The most interesting contest on the 6th of May, and the one that will be most consequential, are elections to the Scottish Parliament. The big story here is whether or not Nicola Sturgeon can win an absolute majority, which she says would serve as a mandate for planning for a second referendum on Scottish independence. And we can go to the latest projection from Britain, Alex, in conjunction with the new statesman, and that puts the SMP on 62 MSPs, just short of the 65 needed for an absolute majority. That poll also suggests that the Greens, who also back independence, could double their representation in Hollywood from 6 to 11. So, obviously, even if the SMP don't get that absolute majority, there will be an absolute majority for an indie ref too. Now, to find out what the Scottish Greens will do if they become kingmakers in the next Scottish Parliament, I'm joined by Ross Greer, a member of the Scottish Parliament with the Scottish Greens. Thank you so much for joining us this evening. Thanks for having me. Good to see you. Current polling suggests in practical terms the next government could be a lot like the last. You'll have an SMP minority government with the Greens sort of propping it up essentially, voting through their budgets. I suppose potentially you could also join a formal coalition with them. My question for you though is, at the moment, you've got SMP government supported by the Greens. You're about to have probably SMP government supported by the Greens. Is a vote for the Greens a vote for the status quo? I mean, from our perspective, you just need to look at what we've been able to do with that position of influence over the last five years. And it's worth saying, yes, we've agreed, budget deals with the SMP most notable, each one of the last five budgets, they do vote with the Conservatives in the Scottish Parliament just about as much as they vote with the Greens. So, particularly on issues like housing, the SMP and the Conservatives are always very keen to defend landlords, whereas you see the Greens and to an extent Labour standing up for tenants. What we've done over the last five years though with the budgets, we've been able to introduce stuff like free school meals for all primary pupils. So that will be coming in over the next year, because that was our price of this year's budget, free bus travel forever and under the age of 22, about half a billion pounds back into our local government budgets, and stuff like we, the Green Party rewrote the income tax system in Scotland. So income tax was devolved for the first time after the 2016 election. And one of our prices, the 2018 budget, was that the income tax system be rewritten. So the richest people, the highest earners pay more. And those on the lowest incomes actually got small tax cuts. So you've seen the influence that we've been able to have over the last five years. It's not been on every issue, on other issues that the government can count on other parties support where they want to preserve the status quo. Where we've been able to really make a mark has been on issues where other parties, frankly, wouldn't work with them. I mean, the controversy with the Greens role, as far as I understand it, is, I suppose, voting for budgets which put through quite severe cuts to local councils. So this is Labour Party research reported by the Herald, which suggests that 937 million pounds in cuts have been made to local government over the past eight years. Obviously, only four of those years you were supporting the government. I mean, is there anything you think the Greens could have done differently? Could you have played a harder bargain in trying to limit those cuts to local government? I think we definitely got better at negotiating year by year. So you can see that the last couple of budgets where we got stuff like free bus travel for young people, free school meals, direct cash payments for low income families this year. So they'll be coming in June, August and December because we put them into the budget. Yeah, we've got better at it than we were in the first couple, but Labour will deliberately use longer periods of time, because that's the period of time you'll have for those budgets. The SMP was in majority and passed them themselves. What we've done over the last four years is essentially stop the bleeding. Local government was being hammered for the last couple of years. It's not been, I wouldn't for a moment suggest that the Greens have reversed all this austerity that local government has suffered between 2010 and 2016 when we started holding the balance of power. But we put half a billion pounds back in, which in Scottish terms is pretty significant, and it stopped the massive austerity that was happening up until that point. Yet we need far more fundamental change. I mean, the haggling that we do over a couple hundred million pounds every year doesn't fundamentally solve the problem of local government finance. Council tax rates in Scotland were last set in 1992. I wasn't born in 1992, so we have members of Parliament who are fiddling around the edges of a local government tax system that is older than I am. Most people don't even pay the right rate of council tax and trying to get not just the SMP, but Labour and others on board with that more fundamental reform, the really transformational stuff that the Greens want. That's been what's challenging. We've done the best we can with the position of influence we had. I think for five or six MSPs, we've punched above our weight. We're proud of what we've done. Would it have been different if it had been a green majority government? Of course it would have been. These weren't green budgets. They were SMP budgets that we were able to really significantly influence. Comparing contrast that with Labour, whose strategy and budget negotiations was to try and force the SMP into a corner where they could only do a deal with the Tories, and it would be a really bad budget, and Labour could turn around and say, we told you they were just the Tartan Tories all along. We never wanted to cooperate with that kind of strategy because people suffer as a result of that. Those aren't good budgets for public services. They would have been even worse for our councils. Labour's strategy was never to get a good budget. It was to, for political opportunism reasons, deliberately force the SMP to pass worse budgets for their own electoral advantage. There are some uncertainties when it comes to this election, but it looks pretty certain that there is going to be a majority for a second indie referendum for Scottish independence after the elections on the 6th of May. What's your position on what should happen next? How that should happen? We can presume Boris Johnson is in power and Westminster is not going to say, go and have the referendum. Do you think they should go and do, I suppose, an illegal referendum where you just organise it without the support of the Westminster government? The kind of trite answer to this, I suppose, is that I don't want to live in exile in Belgium and you just need to look at the experience of our friends in Catalonia to see that that's not a path to independence. There are avenues that are available to us, though, but most interestingly has been the briefings that have come out of Downing Street and the Tories over the last couple of weeks, that instead of this hard-line position of opposition, you can never have a referendum even if you voted for it. They're beginning to recognise that that's actually just pushing more people towards the independence cause, because it's validating our argument about the democratic deficit. So what the briefing now is that they might try and bounce us into an early referendum and to try and have it really quickly and they think that the no side, the unionist side, believe that they would stand a better chance of winning if we've not had a chance to prepare our post-pandemic, our post-COVID argument for independence. So we're seeing a shift in their tactics, which I welcome, because it's obviously to a slightly more democratic or a slightly less anti-democratic position. But we don't actually have any legal precedent on this in Scotland. So it's never been tested that the case for whether or not the Scottish Parliament actually has the power to hold a referendum, it's never been tested in court. So if we end up in that situation where we've won the pro-independence majority that it looks like we're going to, the Scottish Parliament has passed a resolution saying there should be a referendum. If the UK government rejects the Section 30 order that's required to do it in what would be constitutionally and unquestionably a legitimate manner, if they reject that, we can go to court and test the case on whether or not the Scottish Parliament actually has the power to do this itself, because that's not settled and there's strong legal opinion on both sides of that particular debate. So we've got legal avenues that are available to us, though obviously that wouldn't be the preferred path. We would prefer that the other side simply recognise the democratic legitimacy of the mandate we will win. As far as I understand it, what you guys want is to leave the UK and immediately join the EU. And as well as there being a lot of currency issues which were exactly the same issues that you know arose, I mean in EF1 in 2014, you now have this massive issue of the border. And you know we've seen what happened in Northern Ireland, it was very difficult, they also had the Good Friday Agreement. So there was you know I suppose a necessity for both sides to have an arrangement whereby you could have a bit of a soft border on the island of Ireland. How if an independent Scotland joins the EU and the rest of the UK still has a fairly hard Brexit situation with the European Union, are you not going to have a hard border between England and Scotland where people have to you know have checks before they can cross that border or at least goods will have to have checks before they can cross that border? Yeah I mean I'm not going to stand here and pretend that there's a straightforward answer to that but we would also, we wouldn't claim that an independent Scotland would be you know joining the EU instantaneously. That's a process now. We would be better placed than most countries that start the accession process for joining because at the moment obviously we abide by most of the Ikea because until very recently we're bound by them as part of a member state. The discussion around that is in a totally different place to what it was in 2014 and obviously where we've been gradually moving to since 2016. It would be an absolutely no one's interest to have a hard border between Scotland and England. It is notably different to Northern Ireland so I set on one of the bodies that monitors the Good Friday Agreement. Obviously we're in wildly different places. For us it's not conflict related situation but it would be incredibly bad economics obviously to be setting up a hard border. The process by which Scotland can move back in towards the European Union and the process by which we become independent and avoid a hard border. They're not necessarily mutually exclusive. I mean you know no one is suggesting that the common travel area would end for example and we're not suggesting that overnight these issues are easy to solve but what we're asking for here is the opportunity to have that debate so we want an opportunity. We've not set out our detailed prospectus for how independence would look like on day one because we've not yet won the mandate to have that referendum. That's what this election is all about and we recognize the plans that were there in 2014 they don't apply anymore to quite a significant extent on both sides. Both sides arguments have completely changed since 2014. What we as a country deserve is the opportunity to have this debate and to thrash out exactly how that would work and for those of us who are pro-EU and believe Scotland's future should be should be in the European Union that requires a different case to the one that existed in 2014 and we need the time the opportunity to actually build up and develop that case. What we're trying to do between now and Thursday is the pre-debate, the pre-argument the the debate about whether or not we should even have this debate should Scotland have another opportunity at self-determination and you know clearly it looks like we're heading towards a majority for that at least. Once you get to what independence looks like that's when there are much much sharper divisions because the Greens you know as a part of the radical left we want something quite fundamentally different from the SNP so it's not just that we want something different from what our friends in the Unionist side would argue for. Our vision of independence is of complete social and economic transformation the SNP's I would argue is you know the status quo in Edinburgh but rather than London. Those debates are debates that we can only have if we are having a referendum and then actually become independent. At the moment it's all kind of theoretical and academic where all we're asking for is the opportunity to actually have that argument have that debate about self-determination.


and Rich Picks Daily. And today we're gonna speak to you on a new company that I am very excited about, that I think has enormous, enormous potential. And I think that is one that everyone should put on their watch list and put on their radar. They also have some news that we're gonna go over and we're gonna go over the charts as well. So right off the bat, the symbol in America is PYNKF and the symbol in Canada is PYNK. Now, anyone who's been watching Rich TV Live knows I love disruptive technologies. You could see here, PYNK currently at $4.24. And this is a company that has disruptive technology and I love disruptive technology, okay?  Always been a big fan of disruptive technology. And if you've been watching Rich TV Live for quite some time, you know that we've brought winner after winner after winner using our strategy of identifying disruptive technologies, companies that are changing the world, companies that are changing their industry, companies that are unique. This is the key to our success, identifying undervalued, underappreciated, underexposed opportunities before they explode. We've done it better than anyone else on the planet. Now, a lot of times you're gonna notice that with a lot of the companies that we talk about, they're not gonna get as many views and that's because we're on YouTube, which is a search engine and we're talking about stocks before anyone else in the world. So the reality is nobody's searching those stocks because nobody knows. So what we realized is, wow, if we become that place that everybody goes to, every time somebody's interested in finding something new, then yeah, maybe we're not gonna get as many views as other people, but we're gonna be able to identify all the winners first. That is the modus operandi for Rich TV Live. The modus operandi is we are the place that everybody goes to win. And Rich Pigs Daily is our trading club, as you can see behind me, and we take a lot of pride in making sure that our members are winning and having success. So if you guys are looking to learn how to trade, if you're looking to gain more picks, you're looking to gain more success with your trading, you can join our trading club at richpigsdaily.com and you too can be a winner. So Rich Pigs Daily is an online community designed for trades and traders to exchange smart, straightforward and profitable investment ideas. Join us today on www.richpigsdaily.com, subscribe to our YouTube channel to learn useful stock trading secrets and discover the next 10 bagger. So this is the news right now that we're gonna break right here pre-market, Perimeter Medical Imaging AI. I love artificial intelligence. Absolutely love artificial intelligence. Perimeter Medical Imaging AI announces clinical study at Northern Arizona Healthcare Verde Valley Medical Center. This is the big news breaking this morning, Perimeter Medical Imaging AI Inc. Symbol pink in Canada, PYNKF in America, for PC in Frankfurt, Germany, a medical technology company driven to transform cancer surgery with ultra high resolution, real-time advanced imaging tools to address areas of high unmet medical need. Today announced that Dr. Beth Dupri, a surgeon at Northern Arizona Healthcare Verde Valley Medical. Wow, Medical Center expects to enroll up to 100 patients in a study that will evaluate the use of perimeters, optical coherence, tomography, OCT, imaging system during breast conserving surgery. In a previous successful collaboration with Perimeter, Dr. Dupri generated compelling data from a smaller 20 patient study completed in 2020, which prompted the initiation of a larger study.  And if we could find a cure or we could find something to make things better, that would be huge. Many breast cancer patients undergo a lumpectomy to remove the tumor and conserve the breast. The complete removal of cancerous tissue during the surgery can be challenging. The surgeon's goal, which is the total excision of the tumor to prevent recurrence while removing as little healthy tissue as possible is achieved by excising the tumor plus the surrounding layer of healthy tissue referred to as a margin. If the surgeon fails to get clear margins, re-operation may be required, which presents a greater risk of further complications for the patient and higher costs to the healthcare system. The clinical study led by Dr. Dupri aims to demonstrate that surgeons can effectively use perimeters OCT imaging system during a clinical procedure to aid their decisions if additional tissues needs to be excised. This study will also calculate the average time to re-exise while in surgery instead of a separate follow-up procedure. In addition, the data will calculate the percentage of patients that require a second surgery. Dr. Beth Dupri, breast surgeon at Northern Arizona Healthcare said, I'm excited to lead the study using perimeters OCT imaging system, which provides ultra high resolution visualization of a specimen in real time. Game changer. Remember, I'm bringing you guys this first. Never heard anybody talk about this company and the company name is Perimeter Medical Imaging. We love game changing technologies and disruptive technologies and technologies that are changing the game. And this looks like it's doing just that, which provides ultra high resolution visualization of a specimen in real time the use of this technology in or in my or adds to my confidence on the final margin status. For patients, the need to re-operate can cause emotional stress, discomfort and inconvenience while potentially delaying other treatments and therapies. This is a non-invasive technology that aims to provide better peace of mind for both the patient and the surgeon while lowering the likelihood of adding to reschedule another surgery. It is my hope that the date generated from the study will help validate the use of the technology in breast conservation surgery and ultimately create better results for my patients. Hey, if that's what it can do, then that's a fantastic, fantastic, fantastic game changer. Jeremy Sabota, Perimeters Chief Executive Officer stated, this is the CEO. We are excited to announce the recent installation of Perimeters OCT Imaging System at Northern Arizona Healthcare Verde Valley Medical Center. Our hope is that the data generated from the study will further validate the potential clinical benefit of bringing real-time margin visualization into the OR to assist surgeons with their decision-making. In addition, we have worked closely with the clinician users to ensure the Perimeter OCT imaging system can be easily integrated into current intraoperative workflows. We are focused on creating better long-term outcomes for patients while lowering costs to the healthcare system and look forward to the results from the study with Dr. Dupri. The study is open to patients to Northern Arizona Healthcare Verde Valley Medical Center. Dr. Dupri, M-D-F-A-C-S-A-B-O-I-M is a board certified general surgeon specializing in diseases of the breast with additional board certification from the American Board of Intergrade of Medicine. She is a nationally recognized breast cancer expert who has chosen to bring her 26 years of experience and surgical expertise to the Verde Valley. Dr. Dupri earned her medical degree from Henneman University in Philadelphia and her undergraduate degrees in behavioral neuroscience and the history and philosophy of science at the University of Pittsburgh. In her first book, The Healing Consciousness, A Doctor's Journey to Healing was released in 2006 to excellent reviews by Christian Northrop, M-D and Bernie Siegel, M-D among other well-known experts in the woman's health field. Dr. Dupri is a keynote speaker at many national events primarily addressing women's health issues, state of the art, breast cancer therapies, healing and wellness. She is often featured in live tele-surgeries and WebEx online video conferencing to educate physicians and healthcare professionals on cutting edge breast care issues. She has traveled throughout the United States and globally to train countless physicians in breast surgical oncology techniques. Her numerous honors include the Clara Barton Humanitarian Award from the American Red Cross for ongoing contributions to the treatment to breast cancer. She was selected by her peers for Philadelphia Magazine's top docs and surgery in 2016 and 2017. She serves on the advisory board of breastcancer.org and often hosts live chats and podcasts on current breast cancer issues. Dr. Dupri was chosen by her peers as Verde Valley physician of the year in 2020. What do you guys think of this news? What do you think about this deal? Remember Rich TV Live is strictly for education and information purposes. Please do your due diligence, do your research before you invest in anything that we talk about here on Rich TV Live. If you like the video, smash the like button, comment down below, share the video everywhere and subscribe. Now let's talk a little bit about Perimeter Medical Imaging AI. I love artificial intelligence. It is a game changer and Perimeter Medical Imaging AI is using AI artificial intelligence. Symbol in Canada is pink on the TSX Venture Exchange, TSX-V. Symbol pink, P-I-N-K. On the OTC symbol P-Y-N-K-F. And in Frankfurt, Germany, the symbol is for PC. Perimeter Medical Imaging AI is a Toronto-based company with US headquarters in Dallas, Texas. Everything is bigger in Texas that is developing with plans to commercialize advanced imaging tools that allow surgeons, radiologists and pathologists to visualize microscopic tissue structures during a clinical procedure, game changer. Perimeter's OCT imaging platform is a point-of-care imaging system that provides clinicians with real-time, ultra-high resolution, subsurface image volumes of the margin one to two millimeters below the surface of an excised tissue specimen. The ability to visualize microscopic tissue structures during a clinical procedure in addition to standard of care tissue assessment for decision-making during the procedure has the potential to result in better long-term outcomes for patients and lower costs to the healthcare system. Perimeter's OCT imaging platform is cleared by the FDA as an imaging tool in the evaluation of excised human tissue microstructure by providing two-dimensional, cross-sectional, real-time depth visualization with image review manipulation, software for identifying and annotating regions of interest. In addition, Perimeter is developing advanced artificial intelligence machine learning image assessment tools intended to increase the efficiency of review. Perimeter's ticker symbol pink is a reference to the pink ribbon used during breast cancer awareness month by the Canadian Cancer Society and the American Cancer Society, driving home the company's dedication to helping surgeons, radiologists and pathologists use Perimeter's imaging technology and AI artificial intelligence in the fight against breast cancer, which is estimated to account for 30% of all female cancer diagnosis this year. That's the news. What do you guys think of this news? If you like the news, smash the like button, comment down below, share the video everywhere and subscribe. Love to know what you guys think about this big news. Now, I want to go over some more information about Perimeter. So this is their corporate overview. I'm just going to go over the key points. So I'm not going to reiterate what I've already talked about. So right off the bat, their US FDA 510K clearance of Perimeter's commercial ready, optical coherence, tomography, OCT imaging system with further validation from world-class key opinion leaders, artificial intelligence, AI machine learning tools in development, I love artificial intelligence and this is another AI company, exceptional management team, board of directors and scientific advisory board, 3.7 billion total addressable market for high margin per patient consumable. Well-capitalized, traded company, 7 million added to the balance sheet since Q3 from warrant exercises and breast cancer is a global problem and cancer left behind after surgery leads to re-operations. Two million women worldwide were diagnosed with breast cancer in 2019, 317,000 in the US alone. Recurrence risk doubles when positive margins are not excised. $16,000 is the cost per re-operation. Wow, terrible, terrible costs and 48% complication rates for re-operations. Wow, those are scary numbers. So the tissue assessment workflow, current standard of care versus potential future improvement. You can see the current standard of care takes two to seven days. Gold standard, post-operative histology tissue assessment, two to seven days post-procedure and in the future, perimeters, intra-procedural tissue assessment should be available in five to 10 minutes. Wow, see that?  You're talking about right now, it takes two to seven days for the current standard of care and perimeters, intra-procedural tissue assessment will be available in five to 10 minutes. That's what I call disruptive.  That's what I call perimeter medical imaging. Put it on your watch list, put it on your radar. Remember, Rich TV Live is strictly for education and information purposes. Please consult the financial advisor before you invest in anything that we talk about here in Rich TV Live. Always invest in the best, the best is blessed and you absolutely deserve the best, in my opinion. Perimeters medical device platform is a disruptive technology comprised of four key components. You can see here, perimeters OCT imaging console, perimeters tissue immobilization system, perimeters proprietary image library and perimeters proprietary AI IMG assist. And you can see what they do. I love technology and technology is just moving at such a fast pace. And a lot of people can't keep up with it, but that's why people are watching Rich TV Live because we can keep up with the technology. We can keep up with the winners. We can bring you the winners before anyone else on the planet and you can stay organized and you can win. Let's see if there's anything happening in the stock so far. Looks like the stock is just kind of getting started. Doesn't seem to be doing too much movement yet. And if you guys have any questions, feel free to message. Let's quickly take a look here. If you guys have a question, please put it in the chat. We are live and interactive looking at the markets. And this is their website, perimetermed.com. Pretty cool website. I've already signed up to their newsletter. So anytime they have any big breaking news, I will get access to it first. You can learn about their information right there. A lot of the companies that we talk about are pre-revenue. A lot of the companies that we talk about are early. So you're gonna find that is a pretty standard thing with a lot of the companies that we talk about. So I don't think that that's anything new because we bring you companies first that are undervalued, underappreciated, underexposed. Most of them are pre-revenue, but they are going to grow in revenue. And that's why we love the picks that we talk about because we're bringing you companies that are growing. Some of them are pre-revenue, some of them have revenue, but at the end of the day, every single one of our companies is growing in revenue or going to be growing their revenue. That's why we've had so much success. And I believe that's why we will continue to have success. And like I said, this is the website perimetermed.com and there's lots of information for you to learn about the company on the website. So I'm not gonna go through all of it right now. And let's just go through some of this information that others are talking about with perimeter. Once again, Symbol Pink in Canada, PYNKF in America, for PC in Frankfurt, Germany. This company's disruptive imaging technology could drastically reduce the need for breast cancer re-operations. That's absolutely huge. I mean, even getting one operation is bad enough, but a re-operation, that's just like a nightmare. Perimeter Medical Imaging Pink and PYNKF, one-of-a-kind imaging technology that could transfer breast cancer surgery by giving physicians real-time imaging of the affected tissue during operation. Wow, I'm not gonna go through all of the same information. Very similar information talking about here. So here we go. Perimeter's disruptive medical device platform consists of four key components. First one is optical coherence, tomography, OCT tissue imaging console, wide field OCT 100 times coverage. OCT tissue immobilization system, single use consumable, aids in positioning diverse tissue types. Perimeter number three, Perimeter's proprietary image library, ATLIS, broad-based tissue library with pathology verified tissues, structure correlations. Perimeter's proprietary AI, IMG Assist, provides look-here guidance to speed up image review. At present, Perimeter is focused on providing its revolutionary medical technology to significantly reduce breast cancer re-operation rates, but the company's device platform has endless applications in pathology, interventional radiology, tissue imaging for the lungs, prostate, colon, head and neck, pancreas, kidney, liver, the list goes on. Wow, this is game-changing. In July 2020, Perimeter imaging AI went public through a private placement, offering and reverse takeover of New World Resource Corp. Perimeter raised 9.4 million with potential future proceeds of 13.5 million from the exercise of associated warrants. Perimeter's previously accelerated warrants have been successfully exercised, adding over 7 million to the balance sheet, which will help to contribute to the company's sales efforts. Six reasons why Perimeter Medical is transforming breast cancer surgery. Let's take a quick look at the chart here. So this is pink. And if we were to go to the one-year chart, you could see that this has already been a huge success. This was at $1.46 in July. Pretty much when they were getting started, you could see that this has gone from $1.46 all the way to a high of $4.84. That is extremely bullish. I really love the chart. Currently sitting at $4.24, so that is the chart, and that's the way it is trending. Very, very, very bullish. Right here, as you can see, for pink, in my opinion, and let's go back to the websites. And six reasons Perimeter Medical is transforming breast cancer surgery. Number one, FDA clearance. FDA clearance patented specimen imaging system based on proven optical coherence tomography OCT technology and validated by the world-class key opinion leaders. Growing body of intellectual property currently has four issued patents with another five pending approvals in the US and internationally. An exceptional team. Management team, board of directors and scientific advisory board with medical device knowledge, breast cancer expertise, M&A experience, and more. Smart tools, artificial intelligence, AI, machine learning tools, in development to assist surgeons in the operating room in real time, while capitalized approximately 9.1 million in cash, cash equivalents, and 2.9 million in investments as of September 30th, 2020. Immediate addressable market of product, the market for its OCT system is estimated to be worth 3.7 billion. So these are some of their competitors. A quick look at other medical device companies that have developed breakthrough technologies reveals just how big of an upside potential perimeter could turn out to have for early investors. So you can see some of their competitors. You can see their market caps and clearly perimeter's market cap is by far the smallest when you compare them to some of their peers that are on the NASDAQ. And there's quite a few. One, two, three, four, five, six, seven, eight, nine, 10, 11. 11 companies with massive market caps much bigger than perimeter medical imaging AI. And this is a company that could potentially change the game forever for breast cancer survivors, breast cancer re-operations. Here's one of the companies that's actually very, very bullish for me, Butterfly. And also a big fan of Butterfly is Cathie Wood, the famous fund manager, many are calling the top fund manager in 2020 with a 148% return. She loves Butterfly Network Inc. And look at Butterfly Network Inc. Trading at $17 with a 3.3 billion market cap. Well, I like perimeter medical imaging with a 168 million market cap and trading at only $3.91. It's actually the lowest price out of all the companies on this list and I love what they're doing. It's absolutely a game changer and that's why we're talking to you about them today.  So we've talked about a lot of the key characteristics. You can see right here, perimeter is one of a kind. You can see why, when you compare them to their competitors, the five to 10 minutes is a game changer. There really is nobody that can compete with them and that's why I absolutely think you need to put this on your radar, put it on your watch list immediately. This is a company that can be an absolute game changer. Now, in addition to what they're doing, their business model and why I think there's so much potential here, let's take a look at some more information about the company, like the outstanding team behind the company. This is Jeremy Sabota, the chief executive officer. Sabota has plenty of experience and a track record of success in the medical device space with deep expertise in go-to market models across a variety of payer provider environments. He was responsible for deploying over four billion in capital for strategic M&A and MedTech and led the financing for strikers, surgical business unit, operating room equipment with women's health from 2008 to 2019. He held roles in increasing responsibility at Striker including intranural audit, finance transformation, commercial excellence, financial planning and analysis and ultimately leading finance for their surgical business unit. He was also the former controller for the orthopedics division of Smith and Nephew. So they're going through all of the key members, feel free to go through all of this. Love to know what you guys think about the members. I'm not going to break down every single member but you can see that they've got a team of highly skilled, highly qualified members so that they can grow and be a game changer. So six reasons why we love perimeter medical imaging AI. Number one, one of a kind imaging system that could revolutionize how breast cancer will be treated. Number two, already received FDA clearance for its imaging system which is based on optical coherence tomography technology. Number three is currently developing AI machine learning image processing tools that will complement the company's imaging system. Number four, strong leadership and management team that has lots of experience in bringing in a medical device to market. Number five, 3.7 billion total addressable market from high margin per patient OCT system. And number six, well-capitalized publicly traded company with approximately 9.1 million in cash, in cash equivalents and 2.9 million in investments. So I'm really good reasons why you should be putting them on your radar and putting them on your watch list. Once again, we don't see any change in the price yet. Love to know what you guys think about this. Pink in Canada. And if I wanted to go to TMX money and I typed in pink, there it is. Okay, it's up a little bit, it's now at 427. I don't know why there's a delay on Weeble, kind of odd. There's this delay on Weeble but you could see that on TMX money, it is actually already up slightly at 427. It's good news. And this is what I love to look at is the share structure. Love their share structure. I love tight float stocks, okay? This is what we call a tight float stock. Good morning, crypto man. Good morning, Robert. I hope you're all having a great day. I got another winner for you to put on your radar, put on your watch list, pink. Look at the shares. One of the most tight float stocks out there, 44 million shares listed issue note standing, super tight float stock. We love tight float stocks. Look at the market cap, 184 million market cap with all classes, 188 million market cap. That's still a baby market cap for a company that could potentially change the world for breast cancer and breast cancer survivors. I think that this could grow to a 1 billion market cap company, which gives us 500% upside in my opinion. I'm not saying it's gonna happen overnight but I think the potential is there. In my opinion, the potential is there. This is game-changing technology. Once again, the symbol is pink, PINK in Canada, in America, PYNKF, and in Frankfurt, Germany, for PC. Let's just see if they have here PYNKF. If they're updated that, PYNKF, okay? Currently says $3.38 in America. Let me know what you guys think. Is your boy rich or immersed to be live? We bring in the winners and we bring them to you first. What do you think about perimeter medical imaging? Another winner that we brought to you first from your boy rich for a mission to be live. If you're not winning, you're not watching. We bring in the winners and we bring them to you first. What do you think? Let me know. Your boy's out. Peace. Stay tuned. I got more news coming. Get your popcorn ready.


Hey, how's everybody doing? I'm Aaron Davis. I also go by Kamavis, whichever you prefer. And today we're going to be talking about MetaMask and how we get Ethereum into the browser. So our core goal is ease of adoption. And one way we sort of measure that is time from first account to first ether to first transaction. And I'm going to bring Frankie and Kevin on to talk about that. Yeah. Hello, dear devs. Let us make you roar about MetaMask made by us four. I'm sorry. OK, this is not good. OK, oh yeah, OK, sorry. A browser extension for Chrome more, but there's even more browsers and mobile coming up in store. MetaMask injects web three in your pages real lazy. Web dapps running in a normal browser, that's crazy. There's no need for a local full node. First time user, you've hit the mother load. We protect you with contra screens so that you don't lose your ether like spilling the beans. On top of that here, have some wallets to hold all that ether so that you can haul it. We love dapps. Ether needs them in stacks. Give us more of them like Flapjack. I don't have anything else to say. So here's Frankie. Yay. Thanks, Kevin. Hi, guys. I'm Frankie. And I'm going to talk about MetaMask from a user experience from first installation to dapp use to first transaction. Louder. Louder? Even louder? God. OK, we are live on the Chrome store. So user goes and downloads MetaMask. And we create a vault. It is a deterministic key ring. I had to remember this this morning. After copying the 12-word C phrase to a safe spot, we now head over to Token Factory, which is a dapp that generates your own tokens. We go through, we fill it out, we hit Create Token, and our beautiful MetaMask confirmation screen will pop up. And you can either accept or reject it. Now, in the case that you don't have enough ether, a Buy Ether button will pop up, and you have two options. If you're in the US, you have Coinbase or you have Shapeshift. Very simple. Just enter your email in, confirm your email, enter your phone number, confirm phone number, and then enter your credit card information. This has a $5 a day limit. Very go straight to your account. Unfortunately, when you get there, you can accept and reject. Once you've gotten your ether, we'll hold the transaction, and voila. You've created your token. Unfortunately, though, if you are not in the US, you don't have access to Coinbase. So if you guys know any products or are working on something that offers fiat to ETH, that would be amazing. Please come talk to us. We would love to hear from you. Our status right now is Public Beta. We have done 44 releases since March. We have weekly updates. We have over 2,000 users and 14 million RP receive requests per day, thanks to Infira. We are Chrome Live, Opera Edge, and Firefox. We are pending and ready. We're just going through the process of getting it into the extension store. Unfortunately, we won't really be pursuing Safari. They don't follow the extension standards. And as far as mobile goes, it's hard to get. We really don't have any mobile browsers that support extensions. And now to talk about how it works, I'd like to pass it off to Aaron. Hello, hello, hello. Cool. So MetaMask, how does it do that? So yeah, you want an Ethereum in your browser. So just take an Ethereum client and throw it in an extension, right? Unfortunately, that won't quite work. You're blocked on networking. The networking uses TCP, UDP. You don't have access to that in the browser, even in extensions. So what are you going to do instead? Oh, yes. Also, for the kind of adoption flow we're looking for, the mandatory sync time is kind of a non-starter. The light client protocol has improved that significantly. But so what are we left with? We can do RPC against the trusted node, where we'll run the nodes for you. And so you have the zero sync time. What do you do for ID management? We do RPC interception. So anything like send transaction or sending a message that involves usage of private keys. We intercept that, sign the transaction, and pass that on as a raw transaction to the trusted endpoint. So here's a little chart of how it works. You have the user at the top. They're looking at the DAP in the browser and the MetaMask UI. The DAP has the UI code in it, as well as the Web3 injected by MetaMask, much like you'd see in MIST. The Web3 generates RPC requests that are forwarded onto the background. This goes to the provider engine, which I'll explain in a second, and onwards onto the trusted RPC. So the provider engine is a tool for making your own Web3 provider. It's basically a stack of RPC handling middleware. It might look something like this. You have a cache layer. For example, if you're asking for the balance of an account twice in the same block, it's going to be the same. You don't need to hit the network for that. Then you got your ID management. That's where your private keys are. And then the rest of the data look-ups fall back to the node. A node on filters and load balancers filters. You install them, and then you check them. You pull against them.  But this turns out to be something that's pretty easy to solve in the provider engine level. So MetaMask has a bit of a different trust model than, say, Mist. Because we provide out of the box, by default, the trusted nodes, the trust model is different. But we could add optimizations. New features quite easily using custom API. But we want you to be able to switch over to your own local node at any time, if you want. So we use 100% RPC to maintain that standard. Again, if we use custom API, you'd kind of be locked into our MetaMask servers. And we think the lock-in sort of devalues the product. And now Dan's going to talk about lessons from working with DAP developers. Thanks, there. Hey, guys. So since we make a product that injects Web 3 into a browser, we get to talk to web developers pretty much all day. And that's great, not just web developers, web DAP developers. And so we hear cool projects. And that makes the job very inspiring and a lot of fun. And also, we end up being kind of the front guard of questions and frustrations and things like that. So I'm going to talk a little bit about the lessons and things that we've learned about the expectations of a web developer in the context of writing Ethereum web apps. So Web 3 right now, it's your portal to the blockchain. So it's so cool. But right now it has a very constrictive API surface. So we want to find ways to make that do what we want. One way that it's kind of opinionated right now, and it may not reflect the truth of a blockchain, is that it kind of has a single callback nature. We've got callbacks. We've got putting to wrap things in promises. But these paradigms that work well in web development, where there's a single resolution, aren't completely representative of the kind of statistical certainty of a blockchain, where you might have a single confirmation, but you don't know how many blocks forward it is or how many additional confirmations. There's a lot more, there's kind of a gradual trust process. So yeah, if you want one callback, you're kind of trusting it. But what else could that look like? Well, coming from JavaScript and Node, sometimes you might want like an event listener. So this is a kind of suggestion. This could be done as a layer on top of Web 3, but it could be optimized much better if it was implemented at the RPC layer. So in this case, you might get an additional callback whenever there's an additional confirmation or whenever there's an additional block or maybe there could be a callback when there's a fork in the blockchain, because right now the RPC doesn't really give you any indication when there's a block fork, short of you querying the latest block and going back because you're querying by block number, not by hash. So for an RPC trust and client, like a web browser, you have to pound your server very hard just to answer these very basic questions. And these are things that the user interface should reflect anyways. Also, web developers always want to just query the database and some things are very simple questions in relational databases, but end up being very difficult on the blockchain. Consider the case of a decentralized Twitter, you just want the 50 latest tweets. The current basic model is somebody basically querying all the tweets in the blockchain. A lot of developers are coming in and they're saying, oh, the cheapest gas thing is logs, so I'm just gonna use logs as my database. And then the method is basically logs since the genesis block, and now the server gets every tweet of all time and then sends it and it all gets loaded up in the browser, which is the interface thread, which then does all the sorting. It's impractical, right? So what are solutions to that? One solution is to write your sorting logic in the contract layer, and so now it's kind of inflexible and it gets run on the chain. It's still one batch, but you could write some kind of pagination logic maybe. There's a cool EIP out there, 144, which would let you send some custom bytecode, some EVN bytecode up to the server, that it would run so you could actually request like a custom result for the state tree. That's an interesting one. I encourage you to check it out, throw in your two cents. Oh, there's also another idea like maybe there could be an additional method on the RPC, like a SQL like query method, but this kind of requires like indexes, and so maybe we need incentivized indexes because logs aren't incentivized indexes. Maybe they could be a contract. Maybe we should have standards around those. Those could be helpful. Sometimes people ask us, why is this method not acting like geth? And it turns out that there isn't really a standard right now. We go to the wiki and then if that doesn't do it, then sometimes we have to go to the individual GitHub repos or oh, it turns out they were using parity. There are fine distinctions and there isn't a singular source of truth right now. So I would just kind of like to take this opportunity to say, hey, how about we develop a Web3 standards body? Maybe we could be decentralized and cool on a blockchain or something. Have votes, have a maybe signing key per implementation and they could say we definitely support this implementation and maybe there could be on chain validation. I don't know, whatever. I'm sure you guys have lots of great ideas about that. MDN is a great example of a standard documentation. So that's the time I'm gonna spend on that. Now to talk about the future of MetaMask. Here's Kumavis. Thanks guys. Hello? Oh, okay. The future in space. So one thing we've been asked for a lot is multiple account types. Right now we just do the deterministic key chain and that's nice, really easy to back up and recover but people want to use proxy contract identities like those being developed by Uport, maybe remote key stores, like you have Coinbase hold your private key and we use an API to get over there. That's in the near future. Also there's some experiments coming from the Ethereum.js community like turning the browser into a light client by having a separate WebRTC based peer-to-peer network and some bridge nodes. A little experiment we've been working on is the Ethereum transaction visualizer. Visualizer, yes. It looks a little something like this. This is one of the DAO attacks and so when you play this one you can see it spiraling in this circle using that attack vector. And so my favorite project right now is this little thing we're calling Masquera and it's sort of a polyfill for MetaMask. So if you have MetaMask in, oh yeah, so it's a little JavaScript file you just drop in your DAP. And if you're using Mist or MetaMask it works fine and if your user doesn't have MetaMask but you have Masquera installed it'll work just as if MetaMask was there. You'll get a little pop-up with your identity management. The keys are stored in a separate context so the DAP can access them.  And likewise you can, any DAP that uses that polyfill can send transactions to that thing and your accounts are already there. So thank you very much. Thank you team. MetaMask.


Our next speaker will tell us more. Let's welcome Ruben Martínez, data scientist and AI researcher at Data Hack. Ruben, hi, how are you? Good afternoon. Hi, good afternoon. Ready to talk about causality? Yes, if you want, I can start right now. Take it away, Ruben. It's all yours.  Yes. Okay. Well, first of all, I'd like to thank you for being connected in this event where I've been lucky to participate in the last few years. I'm going to introduce myself. I'm Ruben Martínez, computer engineer and I work as a data scientist at a company called Data Hack. And what do I dedicate myself to? Well, in the last few years I've been developing a research project precisely with the friend you can see in the picture on the screen, with Robert Pepper, in which we've been coding and displaying a series of deep learning models. Why? Well, in the end, so that this robot can assist people with Alzheimer's problems. And precisely, when having to display those models in real environments, that's what has led me to see both the potential and limitations of artificial intelligence paradigms such as machine learning and deep learning. Okay? Well, I'm going to leave you here a series of contact guides, my LinkedIn, my Twitter, if you want you can write to me. Normally I use Twitter account to share papers, information related to quantum computing, with data science, with robotics, my website and my GitHub repository. Okay? Well, the question that I would like to ask and the one that has motivated this talk is why do we need causality when we already have paradigms such as machine learning and deep learning? Okay? Well, in this slide we can see how there are many cases of use in which these types of technologies are already being applied, these types of algorithms of artificial intelligence, well, in sectors such as health, financial services, government services, development, optimization processes, both for energy, for transport, travel, for industry. And, well, in the end we are seeing that these types of models are being applied quite successfully, with, at least, media, okay? That is to say, from doors to outside, we see that there is a great boom in which it tells us that we must invest our money from our company to develop projects that carry this type of algorithm. Okay? And this information we can see in such important media, for example, like the Forbes magazine, in which we already talk specifically about the power of deep learning, okay? That is to say that it is something that is not the future, but it is already being implemented. And as I said, well, basically it is being bombed with a lot of articles of how this type of algorithms are going to help the company precisely to take off, to maximize its benefits, to improve the products and to improve the services that are given to the users. And one thing that is heard a lot is precisely the recognition of patterns that are going to carry out this type of algorithms of machine learning and deep learning, okay? That is to say that these two types of algorithms, these two types of paradigms are pieces of what would be the field of artificial intelligence, which is much larger, much wider. Okay? So we are going to introduce a few basic statistics with which we are going to work, because what we are going to do is a trip to take precisely from that famous phrase of correlation does not imply causality. So to be able to understand better what we are going to know in the talk, in which I am going to try to do it in the most practical way possible with Python, okay? Well, we are going to see a series of basic statistics. Well, first of all, is to distinguish the concepts of population and sample. Population, well, basically as you can see in the slide, it would correspond to all the elements of our dataset, okay? The complete set of the elements of our dataset. But as many times this set is too big, well, that's going to make it very complicated to work with it. So what we do is take samples, take sub-conjuncts or samples. And the idea is to work with these smaller sets, with these samples, and then try to infer if the results that we have obtained from the sample are extrapolable to our population. Okay? It is a bit what leads us to inferential statistics. Okay? For now everything is very simple. And a series of basic metrics, of basic statistics, well, one that is used a lot, well, both the arithmetic media and the geometric media. Well, throughout the presentation I am going to put a series of formulas, but precisely to lose the fear of formulas. Okay? As this talk is a presentation in which there may be all kinds of profiles, well, the idea is precisely that after this talk the fear is lost, because in the end what they want to say are concepts that in the end are not complex. They are a arithmetic media, well, we simply add all the elements of our population and divide them into the number of elements. And the geometric media, well, we make the root of the product of all the elements of our population. Okay? Simple. Well, then it is also in the world of data science, and also what leads us to machine learning and deep learning, well, it is also very interesting to have dispersion measures that measure how much an example of a population is separated from the average of that population. And that can lead me to measures such as the variance in which what we do is for each example of our population we take that example and we subtract the average to avoid problems between negative and positive values that can be obtained when making this difference because we raise it to the square and in the end we promise it between the number of elements of the population. Well, as we have raised it to the square the result of this dispersion measure because perhaps it is not very representative of the values of our population. So what we can do is just apply the standard deviation, the typical deviation, which is just apply the square root of the variance so that in the end the result is more representative of the values with which we are going to be working. And in all these very basic statistics with which we work daily within this sector. Well, this leads us to have started counting this series of statistics because it leads us, for example, to want to compare two random variables and a way of doing it is through the statistics of the covariance which is a measure that will tell me the average grade average in which the examples of each random variable are separated from their respective media. That is, we take, for each example of the data set, we take the element that is in the position i of the random variable x and we subtract it to the average value of the random variable x and we multiply that by the element that is in the position i of the random variable y minus the average value of the random variable y. And in the end we promise it between the total number of elements of the population and in the end this measure will be understood in an interval between minus infinity and plus infinity. Well, what interpretation does this have? Well, if the result of the covariance is positive, we can say that the random variables x and y are positively related, that is, as a variable grows, the other one grows. And if, for example, the result of the covariance is negative, then they will be inversely proportional. That is, as a variable grows, the other one grows. It is an interpretation that we can have an intuition of the results of the covariance. But of course, when working with a large range of values, as it is from minus infinity to plus infinity, then that can be a little scary. And that takes us to another measure, it is used a lot, which is the coefficient of Pierson correlation. What we are going to do is to normalize the result of the covariance between the product of the typical deviation of the random variable x by the typical deviation of the random variable y. What we have achieved with this is to reduce the interval of possible values to minus 1 and 1, maintaining the same interpretation of before. The problem of this statistic is that it makes a series of assumptions. Well, as the random variables x and y must follow a normal distribution, that must be linear, or must be homosterastic. What does that mean? Well, that the variation of the examples of the data set must remain constant. That variation must remain constant. So maybe those assumptions can also be a little restrictive. And that takes us to a last statistic, which is the coefficient of Pierson correlation, in which what we are going to do is to order from greater to smaller, or from smaller to larger in this case, the values of the two random variables. And then we are going to calculate Pierson's linear correlation coefficient of pairs that have the same range. And in the end we can edit that requirement that the two random variables must be normally distributed. Well, at this point, if you have not run yet, you can ask me, and this brick, why don't you let it go? Well, basically, this brick, I have wanted to let it go. Why? Well, because the models of machine learning and deep learning what they do is find correlations. Correlations that can be linear or non-linear within the data set. But they do not do any type of study of causality for themselves. That type of algorithm of machine learning and deep learning by default is not in charge of analyzing what causality relationship is between the variables. They are simply going to measure what type of correlations there are between the variables and the label with which we are going to work. So that leads us to the concept of pure correlations. Well, I wanted to share a page that is quite entertaining. If you want to take a look, that is also put here in the slide, in which you can see correlations. For example, the consumption per capita of cheese that is positively correlated with the number of people who died in real life between their Saturdays. In this case, as we see by intuition, there is no type of causality but instead, a model of machine learning or deep learning would find this correlation. And that is already dangerous because although this is very intuitive that there is no relationship of causality in other situations, we can think that with the model of machine learning and deep learning what we are finding are causes that produce the objective that we want to predict. Another example of pure correlations for example, the consumption per capita of mozzarella that is also correlated positively with the awards of doctorate in civil engineering. In this example, there is no causality relationship but a model of machine learning would not find this pattern. And that is dangerous. Well, a last example showing a little bit of code simply, I have loaded a data set in which the monthly number of shark attacks the monthly sale of ice and the monthly temperature and if we for example obtain the coefficient of pierson correlation that we have seen we can see that the monthly sale of ice and the number of shark attacks is very positively correlated and we could think that maybe one causes the other and that would lead us to a wrong conclusion. Why? Because if we analyze more the data that is precisely an analysis of causality would lead us to a concept that I am going to advance, although we will see it later which is the one of confounding variables. Confounding variables are variables that cause both our treatment variable and our objective variable. The treatment variable is the one that we want to know if it is the cause of the objective variable. So if we have a variable that causes both our treatment variable and our objective then in the end that confounding variable is the real cause that the number of shark attacks increases as the number of ice sales also increases and in our case that confounding variable is the temperature. If we see here as the temperature increases also increases the number of shark attacks and also increases precisely the monthly sale of ice. That is why it is very important not to just stay with what the other models of machine learning are. That leads us to talk quickly about the limitations of machine learning and deep learning. Well, this summer we have seen a boom, especially with architectures such as the Transformers, which have been applied both with great success both in the processing of the natural language as well as in artificial vision and for tasks for those who had not been trained. But in the end if we analyze the functioning of this type of algorithms specifically the GPT-3, which is based on this Transformers architecture, we realize that precisely it is the amount of data with which it has been trained which allows it to apply it to so different fields. In which originally or initially we could think that what the model has done is learn when in reality what it has done is memorize the great scale. And for example I wanted to finish this part with a quote from François Solet, the creator of Keras in which he tells us that the models in this part tell us that the models of deep learning are simply a chain of geometric transformations that what they are going to do is map a vector space in another, a manifold in another. But there are many situations in which that mapping is not evident or even can not be done with simple geometric transformations and therefore not even increasing more data, increasing our data set or increasing the number of layers of our model we will be able to solve the problem with algorithms of deep learning. Particularly there is one thing that always worries me a little, which is that from the business area you can think that the data are the new oil, which is a phrase that has been made very famous. In reality, I would say or I would like to sing a little more about what the focus should be on the generation process of data because what really interests is precisely the physical processes that have been responsible for the events that we want to predict. And for that we would have to go down to a series of physical laws, both macroscopic and microscopic, in that case quantum physics. And in the end when we go up to that macroscopic physics, how those quantum physics phenomena collapse when we observe them in those laws that we can observe in the day to day. And in the end it is a little in what would have to pay more attention and focus on those generation processes of data and not so much in thinking that if a large number of data will be able to predict through machine learning models your goal, that is, the machine learning model will not be in charge of looking for causes within the features that produce the event that we want to predict, but it will simply find linear correlations or non-linear, okay? So that leads us precisely to the subject of this talk, which is causality, the causal inference in this case. Well, how could we define causality? Well, if we have two random variables x and y, then what will interest us, for example, is to know how the random variable changes and when I do that the random variable x has a series of values, it has certain values, or more specifically how the distribution of the random variable changes and when I force that the random variable x takes a certain value. Well, this, forcing that the random variable x, what would be our treatment, takes a certain value, is what is called intervention, and that normally is represented as the probability of the random variable x conditioned to du of x, that operator du, du of y, sorry, that operator du, what it would do is precisely to force that the random variable x takes a certain value. And when we are working only with observational data in which we cannot force that the treatment variable x takes a certain value, then in that case, the only thing that we will be doing is working with an observational distribution that is called, and there what we can calculate is the probability of the random variable y of the label conditioned to observe that x takes a certain value, but already without the operator du of x, okay? Well, then the thing is, how can we address causality problems when many times we only have access to observational data where we cannot perform interventions, because as we have seen to perform a pure analysis of causality, it would be necessary to perform those interventions, but many times it is not possible because the means are not enough or because it is not ethically possible, okay? It is not ethically possible then it is precisely what brings us to the techniques of causal inference, that is, how to infer causality from a set of observational data where we cannot perform interventions, well, well, now what we are going to do from this point is to work with a dataset, which is a study that was carried out during the years 71 to 82 in which what we wanted was to check if smoking stopped affected the weight increase, okay? because, again, there is the belief that if you stop smoking you will get fat. Well, precisely a study was carried out during 11 years in which it was tried to check this fact, okay? In our case, well, I advance that the treatment variable is precisely the stop smoking and the objective variable is the weight difference that there was between the beginning of the experiment that the individuals began once, at the beginning of the experiment in the year 71 and they began again at the end of the experiment in the year 1982 so the label is going to be that weight difference between the 82 and the 71 and the treatment, as I say, precisely the stop smoking. Okay, well, here, I'm going to pass this a little quickly, if someone has curiosity, I'm going to share the code it is precisely on my website, okay? because it has curiosity, or if you put in contact with me and I can pass more code than I have done of this type, okay? Well, we are going to be working with a library that is the library Causal Inference, okay? There are others too that allow you to work from Python with Causal there is one with which I want to put myself, which is Dubuay from Microsoft, okay? But if someone has curiosity, then contact me and we will continue talking, okay? Well, above all, I will bring that, well, our dataset we have loaded here, this is the URL if someone has curiosity to download it okay? and well, here I have done a little bit of data exploratory analysis of this section, I'm going to skip some slides because of limited time and what I want is precisely focus on one thing what is going to be the treatment feature the label feature and what are going to be our co-variables that is, features that also influence but that are not the treatment or the label I agree, well, here we see some of the features okay? I'm going to go down a little more here I have checked if there are null variables okay? here I have written a little the dataset, here I have seen a little the head of the first five rows and here a little of types of columns, okay? here the save because we have 1629 rows and 64 features okay? and the columns because they are all these that we see our treatment feature, and I advance which is quit smoking, which is qsmk, which we have here, okay? and our label is going to be precisely the difference of weight which is this one that is between the years 82 and 71, okay? and then we are going to have a series of co-variables that we will see okay? well, what an assumption that we take is that as it is an experiment that lasted that lasted 11 years, well there are people who died during the experiment unfortunately and then what I have done when I cleaned it of those individuals who did not to weigh themselves in 82, okay? well, well, here I am going to show what the features that are the treatment, the output and the co-variables, okay? well, in total we have 1566 smokers initially, okay? the treatment variable in which we are going to call a, because it is quit smoking, okay? and we are going to do it because it is going to be binary and A is equal to 1, is that the individual quit smoking and if A is equal to 0, is that the individual does not quit smoking okay? okay, well, here we see as there is a imbalance there is a big imbalance that this does not concern us okay? that is, there are 400 or 300 people who quit smoking and 1163 who did not quit smoking okay? and well, here normalized those values and that variable the QSMK law we keep it in the QSMK variable the exit there is this feature is the difference between the weight between 82 and 71 of each individual well, here we have the distinction of the values that this variable takes, because it is an individual who won 3 pesos the negative values are that they were lost, for example, 4 kilos okay? and so on and that we keep it in the QSMK variable okay? well, and now the confounding variables that are as I said before the variables that influence both the variable and the independent variables, such as the dependent variables well, causing with spurious relationships well, they are going to be, for example, these that we have here for example, ACTIV which is a measure of the daily activity that the individual had in 1971, age, education recreational exercise especially we are going to look at ACTIV I agree that it is a bit the daily exercise activity that the individuals did that takes 3 different levels but that an inactive person a moderate exercise and 2 that is a more intense activity okay? and well, another series like sex, the intensity of the smoker, the number of cigarettes I agree and well, the description of these values and above all as I said, what is the distribution of the label for that would be the control group that is, those who did not stop smoking well, we have 1,163 individuals okay? and for those who did stop smoking well, 403 and as we see, it is very unbalanced and then well, here what I have done has been well, graphically that is, painted a little with KDPLOT and histograms okay? this simply to give us an idea of how of how they are distributed the data between what would be the treatment group and the control group and one thing that we can also see is the average of the weight of the difference of weights between the end and the beginning of the experiment for the control group well, which is 1 kg that was won, okay? of average and of average 2 and for the treatment group for those who stopped smoking well, 4 kg of increase of agreement and 3 kg of average and this makes us think first of all, doing this simple exploratory analysis because stop smoking has a positive impact that is, the treatment stops smoking has a positive impact on the weight gain okay? well, well, let's start with what would be the analysis of cause-and-inference why cause-and-inference? because in reality we are giving these data we are not doing interventions properly said okay? so, well the analysis of cause-and-inference well, a pure cause-and-inference could be done as I said doing interventions for example, doing what would be a randomize control trial in which our entire population is randomly divided in two groups of the same size one that would be the treatment group and the other the control group the key part here is that it is divided in a random way to the individuals with that randomity, what we are trying to remove is what is called the selection bias the selection sesbo okay? and that in the end would allow me to calculate a measure which is the average treatment effect for the entire population that is, the average effect the average effect of the treatment so, if I am able to do that what I would do would be to take I am going to put the formula here I am going to tell you a little about the formula well, what we would have to calculate for the average treatment effect is precisely a formula which is the expectation of the output for those individuals in which the treatment has been applied minus the output for those elements in which the treatment has not been applied okay? that leads me to a framework which is the one of the potential the one of the potential outcomes in which we are going to see a notation with which we are going to be working well, when the output corresponds to an individual who has not received the treatment to that output I am going to call it and sub-zero okay? and when the output corresponds to an individual who has received the treatment I am going to call it and okay? well to be able to calculate the average treatment effect well, as I said, we would calculate the expectation of Isu-1 minus Isu-0 that is, we would take the column of Isu-1 for all the elements for all the individuals who have received the treatment that is, we would take the weight the difference between the end and the beginning of the experiment for all those individuals who have stopped smoking and we would do the average we would do the same with all the individuals who have not stopped smoking and then we would subtract them we would subtract those two quantities that is, the average of Isu-1 in all the individuals of the training set minus the average of Isu-0 in all the individuals of the control set and that would be the average treatment effect okay? that was the case that we could do that randomize control-trial okay? but if we have an observational distribution the only thing we can access is precisely this is a little what I told you and what we have calculated the expectation of Isu-1 minus Isu-0 but if we only have observational data the only thing we can access is to this formula to the expectation of I conditioned to that A is equal to 1 minus the expectation that I that individual has not received the treatment or what is the same? the expectation of Isu-1 conditioned to Isu-1 minus the expectation of Isu-0 conditioned to Isu-0 and this normally is different from the average stream-benefit real that we have calculated previously doing a randomize control-trial and you can ask me why these two quantities are different? well, precisely because if we calculate the expectation of Isu-1 conditioned that the variable X is a certain value that is different to calculate the expectation of Isu-1 why? because we are working with different data sets to calculate this expectation we are going to work only with those individuals we are going to take the Isu-1 of those individuals in which the value of treatment is equal to I that is, we are going to work with the individuals that we observe that they have received the treatment but in this case we will be with the whole population or with a randomized selection not only observational but it has been randomized the selection of treatment of the treatment group or the test group the data sets that we are going to work from an observational distribution or from an interventional study are different to see if there is any effect that can be obtained from an observational distribution it may not be the same to see if there is any real effect then what we are going to have to do is a series of assumptions when we run a randomize control trial what we are doing is randomize the assignment of the treatment and what that causes me is that the outputs are independent of the treatment and that would lead me to and sub-zero and sub-one are independent of the treatment A and if we do the join distribution that is, if we do the probability of the treatment A, sub-zero and sub-one then that would be equal to the probability of A for the probability of sub-zero and sub-one because they are independent then we can think that the expectation of ISUI conditioned to X is equal to the expectation of ISUI then that leads us to what I have put in this slide that if we want to run a randomize control trial from an observational distribution we have to assume that we have enough additional information to explain in a complete way the assignment of the treatment to each individual and if we to that additional information that explains the assignment of the treatment we call it as the set of X variables that can be several then we have this that the outputs that is, sub-zero and sub-one are independent of the assignment of the treatment as long as they know those co-variables X that leads me to the joint distribution of the treatment and the two potential outcomes conditioned to the co-variables will be equal to the probability of the treatment conditioned to X by the probability of the potential outcomes conditioned to X, that is to say as they are independent conditioned to the co-variables then we have this result of the joint distribution and this is what is called the Assumption and the Ignorability Assumption because it has the theme that if our set of co-variables this set of co-variables does not contain all the possible confounding variables then any prediction we make because it will be successful it will not be reliable and that is a problem and then it has the limitation that is called the causal inference and this individual we will not be able to simultaneously observe the two potential outcomes why? because if an individual receives the treatment then we can observe and this is what we will call the the actual output and this will be the counter-factual it is precisely Ruben, I'm sorry to interrupt you Ruben but we really are running out of time you did warn me you told me that you had a lot to say exactly would you mind just wrapping up in 30 seconds to a minute and then we'll ask you some questions so sorry to interrupt perfect, I'm going to finish right now I'm going to make an overview what I wanted to tell you is from these assumptions what we are going to do is with machine learning models that we are able to observe and from that model of machine learning that we are going to use to model that output that we do not see then we are going to be able to calculate that open streamer effect that we have told you before and that actually takes us for example to use a linear model as we see here and when we apply precisely that linear model then it takes us to have positive values this would be a technique in which if you use a linear model then you can use other types of models like for example matching you can try to solve the imbalance between the two groups with techniques such as trimming or propensity score all of that if you are curious I have this whole notebook on my website and there you can check it the trick is precisely the frame of the potential outcome is to model the label that we do not know through some model of machine learning and assume that ignorability assumption that the co-variables explain the complete treatment and this would be a little the summary of how to address these types of projects if there is any doubt if you want we can answer it this is a very interesting talk it has been a privilege to hear such an expert and I think you could have gone on for so much further as well you had me very worried at one point I was very scared when you started talking about cheese I love cheese so I had a small it is very dangerous you have to have high teeth you have to have high teeth that the relationship goes back there it is getting tangled up there we don't have that much time but there is one thing I was curious about Ruben when the Covid pandemic started to really grip the world in April and May and I found myself at home like many of us with more free time perhaps than we were used to I found myself in Excel making a column for each country looking at the death rates and the infection rates what could be the causes for why there was such disparity why were some countries so much worse than other ones it didn't seem to be anything to do with the wealth of the country I looked at variables such as religion and all kinds of things but obviously very superficially I wondered if you had applied causal reasoning in any sense to what is happening right now in the world I have to say that it is very difficult to carry out a causal inference project because in the end it is a physical project and if we have a lack of understanding of the physics behind your problem it is very difficult to get an accurate result and because of that as I said in the beginning of the talk you have to dig into the real confounding variables of your problem you have to have a very good understanding of your problem because the key here is to identify the confounding variables that you could have in your problems and these confounding variables could be unobserved confounding variables you have several techniques to fight against these kind of problems and if there is anyone interested in these kind of topics you could keep in contact keep in touch to talk or to discuss about these kind of problems and solutions Okay Ruben, let me just finally just ask you a very I'm going to need a very quick answer but someone here was curious to know if there are any real business applications for what you've been talking about Wolf No Every business you could apply of techniques and I have to say that you should apply these kind of techniques to every business to every model because it's a it's a technique that is a must in the end because machine learning and deep learning models have limitations and causality will be the next step on your analysis of your project if you don't do this kind of of analysis you are going to miss a lot of information a lot of useful information So it's all to come In the end you should try this kind of analysis to be sure about your conclusions Ruben thank you so much indeed that was a fascinating talk I'm sorry to have cut you a little bit short but the time is our enemy here so we've come to an end thank you once again Ruben and we're going to take a 4 minute break and then we'll be back here at the garage thank you very much