clip. I'm gonna. Because it was amazing. And then, you know, folks, of course, we're kicking into Thanksgiving and we got some heavy storms, depending where you are in this great country of ours. You know, I almost put that, I didn't have enough time. I almost put that in the morning report as well because yeah, it's remarkable. And it really is. There's a lot of weather, that Denver airport, right? People just stranded in Denver. Boy, oh boy, you know, we're very lucky to live in Florida. We know that. And we really get reminded of it this time of year because boy, oh boy, those cold storms ripping across the country. Yeah, and so this one here, they got nasty Midwest storms and gusty New York winds, folks, okay? As Tommy was just saying, Denver received eight to 12 inches of snow with treacherous road conditions though. That's, you know, that's the bottom line. And then, of course, in New York City, now this is coming in on Thursday, they're gonna, hopefully the winds won't be as bad as they think they are right now, but 25 mile an hour winds. And of course, you get the Macy's Day parade. Yeah. And then for all the ski is in California, they're gonna be all set. I mean, the Sierra, Nevada's and the mountains out there, they're getting, they're getting a foot of snow. I guess, yeah, the foot of snow is one thing, but this is sad. So, because after all those fires, now Southern California, they're thinking it might touch off mudslides. That'll be a problem. That'll be a big problem. Yeah. And I saw the headline just to bring it back to Denver. 500 flights closed Wednesday in Denver. Cancel, excuse me, I believe. Yeah. And that's a tough one. One of the biggest travel days, if not the biggest travel day of the year, man. There's no doubt. Look at this, Chicago. Excuse me. 50 mile an hour winds right now. Yeah. I'm a man, Mr. Kevin Hinks. He must be, he's, and here he is. I hear, I hear it. He was just on TD Ameritrade. Perfect. We'll be able to talk to him soon. They're from the Windy City, man. They're used to that wind. They're troopers out there in Chicago, man.  They gotta be troopers out there in that Windy City of Chicago. Yeah, there's no doubt. And listen, folks, every trading day right here, you wanna understand option, option strategies, futures, great program. If you haven't test driven to think of swim platform, real easy to do, just hit that button and the banner right when you're on site at TFNN, coming from the Windy City. Oh man, Mr. Kevin Hinks, what's going on? Oh, no Kevin yet. Okay. So he's getting, he's still getting blown away out there. That's all right. He'll jump on. Look at that 50 mile an hour winds, oh my. That is, man. That is. And I, you know, it's, I say they can handle it, man, but boy, oh boy. I mean, especially in the winter, the windshield numbers, as I think we all know, if you've seen the weather, man, they put up some amazing windshield numbers because when they have the cold weather, some of the areas of the country you have, then you combine the wind with it and they got negative 15, negative 25. I mean, that's where it really, you lose some fingers and some toes if you're not careful out there. Oh, there's no doubt. And we're getting an update, the den right now. So Western Denver, 14 inches of snow, seven degrees. Snow plow operators already on Thanksgiving holiday. I don't blame them, man. Oh my God. I don't blame them, yeah. So, you know, market-wise out here, Tom, bottom line is that, let's take a look at this. Let's take a look at these futures because there's no doubt that this got right to the high, I believe, as soon as we were open. Let's see, so. If you want to minimize that area in the bottom, so we're only seeing that way. Thank you. Okay, so let me pull this. Yeah, because GDP number was strong just as you're pulling it up, right? And the durable goods was a big beat as well, man. I believe durable goods came in at 0.6 or 0.8% and there was an expected decline. And that number out at 8.30 as well. So with those two, I mean, you could have seen the market even higher, I think. Yeah, no, I can see that. So it looks like the futures made a high at five o'clock this morning. Yeah. But the spy made a high on the open. That's what we did do out there. Stay right there, folks. Tommy and I are coming right back. Our phone number is<PHONE_NUMBER>. Right back, folks. Thank you. 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. 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Hear all of the TFNN shows, plus see all of 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. TFNN has launched our brand new website. You can still visit us at the same tfnn.com URL but when you do, you'll see a new and improved homepage with a much simpler navigation whether you're watching 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. Call now toll free at 1-877-927-6648. Internationally at<PHONE_NUMBER>. Welcome back folks. Now I was down 29 now except 29 S&Ps of flat. That's after all the indices reaching an all time high today. And let's go to our man in the windy city of Chicago folks. Our man Mr. Kevin Hinks from TD Ameritrade and don't forget every trading day right here. 11 to 12 Eastern Standard Time Outstanding Program. You want to understand option, option strategies, futures. Great program, Kevin and his team. If you haven't test driven yet the think-as-one platform so easy to do as you're watching TV right now just hit that banner. Bring it up. They'll allow you to trade paper money. Kevin Hinks, we heard it was windy 50 miles an hour out there today, man. Good morning, Tom O'Brien. The only thing that's inconsistent about Chicago is the weather, Tom. If you don't like the weather just wait a few minutes, it'll change. And people in Chicago, they're used to it now. The national weather service talks about weather in Chicago and people are like, yeah, just a Wednesday. I'm with you. Yeah, because your headlines out there today.  California's getting something. They're saying that Chicago 50 miles an hour, 50 miles an hour winds, man. That's pretty intense, man. This time of the year, guys, if the temperatures are a little warm, which actually you say out, they're up there 50 degrees today, chances are that's because of wind. Wow. And so that's just what you have to live with. If you get a little better temperature, you gotta deal with some wind. That's pretty cool, man. That's, yeah, no, there's nothing wrong with that, man. 50, with wind, I'll take that all day instead of 20, right? Absolutely. Or 20 below. Yeah. You'll take that. You're done, right? You will. So, hey, listen, another day coming into Thanksgiving. They reached all-time highs again. You know, a lot of action, man. I mean, it's slow, but there's a lot of action, right?  Yeah. Because they literally took most of the information for the rest of the week and just put it out today. So we got some nice data points. Some of them were strong. I thought the number that just came out at 9 a.m., personal income and outlays was a little soft. That's why you saw bonds rally back to about half of their losses because the durable goods number was really strong. Yes. The GDP number was a nice upgrade for a second look at third quarter GDP. That made bonds weak, but now they've halved their losses because this income and outlays number was pretty soft. No inflation in the pipeline at all. It was, you know, pretty light numbers. So bonds got a little strong based on that. And, you know, we got a little news in Bowling, holding the Dow. That's why the Dow futures are a little weak. We, you know, John Deere's earnings are probably hurt in Caterpillar a little bit this morning, but all in all time as we break, go through the day to day, I think more and more eyes will be turning off the computers and focusing on Thanksgiving. Yeah, there's no doubt, you know, by noon, I expect that things will slow down. You know, Kevin, these notes and bonds, man, I know he keeps saying it, but it's amazing. They're relentless. They just, you know, because when I grabbed this one, earlier this one, as Kevin said, folks, the note and bond microwaves down pretty good actually, okay?  And then all of a sudden, by the time I got to the office, just as you said, they took back half of it. It's like, okay, man, people are still buying those hand over fist too. In the last two days, you know, we had monster volume in both of those contracts, which it's like, man, you know, there's probably a rollover happening too, but that's a, there was a big numbers inside those contracts the last two days. Yeah, indices are drifting here, Tom. I mean, there's really no reason to get in their way, but that being said, you know, some strong durable goods and GDP may have put a little trade into this market, but I think personal income and at least just kind of muted it. So with three good data points that we got this morning between 7.30 Chicago time and nine, it's too good, one, one kind of soft, all in all it's kind of neutral and it's time to look towards turkey day, frankly. No doubt. I'm ready. Yeah. Hey, let me ask you something. You know, like when we came into the summer, yeah, you basically always saying you gotta be careful in the markets in general. So you're trying to push things out to September. Right now, if we're doing spreads inside the, you know, do you push them out to January? How do you deal with that? Like right now? I'm like trading, here's what I, here's the way I think. And it's from years of looking at these things. I love trading January from the long side and then selling between now and December. Okay, cool. Between the end of December because it's a holiday market. Yeah. January, remember what most Jan options are if you open up your option screen. There's a long-term option that has now moved up on the calendar all the way to January. So there's big open interest, usually big interest in Jan options in general. Okay. So yeah, you're right. With lower numbers into November and December, I like to buy the Jan options and sell the Nova Dees. That's great. That's cool, man. Yeah.  People are looking as to January now.  You can imagine the amount of, some of these portfolios are gonna be lopsided with some of these equities, the gains are so dramatic. Sure. I mean, so it's like. Yeah, you know, you've got a lot of money managers time with a lot of big numbers. Yes. On their sheets. The last, you know, looking at the overall year, I'm sure they'll be, you know, at least lightening up on any rallies here. So I think this market will get a little, you know, it'll drift, but I don't think it's going anywhere significant during this period. But January should bring in inflows, right? And people are making more money. So I think January will probably a pretty bullish month for this market unless something comes to change it, obviously. Sure. So the object to change without notice, Tom. Yeah, there's no doubt about that. And then, you know, what does happen is this, a lot of people are gonna be pushing more money into their IRA. That's gonna get put to work, right? I mean, what does happen is that people look at how much they've made, you know, how much they've put in there and say, hey, I can put another couple grand in there. And that times thousands of people adds up to some real money on a longer term basis too, which is a different ballgame altogether. Do you know what I mean? Yeah, I think that, you know, that's money that's gonna be invested in the market most of the time, right? That's going directly into the stock market, depending on the age of the person doing it. Obviously, as you get older, you should be transitioning a percentage of your money into fixed income and things like that. But fixed income, it's very difficult for the elderly and the people in their retirement to be relying on fixed income because the yields are so low. There's no doubt, man.  Right. And we take a look at this from that perspective, you know, we've had quite a year. Of course, the high's been three, the low's been 1.45, you know? Right. But even a three, right? Okay, three percent, three percent. I guess three seems like a lot now. Right. People would be killing themselves to get three percent right now. But that's what this does. Lower rates pushes you toward stocks, right? This is when the smart portfolio managers have to be looking at dividend play. Dividend stocks to replace the yields and then when the market flips and rates go higher, then you transition back into those. Yeah, there's no doubt. You know, it's amazing too. And when we was talking dividends, it's amazing that, you know, these big phone companies, Rise and Marbelle, they've been paying four, four and a half percent forever. Yeah.  It's like pretty amazing, man. Well, listen. A lot of cash flow out of those. Huge. Folks, right here, 45 minutes from now. Kevin, you have a great one, a safe one, a great Thanksgiving, great weekend. We look forward to speaking to you next Tuesday. Thanks for having me on, guys. Have a great Thanksgiving, O'Brien. Thank you. Thanks, Kevin, you too. Stay right there, folks. Tom and I are coming right back. Dow is down 23. Nasdaq up 31. S&P's a flat. Come right back. Hi, folks. Tom O'Brien here. If you'd like to get my daily newsletter and market insights, then now is a great time to sign up for a 30-day free trial. Every morning by 9.30, I send out my morning letter to subscribers with market commentary on a variety of markets, currencies and commodities to keep investors up-to-date on the day's trading action. 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Don't miss out on this incredible new piece of software. Get your copy of The Art of Timing the Trade Charts today by visiting tfnn.com. 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. ["Think or Swim Banner"] Welcome back, folks. Now I was down 23. Nasdaq is up 31. S&P's a flat. Let's go inside that dial and see what is the strength versus the weakness inside here. Put this baby up. Point-wise, what we have is that... Oh, yep, it's Boeing. Boeing's putting 30 negative points. Caterpillar, nine. Dow to Pond, nine. Not that bad. Apple's putting 11 positive points. You got 3M putting nine. Big Mac is putting eight. And let's go over to Deer, Tom, and see what they have to say. They... There's no doubt this is... You know, it's down eight bucks. Yeah, off the lows, but they had a little bit of negative action this morning on their earnings, that's for sure. Yeah, so let's see what that... Let's see. So the headlines is, Deer Outlook Disappoints as Trade War Keeps Farmers Frugal. Can't blame them, man. Yeah. That's the sales of agricultural turf equipment forecast to fall 5% to 10%. Let's see. Deer delivered a more cautious outlook than expected for a year ahead as simmering U.S. trade tensions and growing... And difficult growing conditions keep North America farmers from replacing large equipment. Demand for machinery has taken a backseat as trade concerns of farmers worried about who will buy their products. Man, it's got to be pretty intense to be in a farmer right now, man. Oh, man, it's not much tougher that you can be going through, I imagine, from what I continually hear what they're dealing with out there, yeah? Seriously, man. Okay, so given their first guidance for fiscal 2020, the newly appointed chief has got the executive office, John May, projected net income in the range of 2.7 billion to 3.1. And that compares with the 3.46. So that's quite a... That's staggering, right? I was waiting again. I mean, that's just... They might have income of 2.7 billion and they were gonna have maybe 3.5. That's $800 million to the bottom line that just might go poof. Yeah. So that's serious business, man. And that's keeping in mind that that was the average analyst estimate which already factored in some struggles. Yes. You know? Right. So it's, yeah. Wow. Let's see. Fiscal fourth quarter, the top truck to make reported better than expected sales and earnings that were broadly in line with estimates. There, let's look at the positive. There are positives. The deal between the U.S. and China could provide stimulus. Well, good, guess what? You're waiting on that, man. Seriously. Yeah. If you were a farmer, just pitch this hook. If you were a farmer and a deal comes down and you have to depend on that deal staying in place, would you go spend the hundreds of thousands of dollars on equipment? That's right. That's pretty interesting. No, it's a great point, exactly.  Yeah, no doubt. So that's the deal. Let's go over to Caterpillar and see what that's doing to Caterpillar. Yep. Hitting Caterpillar a little bit this morning, as you might expect. Right. Not as bad, though. Interesting, right? No, no, yeah. And I think they're both, you know, they got, of course, the farming equipment, but they're both into mining equipment big time too, I believe, let's see. So under the scripture of Caterpillar, you got construction mining forestry. Let's go into deer and see maybe deer is just a bigger agricultural deal. I see, that's interesting. They're into construction of forestry also, but agriculture is the first one. Yeah. Yeah. So if I put the revenue, if they break it down. Yeah, that's what I was waiting for. All right. Okay, so this is cool. They do break it down. So deer, revenue-wise, agricultural and turf, 23.7 billion, construction, 11 billion. Okay. Now, let's do Caterpillar. So we gotta remember those numbers. 23 and 11. Okay, cool. And then inside Caterpillar, let's see if they even break it down for us, right? Sometimes they hide the sauce. They do. They do, and there you go. Yeah, machinery energy transportation, 51 billion. Yeah, it's a little different mix. Yeah. Hey, how about oil if we could jump over? So we just had those crew numbers come out, quite a miss, man. We were looking for, we talked about it yesterday on the show, the estimate was looking for an EIA inventory decline of about 800,000 barrels. And it looks like we still got plenty of oil, man. That number coming in at 1.5 million barrels on crude. And I'm pulling up the chart. We initially got a little bit of a thrust downward. I got the chart up there. We spiked to 58.16, but oil right back to where we were, man. $58.37, we didn't miss a beat and we're actually trading higher on that number right now. Wow. Pretty remarkable. And the remarkable run we had last night, man. You look at that high we had at 6.50. I call it last night, 6.50 AM this morning. We're up at $58.66. And boy, oh boy, crude catching a bid right now at 58.49, as it looks like even a surplus, an unexpected surplus on that EIA number. Can't hold it down. That's pretty wild. And what you're doing here, folks, which is gonna be interesting here is that you're going right, we've been up here a few times and 58.74 is the number that it's trying to break through right now. Look at this. Well, we might get there by the end of the segment, let alone show. No, no, totally. Totally, I can see that. No doubt. I mean, talk about a trend of higher highs and lower and higher lows, right? I mean, that trend is just poof. It's pretty clear. Let's put that on a yearly. Well, I actually got to do a CL. And CL one? Yeah. There we go. Let me get the gas inventory up there as well. Plenty of gas, 4.3 million barrels of gas inventory. Oh, no, excuse me, that's the API. Gas came in at 5.1 and the estimate was only an increase of 800,000. So plenty of gas as well coming into Thanksgiving. Yeah, you can see, they have this long-term chart up now of oil.  I mean, this thing back down and well, actually a year ago, that's when we had back down. Look at that. So in September, last September folks, you're at $76 and in the middle of December, we were at 42. Yeah, that was crazy run. Yeah. And that's of course the market, it took the market down simultaneously. That's in the market itself also. Oh yeah, I'm sure. You look at that December low, some of those big bars, that's when the market, good old Christmas Eve was screwed on Christmas Eve coming for all the cash. Coming for all the cash on Christmas Eve. And he got it and he just had to give it back the next... That's right. He started giving it back the next week though, right? Seriously. Yeah, no doubt.  Asia was mixed last night. You know, Hong Kong still, Hang Seng still can't move. So there's not a lot of movement over there. And what does happen, even though that they are not opened, though we are not opened, there's not really a lot of action even the next couple of days. It's always intriguing to me that if our markets are closed, their markets are open, but they're a lot slower, you know? It would make sense. We're the biggest economy in the world.  And even we're closed on Friday, one of the only days really that we're closed when the market is open. Stock market open for a half day on Friday, but really, I mean, if they didn't have the regulations, I think, that say that the stock market can't be closed for four straight days, they have to sneak in a half day open there just for, you know, price discovery to make sure that things aren't closed for too long, but very few traders in the office for Friday after Thanksgiving. That's quite a rule, huh? It should be because bad things can happen. You start closing the market for four or five days when you reopen, you know, a lot can happen at that time. You want to make it to mark, to mark, on against this new lease. Stay right there, folks. Tommy and I are coming back. We got our man, Mr. Teddy. Case that, we're going to be talking comments. He's coming right back. If you are in the CD market and looking for 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 a four-year CD in the country as of February 20th is 3.1%. 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The prospectus or summary prospectus should be read carefully before investing. An investment in the funds is subject to risk including the possible loss of principal. The funds are designed to be utilized only by sophisticated investors such as traders and active investors. Distributor, Four Side 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. Welcome back, folks. Now, now it's down to 19 as it's up 30 S&Ps of flat. Let's go over to my amendment, Mr. Teddie Kakes. As we do each and every Wednesday at 40 past the hour, you can reach Teddie every trading day, folks, at forex-trading-unlock.com. That's forex-trading-unlock.com. Teddie Kakes, that, happy Thanksgiving, man. Happy Thanksgiving to you guys, too. Totally. So what kind of presents are you gonna give me here today in the currency market? Well, we're looking at the US dollar yen bulls, huh? Yeah. We're looking at a new high, I think. It's pressing that 109.23 right now. Yeah. Right. So this could be a little trouble inside the gold market, folks. If this thing holds, let me see this thing. Oh, that's interesting, though. Okay, we get to 109.31. I think, yeah, 109.32 is, oh, man, look at that. It doesn't need very much to all of a sudden accelerate a rally up to that 110 area. Look at that. This is pretty wild. The benchmark number, folks, that I'm keeping my eye on is July. It's 109.32, and we hit 109.31 today, man. Just crazy when you see these things pressing, right? And it's like, how does this work? We know that technically that's what they like to do, but it's like so weird, man. We're talking large numbers, presses, presses, and yet we know it's either gonna blow it away or back down, right? Pretty wild. Well, the dollar index right now is kind of helping it out right now, I think. Yes, yes. Yeah. It keeps flirting with this resistance, and for some reason, the bulls can't, you know, they keep popping these highs, you know? I know, I know. There's no doubt, man.  Right. Yeah, interesting. So what normally does happen? Does the currency market slow down like right now, too? Or, you know, is currency like, you just keeps moving, meaning? You know, and there was numbers that were coming out, like there's all the significant numbers are out for the week already. Okay. So there's really no reason to shake up anything in the EU, the UK, Japan, anywhere. Now, there is something that's gonna be, might be interesting though, is later today, you have the SNP from the UK that they're, I guess, putting out some kind of policy about their platform for election for next year. I see. For Brexit. Okay. And they're saying that there's not going to be any trade deal in 2020. They're expecting that. Wow. So they're saying that Brexit's gonna go on without a trade deal and they're gonna fight Boris and they're gonna push for it. And that's pretty much what they're gonna lay down, I guess, today. That's the news that everyone's waiting to hear. Yeah. So that's gonna, that's, yeah. I guess that the pound and the euro, that's what's gonna dictate where the dollar's going, right? I mean, that's what it really seems to come down to, you know? No doubt. So that could be the one sleeping little thing that could happen over the next couple of days. I don't know if it's really gonna shake it up, but if that makes, if that would cause the pound to rally, then I think you'd have the dollar index probably take a little dent, a little pullback and then, you know, so that also might affect the US dollar yen trade as well. And then we have the trade talks, which supposedly once again, they're saying they're going well or whatever. That's kind of fueling this US dollar yen trade. And if that all of a sudden falls apart, somehow over Thanksgiving, who knows what we get? We have to be thankful if everything's just quiet, I think, the next couple of days. I agree. Hey, let me ask you something.  I don't think you do anyway. But Tommy and I were looking at this yesterday. So, I mean, this is the weakest it's been, like when I pull this up forever, right? So what makes that happen? Is it the economy? Why is that happening? Well, it's definitely the economy. There's a lot of, I mean, the country's in so much debt to begin with. And there's just no faith in their currency there, you know, most people because of the stability. It's pretty wild, folks.  And yeah, you bring it back to, look at this, even 2011, it was 1.5.  That's quite a loss and wealth. It's a huge, huge, huge move. Yeah. Yeah. Wow. That's the kind of thing that breaks a country. That causes social unrest, that kind of thing. Yeah. Literally, it does. It's too fast. It's too much. It's way overdone. So, and it's just, it's because of economic policies and all kinds of other, there's a lot of macro things that have led to this. That's for sure. So, and how they did the two things to try and prop it up, that's just another sign that they're in a lot of trouble, I think. Well, you know, it's so intriguing and I, I stopped hunting it last night. Like, I, I buy a lot of stone and, you know, you know, it's quartzite, marble, all this folks, and Brazil has beautiful stone, man. So I'm really, I'm looking at this, I'm saying to myself, I wonder the correlation in US dollars versus their reels, you know what I mean? What comes down? And of course, there's plenty of gold mines and, and Brazil and it's like, okay, so is that correlation? I suspect that the land value probably went up as this is going down, right? Cause if it didn't, like everyone would be down in Brazil getting the commodities, right? Sure, sure. I would think that there's rampant inflation without a doubt, there has to be. Right, no, I'm with you. So that's always intriguing how that works, right? Hey, let me- It's not a fun situation. It's definitely something that hopefully stabilizes. Yeah, pretty intense, man, no doubt. But I think we're gonna see a lot of inactivity the next couple of days, like you were saying, like the markets are, when we're done, when the US is quiet, the rest of the world doesn't have a lot to do. Yeah, pretty wild, man. So except for the Euro, the Euro and US dollar right now, it's pressed and low, it's just like the US dollar yens making highs. And the US dollar Swiss is also right hanging below parity there, which I don't know the parity dance that keeps on failing. So I guess maybe that's gonna be our key to watch to see is the dollar index gonna really rally? Can the Swiss finally get above parity? And will the US dollar yen break out to the upside? Or is it gonna be a failure? Maybe we're coming into the turn again. So I think it's tough with the holiday markets to get a gauge on anything. It is, no, listen, man. And one of the biggest moves I've ever seen, now I'm going back, man, like 1996 or something, was on the Friday, this was in the equity markets. Teddy, but it wasn't the Friday. Like at that point, the markets were trading a lot lower. So maybe like the S&Ps went up 20 points, which was like insane, you know, in 96. And then they imploded the following Monday. It was like on Friday, we're all in the desk and we're just hitting buttons because it didn't matter what you bought. You were making money. But you know why? Because that's because the guys in the S&P pit was really thin. They just ran it up. Exactly. It was like, wow, this is pretty cool. There was nobody in New York. There was just guys in Chicago bidding it up. Totally. And then on Monday, boom. I mean, everybody can now, but I would never forget that day cause like we all thought we were geniuses. Well, we knew, we knew that it wasn't right, but we left there like, hey, this is pretty freaking cool, man. You know, it didn't, didn't matter, folks. I'm not kidding. It didn't matter what you bought, you know? Hey, hey, what about- Happy Thanksgiving, guys. Happy Thanksgiving. Happy Thanksgiving, Teddy. I heard it's pretty windy up there right now, right? Oh, it's a huge windstorm right now. Okay, man, listen. Always a pleasure. We look forward to speaking the next Wednesdays, Teddy. You too. Stay right there, folks. Tommy and I are coming 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. 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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 five 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. Since 1984, Basel Chapman has been using the Chapman Wave methodology to advise traders of his expert market opinion. 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For more information, just click the Think or Swim banner on the front page of tfnn.com. Welcome back, folks. Let's go to our man, John and Philly. John, what's going on, brother? And happy Thanksgiving to you too, man. Absolutely. Say a real quick question as you close up, please, on First Majestic Common Stock. The stock has gone sideways now since Labor Day. Very specific question, Tom. If this rally's back up to 1150 or so, what is it that you need to see? If it does that, that tells you you've got a CDD rally going on up towards 15. I'd appreciate it. Yeah, on the daily, you know, you're going to be looking for basically five to eight million shares trying to break that level up there. And I think what we have, John, is that what happens, folks, is that this is a good stock to trade, but it's a very tough stock because it moves very fast, folks, up or down. And I suspect that the way this week we try to get to a higher high, and you can see the volume is dying on the vine, so it's like this consolidation is still here. So I suspect it's going to probably try to get, whether it's $0.30, $0.40 off of this level, meaning maybe the $10 low before it makes another surge. But I like the setup, but we know that this is a tough one, man. And I don't like buying this one closer to the highs. I like buying it at the bottom of the consolidation. You know what I'm saying? I do. Great. Thanks so much. OK, man. You have a great one and a safe one. Yeah, so we get four days, Tom. Bottom line. We sure do. We'll see. Got to love it. I know. We'll see whether they're going to rock this market. And what I do expect, we're going to see, folks, is that by the time I'm on this afternoon, you're going to be all green. Right now the dial is down, too. You got to love it, man. Definitely. Folks, you stay right there, folks. We've got a TD Ameritrade coming up next. Then our man, Mr. Basil Chapman, Steve Rhodes. And I will be back this afternoon. Tommy, have a great one, safe one. Love you, man. Love you. Talk to you a little bit later. OK. Don't get it, folks. Red! Oh!


Military mutiny brewing in Russia. Ukrainian Major General warns a military mutiny could break out in Russia. It may arise when the Russian occupants realize that Russian President Vladimir Putin is using them on a roll of the dice. The opinion was expressed by the former deputy head of the security service of Ukraine, Major General of the reserve Viktor Yahun in an interview with Glavred. He notes that part of the occupation army does not understand what it is doing in the war. At a certain point Russian military will realize that they are being used on a roll of the dice. Putin tried to realize similar events in Ukraine but we have a different situation altogether. The Ukrainian military are conscious. They understand what they are defending and why they are at the front. However the situation is different among Russians. A certain category of their military, especially the lower ranks, do not understand what they are doing in the war, Yahun said. According to him one part of the Russian occupiers came to fight in Ukraine to avoid problems. The second part rejoices with blood and the third part makes money from the war. But the vast majority don't understand at all what this war is about and what the Russians are doing here. They do not understand who and from whom they are liberating when they are not loved. Here he noted it should be noted that Russia is on the verge of turning against Vladimir Putin as the country's economy is running out of ways to fend off crippling Western sanctions experts have warned. After Russia's invasion of Ukraine in 2022 the West hit Moscow with brutal sanctions aimed at isolating the country on the world stage. Putin has managed to minimize the damage to Russia's economy so far by burning through his cash reserves. But Yale's researchers and economists Jeffrey Sonnenfeld and Stephen Tien warned the lives of Russian citizens will soon get a lot more difficult. Tien told Business Insider people are living regular lives for now but it's a completely unsustainable strategy and the fundamental growth drivers underpinning that economy are deteriorating in front of our eyes and if Trump's not elected then all of that will rise to the surface. The House of Cards will come crashing down. Sergei Guryev a Russian economist and the incoming Dean of the London Business School told the same outlet that Russia will be hit with a deep recession that could cause social unrest. He said Putin doesn't believe that Russia is doing very well. Putin understands the deficiencies of the statistics but he needs to convince Russians that everything is fine.


What's happening guys? It's Shane here, so here we go. This topic has been requested like crazy since the incident that shall not be named happened. And my audience knows that I always deliver as long as there's enough demand. And by the way, we're like 8 seconds into this video and you still haven't smashed like. What's up with that? And I really feel like this one is especially important to watch, so I spent a lot of time reflecting on it. And I realized that if I'm gonna make a video like this, I might as well make it as impactful and useful as possible. Because I talk a lot about investing on this channel and I realize that not everyone is at a point where they can invest. So everyone's seen the reports and it's been really emotionally draining. At the time of writing this script, the economy has lost well over 700,000 jobs and it's probably gonna be a lot worse by the time you watch this. Now there's a lot of debate about whether we're in a recession right now or whether it's temporary and it's going to rebound once we get rid of this issue. But if we are heading into a recession, which there's a very good chance that we are, there's a few things that we can do. Now everybody looks back on the 2008 crisis and they think back to that time and they say, oh I wish I would have dumped all my money into the market then. But before you can do that, you either need to have a good career or at least have a steady source of income. That way you can get to the point where you're not living paycheck to paycheck and you can invest your money. And I think this is incredibly common for a lot of people. So I decided to make this video to talk about the most stable careers that are going to not only survive but maybe even thrive in the coming recession. We're going to dive pretty deep into this and I can almost guarantee you that by the end of the video you're going to know more than 98% of people out there about which jobs are actually stable and which ones aren't. There's some careers that will pretty much be unaffected and then other careers you'll have about as much chance of finding work as a stuntman after a work from home order. And as always, this is just my opinion based off of my life experience as well as a ton of research that I put into these videos. So without dragging this intro into something that's unnecessarily wrong, let's jump right into it. So the first one on the list is going to be education and by that I mean all different types of teachers. This goes from elementary school teachers all the way up to college professors. Now these might not be the highest paying jobs ever, but they're extremely stable overall and they do especially well when it comes to recessions. For elementary, middle school and high school teachers, they're pretty stable just because of the fact that they're pretty essential for a modern society. And then the second thing is they are technically a government job, although they're usually part of the local government which isn't quite as safe and stable as a federal government job. Then in the case of college professors, when a recession comes a lot of people basically either quit their job or they get fired from their job and they decide to go back to school. This means that colleges end up getting just as many if not more customers, I mean students. So college professors and other people that work for colleges generally don't have to worry too much about losing their job. Now the second one on the list is going to be careers that are in the medical field. Now I think this one is fairly obvious, just look what's happening during the current situation. Unfortunately, a lot of people either lost their jobs or they got laid off temporarily or they're working part time, whereas medical workers probably ended up working even more than they usually do. And sure there's always going to be a few exceptions, but overall there was an obvious trend of medical workers being even more needed than they usually are. And this has been shown to hold true throughout pretty much all the recessions in the history of the United States. Think about it, if the apocalypse came and we were like a week away from the end of the world, who would still be working? Doctors, nurses and other medical professions would still be taking care of the sick and the injured. There's pretty much no situation where these professions wouldn't be valuable to society because they're pretty much the definition of essential. And full disclosure here, I am a healthcare worker so I might be a little bit biased, but I think the research tends to support my opinion. First of all, if you look at the future growth of jobs, many medical careers have ridiculously high expected growth. For instance, for a two year degree like medical sonographer, there's a 14% growth rate over the next 10 years. An example of a degree that takes four years to get into would be registered nurse, which has a 12% growth rate over the next 10 years with over 3 million jobs already. An example of a degree that takes about six years to get into would be physician's assistant, and that one is growing at a ridiculous 31%. An example of a degree that would take eight years to get into would be medical doctor, and that one's growing at 7%. Now, if you compare all of these numbers to the average of another profession, like engineering, for instance, which is usually around four to 6%, you'll see why health careers are so stable. Now, at this point, you might be thinking that I'm cherry picking specific health careers just to try to prove a point. And you do have a point here, there are some health careers that don't have these amazing stats. But the Bureau of Labor Statistics has stated that as a whole, health care careers are expected to grow at 14% on average. This is much higher than the average of 4% to 5% for all other occupations. On top of that, 1.9 million jobs are expected to be added in the next 10 years, which is also way higher than any other occupation group as well. I think you get the idea here, most health care careers are going to be extremely stable no matter what happens to the economy. Now, on a personal level, I've asked nurses, doctors, pharmacists, other health care professionals what happened to them during the 2008 financial crisis. And overall, most of them said that they were pretty much unaffected. Next on the list is going to be government workers. And this is another really, really good one. Government workers are about as safe as a bubble wrap turtle in a bank vault. Now, if you look at this list, government workers were the only one of three jobs that actually gained during the 2008 recession period. The other two being the first two that I went over, which were education and health services. Now, what happens in the past doesn't always happen in the future as well. But in this case, there's a very good chance that it will repeat itself. The government, especially if you get a job for the federal government, rarely if ever fires its employees. And of course, there's always going to be the person in the comments that comes up with the one counter example to this, but as a whole, it very rarely if ever happens. They may reconsolidate and move people around, but they rarely ever fire them because they consider government employees to be essential to running the country. And you could make an argument that during a recession is when we need the government more than any other time, although Ron Swanson might disagree. Next on the list is going to be essential trade careers and public utility jobs. Now, a friend of mine lived in Pittsburgh during the 2008 crash and his whole family was from there. And he basically told me that him and his family barely even felt the 2008 recession because they were all in different trade careers. And this makes sense if you really think about it. If it's in the middle of winter and your furnace stops working, you're going to call somebody to come and fix it.   Most people don't have the skills, time or energy to fix those things themselves, whether they're in a recession or not. Sure, you can make an argument that some types of trade work is going to go down during a recession. I'm not going to argue with you there. For instance, there's not going to be as many jobs for construction workers. And if you're an HVAC mechanic that specializes in fixing heaters in Los Angeles, it's possible that if somebody's heater breaks, they're probably just not going to get it fixed because it's not that hot anyways. So as always, you might have to do your research here and you might even be in a situation where you have to move somewhere. But overall, trade careers are relatively stable. Next on the list is going to be remote or work from home jobs and specifically ones that are in really good industries that are extremely solid, such as the tech industry. So tech jobs just because there's already so much money in these industries that they have a surplus and they can write out just about any storm. Recessions generally only last a few years. So tech companies can usually just take an L for a few years and they're going to be completely fine. And on top of that, tech companies are so competitive with each other that they would probably look at a recession as an opportunity to gain an upper hand on their opponents. Big tech companies know that their employees are the most valuable assets in their business. And so they'll do whatever it takes to retain them. And there's always exceptions. Of course, there's some companies that will probably end up laying off a lot of their employees. And if you're in the wrong situation at the wrong time, it could happen to you. Even if you're a top talent, you're one of the best employees in the company. If you get unlucky, you can still find yourself in a bad situation. But overall, because of how coveted these skill sets and technology and computer science are, you would probably be able to find yourself another job relatively quickly. And what makes it especially good, considering the recent events, which I still can't mention because I don't want to get demonetized that happened, is that tech jobs can generally be done remotely. And that means that you're not just stuck with the jobs you can get in your local area. You could get a job that's all the way across the country or even in a different country. That means that there's just a lot more opportunity out there. And you're probably not going to be stuck in a situation where you have to move all the way across the country just to be able to get a job. So to me, that's what makes technology extremely deserving of being on the list. Now, there's been a lot of studies out there about which jobs are best to survive a recession. So one example is a study by Bankrate, and you can see there's lots of medical government and trade jobs on here. But what if you're not in one of these industries, you're not in one of these jobs? What can you do to still give yourself the best chance of being able to have a good career during a recession?  You don't want to be dead weight, the person who never gets anything done a bottom feeder or anything like that, because you'll probably be one of the first people to get cut. Another thing you can do is be someone who specializes in a certain thing in the company that maybe nobody else or very few people know how to do. By doing this, you can be invaluable to your company, and you'll be the person that everybody comes to whenever they have a problem. Another thing is you always want to be positive.  Now, if you can't be in a recession-proof job, you could try to be in a recession-proof industry. So there are certain industries that are more essential than others, and a few examples of these would be utility and medical. So if you have a really flexible degree like you're a business major, for instance, this would be great for you because you can hop into an industry where it's a lot more stable. And then the next thing is you want to try to have multiple streams of income. It's always good to have a secondary source of income, some kind of skill or a side hustle that you can jump on if you need to. This way, if you do get fired, you'll have another skill that you can maybe do a little bit extra of and make some extra money while you're looking for another job. Check out my videos right here. I made them just for you. Go ahead, smash the like button, hit the subscribe button, ring the notification bell, and then comment down below any comments, criticisms, etc. that you have on the video. Thank you so much for watching and bye for now.


Welcome everybody. Thank you so much for being here. My name is Danny Laguna. I'm an assistant professor here in the iSchool. Hello everybody who is here live. And I I guess I've been here for for some years now and I do research in many areas but mostly in trying to understand science and a lot of the things that I will present today or I would talk about today I use in my own research so And I would today I will present about applied deep learning I teach two courses primarily big data analytics where we use We try to understand why big data is so popular and what kind of value can it can bring to your organization and Apply deep learning where we look at software packages and things that we can use directly in and many different kinds of Mods of media images text and things like that so first If I can go to the next slide, please. I'd like to just acknowledge the help of many colleagues and students who have who helped me In my research. I run the science of science computational and computational discovery lab here And and here's a sample of the funding. So thank you very much to the funders And I just wanted to start by just saying the the final message of this talk, which is I believe deep learning offers a tremendous opportunity to businesses and Because of any size and any industry And that's shifted from from some years ago where most of the value Was being Taken advantage of by big companies in stuff today. I think any business of any size can take advantage of this new technology and The idea is that we can use the data that your organization produces I Guess I can remove these I'm just too conditioned to use a mask everywhere. I probably sleep with masks so You can use the data says that your organization produces for for and analyze them with these kinds of technologies and also we have these software packages that allow you to to download models and and Techniques that have been developed by big companies so you can take advantage of that now the key in my view is that your Organization needs to have the right kind of data scientists as the right kind of Understanding that it's just not it's not just developing and getting the data But it's also the infrastructure around using this technology So it offers a lot of opportunities and a lot of limitations to not everything can be solved with deep learning so you have to be careful about that and lastly but Perhaps even more importantly moving forward is that a lot of these technologies that I will talk about today hide complexity and they hide ethical issues and that has implications Lee in the legal dimension and ethical dimensions, too. I don't I don't know what time it is, but please let me know if I run out of time All right, so much you learning and deep learning Historically have been used by big technology Tech companies, you know Google the usual the usual suspects Google Facebook and things like that and If you if you look at what they say about how they use AI and how much AI kind of trickles down into the organization to drive up revenue Recent surveys show that For almost a fifth of them They see an increase of six to ten percent in revenue improvement through the use of AI and mature learning And Kobe just has just accelerated it is because you don't have Interactions you don't have really a lot of times you can just skip many steps When you're trying to sell a product or service and then you can use AI to try to replace many of the steps that Usually took place in the real world. So this has just accelerated over the last two years So let me just give you an example of what I mean by this So I'm sure you are all familiar with the process of buying houses And that's a very I mean traditionally is a very long process where you you have an agent You go and look at many various houses And then you you say, okay, this house is great.  You say, okay, I'm gonna pay $19 and Then there is a negotiation process with the agent. This is very complicated and it takes, you know It might take several days. I don't know about these days But maybe in the past usually it takes, you know, several and sometimes weeks But if you think about it, it's a very convoluted process And there is a lot of uncertainty from from everybody like it's a very mysterious process But ultimately what you would really like is to try to and say, okay What is the final selling value of the house? Like after all of these back and forth You want to say, okay, I think this house is gonna be sold at this price So maybe the buyer can say, okay, I'm gonna offer the final value and the buyer will say, okay That seems fair. So they agree and the deal can be closed in 10 seconds instead of being a two-week process Now The process is mysterious, but if you think about it with websites such as Silo They have a lot of data about this about many many places in the country Many kinds of houses that have been bought and sold Of all kinds, some shapes from different buyers. So if you think about it They have a lot of features about the house Okay So you in real estate people talk about location But it's a lot of other stuff when you go and look at a place even to rent you click at the pictures So the picture is giving you for some information about the house How nice it looks how much light you get from outside And that all these kind of set of things that you use mysteriously in your brain For predicting how much you're gonna offer. Okay, so if it looks outdated you say that that asking price is crazy So I'm just gonna offer something lower or if it looks great and it's in the neighborhood looks great You might make another offer. So There are things that many Many features of the of the offer of the place Can inform How much is gonna eventually we've sold for and so it's the pictures the location to and and things about the buyer itself so The next slide the next slide please So So this is usually how how this works and you you get the day you well You formulate a problem you get the data in the case of Z-Lo they get, you know Hundreds of thousands of data points where you have this is the house And this is the location. This is that picture and then they try to predict, you know a two-week process How will how much this house will be sold for and that's what they call as estimate Okay, so you deploy the model But then there are all these processes where you have to tune the model you have to deploy it Make it available for for the users, which in this case is just Z-Lo But you can make it available for other companies and then you have to monitor Maybe it's making some mistakes or you have to update The next slide please now in data science We usually don't think too much about these things when you when you are teaching in an environment like this in the university But I tried to well, we tried to teach it here in the school There is all these of this infrastructure around building these models So not just getting the data, but it's also the deployment of the of the of the models So you have to create API you have to create versions and also you have to monitor your things And this has become very important in the Recently because you might have you know biases you might have you might be breaking the law Because you are maybe you're taking the zip code in in the prediction Okay, in the prediction of the value and you know without you realizing In the US at least You know areas of certain cities are very segregated. So the zip code might In some sense be informing the the prediction and making all kinds of bias predictions About that so next slide please so So data science is this kind of role that kind of feels that is like a full pack full stack data mining role that goes from You know getting the data having a little bit of domain expertise developing the model fine-tuning them and then Deploying them and and monitoring them. So here's a survey of how much time people spend on different on all of these Tasks in data science and you know Building the model and thinking about these things is just a tiny fraction of the rest you need to explore the data logic deploy it and Clean the data so next slide please All right. So how is what what is deep learning? What's what's going on here? So because this talk is about deep apply deep learning So what's happened is that in the past you could do the things that I show you you can say Okay, I have this data set and I can kind of skip the middle man and make me the middle woman and make a prediction About the say this the price of house, but what has happened over the last Ten years I would say or maybe even five years is that in the past? Let's say if you look at this cartoonish plots of How big the models have been in the past like this these models that take the features and make the prediction How much computation of power you need to make the predictions what has happened is that in the past? since we're like slowly Increasing in size in computational power. Okay, but around 12 2012 There was a break in that trend in that to now Get predictions that were really good You needed to start increasing the amount of power that you put into this model by match more and what happened there in 2012 2012 was the introduction of this idea of deep learning. Okay, and I will explain what that means, but basically Now for your organization to take advantage of these kinds of models You need much bigger and complicated models more data more computational power So I will suggest that you look at this economist article from open AI that goes through this idea The next slide please So, okay, so what is deep learning? So if you think about it artificial intelligence is a very broad goal of trying to reproduce intelligence like we what we all think is intelligence in the in the in nature Virtually or artificially in a machine. So that's a very broad Goal that has both like technical implications and applied implications, but also has philosophical implications Now if you if you look if you kind of specialize more you can think of much you learning where you're trying to use data to Build models that learn from that data and improve future performance So you want to take you know the zero data sets and make a prediction so that you reduce the error between the The asking price sorry the selling price and the actual prediction that you are making about the price of the house So that's a very standard thing that has been going on for 50 years But now deep learning is a is a more specialized form of material learning which you're using a specific kind of model for making such Tradition and the specific kind of model is based on how the brain works or at least is Inspired by how the brain works and more specifically about how neuron neural networks work in the brain So here's my favorite kind of Structure that we have in the brain This is from the visual system when you are looking at something Let's say you're detecting food or maybe you're trying to look for a place without snow You you are looking with your eyes and you can think of of the eyes kind of capturing the input from the environment And what's cool about the visual system in the brain is that? That light that goes into the eye Hits an area the primary visual area of your brain and that area through various Experiments in neuroscience. We have determined that they compute like little features about the environment like little you know Borders little colors like very simple things Okay So the first thing that that the brain computes are like these very simple features about the environment What's cool is that once you start stacking this area once this the primary visual system sends The signal what he detected these borders into the next area the next area then combines those features hierarchically So you get you get things now that are more like shapes instead of just being borders It's more like okay. How many squares there are how many circles and then you keep going up and up Until at some point there is an area in the brain where there is a neuron that will fire if you see you know a delicious Sandwich or you see you know no it's not or something like so there is this hierarchical Structure where you start detecting simple things and it goes all the way up to very complex concepts faces and things like that so artificial neural networks try to reproduce that in the computer by creating By extracting simp the three basically simple concepts the concept of a neuron Which is a something that receives an input or a set of inputs and it makes a very simple computation The other concept is that that's output of the of the neuron connects to other neurons and the third concept is that that Connection usually is through layers. So you have layers of neurons that connect to the next layer of your neurons And you don't really have connections within a layer Next slide please. So here I'm showing you an artificial neural network. I'm taking this from three blue one brown a great YouTube channel. So And this is a classic example of how a neural network learns to detect digits Okay So the inputs for this network is an image of 28 by 28 pixel Grayscale image and the output is a label that says okay I think these new and these numbers number two these numbers number nine and so on and so forth and Here I have a very simple fully what's called a fully collected neural network. This is artificial So the impulse are the pixels and then I have two hidden layers And I just initialize the network to just having random Random weights so it starts randomly and it starts just guessing So you pass the pixels and he makes the prediction and then what's cool about this is that we make the network So that is differentiable. So we can compute the Difference in error that will make if I change any of the connections in my network Okay, so through a process coil call forward propagation We compute the errors that we're making and then we can back propagate those errors to fix the neuron Okay, so through these processes by showing examples and labels Show example and labels the network will start learning and the idea of deep neural network Is that now we've gone through just two neurons of any two layers? You can see these layers here up to now We are like hundred layers and that's what called that's what called deep and the deeper you go the more sophisticated features are the next slide please now what I've seen is that in the past To apply these kinds of models you needed, you know very specialized knowledge You needed to know Some special obscure parts of the language to implement this neural network to yourself But what has happened over the last five years is that there are all these packages PyTorch TensorFlow MXnet that allow you to anybody really any organization to take these Models and apply them to any data set they have so and is all very standard and It's backed by big companies like Facebook Google and Amazon and there is infrastructure now for taking these models Deploying them on the cloud. So there is it's all these this kind of support that allows you To use these kinds of technologies in your data set. So next slide, please So what is now what's applied deep learning because deep learning could be researched you kind of are developing new kinds of architectures You are processing new kinds of data But applied deep learning is really just taking what other people or other organizations have done not really created a new architecture By taking these little pieces and combining them So you might want to connect combine, you know images with texts and you might want to combine those things So apply deep learning is really this idea that you can take the big data set that you have you can use them You have a storage for For storing data that you want to learn from you have access to GPUs to be computational power power infrastructures and You have access to this much sophisticated models and methods of training these systems So here I'm showing you an example of something that would have been crazy ten years ago well, maybe maybe 20 years ago where This is a classic task in computer vision where you want to detect objects that are present in an image So you take a picture and you want to see if there are people there dogs, you know, if there are all You know cars and things like that So something that would have taken you know a couple of PhDs to to to build now It takes like two lines of code in Python. Okay, so you can use something like pytorch and say Which is a package backed by Google and you can say okay take this model that was developed by Google It cost them, you know hundreds of thousands of dollars to train them on millions of images. Take that download it And then I'm gonna detect based on an image that I have in my organization I'm gonna take the objects that are inside. Okay, so that makes it very easy for any organization to use this So next line, please So let me see let's review a couple of applications of this So well health care, I think is a is an obvious application because many many processes in health care You think about it about them.  Yeah, so here's an example of cancer detection in skin cancer and So if you think about it for a doctor to make the decision of whether let's say a mold It's suspicious and maybe you need to do it like a biopsy They have to they have to train right they go to med school and In somewhere there is like a cancer class. Let's say and they show different pictures You say this is a normal mold is like You know the borders are even and it's not growing and the color is like brownish that color clear brown so these are fine and then these are These are not fine. These are like irregular. They're growing in size. The color is a little darker So this you should pay attention to this. Okay, so the the the dermatologist will will learn this separation of these two classes and It will make a decision once it's in the practice It will you will look at a skin of someone as well. I think this is based on my experience. This doesn't look right So maybe we need to do a follow-up Now because we have huge data sets and here this is the work that was published a couple of years ago in nature They took data from UK. They have the national health system. I think school So they have hundreds of thousands of of images and they have the expert opinion of a dermatologist They can feed these images through computer vision Deep learning systems that can make the prediction from the image just the raw pixels into saying this is okay And this is not okay Okay, and this these kinds of networks are called convolutional neural networks and the idea is that you pass these filters through The image and as I was saying before they the first kind of filters detect simple features like borders and things like that But then once you move up in the hierarchy, let's start detecting more sophisticated things like irregular borders or colors and things like that And now here where I'm showing is is the kind of the true positive rate the false Both the kinds of mistakes that the dermatologists are making and the the closer you are to to here to this corner The better okay, and all these dots here just show different dermatologists where they are located What kind of errors they're making and what's cool is that this deep learning Network actually is making in general less mistakes compared to the experts and on average This is the average here. I know if you can see it on the screen. There is a really a little green dot The average from many many dermatologists is lower in performance than Than a computer vision system Now there are many caveats. There have been many follow-ups of these research So it's not completely a hundred percent true because the data said wasn't like completely Okay, and there was a lot of there was a lot of overfitting but in general we see this like We see the performance of computer vision systems approaching human performance Okay, so next slide please Now the applications of these you know, it's really just this this is just one application with cancer research But you can think of something like precision mason where we're trying to to produce a Program for each individual, right? So we're trying to produce a treatment program based on the particular history of each individual So if you think about mason over the last 50 years because of time and because of expenses We kind of have these rules of what we should do with certain Symptoms this is we should do this and because you know, this is what applies to most people So in general on average, this is good, but for individuals it might be very different So the treatment might be very specialized. So precision mason tries to make that now in the past to do precision Mason you will need a team of doctors looking at a case and kind of Tweaking the treatment for the person But what we could do we could take an AI system and we can take data from previous Judgments on individual cases and say, okay, you given these symptoms What should be the treatment for that this particular person? So we can include AI into that to make that that prediction for each individual so next slide please So recently there was I guess I guess this year there was this breakthrough on another Kind of problem, which is protein folding It turns out that a lot of the functionality in our brain in our body and in our brains is made by this protein That are just sophisticated machines that move inside cells that are 3d structures and they perform various tasks They're like mini machines. Okay, so if you see them live, it's pretty fascinating five minutes. Okay, good. So Now the idea is that there are all these these we don't really understand how the the this 3d structure is computed from the from the initial encoding of the of the of this protein so we need it will be great to understand how that how that process goes and and I Guess this year there was this deep learning Technique that took the sequence of amino acids that predicted very accurately much more accurately any any any before Anyone before the structure of the of the protein. Okay, so if I can move here Now there are more like cute applications of these technologies So if you use Instagram that are always filters that you can apply now this very sophisticated deep learning techniques that allow you to parse an image To parse a face and detect where the where the eyes are and things like that And you can and then based on that they and they run really fast at 30 frames per second and they can start You know understanding the the 3d structure of a face and then put You know different kinds of of things on top of it There are all other apps that can reverse H you they can make you younger or older Because they have learned the progress of the image of someone Translation if you speak another language you've seen the tremendous progress in translation the same thing because they have these massive Data sets and they can learn and do equally well almost to translate now in my own research I I use this model so with text Analysis I am using at these models a bird for example a big model for Processing text. I'm also processing images and it's just remarkable how well I can tell The location of pieces of images of graphs and detect whether that's trying to mislead other scientists So I'm gonna move now. This is a cool new development where we have these huge Models that are trained on different tasks and these are called foundation models And this is the latest and greatest and you can read more about this just Google foundation models And it's the next iteration of deep learning so I wanted to finish by kind of By kind of warning that The problem with these models deep learning models that they hide a lot of bias So here's an example a really nasty example of a bias that was hidden in an API in Google So if you if you painted the hands differently The model was guessing that's you know a white hand was holding a monocular whereas a black hand was holding a gun So what happens with these models that they're so complicated that is it unclear how it's making the decision So it's really hard to inspect what's going on inside. So I highly recommend this book that goes through many of these examples Okay, and with that I will finish we have research in our own lab where we have similar things where some Text techniques are bias against woman and against minorities. So we have to be in general careful about apply deep learning Okay, so with that I'll finish. So thank you very much. So maybe just One or two quick questions. Yeah, also, it's a nice segue to the next talk So you talked about ethics the whole next talk is actually going to talk about ethics as well There's a question that came up. You mentioned skin cancer detection and I actually talked about that even yesterday So it's pretty common one to talk about Does that work equally well depending on your color of your skin or not? No, actually, that's a great question. So he has a real real trouble with darker skins that's that's a problem that across computer vision and Because the examples that they that the systems have are bias and most of the examples are of certain ethnicities And they're from the UK. So it's already bias. So my understanding is that doesn't work too well with dark skin Yeah, and one last question So under what circumstances is deep learning better than traditional machine learning algorithms Or maybe another way to say that is under what circumstances are traditional machine learning algorithms still useful? Yeah, I think you should definitely always try traditional machine learning algorithms deep learning Works mostly when you have a lot of data already or with somebody else train These foundation models in a bigger data set. So if you have small data sets or it's very noisy Definitely don't go with deep learning. Just it will overfit your data