Welcome to Digital Asset News to get top stories in Cryptocurrency Digital Assets and break them down to bite-sized pieces. They were going to continue on with our trinity of trading and there is three criteria for these such things. One of those is sentiment analysis. This is Weston Nelson. He is head of business operations over at Trade the Chain. Technical analysis brought to us by CJ for Market Rebellion and then I'll be doing the fundamental analysis. So if we take all three of these things together, we should find a pretty good pick. And just as a reminder, let me share my screen right quick. So our two picks that we've done recently are both right there for you to see. And the one that we do today will also be on there for whatever we call. Everything starts with the sentiment analysis with Weston. We're going to move on to CJ and the technicals to make sure they all line up. If they do, it gets to me on the fundamentals and we'll see if we can move forward. So gentlemen, start your engines. What do we got? So no pressure, right? No pressure. Starts with here. All right, well, let's pull up these Trade the Chain sentiment dashboard. Make sure that you can see this AOK and should be seeing the spotlight page, which is the main page. And then now go over the coins.  Just, you know, sentiment analysis. What Trade the Chain does is it actually crawls over different websites and blog posts for some of the biggest names in the industry as far as cryptocurrency. Plus it has one of only four cryptocurrency companies that has a direct API into Twitter. And it takes billions of tweets and gives us a sentiment analysis. And that's what we're going to look for today to see what could be a potential big winner. Yes, yes, very well said. I think it's important to note crypto assets, a new asset class, it almost stands to reason that you'd have a new tactic for identifying performance in that new asset class. And that's kind of what sentiment is. I think of it like momentum, news, sentiment, all similar in nature. But now we have a way to quantify it. So we are in the coins page. So if I were to be looking for a trade, again, typically I'm looking for a short term. One a couple hours or a swing, maybe three to five days. I'll start right here. One hour projected price range. Kind of scan through. This one is very easy. Synthetics network token. I mean, we've got so our range down 4.45%, which is, you know, considerable, but higher to the upside with a 4% mean. Relative trading volume is up 123%. Tweet volume is through the roof. And then look at that daily and hourly sentiment as well as the one hour change hasn't been that crazy yet. And then there's some fundamental things I know that are going on. But I'll leave it just at the sentiment for now for you guys. So that would be that would be my pick. And I would say this is a, let's see what's the day to day job. This would be a multi, well, a short, this would be both a short term and a multi-day home. Okay. CJ, let's take a look at the technicals. Yeah, I would love to pull up my chart. And guys, Weston is the best sentiment trader I know just based on his hyper focus and attention to detail and his track record thus far. But I have to say with this pick, I'm liking it. It's pretty green across most time frames on the sequential indicator, which is what I use to enter and exit trades. Now, the one thing that we've got here on the one hour timeframe, because I know that's a little bit more how Weston likes to play these sentiment trades and then a shorter term timeframe. I want to see this red one flip to a green six and what it would need to be able to do that is close above this previous two. So it's got to close above 15, 24. Once we get that initial positive price action, that shows me that, okay, the market's telling me that I'm probably on to something or I'm probably right on this one. And then get into the trade and then I would presumably ride that out to upper levels here. But it's very important to me to confirm that the momentum is going in my direction at the exact moment that I enter the trade. I don't know if that's true for Weston. This is just what I'm seeing technically on the one hour. So is that pretty close, it looks like, to where you would be then? Yeah, let's get the exact percentage. So I'm looking for, I would wait for price to increase about two to three percent, then based on the close of this hourly. And if we close and get that green six in the next 41 minutes, that's just what I would be looking for. But the nature of these trades, I think when you're looking at sentiment is much more fundamental sometimes than technicals. So I take Weston's opinion to have a little bit more weight on these trades. I see it as you playing it a little safer almost in a way here. I might get a couple percent more maybe, but you're waiting for confirmation, which is just another added bonus. That's yeah, waiting for confirmation. It's just what I'm doing is sacrificing a little bit of that upside to confirm that I have a higher probability of the trade going in my direction. Rob? So what do you think here? So I'm looking at it, it sounds pretty good. I mean, we look at the tweet volume and the trading volume and the sentiment, we say like the technicals, we'd like to wait a little bit more. And I think if we wait a little bit, it wouldn't be so bad. We might sacrifice some profits, but we could actually be a little bit more safer in that play, right? Yeah, I mean, think about it. You wait two, three percent of bullish price action, but I mean, Weston, what do you think is the projected upside on this 10, 15 percent move? What do you typically see on these types of trades when you get the sentiment analysis? I know that's kind of a difficult question. Yeah, right. Yeah, what exactly will it be at 12 p.m. tomorrow? I wish I knew. So this one is really interesting right now for a couple of reasons. There's some big fundamental factors that Rob may or may not get to in a moment here, but I'm looking for a pretty solid gain from this over the next couple of days. Somewhere in the range of 10 to 20 percent, ideally. I mean, it's up like 30 percent over the last 24 hours already. So that's pretty significant, but with what's going on, looking at the sentiment, the trading volume seems to be increasing. So I don't see slowing down. Okay, so let's take a look at... So if it was me, I'd probably put a little bit in. Not a big trader.  That's why I have you geniuses here. So I'm going to take a look at a little bit of the fundamentals here. So let me just share my screen and see what I got. So the first thing I'm always going to take a look at is CoinGecko.  Well, it's number 17 in a top 20 for CryptoMarketCap. It's $15 right now.  It's only going to buy a percentage, so we haven't missed something fantastic. Over seven days, this is what we've seen pretty volatile, right?  So a great thing about CoinGecko, if you scroll all the way down, which I should have done on the last video that we talked about, this will tell you exactly what it is and what it can do. So synthetic, it's a collateral to mint synthetic assets. They can have a mentor, stake their synthetics, smart contracts, create pretty much anything that you really want to. And then what else we got? They can earn rewards. Of course, this is all about DeFi. And of course, if you're talking about farming, synthetic also offers other time-limited incentives such as liquidity provider rewards can be viewed on mentor. And let me just read this. Synthetics is a decentralized synthetic asset protocol founded by whoever. Okay. It was known as Haven, launched in September 2017. It's a decentralized exchange for derivatives in the decentralized finance space, 700 million total volume locked. What are synthetic assets? They're derivatives that provide exposure to real-world assets on the Ethereum blockchain. Okay. Got it. So it sounds like it's a pretty good thing. Ethereum, we know what's going on right there. A lot of things going on with Ethereum 2.0. The fees are outrageous right now, but we'll see how it works. What I really want to see is I took a look over at coin status to see if there's any new stuff on my phone.  The only thing that's been recent just says Solana compound stellar and synthetics are already recovering from yesterday's crypto crash. That's not really news. That's just assessments. So let's take a look over here and see if we can find something. I'm also a big fan of Cain personally, just since that synthetics protocol, that guy right there. That guy right there. Okay. Sounds, don't know who he is. All right. Sounds good. If memes were an indicator of growth, will Dogecoin be number one? That's all I can tell you. I don't see much. I don't see much. So I don't see why there is such an increase in the chatter in the Twitterverse. It is something going on that we can't see. What is this? This is January 10th. Total value locked in DeFi. Make or Ava Uniswap compound synthetics, 19%, 2.1. Okay. Back up. Tell me that again. What's happening January 15th? Thank you. I'm January 15th for synthetics. Okay.  Everybody knows that. Trying to find solutions. You got sidechains, Matic, Stake. You got ZK rollups, LRC, I think OMG. And then you've got Optimistic, which is their way of scaling. Synthetics, Chainlink and Uniswap have agreed to roll this out to test it. Synthetics being the first one to go live on January 15th. Excellent. That's the fundamentals we need to know. Thank you so much. Here's the next question. Where can we find synthetics? I was looking on Voyager. I didn't think you could be there. Let's take a look at the pairs. Binance, Coinbase Pro, SushiSwap. Where else can we find it? Uniswap. Uniswap. So here's what I'm going to do. I'm going to throw $100 in the day and see what happens. Today it is. What time is it? First of all, what's the date? January 12th, 1.30 p.m. Mountain Time. So let's clock it right here. What's the price? $1508. Okay. I'll put it in there for this. We'll see how we do. When do we want to get out of this position? Coming to me. Coming to you, Weston. So this is one that is a little unique because I'm going to watch it. Like a hawk. But I would say right around the 14th, 15th, or 16th. Days. Okay. So yeah, I just activated my... CJ, what do you think? You know the trend is just really, really strong. I'll go one more time back to the chart because I showed it on a shorter term timeframe on the one hour, but now that Weston has kind of been saying that his timeframe is to hold this for about a week or two. This trend has not put in any weakness thus far. So there's really no reason for me to sell. We have a green three closing above the previous one following the nine. That's all very bullish for me technically. So when the trend gives me some weakness, whether it's a price flip to a red number or a lower high, I'm going to hold this one so we'll see. We'll see. I'm looking for somewhere around 20% profits. If I go from 120 bucks, I'm pulling out. That's all I'll say right there. I'm very... Okay, you're going to take it from there. All right, I would ride it almost a little further. I'd say that's a little aggressive. Yeah, 20% in a couple of days is good. Sounds good. All right, everybody. So that is it for Trinity Trading. Again, awful trader, but I got these two guys with me. Before we take off, trade the chain. There's a link in the description below if you want to take a look at that. Not only do we have trade the chain and how it actually works, but remember there is a robust community that's growing every day. There's a 14-day no-questions-ask refund. Also on top of that, CJ over there, Mark Rebellion, if you want to learn TA and not be like me, who knows absolutely nothing, go ahead and take a look at them. You can start up for a dollar and they got a nice new interface. CJ, tell us what it's all about. Yeah, so what we're doing, we are a community of beginner-to-advanced traders all throughout the world. And what you get with our service is trade ideas, insights for myself and our other analysts at Market Rebellion. Also a macro portfolio where we change the allocations fairly regularly. We're also, tonight, are activating our new education curriculum right here. So we have a couple courses. We're going to try to drop one course per month going forward on various technical analysis. We also have weekly webinars that you can follow along and ask us direct questions. Our trade insights, like I mentioned, and then our platform with our live chat and charting services and indicators. So you want to check out all of that. It's marketrebellion.com-crypto and you can sign up now and try all of that for just a dollar. Another thing before we end here is that I strongly encourage you guys to check out Rob's video. It's 2021 and you're becoming a millionaire because this video goes on more than just crypto. I really like how Rob touched on life principles and happiness, things that go further and are somewhat more important than crypto at the end of the day. That's a great video. Yeah, and also you get to see all my screw-ups. So those are always entertaining. So, all right, so everybody, so thanks so much for watching, Trinity Training. All the things we talked about is linked in the description and we'll see if this one does. Remember, you can find a link to the spreadsheet and I will be posting that in the description. So thanks so much, everybody, for coming by. I appreciate it. Thank you. Thank you. See you in the next one. See you in the next one. Have fun. Bye.


What's the matter with this thing? All right, girl, come and trust me. Stress, what is it? Who does it affect? And what can we do to control it? Stress is the sum of biological reactions to any adverse stimulus, be it physical, mental, emotional, internal, or external, that tends to disturb the body's natural balance. Although it's difficult to determine the full effects of stress on a pilot, it's important to be aware of the stresses we know we can't control. These controllable factors are called self-imposed stresses. These stresses have adverse effects on you and can affect the safety of the flight. These controllable stresses include alcohol consumption, self-medication, drug use, tobacco use, inadequate diet and nutrition, and psychological stress. Fatigue, poor physical fitness, and dehydration are additional stresses, but will be covered in a separate training module. Each self-imposed stress can degrade your piloting skills and can affect your ability to fly safely. It is important to remember that these kinds of stresses are cumulative. Alcohol is a depressant, hypnotic, and addicting drug that in any quantity can have adverse effects on your flying abilities. Brain effects include impairments in reaction time, reasoning, judgment, and memory. Alcohol decreases the ability of the brain to make use of oxygen. This adverse effect can be magnified as a result of simultaneous exposure to altitude, characterized by a decreased partial pressure of oxygen. Visual symptoms include eye muscle imbalance, which leads to double vision and difficulty in focusing. Inner ear effects include dizziness and decreased hearing. Alcohol can cause a significant impairment in performance and can vary in the severity of its effect from person to person. Pilots have shown impairment in their ability to fly an instrument approach or to fly IFR, and even to perform routine VFR flight tasks while under the influence of alcohol. Regardless of the extent of the individual's flying experience. Alcohol is quickly absorbed and transported by the blood throughout the body. The factors that influence alcohol absorption are the type and quantity of food in the stomach, degree of dehydration, concentration of alcohol in the beverage, how fast the alcohol is consumed, and the person's body weight. Our bodies are capable of eliminating approximately a third of an ounce of pure alcohol per hour, even after complete elimination of all the alcohol in the body, there are undesirable effects. A hangover that can last as long as 48 to 72 hours following the last drink.  A pilot with these symptoms would certainly not be fit to safely operate an aircraft. The use of alcohol and drugs is regulated by FAR 9117. The regulation states in part that persons may not operate or attempt to operate an aircraft when they are currently under the influence of alcohol, have consumed alcohol within the past eight hours, have a blood alcohol content of 0.04% or greater, are using any drug that adversely affects safety. The number of serious errors committed by pilots dramatically increases at or above concentrations of 0.04% blood alcohol. Be aware of hidden sources of alcohol, such as cough medicine, tonics, et cetera.  As a minimum, adhere to all of the guidelines of FAR 9117. A more conservative approach is to wait 24 hours from the last use of alcohol before flying. This is especially true if intoxication occurred, or if you plan to fly IFR. Cold showers, drinking black coffee, or breathing 100% oxygen cannot speed up the elimination of alcohol from the body. Ideally, total avoidance of alcohol should be a key element observed by every pilot planning or accomplishing a flight. You should be aware that over-the-counter medications, as well as prescription medications, can affect the safe operation of your aircraft. People tend to have the false impression that if a drug is bought over the counter without a prescription, then it must be safe enough to pilot an aircraft while under its influence. Some drug effects can be intensified when taken at altitude. Consult your local aviation medical examiner to determine any possible side effects that a medication might have on you as a pilot. Self-medicating with over-the-counter medication is not a good idea, as any use can mask unsafe conditions. The desired effect of a drug can have an undesirable effect on a pilot's ability to perform critical tasks. For example, if you were to take an antihistamine for cold symptoms, you might experience side effects such as drowsiness, impaired coordination, or blurred vision. Many side effects of drugs are unknown, and the reaction to medication varies with individuals. Remember, the condition you're self-treating may be as disqualifying as the medication. If an illness is serious enough to require medication, it is also serious enough to prevent you from flying. Our recommendation is, if you have to self-medicate, do not fly. There are several other harmful substances that can affect your ability to fly. For example, the substances one gets from inhaling tobacco smoke. Three substances in tobacco smoke in particular are of importance to a flyer. Those substances are carbon monoxide, tar, and nicotine. Carbon monoxide constitutes up to 2.5% of the volume of cigarette smoke, and even more in cigar smoke. Carbon monoxide combines with hemoglobin about 250 times more readily than oxygen. The result is the hemoglobin is not available to carry oxygen to the tissues. This lack of oxygen produces hypemic hypoxia, which reduces your tolerance to altitude. Tar is the viscous residue left from tobacco smoke and is one of the major cancer-causing agents. It destroys the delicate mucus membranes of the respiratory tract. It also interferes with the natural cleansing action of the lungs and impairs proper oxygenation of the blood thereby reducing tolerance to hypoxia. Nicotine is a potent drug that primarily affects the nerve and muscle tissue. The amount of nicotine found in two cigarettes if injected directly into the bloodstream could be fatal. As smoking becomes less acceptable, some people are opting to chew tobacco or use smokeless tobacco. Unfortunately, the same detrimental effects are present in these products. Practicing good nutrition and having proper eating habits are important in the aviation environment. Unfortunately, disruptions of normal eating routines due to flight schedules are common. Three balanced meals each day are recommended. A balanced meal should consist of 50 to 55% carbohydrates, 15 to 20% protein, and 30% fat. This balance will help the body maintain a good store of energy reserves. Carbohydrates are broken down in your body to glucose, or sugar, which is a readily available source of energy to be used by the muscles and the brain. Proteins are the basic building blocks of all cells in your body. Fats are a very concentrated source of energy and, if not used, remain stored in the body. Vitamins and minerals are also an important component of any diet. For persons who skip meals, there is a possibility that they may become hypoglycemic. Hypoglycemia occurs when the body has low blood sugar. Possible effects of low blood sugar are weakness, headache, irritability, nervousness, trembling, and, in extreme conditions, fainting and convulsions. You can see why skipping meals is not a good idea. Use common sense when preparing for a long flight. Take with you a prepared box lunch. Psychological stresses involve physiological, mental, and emotional responses to socio-cultural family and job-related situations. Personal life changes known to produce significant stress include death of a spouse, divorce, moving or changing jobs, death of a close family member, marriage, being fired from work, and major changes in health. You can also experience stress when confronted with emergencies, unexpected situations, or unfamiliar events during the operation of an aircraft. Some examples of events that can be the direct cause of stress and or aggravate pre-existing stress include flying in bad weather, night operations, performing an instrument approach to minimums, flying in a high-density traffic area, flying into unfamiliar airports, becoming temporarily lost in flight, equipment malfunction, conflicts with other crew members or air traffic control personnel, being subject to flight checks. Stress, per se, is not bad. Some degree of stress is necessary to stay healthy, motivated, and alert. It eliminates boredom and complacency. On the other hand, excessive stress can be unhealthy. It can impair performance, lead to errors, and cause incidents or accidents. Individual tolerance and susceptibility to stress vary from one person to another, as do the individual mechanisms to cope with stress. Stress tends to be cumulative, and, if it is excessive, it can overload your ability to safely operate an aircraft. Even the best pilots in the world are, at one time or another, subject to significant pre-flight stress. This can include problems involving family, job, finances, health, and others. You may not have sufficient reserves available to cope with the demands of flying an aircraft. Stress may be manifested by any of the following signs and symptoms. Anxiety, irritability, excitability, impulsiveness, aggressiveness, emotional or physical isolation from others, problems concentrating, confusion, difficulty remembering important information, increased self-doubt, nightmares, fatigue, trembling, weakness, diarrhea, indigestion, frequent need to urinate, migraine headaches, grinding of the teeth, cold sweating, increased smoking or overeating, loss of appetite, and alcohol and drug use or abuse. An effective approach to deal with stress includes defining the source of stress, evaluating available resources for problem-solving, including professional advice from a psychiatrist or psychologist, exploring possible solutions, including relaxation therapies, taking action, evaluating outcomes, making corrections or changes, if needed, and trying again. You must evaluate your interior and exterior environment on a daily basis. The best decision you can make when experiencing a significant level of stress is not to fly. Stress is the sum of biological reactions to any adverse stimulus, be it physical, mental, emotional, internal or external, that tends to disturb the body's natural balance. It's important to be aware of the stresses we know we can control. These controllable factors are called self-imposed stresses. These controllable stresses include alcohol consumption, self-medication, drug use, tobacco use, inadequate diet and nutrition, and psychological stress. Alcohol is a depressant, hypnotic and addicting drug that, in any quantity, can have adverse effects on your flying abilities. You should be aware that over-the-counter medications, as well as prescription medications, can affect the safe operation of your aircraft. Three substances in tobacco smoke are of importance to a flyer. Those substances are carbon monoxide, tar and nicotine. Practicing good nutrition and having proper eating habits are important in the aviation environment. Psychological stresses involve physiological, mental and emotional responses to sociocultural, family and job-related situations. Even the best pilots in the world are, at one time or another, subject to significant pre-flight stress. This can include problems involving family, job, finances, health and others. An effective approach to deal with stress includes defining the source of stress, evaluating available resources for problem-solving, including professional advice from a psychiatrist or a psychologist, exploring possible solutions, including relaxation therapies, taking action, evaluating outcomes, making corrections or changes, if needed, and trying again. The best decision you can make when experiencing a significant level of stress is not to fly.


Thank you. Good morning. A few months ago, the organizers of Big Data Spain asked me to come up with a talk about some of the things that I've done in my career, but especially could I speak about F1? And it's funny that every time I talk to someone about my, you know, the things that I've done in my career, they always end up asking about Formula One. So I was wondering why this is, you know, why do people feel such need to ask about that particular area, of all the other things I've done, which I think some of them are possibly more exciting. And it got me thinking, can I put together some sort of presentation that actually ties up two or three of the industries I've worked on and the commonalities between them and what methodologies I've learned in one that I've carried across to the next. And so I think that one of the things that unites them all in this particular area of Formula One, energy, environment and healthcare, is the driving force behind all of them. And I do not mean money. So passion. Passion is the one thing that actually makes people who work in Formula One or people who work in energy or people who work in healthcare do what they do. It's passion for the race, passion for the fans, for the sport. It could be passion for the environment to have a climate that does not change and we can have an environment that our children, their children can't live in without fear. Or the passion for the people who look after others, who make us feel better when we're poorly. In general, all of the people in these sort of industries have a mission and that mission is either make people happy, make people healthier, make the environment cleaner and all those things are the ones that actually make these sort of industries move together and achieve challenges that possibly couldn't have been achieved without the dedication of all those people.  And these challenges are common in all industries. I'm not claiming these are exclusive to these areas. In fact, I'm sure you'll recognise all of them. I've picked sort of four representative ones that I think will be quite interesting and I'll sort of put some examples or anecdotes on some of these different industries and see how those challenges have actually happened. The first one is what we call the human factor. The human factor has different dimensions to it and the first one, probably the most common ones is that knowledge takes time, experience takes time. It's quite a hard thing to, for example, make it in F1. You have to come in as an apprentice, you have to go from team to team, learn the skills, become either a race engineer or a vehicle scientist or all the different things that you can do within F1. It's quite hard to come in from the top. It's normal to go and take years and years to get to the point where you actually can make a difference. And similarly, we all know how hard it is to actually become a clinician, to become a doctor or a nurse.  And in energy it's the same if you imagine what we call the network design scientists. So these are people who have studied physics for years and they work out the power electronics and everything it takes to build a massive network that conducts electricity across the whole country or even a continent. That is a very hard job. It takes a long time to learn it and it takes a long time to trust you enough to design a country-wide network that carries power through. The other two are quite related. The people have a natural aversion for machines. Possibly nobody in this audience has that problem, but people who are not like us, they're not scientists or engineers or technologists, find it quite difficult to trust machines. And some people think that if we automate everything, then what's going to happen to jobs? That's the threat of AI everybody's talking about at the minute. What happens if I automate away this particular role I'm doing now? Where am I going to do next? And this is quite an interesting effect that we're seeing as technology advances and it moves across. And I think that one particular case in health tech, a lot of people have this sort of distrust for AI with reason. I mean, we haven't validated that any other decisions that artificial intelligence making on someone's health can be trusted. And there's two camps in this area. One camp thinks that AI will replace doctors in a few years. We won't need them anymore. And you will be able to be diagnosed and treated by a machine. I'm on the other camp. The camp that says that AI, machine learning, and all the technologies that we're learning will empower doctors to do the right jobs and will empower patients to fight the right treatment. And that's not necessarily seeing the doctor. It could be something else like stay home and take some tablets or go and talk to someone about mental health, things like that. So this sort of divide between the two areas is quite telling of what the two sort of groups of people think of what's coming next. The other challenge that I want to talk about is complexity and complexity manifests in lots of different ways. It has three examples of them. And I'd like to tell you a little bit about a story in Formula One that tells you a bit about the complexity space. We think that the complexities in the... Well, most people think the complexity is in the algorithms and how complex they are, but actually sometimes complexity comes in the environment around the algorithm that makes the algorithm almost useless. To give you an example, a couple of years ago, when I was in Formula One, we were the smallest team in the grid. We had tens of times less money than the big guys. And so the money we had, we wanted to spend in technology rather than vehicle engineering. This is because we didn't have hundreds of millions to spend in a car. We had tens of millions to spend in a car. And so even so, there was that difference. We were literally fractions of a second behind the fastest car. The reason that this was possible is because we were using technology to make some decisions that other teams were more traditional about. But the example I want to tell you about complexity and how it makes it very hard for any predictive algorithm to actually work is the race in Brazil in 2016. I remember very distinctly because it was the one time I actually decided to invite my family to come over to the factory to go into the op-center and watch the vehicle scientists and the strategists and everybody run around the screens making decisions, shouting things on the radio. I thought it would be quite exciting. At that point, we were number 10 in the 11 teams. And for those of you who don't know about Formula One, only the top 10 teams get the prize money. The 11th team gets nothing. And so it can be a game changer if you're not in that top 10. We were top 10 for most of the season. Then Brazil came. And it was a horrible day. It was raining all the time. We thought the race would not happen. We had prepared for it. We had simulated all the different scenarios. We thought we were ready. And then the race started. We were in a good position. We thought it was going to be cancelled at some point and we were in a position that we would retain our 10th position. The race stopped because of an accident and we thought that was it. Then the stewards decided to restart it. The race started again. Again, we retained our position. We were in the money. If we stayed where we were, we didn't let the bad guys behind us take us over. We would have retained the top 10. And so there was another crash and another stop. And at that point, we thought, right, we're in it. And now the stewards decided to start again. And this third time, literally almost the two hours of the whole race were up. We thought, you know, it's now or never, sort of thing. And in the last few laps, our direct competitors, which are number 11 at the time, over two casts, the race was over, the team was over, the money ran out. And so that's why I'm not in F1 anymore. But that example is just to tell you how complex an environment can be that doesn't matter how much simulation or how much AI you throw at it, you will always come across situations where it's very, very hard to predict what you can do and to fix it quickly enough. The second condition, the physical limitation is actually a very interesting problem and it happens in all industries. There's things that you just can't do physically. We don't have the technology or we don't have the knowledge to solve a problem. However, if you think hard enough, you can come up with solutions. And one good example when I was back in energy after Formula One, one of the big problems that we all have, we all want, I think, 100% green energy running our grids, right? We want wind, we want solar, we want to get rid of coal and gas and all those things that generate carbon. And so the problem we have with the national grids in every country is that they were designed 100 years ago. They were designed for these power sources that we call inertial. And you can imagine an inertial power source to be this big power station running coal or gas which heats up water. The water makes this big weight spin very slowly and that spins actually an electromagnet that's generating electricity down the grid. And that's what we call inertial because it takes a long time to start and a long time to stop. So the way we see this wave, 50 hertz to 60 hertz depends on what country you're in of energy going through the grid. When that 50 hertz to 60 hertz gets a little bit off, like 49.6 or 51 or something like that, the grid is at risk. The grid can actually black out. It's not designed to be outside of this very narrow band of tolerance. And so you'll have these guys in power stations literally twiddling knobs to actually say oh, we're getting a little bit higher, let's change the load and balance to keep it straight. Then along came solar and along came wind and those are what we call non-inertial. The reason why it's non-inertial is because there's no big way to move.  A cloud comes over a solar panel, everything stops. A wind gust comes over the wind turbine and a lot of power is generated. So this generates a lot of noise from the grid and so that nice smooth curve becomes really noisy and that noise is dangerous if you get outside that narrow band. And so general country-wide networks limit the percentage of green energy that can go into the grid to preserve its stability. And so I think in the UK I mean it's about 40%. So when you get more generation from solar, which is really what you want, they actually won't let you put into the grid because you're risking the stability of the grid. So a few of us got together, how can we solve this problem? This is a physical limitation of the grid. Can we do something to actually avoid this situation and increase 80, 90, 100% of green energy into the grid? And so we came out with the concept of a smart grid. And what a smart grid is, it's a bunch of devices that you put everywhere in the country, wherever you can actually consume load, for example, a fridge and air conditioning, a heater, a radiator, things like that. And then the things that you can switch off and on very quickly without the user actually noticing. So if I switch off your radiator for two seconds, you won't tell the difference. The room will continue to be warm. And so we have this really complex network now of millions of devices out there that are making these decisions really quickly to actually try and balance the grid. So when there's a lot of noise in the grid, these devices will switch on and off very, very quickly to try and do the noise cancellation like you were doing headphones, for example. So that's a good example of using algorithms to actually try and solve the sort of physical limitations, limitation of a system. And the third one is like the more understood, I mean, Formula One is incredibly regulated. The rulebook is bigger than, you know, and the energy markets, you know, how to actually generate electricity, how to put into the grid. All those things are very regulated. But healthcare is by far the most regulated industry. I think that it deals with people's lives and so we want it to be regulated.  It'll save your lives, I think. And then they walk away with something that was wrong. So healthcare is probably the one that's lagging behind in technology development purely because we want to be safe. And sometimes we create technology that we're very excited to use, but at the same time we have to make sure it's safe. We have to validate it. And regulations move slower than technology. Everybody knows that. So we're trying to lobby the regulatory bodies to actually try and catch up with us, but at the same time we don't want to jump ahead too much because people will know us playing with our lives, really. The third challenge, scale. We all deal with it. Three examples because scale can mean many different things. In Formula One, there's only two devices. If you think about it, there's two cars in the track for a team. And those two cars have hundreds of sensors in them. And those hundreds of sensors generate thousands of streams of data. Some of those streams of data are in the kilohertz scale, so we have thousands of samples being taken at any given second for a particular metric. A good example of that is we have these cameras that take 32 points of infrared temperature sensors against each tire thousands of times per second. So you can literally draw the heat map of a wheel of a tire as it spins. And so you can predict degradation, grip and all these other things. So here we have just two devices generating huge streams of data that you need to process really quickly because if you want to make a decision as to whether to pit the car in the next lap, you have to make the decision in about 30 seconds. And so we're talking about huge amounts of data. This is something that Professor Roddy mentioned yesterday actually in his keynote. When he said aerospace challenges to make decisions to like 40 milliseconds, read lots of data, pull the cord, if the rocket is going to blow up you want to save the crew. We're in a similar situation, but at the 30 second scale. So again, lots of data to be processed very, very quickly. There's huge amounts of devices in the example I've given about the smart grid. We have millions of them trying to control what the grid does very, very quickly. So you need the infrastructure to cope with millions of connections, 24-7, sub-second response time so you can regulate these noise corrections and things. We're also a very, very interesting one to deal with. And in healthcare we have huge amounts of people. If you want to provide a health system that serves the world, I think at the minute we're about 7.6 billion of us in the planet. So if you want to provide a system that deals with them and can keep the medical records of 7.6 billion people and the interactions with doctors, the interactions with other clinicians, the tests, the insurance claims, all these things, we're talking massive scales. So that's an entirely different problem in itself. And the one that I think everybody's concerned was really good talk yesterday from Spotify about what they talk about the sensitivity of data, the privacy of people's data. And that's a good example for healthcare, for example. That's our number three there, how do we protect data so nobody gets access to very private information you might not want your employer to know about or other people. The priority information is a very good example of Formula One. Formula One is incredibly protective of their design and their systems. And so when you build a new car or a new part for the car you do not want your competition to know about it because obviously it makes your car slightly be faster than them. And so people who quit the team to go to another team actually get put into what we call gardening leave, which is you will not work for anybody else for a year. So they get paid to do nothing for a year so that the knowledge they have when they go to the competitor is already too old. So this is how paranoid they are about this proprietary information. In the grid you can think of we have platforms now that can control the whole grid of a country. Can you imagine what if a bad actor, a nation state that's trying to attack the country would break into that system. It could completely collapse the economy. It could do all sorts of damage by switching things off or even creating a resonance wave in the grid that would actually break the devices attached to it rather than just switching them off. So it could be catastrophic and so security in the energy space is huge. And the last one, the gaming system is actually quite an interesting one because I'll give you an example where recently Dr. Ling, where I work now, we just released a new version of our diagnostics engine. It's basically an AI chatbot where you can go in and if you feel bad, you feel poorly or something, you'll go in, it'll ask you a bunch of questions and it'll actually work out what's wrong with you. If you need a doctor's appointment it will actually allow you to book right there and then make an appointment. And that if you don't really need to be seen by a doctor so if you could go to the pharmacy or you could just go and stay home and take some paracetamol, something like that we'll try and do that. Some patients really want to see the doctor no matter what, so as soon as we release the new system just a small portion of the population just to test it. We included this feature which at the beginning of the chatbot it would ask you are you playing with the system or is this a real case? Just so that our stats were nicely partitioned a few people in one day that chose I'm playing with the system and they did lots of different chatbot traversals until eventually they got to one that gave them a doctor's appointment that day and then they switched to this is a real case now and they did the same traversal to get the doctor's appointment so within 24 hours of releasing the new version they already worked out a way to cheat the system and get that appointment that actually they didn't need so we now have systems in place to do anomaly detection stuff to actually rectify the situation very quickly but security is not only a concern of people coming from the outside but also people coming from the inside that have access to the system trying to cheat it now that was the preamble to what I want to talk about now which may or may not be controversial depends on how you think about artificial intelligence machine learning but I think this quote is quite quite interesting because it sort of sets the mood for what I'm going to talk about next so how do humans learn there's lots of ways of explaining the human learning process but one way of representing it is this we dream or create or build something we use it for a while we get some feedback as to whether it works or it doesn't work we find the issues that we had and then we just go around the loop and make it better a very good example that occurred to me the other day because I was looking at some old photos a while ago photos when they were still filmed we didn't have digital then the photography companies wanted people to have easy access to photography and to make these nice cute little point and shoot cameras and one of the features they had was the flash everybody uses the flash now and they wanted to encourage people to take photos in parties at night in dark conditions and so they released these cameras and everybody loved them they all took photos and obviously they were in digital so you had to wait a few days to get your lab photography developed and your film comebacks in pieces of paper and then they realized that everybody's eyes were red like demons and this is like what's happening why is this happening and so the manufacturers realized that the human pupil dilates in the dark and so when you shine a flash into it it reflects it back and it looks red and so they thought about this and thought oh I know what we'll take an initial flash and then a fraction of a second later we'll do the proper photo with the flash and so they changed the cameras the released new version people started using it and now everybody's photos were wrong because everybody was looking away after the second flash and so this sort of cycle then realized humans had to learn to actually respond to the change in technology say okay now every time I get photo taken in the dark I wait for the second flash if I don't want to look like an idiot so that sort of thing is the theme I want to run through next is how humans learn, design, build the machines tell us what's wrong with things and then humans learn from that and develop better machines but also we change our behavior we realize that we also have to change and so I can't I can't claim I came up with this concept I just thought of a name for this thing it's a machine assisted learning this is not the machine learning you used to hearing and you've heard a lot about this few days this is the process whereby humans design an algorithm and that could be a mathematical algorithm it doesn't have to be an ML model this is an actual physical algorithm then they run it in production and let's say the example is I do a model of a car and then I want to run it in a simulation and I want to see that that car looks realistic then I gather data that could be telemetry from the car and then the machine learning model this is where I sort of added this bit that I call machine assisted learning a machine learning model will then do anomaly detection, pattern detection and other things and work out what the differential between my model and the real world was and then the human learns how to actually tweak their models and become more accurate and learn things that they didn't see before because the machine is much better than us doing that sort of thing and so you refine your algorithm and you do the same loop so this is the concept I want to run through and I'll give you an example of each of those three industries just to see and I probably could think of a lot more many more examples like this but I'll give you specific ones which will make it a bit clearer so formula one one of the things that when I joined formula one it was quite interesting that because they've always done ways in a certain way they didn't quite favor technology as much as the favor engineering as you would imagine so they spend millions of dollars in a car they spend a few grand on computers right it's not quite the same as we're used to in tech and so I sort of had a look and said well we have all these data that's been created by the car at the minute there's really old slow processes to deal with it can we make that faster first so my first three weeks I just wrote some code add the data out of the car get it out in a couple of minutes do a lot of analysis and so within a couple of laps I could do something with it so then we realized well if we can our vehicle scientists which are I guess the equivalent of a data scientist but specialized in physics of a car could model mathematically the car and I'm talking to excruciating detail like the model of every spring the model of the aerodynamics of every part of the body that can be changed to change the aerodynamic performance of the car the stiffness of the springs the angle of attack of the wings all those things that the grip of the tires the type of tires the type of tarmac whether it's wet or not whether there's wind coming all those different environmental simulations mathematically and I could generate a lot of simulations so when we started we only did two or three simulations before a race by the time I included cloud and massive parallel computing HPC and stuff we were doing about race simulations in the weekend before so that we can then have this massive array of parametrized simulations with all the different configurations that we thought would benefit that particular race so say Barcelona race you would want more drag force you would want more downforce to get the car to stick in the fast corners you knew it was going to rain so you would do something with the tires that sort of stuff would run all these huge models and you get this n-dimensional very hard to visualize map these possibilities and then we had some sort of parallel coordinate system where you could highlight a few of those and it would massively massively reduce your sample of simulation runs and so we come to Friday those of you who don't know Formula One on Fridays we do what we call free practice free practice one in the morning free practice two in the afternoon that's where you put the car on the track and you do configuration changes and see what works because you know this is the real world all the simulations in the world are going to happen until you try it and so the race engineers which are very expert in this will tune the car as they think will fit that day those conditions that track and they will run the car a couple of times and then we'll identify some of the run simulation runs that will fit closest that performance and so we'll get rid of all the others and stick to those then do a few more changes and eventually we'll iterate within a few laps in free practice to the simulation that looked the closest and here's the simulation so you can see that there's a combination of machine and humans working together then you get to the simulation and you run another set of different simulations we call the strategy simulation so these are the ones that actually do gamification they simulate all the other cars they work out what happens if this car crashes here what happens if there's a yellow flag or a red flag what happens if we have to stop the race because of rain that sort of stuff and it gives you all the different possibilities because your competitor is probably going to pit you a lap later and you can actually overtake him that sort of strategic decision very quickly generated with this one simulation that we narrowed down so now you have almost a perfect simulation of your car and then you can run that model into the strategic simulation then we run the race and then we see whether it works or not and strategy as I said is very difficult to predict sometimes the conditions so most of the other times it would work in years the vehicle scientists everybody would get together look at the data which was lots of them machine learning was used to actually help work out what happened what was predicted versus the actual and it would go and tweak the models and you know we go to the next race that's a very nice sort of loop example of what machine assisted learning can do in terms of humans designing algorithms that then get modified or improved by what we learn from machines in energy this is a short example but in energy particularly in the smart grid we did a very similar thing we had data scientists and physicists modeling people's rooms how do radiators hit up your room can we tell that your window is open and therefore you're wasting energy and the room is getting cooler faster than normal can we model the grid can we model power generation what happens if I switch this nuclear power station because there's been a problem then we would come up with algorithms how to control devices out there to make those changes respond to those changes very quick and so we could enable more green energy in the grid and so then we would run this in production the devices will send some telemetry to say this is the power I'm generating this is how hot the room is all these different things and obviously get millions of those and then we use anomaly detection pattern analysis and other things to actually work out how close was our model in a way that we hadn't predicted and so we learn which of our models we then consider ok what if some other power station goes so far we have a massive gust wind in Scotland that's going to generate a massive rush of energy all those different things we'll learn, modify and do the loop again so again another example of actual human design algorithms being reinforced with machines and in healthcare this is very close to my heart because we're actually building this as we speak we have probably the only system probably can't claim that so I won't say that but we're one of the few systems that actually have a set of algorithms that can actually diagnose almost I believe that we're now up to 95% of all conditions and Dr. Ling we really believe on clinicians being in charge of that being able to design algorithms and those algorithms are done with using Bayesian logic using natural language processing using a bunch of different techniques we know about your family history we know about your lifestyle, do you smoke, do you run do you have someone in your family with diabetes all these different things so we have a picture of you and then we create algorithms that will actually tell you when you're feeling poorly and we ask you there are questions can we work out what's wrong with you and tell you how to fix it so we create the algorithms patient comes into the app starts talking to the chatbot and it'll be asked a few questions because we already know about you so we want to ask you are you diabetic if we already know you are that sort of thing so it sort of makes the chatbot a little bit more streamlined and the UX is a little better for the user so we collect all the data we work out what we think is wrong with this person at the same time we're analyzing how long did it take to traverse the questions did it hesitate in any of them could it have gone in a different direction what is the actual conclusion what do we come up with who should fix it and by what time frame and then we capture the data as that patient goes through the pipeline and that patient has to go to the doctor and we see him appearing in the doctor because we put a beacon in the surgery and we can see them showing up then we can start collecting all that data so much so that we now have huge amounts of data that can give us population level analytics so we can tell when there's a flu outbreak about to happen in Birmingham for example there's a huge condition happening in a particular area which could be something with the water so at the same time we're analyzing how did the algorithm perform did they actually predict the conditions correctly did the patient do what we suggested or did they do something else and why can we change the algorithm to make it better so that whole process then gets presented to the clinicians the insights from the machine learning models gets presented to the clinicians on their toolkit design where they design the algorithms and he says right this is the algorithm you start with the best you make to it but it's up to the clinician to then assess whether they're safe and then merge them so it's all like a development process you get your pull request you assess it, you merge it, you put it in production and then we'll go through the loop again so you can see that the process works really well in this case because we don't want to lose the human interface that would decide whether something is actually clinically safe the machine alone would not be able to make that assessment so I think this quote is really appropriate because from the CEO of IBM it basically comes to say that as valuable as artificial intelligence is actually it's augmenting us and we shouldn't forget that the human in the equation is actually very, very important and we're the ones that will improve AI in turn or being improved by AI so just a final note just say we've talked about what's in common with all of this but something that I sort of blocked about years ago was the concept of cross-pollination one thing I normally do if I change jobs is to change industries I don't like to repeat industries if I can avoid it the reason for that is because when I go to a new industry I'll learn a lot that I didn't know because I didn't need to use that methodology or that technique but at the same time I see things they're doing that maybe another industry has solved years ago in a much more efficient way and so that cross-pollination of different industries coming together and solving the same problems because as you see in the methodologies are very similar you can actually resolve issues quicker so Formula One is a great example as I said it's very hard to get to F1 unless you're getting from the bottom and if you get from the bottom then you're going to be taught what F1 does, how they do it and why they do it and the answer is because we've always done it that way which to me is not a reason, it's an excuse so when someone from the outside comes in it's an eye-opening experience for both because I saw, wow, technology solved this so much quicker, so much easier why don't we try this but also I learned about process I mean the way that they manufacture things they run them through the pipeline, they know when something is faulty and they're already ready with their replacement on track, that was amazing I thought, wow, we could use that in a hospital if we could do that with patients moving around different wards and different beds and all that sort of stuff what Formula One does is amazing in that system, so this cross-pollination I think is a fantastic concept that we should encourage and if not by changing jobs by coming to places like this and actually talking to people that are in different industries and see how they solve problems and whether that applies to something that you might have been thinking about that there's a better way to do so just to conclude passion is what makes us what we do what makes us do what we do humans are the better part of creativity from nothing just from our knowledge and experience we can build great things but machines are much better at spotting issues crunching huge amounts of data and then give us that feedback straight away and with this machine-assisted learning concept we close the loop we learn from the machine the machine learns from us and it keeps on going until eventually probably we'll all get much better off and that last concept I just talked about the cross-pollination concept I think is very, very important I encourage everybody to after this go out and network and see who works in our industries and what you can get out of them and that's it, thank you very much any questions happy to talk to anybody outside of there don't want to ask a question in public for the talk I have a question, you mentioned that you used physical mathematical models did you also use statistical models? some of these were Monte Carlo simulations there were a lot of different models put together did you use a combination as in did you use feature extraction using physical models and then statistical models on that? I can't say much but yes we did a lot of overlays of different models and we learned about how to re-order them and how to do them in the right way so there's a lot of combinations of models we don't have any more time for questions that's fine, thank you everybody


So, we can now further train BERT on very specific NLP tasks. All we need to do is replace the fully connected output layers of the network with a fresh set of output layers that can basically output the answer to the question we want. Then we can perform supervised training using a question answering data set. It won't take long since it's only the output parameters that are learned from scratch. Most of the model parameters are just slightly fine-tuned. And as a result, training time is fast.


Ysgrifennid yw'r oedd yn gweithio'r gweithio y gwirionedd! Rwy'n gwneud yn cymryd ag ysgrifennid. Rwy'n gweithio bydd y peth yn ymgylchedd yn oedd ymddangos gyda'r hyn o'r heb ddafod o gweithio'r cyffredinol. Y cyfan y dyma'r ysgrifennid arall yn ymgylchedd yn ymgylchedd. Mae'r cyffredinol ar y cyfan yw'r cyfan yw ddim yn ymgylchedd. Felly, mae'n gynhwys yw'r ddim yn ei dweud o'i gweithio bod i'n gweithio'i gweithio'r ffinanc yma o'r ymlaen. Mae'r gwahaniaeth lleol iawn wedi'i gweithio'r gwahaniaeth gyda'r lleol, yn ymwneud dda. Mae'r gweithio'r gweithio yw'r cyfnod yw'r cyfrwynt hefyd? Yng Nghymru yn ei wneud, a mae'r gweithio'r cyfrwynt. Mae'r gweithio yw'r cyfrwynt cyfrwynt. I didn't expect to actually be going on to a live market and trading exactly what millions of traders around the world are trading. Amplify has a magnificent investment into networking. Meeting industry professionals, former people who used to work in very senior positions in very prestigious banks. That definitely has probably been the highlight for me. So I think it's a whole mix of things. It's kind of a jack of all trades. Everybody who works here has had exposure to the financial industry. You guys really do start from basics and I think just ask as many questions as possible because no one has made me feel like that's a silly question. It doesn't feel like a four-week intense programme at all, you know. The content is really interesting to learn and the more you learn the easier everything else becomes. I think it's a complete necessity that if you're doing this track you should do it. If you're coming into the world of finance you have to do the Amplify programme. To be honest I think it has exceeded my expectation significantly. And it's so much nicer to be able to do that with a group of people that you've bonded really well with so 100% recommend my course. I wish I could stay longer and do it all over again. Without any question for doubt this is the most satisfying thing we do here at Amplify Trading and hopefully one day we'll actually be using them as a client when they're working as MDs or VPs for some of the world's largest investment banks, asset managers and hedge funds worldwide.


And we're live. Hello everybody and welcome back to the coolest dog training channel right here on YouTube. I'm Tom Davis. Welcome to my live stream Today we have two of our boarding trains out with Taylor and our other trainers Zach and what we're gonna do is I'm just gonna walk you through The process because I thought it we were doing this. I said, hey, look, let's grab this Basically, I'm gonna teach you how we manage dog reactivity on the leash This dog isn't necessarily aggressive But if you have a dog that's literally reacting on the leash at everything and every other dog This video is gonna help you how to manage that properly and how to get over the hurdles So with Lupita what we did who she's she came in really reactive What we did is we taught we taught her how to focus on the handler. We also taught her How to heal we taught her how to place we taught her how to do all this stuff. That's that's Lupita here So I'm gonna walk you through really quickly An exercise in a scenario we do here at the upstate Canaan Academy to help you guys out at home with Dog reactivity it's just some things that you guys can do so as you watch you'll see exactly hello Rochester Hello, Alaska. Hello everybody else. So I'm gonna start this process now if you're new to my channel Thank you guys so much for joining Consider subscribing to my channel liking this video and if you guys are just joining this this video live Tell me where you're from so I can say hello. Hello, Hawaii. Here we go Here to get the dogs attention using using positive reinforcement to try to counteract The behavior and manage the the the the reactivity on the leash Taylor. That's a hoax Good So the place command is really good because what it allows us to do is it gives the job multiple jobs at once place There's job one sit job to focus job three So what this does guys is you're noticing that I'm giving her Multiple different things to help her again. This this video is how to manage the situation So if you guys are starting off working with a reactive dog All you can simply do is is just teach the dog some of these things and as you can see as the other dog Is walking around this is a dog that came in with severe Reactivity and as you can see the other dog walking around I'm giving her like Three to five things to do to have her focus on me and I'm paying her periodically with food motivation So what it does is it decreases the stimulation of the other dog and over time? It'll desensitize her from being reactive at all.  Where are you and she'd freak out now What we're doing is we're saying hey, there's a dog and every time there's a dog around something really good happens You get paid. I give you a job and so we're making this experience to be A more positive thing than anything and she's been here for a week or so So she's just really started our program. I'm really really excited about Continuing her education moving forward So I'm gonna do this again just to show you guys a really easy exercise to start managing dog reactivity on the leash Okay She's disengaged off of the other dog Getting a little closer good sit So you guys can tell she's getting over stimulated about the other dog you can tell in her tail You can tell in her breathing so I'm gonna go and get a little bit closer each time to her So you guys can see how I do this process. Okay, break. So what I'm doing is I'm feathering her in so I'm not just going completely in completely in What I'm doing is I'm feathering her in very lightly. So I'm gonna show you that process and how I would do that Get my camera situated. Yeah So as I get closer, you'll notice her stimulation off Excited dog. You'll notice her stimulation will get higher Yeah So that was a big moment there because what happened was hi sweetheart What happened was is is she actually looked over to the dog and she smelled him So she was air-sending so what it did is it turned the reactivity that she normally would have and the overstimulation Into curiosity, which is exactly what we want to do. It's a behavior modification We're literally changing the negative behavior of reactivity and being crazy and what we're doing is we're changing it into something positive So we're getting closer and closer to the dog that normally she would have freaked out about when she first came in and we're making it a positive thing but at the the macro the macro or the bigger picture is the fact that Mentally she's becoming more stable and thinking clearer around other dogs And so what it does is it takes the other behavior away because it's stressful It doesn't make any sense to her She doesn't know why she's reacting and it self perpetuates into this my dog is always reactive And so what we're doing is we're slowly but surely I call it ballooning ballooning in and I'm getting closer and closer to the other dog and making it as much of a possible positive experience as possible and Over time she'll make this connection of other dogs aren't so bad So I'm gonna continue to do this if you guys like this live video. Don't forget to like it. We have nine likes right now There's almost 75 people in here 65 whatever it said So don't forget to like this video if you're here Let me know where you guys are from and if you're watching this in the future Leave a comment and let me know your dog churning questions And of course I'll answer them in the comments later tonight So I'm gonna continue to do this and then I'm gonna put the dogs back and end their sessions for this evening Thank you guys so much for watching again. Let's go. Let's go She's super engaged with me What a good girl So the the main thing here guys is for you guys to understand is That the reward-based system that I was doing and of course watch this On a huge explosion just kidding. Um, but that would happen to me But anyway, so the main thing I want you guys to understand during this process is when I reward her This is key key key key key when I reward her I'm only rewarding her if you watch the video back once in a while. I'm not rewarding her Continuously and continuously and continuously because I'm not I don't want to take away completely of the situation Because if I completely take her focus Through a food drive so dogs are Dog that has food drive as a dog that really really loves food really really gets excited about it If I take if I take her attention onto the food I also need her to be present So I don't want to completely have her be obsessed with the food So I'm only giving her food once every maybe five or six commands or more So that's very important to understand through this process is I'm not bribing her through with food I'm rewarding her with food when she actually completes the task of Looking at the dog and then looking back at me. Yes, but once every maybe five times I'll actually give her a tiny piece of like literally I'll show you guys really quick like literally like this tiny piece of food So you don't want to reward her over and over and over again with food with food Reward but you can praise the hack out of her every single time She's she decides to make a better decision around a dog that she normally would be super reactive to and judging by the behavior I'm seeing my professional opinion is Lupita is very very overly stimulated around other dogs and she just doesn't know how to Maneuver that and so what I have to do is I have to navigate her through the process to really teach her how to make better Decisions around other dogs and to be calmer and to be more relaxed and making the entire experience super super positive Yeah, you're seeing me live. Yay. So anyway, I just wanted to we've been super super busy I was on the road for five weeks. I came back I flew to LA I come back and then I'm flying to Kansas City this weekend and then I'm coming home and then I'm going back to LA and So anyway, a lot of stuff going on. I hope you guys are so well. Thank you guys so much for the support here on YouTube I'm Tom Davis America's canine educator if you haven't yet like this video Let me know where you're from and if you have any dog training questions And of course if you like this video and it brought you any value at all consider subscribing to my channel Thank you guys so very much. I will talk to you next time. Peace