for the last decade, for Rolling Stone advice, and a number of other publications, and more recently we founded Double Blind, which is a magazine that's on both shelves across the country, and also you can find us online. And we have our esteemed panelist here, Jay Pleckham, who is a mycologist. We have Leonard LaRaire, who is the founder of Back of the Arts, Algae Sciences, and then Tony Molesky, also a mycologist for that same. Company. Before we dive in and talk about the challenges to scaling the production of sills, and I just want to give a little bit of context. I don't know how much everybody knows. I think there's a wide variety of familiarity with the psychedelic space in this room. In May, excuse me, in November of 2018, Compass Pathways, which is a pharmaceutical company based on the UK, was granted breakthrough therapy status for psilocybin for treatment-resistant depression. And then in November of this year, USONA, which is a non-profit pharmaceutical company based here in the United States, was also granted breakthrough therapy status by the FDA for psilocybin for major depressive disorder, which is affecting 6.7% of Americans. So it's a massive mental health epidemic that we're facing with depression. And so breakthrough therapy status basically means we are designating this as a breakthrough therapy because there are no other medicines that are effectively treating this condition right now.  There's more than 100 million people around the globe with treatment-resistant depression. So alongside sort of all of the excitement around psilocybin following in the footsteps of cannabis, there has been the rise of the grassroots movement to decriminalize psilocybin at the local level. So last year in May, the county of Denver decriminalized psilocybin, which it's not legalization, it basically means it's a low enforcement priority for law enforcement in the city. And then less than a month later, Oakland decriminalized all naturally occurring psychedelics. So that includes psilocybin, peyote, ayahuasca, San Pedro, anything that comes from the earth, so not LSD or NDMA or any other synthetic derivative of the psychedelic. Now there's more than 100 cities and counties across the United States that are trying to decriminalize naturally occurring psychedelics. So there is a massive movement, it is spreading like wildfire and it's really two things are happening at the same time. We have cities and counties that are trying to decriminalize psychedelics at the local level. And the reason why this has implications for investors and people who are interested in getting into this space is because a lot of the people who are running these initiatives are doing so in the hopes that people are going to be empowered to grow psilocybin and other naturally occurring psychedelics basically in their homes, which means that they won't have to dead it as an FDA-approved dedication. And then the federal research, which is for us, and hi Andrew! Anyway, so there's a little bit of context for you and now we'll just dive into our panel. I'd like to start with what you talked to us. Just give us a little bit of context as to what you're doing and what the implications are for the psychedelics case. What we talked about are the real nuts and bolts. It took Amgen 15 years before they could get a reasonable drug and start the whole antibody therapeutics. And so what we're really talking about is how do you get a safe, sustainable CGNP, good laboratory practice supply of not only psilocybin, but all the empty agents onto the market. We're not only talking here about psilocybin, we're also talking about a whole range of products which are derived from peptide, derived from even raw epithelium. And we think we can do it because we do it already. But it's a long and hard and tough road. It's also a road which requires a huge amount of investment. We just installed now our own brand new HPLC. We have CMS, which is mass spectrometry. And what we basically work on is one thing, sustainable celibate agriculture.  We've got to move on. We're not into genetic modification. We're into basically helping nature do things better. And we've done that already. Our main product is this, which is an extract of spirulina. It's called pica cyanide. Has anybody heard of it? No? Okay. Yep. Okay, good. You can get some for me afterwards. Okay, this is a potent antioxidant. And it comes from a blue-green algae. And we're the first in the world to basically make this in a non-polluting, sustainable way. And it goes basically into the market as a food coloring. But we've been testing it quite extensively for depression, believe it or not, in animal standards. And we found that it wasn't an anti-depressant, but we learned a lot about how to do animal studies. And all our extracts, which we're doing now, which we may be doing or may not be doing, in the epigenetics phase, will go through a similar development process of animal testing, which is vitally important. We can ever distinguish between what is it and why do these things do what they do. So, number one, we are basically cellular agriculture specialists. We have one of the largest facilities of robotic cell culture in the world, but nobody knows about it. Because our work is basically around algae extracts using these as lightages to improve growth. And now, more increasingly, what we found is that the, and we can't obviously talk about this in great detail, is that it is possible to increase the levels of actives in things like wasabi, which, and perhaps, perhaps hallucinogenic mushrooms, in cell culture to a level where it makes it economically viable, never to grow a mushroom again, never to have a mushroom growing on manure, tar-down, horseshed, whatever it is, again. And that's the future of this. So, basically, that's our work. I won't go into too much detail. And I have on either side of me our principal, my colleagues, who basically are the ones that pushed, that opened my eyes to this whole area and pushed me so hard in the direction of, we've got to be able to do this in a way that we can get universal access to what are definitely active molecules that work. And then, I think we're amongst the converted, but it should be easy to convert everybody else, other than my guys. Yeah, Tony, or Jay, will you talk to us a little bit about, you know, what is the silicide that people are using now in the trials at NYU and Hopkins and others, and how, what you're doing, different? The difference with what they're doing, we're just our basic principles of trying to make the cleanest possible cultures of active compounds. And I'm not sure exactly what John Hopkins or what their actual tests, what they're getting to attest them with. I'm not familiar with that, but we're trying to fill that food chain, whereas I know for a fact that there's multiple compounds that are lost in the durianic process, and multiple compounds that just aren't explored, like THCV. There's all these compounds that they're now finding out that complete what really makes THC work in the canvas. And we're trying to get all of those compounds brought out and isolated and figured out what they're worth so that then they can really understand. Is it silicin or is it something else? I'm sorry, is it silicin or is it something else that they're trying, that is causing this? What are the results from, truly? Is it the silicin in the mushroom? Is there other compounds in the mushroom that the results are coming from? And we are trying to dissect all of that and figure it out so that we know what points these are on this HDLC issue, what these points really truly mean to the mind and to the soul. And that's our goal is to have that laid out like a diagram. Yeah, that's very interesting. For those of you who are in the cannabis space, you're familiar with the fact that there's this movement right now to isolate the turkeys and the cannabinoids to figure out exactly what combinations and what things work for what people and what conditions. There's more than 200 mushrooms that have silicin and 200 strains. So Jay, can you talk to me a little bit about all the different kinds of mushrooms with silicin and how your work always can help you navigate that? So we spent the past couple years collecting various strains of mushrooms that are silicin produced in the mushroom. Of some of those strains, a couple produced higher ratio of silicin to silicide them. And the silicin is the part that ends up being oxidized and degraded during the drying process. And we're, you know, looking for all these different varieties of mushrooms, we're looking for the mushrooms that produce higher ratios of silicin and then growing it in a way that it can be processed without degrading any of those aspects. I'm just going to turn to my right here, Lauren, who is running a little bit late. She just joined us. She's a partner at Pillar of P.C. And so a lot of people are looking at silicides and psychedelics and they're saying, it's falling in the footsteps of cannabis and, oh gosh, I got to get in right now. What are your thoughts? Is it too soon? Are people right on time? And what kinds of lessons do people learn from the cannabis investment landscape as they're entering into the psychedelic space? Do I call it as being like, hey, I'm filling in for a partner of mine who is 12 years old, unfortunately, to be here today? I have a lot of cannabis law and we're just starting off to get into this. And it is similar in a lot of ways and different in a lot of ways. But the most important thing to think about whenever you're investing is that this is a long-term play. Even if you're in for the right reasons, you're not going to get rich today. And I think that that was a big problem that the cannabis industry had in capitalizing was presented as green rush. And if anyone knows anything about investing in cannabis, it's going to probably be green rush, especially the operators. But there are a lot of similarities and dissimilaries. As everyone probably knows, you're in full cannabis and about $22.00 is still one of us. That means according to under the crucial Substances Act, they have no mental purpose to be known as doctor. They can't be safely, you know that's not true, and they're highly susceptible to abuse, you know that's not true. So both cannabis and psychedelics really have no place in Seattle-on, but they are classified in Seattle-on. Now, the reasons for that classification for those of you who can know about the legalization of cannabis is really rooted in immigration animosity. We know that the controlled Substances Act is fostered through the NICS administration in an effort to suppress political rights, minorities and through speech rights to those who contested the current NICS administration, and the war on the NICS. So we have mass incarceration, we have mass arrests, we have issues in cannabis that we don't have in the psychedelic universe. So the legalization efforts in cannabis, independent medical cannabis are rooted in social justice reform, and the mass incarceration, you know, fixing the failed foreign drugs and the vital consequences associated with anyone who is improperly arrested in connection with the failed foreign drugs. The legalization of mushrooms and of psychedelics is rooted really in connection to nature. And if anyone takes a look at, you know, for example, the legalization of professions and legalization, the decriminalization measure passed and opened. It reads sort of like a groovy guide to getting high, and it talks a lot about the spiritual connection with the planet Earth, and people having the freedom to alter their minds, and that's really not something the government should be able to jail people for. So for reasons, the policies underlying the legalization of both substances are similar and dissimilar in various respects. But we are nonetheless stepping into a more billion-dollar demand when we think about the medical potential for mushrooms covering large spots of the population, similarly as you would with many of the medical conditions that medical cannabis addresses. Will you talk to me a little bit about your relationship with King's College London? We can't really talk much, but as I mentioned, the development of this product, phytocyanin, the development of phytocyanin, which is an extract of a blue-green algae called spirulina. You have heard of spirulina, of course. The idea was that we thought that it was a potential antidepressant, so these are botanical drugs. So what you do is you do a whole range of animal studies, you make mice depressed very cruelly, and you see if they feel better, if you give them some of this stuff. We didn't work, but it turned out that this particular product, because of its potent antioxidant, is basically the world's next natural gatorade. I'm sure you don't believe me. That's fine. So our ideas, we will be working together with Hadassah Brain Lab, which is the world leaders in animal models of psychiatric conditions, on these various extracts, on the natural extracts. Once we see that there's some effect to the natural extract, which we know there will be, we'll start to dissect our particular active molecules. We're investing more and more in our own analytical capacity for this. And remember, all this is coming from cell cellular agriculture. It's not coming from a mushroom, and we're in some kind of legal penumbra, which is going to cost us a shitload of money to sort out. I don't want my PhDs to be arrested, and all that kind of stuff. We're living in this penumbra. And our whole goal with our R&D is to get out of this darn penumbra.   And then the next thing those mushrooms are being given to patients. That's not the way you do biomedical R&D. I'm an epidemiologist, I'm a biostatistician.  And that's what our goal is, to get this out of the penumbra. And the first thing we could do is torture some poor animals, because that at least is on the edge of the penumbra. We could be quite open, we can publish, we can publish that, and we can get the relevant commissions. This work is being done in the context of a collaboration between Hadassah Brain Labs and King's College. And we are actually having a meeting in 10 days' time at King's College to start outlining this particular liner research. But it's not, it is pre-clinical research. And I think it's vitally important that we go down this route step by step by step by step. As you're getting to the same thing with cannabis at the end of the day, Journal of American Medical Association publishes CPD, no good. Three days later, cannabis, very little effect. Two months later, well, maybe it's actually better as an anti-cancer agent than it is as a psychiatric benefit, maybe minimal, but it's probably a great anti-cancer agent. Let's try to avoid that when we go down this route with the anti-agents, with the other agents, I should say. And Jay, why is your mechanism for, I don't know, growing is the proper word for growing the psilocybin, but why is your methodology more scalable than, say, growing mushrooms? It's kind of amazing here, you know, to actually grow the mushrooms, you have to do transfers to substrate, take the time to let that substrate grow out. There's multiple different points along the way where contamination can mess it up, and then what you get at the end isn't always consistent. There's differences in the mushrooms depending on what stages of the life cycle.  And today you're still going to have to take those mushrooms and homogenize them and make an extract out of it, whereas what we're doing is, you know, just growing out to my sealage and extracting that so you can scale up into a tank just like, you know, going from home brewing to a china brew house and just get bigger tanks and bigger equipment. So, yeah, it's easier to scale up and keep sterile.  Oh, yes, absolutely. How quickly and how big? Oh, sure. I want to know! You know, there's more than 100 million people around the globe, which are your resistance to depression, so if we're waiting for every single mushroom to pop out of the mud, it's going to take a while. Give us the money. The thing that makes me absolutely clear, we're back of the yard's algae sciences. Our business is primarily making sustainable media and growth factors and people above and zero replacement. Because we believe that that's the only way that we'll be able to supply the kind of meat in a non-polluting, non-cruel way. The point here is that what we've found is that in all these sectors, such as lab-grown meat and probably in the whole atheogenic mushroom space, there's so little innovation going on that we have to jump in into the whole thing. In order for us to create a market for our growth media, we're going to have to show people how to grow lots of mushrooms. The problem with that is it doesn't take a shitload of money, but it does take money to do that, especially if you're going to do it well. We have a $300,000, $400,000 that's invested in analytical equipment at the moment. That's to do it well. The transition has been from that black hole where people say we've got the mushrooms and we're doing the clinical trial to we've got those mushrooms. This is what it contains. This is the exact... I want to see the profile of the mushroom which goes into meat if I'm going to be in a clinical trial. I want to see that gel-elected for reasons. Where is it? I want to see the MSDS from the supply. I want to see the certificate of analysis. That's the way you do research. So you talked a little bit before about the human elimination movement as well as the federal research, and I know that a big concern among longtime psychedelic advocates, people who've been in this space for decades and decades, are that basically new players are going to come into the space and psychedelic medicine isn't going to be accessible or equitable once it gets to market. So what are your thoughts on how the psychedelic industry can basically not make the same mistakes that the cannabis industry made and kind of squashing out the long-time players, the farmers who come both and the people who've been around forever. What can we do to bring the capital that's necessary into this space to allow it to scale, to allow these things to get to market while making sure that the people who have been doing this for a long time have a say as well? It's a big challenge. It's about the balance because we need capital for this industry to grow. We need capital for the research projects that you're describing. As a federal and drug research, it's incredibly expensive. We really need to focus on lobbying efforts to get it rescheduled. I know that the Johns Hopkins study, they're trying to make a schedule for and I think going back to what I was saying before, let's not talk about this as another game, let's talk about it as this is a safe, effective potential medicine that's going to heal a lot of people. We have a real opportunity to potentially cure treatment-resistant diseases, resolve crime, resolve suicides, resolve issues that are increasingly plaguing our society. So if we focus it in terms of a really utilitarian perspective, we attract the right people. We don't talk about it in terms of making money. We talk about it in terms of helping and healing. And that's how we start thinking about who your prospective partners are when you think you're synthetic. We talk about priorities. There's no question that this industry attracted the spirit of Charlotteson and the St. Wales salesman because they were stepping into a multi-billion-dollar recreational demand. Civil society doesn't have that. It does have a multi-billion-dollar medical demand so this isn't a drug where people are taking to the streets and it doesn't affect the quite the same population numbers as it does with cannabis, but it certainly could surpass that when you're thinking about the consensual medical applications. So we invest in this drug but you have to be in it for the right reasons and that's to heal people and that's what the money will follow. I work a lot with the farmers out in Humboldt and so much of legalization is advocacy. If you're in the business of cannabis, you're in the business of regulating work compliance and advocacy and it's the same for any kind of psychedelics. We have to shift the stigma. We have to make sure that people understand that this is state. A single one is completely inappropriate for this type of drug application and that's the message we need to be spending and that's how we're going to attract the right people. Yeah, thank you for that. So in terms of investment opportunities what are people talking to you about? As you referenced, psilocybin and psychedelics are very different from cannabis because you can just do psilocybin and walk out your door but I wouldn't recommend it. Really there has been some talk about whether if you criminalization of psilocybin and Denver and other cities and counties is going to allow for some kind of grain market dispensaries to pop up the way that they did in California after cannabis was decriminalized in the 90s but it's really it's a very very different kind of fungi rather and it's a very different kind of experience so what do we think of that? Well make no mistake there are side effects to psilocybin and drug and nausea, disorientation lack of coordination, sometimes overdose these things really do happen and cannabis no one has ever been seriously injured we'll put aside intoxicating and driving but in terms of cannabis use it's only one in the thousands of years of medical application and psilocybin is my understanding of Russia this is the least proof that the ER visit is my understanding so it is safe but at the same time it does deserve a stable and comfortable environment within which to experiment with it so there are some differences between the safety and the widespread use and ability for people to use it as opposed to microdose or whatever that might be but at the same time like I said when you think about the large stocks of the population that have the potential to heal then that's why we really get excited we have to follow right now we have to follow the schedule on protocols for research so this investment research this investment opportunities it's less of an investment it's more of an opportunity to build the market into which you'll ultimately be stepping so supporting the organizations that are doing the research like Max and Johns Hopkins the organizations that are doing those research getting out there and connecting with those organizations to find out who's connecting with them who's trying to submit study defense grants research applications for a schedule one substance that's been taking a long time finding a lab to do manufacturing that's a schedule one registered schedule one certified takes a long time contract manufacturing in general is a tough deal to strike with a manufacturing doctor so lots of different opportunities exist keeping in mind it's a long term critical right now so we're going to do some research and then we'll follow that leads me into my next question perfectly so obviously you were referencing before some of the challenges getting approved to dealing on the manufacturing side of schedule one substances what you know why do you think that or talk to me a little bit about why your methodology you think is going to is more likely to pass the smell test for GMP with the TDA I must be correct as I said we're not expressing on this particular subject I mean we we work to provide the tools to ensure that there is a widespread that there will in the future the near future be a widespread safe supply of entheogens and we believe that the way to do that is through cellular agriculture because that way you never ever have a fully grown mushroom and already that in itself is a tremendous advance from a safety point of view from a good manufacturing point of view we would a lot of what we a lot of what I'm saying has been guided by our experience in the lab grown meat industry where basically even today almost a decade after the first lab grown meat burger was eaten you still can't get another one okay it's an industry which has got a huge amount of hype it's going to have its clear and lost moments and what you see is that essentially the technology has largely lag behind the hype and that means that we are working essentially on things like using an aerobic digest state to make non-polluting media using using algae extracts to make replacements for dead fetuses which are used to grow vegan meat we want to see an industry that flourishes okay where we have not tens of thousands of liters of bioreactives but we have millions upon millions of liters of bioreactives so our role is in this industry the same thing we have to do this R&D and we have to develop a cellular agriculture but our hope is that well if we have to be the world's biggest growers of hallucinogenic mushrooms good we'll look at rich and our investors will get rich it's great you know but it's not really what we we're about we're about the enabling technologies and the engineering that goes behind it but if you want to invest in us to grow a lot of mushrooms come and see me I'll do it


Here's one thing that I think flies under the radar. It's Google My Business. Welcome to the We Are Slam Show where we share marketing agency insights, best practices and ideas to help your business grow. My name is Tyler Kelly. I'm the co-founder and chief strategist here at Slam Agency, a digital first full service marketing agency designed to help you captivate, motivate, and inspire people to action through advertising. And today I wanna share with you how to get more online visibility using Google My Business. Now we all know about Google AdWords. We all know about SEO as ways to help people discover our pages and to appear more favorably in the search engine results pages. We know about social media as a way to drive awareness and to increase target lead generation. But here's one thing that I think flies under the radar. It's Google My Business. Now if you're a local business, you'll probably have a Google My Business account or page. And what you've probably done is you've gone to that page, you've filled out all of your information and you've done this for your local SEO so that you can appear in the map pack. So for instance, you'll know that you have a Google My Business page. If you go type in your business name and in that search result on the right hand side, you see a map that says website, directions. It's your business and it's links to your website. If you have that, then you have a Google My Business page and account and it's important for local SEO because it allows you to appear in the local SEO map pack or that Google Maps directory if you will. And here's one way that you probably aren't using Google My Business but that you should be. Google My Business post. A few years ago, Google released the capability for businesses to be able to post on their Google My Business page which appears in the search engine one post at a time. And okay, so what you do is you leave a post and it's out there and then it expires, then you gotta go back and post another one. It's not like social media where you can schedule this stuff out in advance but what we've found is that it does provide lots of visibility specifically in Google. If a competitor is consistently posting in and on their Google My Business page, then they're immediately gonna look more relevant and more up to date. So when someone types in your business and they see that you haven't had a Google review in a while, you haven't had a rating, you haven't posted anything, you're immediately gonna look dated, okay? As compared to your competitor who is posting these things. Okay, so these Google My Business posts, they last for seven days. They'll expire after seven days but you can post as many as you like knowing that just one at a time will be there in the Google results page. You can post today's special, you can post a community update, a current event, you can post a picture, you have up to 1500 characters that you can use and the best part is you can post a call to action. You can actually say, okay, go to this page, do this thing, call me, visit my online store. So if this is the first time you're hearing about this, maybe you don't have a Google My Business page and that's okay, let me tell you about Google My Business. Google My Business is a free service provided by Google to allow you to list your business in the search engines.  If you've ever gone to Google and said let me find barbers near me or pizza delivery near me, this all comes from the Google My Business platform, from the Google Maps functionality and these types of searches have increased drastically over the last six months. People wanna know what's near them and the way that you can tell the people that are searching that you're nearby is by getting a Google My Business page. You can also set your hours, your info, you can provide a virtual tour, you can invite people to your online store and most importantly you can manage Google reviews. Think of a review as a free advertisement. I would encourage you to figure out how to get people to leave Google reviews. Not only does it help you rank with local listings but it provides social proof to that potential customer looking to do business with you but they're just not quite sure. A rating, a five star rating, a positive review, these things are critical today, they're critical, they'll be critical tomorrow and these are important, you can manage them on your Google My Business page. So these are all things that if you don't have a Google My Business page, there's a link in the blog post, go there and get signed up. If you're not quite sure what to do, give us a call, we'll help you, we'll walk you through that and then once you're there, use Google My Business post. This is a secret that not many people are doing right now but we wanna get the word out because this is important, this is something that when they search your business name, you have an opportunity to provide a customized message via posting for your potential customers, for your community, for your audience. It's something that you definitely need to do and your search engine visibility will increase as a result of it. All right, so here's a couple of tips for your Google My Business post. Number one is set up a calendar. Google's not going to remind you and it's not something that you can actually schedule. So set up a calendar, just a reminder. So every seven days or so, if you're doing the seven-day thing, to get back there and to put something timely, custom, personal on your Google post. And then also, if you have a good offer, if it seems to work, reuse it. I mean, it's gonna expire after seven days but if you've been seeing a lot of business from that specific post, copy, paste, reuse. It's okay to reuse content, especially in this case. Remember, this isn't a social network. This is what they see when they Google your name. So set up a reminder and when it's working, don't reinvent it. Don't try to reinvent the wheel. Use it, reuse it. If it's working, make it happen. All right, if you've enjoyed this show, let me know. Leave me a comment, send me a message. If you're listening on a podcast network, be sure to subscribe, rate and review. And thank you for watching. I wish you the best and we will see you next week. Thanks for watching. If you like what you just watched, subscribe, then hit that bell. You'll be the first to be notified when new content goes live. After that, you can watch more videos from Slam Agency. We've picked something we think you'll love.


What's up everybody, Jason here from jazbeastcasebrakes.com 2020 top triple-threads baseball hobby nine box intercase break Picker jeans number 17 sold out, but a couple rmb's for these teams here from the White Sox down to the Marlins and Yankees Dodgers blue Jays Padres So let me show you guys those results really quick. So these are the customers in this random number block randomizer And then here are the customers in this random number block randomizer And here are the customers that bought them straight up Good luck guys Good luck All right, and it looks like we have a full sealed master case. So we'll use a little die Unroll it and see which side we'll use here and then the second half will be for the next triple-threads break, which We did sell some spots, I think we're almost close to single digits We can always R&B The last few teams I got one two three four five six I was wack hold on There we go two One two first one didn't roll. So there you go This side will be for the next one. Oh, yeah, TJ. Absolutely. It's fired too, man I did a lot of that stuff in personals for the price point can't complain Definitely and if TJ say the first half There was no monsters and you guys should definitely start rolling on that Yeah, because after this guys, I mean, I have a few orders to go through not too many like five but There's nothing else at the moment. That's all Now in case basketball has a little filler and I put backspots in it Hopefully see we can sell it out straight up. I know that break is a little close People have been saying that's been marinating We have the mixtures of course that we can still work on today So there's still quite a few options Still relatively early, I'm usually here until 10, but of course you guys know me I'm here as late as you guys want me to be sometimes past midnight one o'clock So it is up to you guys if you guys want to continue All right, Nolan or not oh to 275 Ricky Henderson Will Clark and Barry Larkin and Cincinnati Reds for the R&B spot one Going to Matt L Giants are on Aaron Ola relic 336 for the Phillies David Chan miles Nicholas for the St. Louis Cardinals 54 out of 99 54 R&B spot four going to James. So there you go James you're already on the board.  You got a Nicholas coming your way Here and how about a seven out of nine? Freddie Freeman First at first Seven out of nine for the Braves No problem. I mean this is very fun and cool, especially when you're hitting But obviously there's no guarantee that you're gonna get something but in your case You already have something and you still have all these opportunities So the R&Bs actually give people opportunity to get some nice hits throughout the whole case and Matt There you go, man, Joe Braves and Luke Boyd five out of 18 Little pinstripe as well Yankees that's five One of Matt our next box have your buys for the Cubs and Mark McGuire for the A's I Believe this how your buys as part of the R&B 161 So spot one Cubs going to Matt Al more on our vera 177 and Thurman Munson 25 out of 99 so spot five and spot seven So seven Matt spot five Matt Al as well Matt Chapman six out of 18 A's going to Howard and a Fernando Tatis jr. 19 out of 36 Relic That is Padres nine Going to Donnie and we got for the White Sox 21 out of 75 that is Frank Thomas Eloy Hemenes and Louise Roberts Triple Relic That's gonna go all to the White Sox or the R&B one Matt Al We got shogo This is 90 red 64 out of 99 64 So that's about four and another one for James. There you go. All right next box guys Josh Johnlinson for the Minnesota Twins in Miguel Cabr for the Detroit Tigers Detroit Tigers part of the R&B 74 Spot four going to James a little base and Minnesota Twins R&B 133 Spot three going to Deborah. You're at Tony Gwynn for the Padres 105 and 1441 as 275 spot one So one is going to Daniel spot five is going to Matt Al Our city is Aquino for the Reds And you're done over this Nick sensual 71 out of 99 little double Jersey or triple Jersey Relic and auto So that's 71 One that's going to Matt Al and we got Ian have 11 out of 27 One going to Daniel got Nick sensual triple relic or sorry not triple relic. Oh, I guess you could say triple relic 13 out of 18 three going to Deborah and Jordan Yamamoto for the Miami Marlins 42 out of 50 Marlins 42 gonna mark out our next box guys So including this box we got one two three four five and this is box six. So so far we've only done three boxes guys Plenty of time plenty of hits all right 75 to 75 an eagle horn here spot five. That's going to Matt Al And Hank Aaron a lot of breaks. We got mookie bets 17 out of 50 for the Dodgers R&B 17 Matt Al and of course right after mookie bets you have a Mike Trout 170 Cal Ripken Jr. Nine out of 18 little triple relic and tip of the cap an autograph Orioles that spot nine We're just going to Matt Al Nick Solak nine out of 18 for the Texas Rangers Xander Bogart seven out of nine local kudos Seven and that is Boston Red Sox Chris Walker straight up Will Smith for the Dodgers the re out of 25 And actually Will Smith is actually just batting right now Three that's going to Roger needs to needs to redeem himself in that the miss catch yesterday And I know what he was doing though. He was just he was already getting ready to tag out of Rosarina, but Can't keep your eye off the ball for a split second because that's usually what happens Muncie's throw. Yeah, which one? Oh, no, I think it just hot. I think it just hopped over there. Yes, Taylor I think Taylor is the one that just threw it, but he's the one that first messed it up Yeah, I don't think it was Muncie. I think because it bounced too much, but I think just will Smith was just looking at tag But Kelly Janssen is the one that kind of messed up to Cody Ballinger 245 out of 275 And Carlos 43 out of 299 so 245 spot five. That's going to Matt Jigga de Grom for the Mets and Reese Hoskins 35 out of 299 Phillies and then Mets R&B. That's 192 Mark L. Oh, did he I don't remember that Yeah, triple relic combo. That's Bryce Harper Reese Hoskins Andrew McCutcheon 12 out of 18 Philly's going to David Chan straight up Brian Abray you One out of 50 There is a redemption Logan, but I don't know if it's the one we want Ronald Acunha Jr. One out of 36 Sam, how's it going man? All right, let's see guys. Good luck rookies base rookies and future phenons autograph relic card of Gavin Lux for the Dodgers That's gonna go to spot zero and Daniel Xbox guys four laughs. Please if you don't see it by tonight, Sam, maybe tomorrow I Don't know if Nick is just wait me potentially waiting on a checklist or maybe waiting to see how many we're gonna be getting Max Scherzer Lou Gehrig, I know we were pre-ordering already Prism, but I think get a plan that just cuz it's gonna be a little pricey so Scherzer and Lou Gehrig Yankees a 28 Spot a going to Matt Medler Resolutions be I expect Latest Sam Tuesday, but I expect it if not tonight by tomorrow for sure Boba Shet spot to Gonna Matthew Nielsen and we got 41 out of 50 George Belanco That's a Minnesota twins 41 spot one going to Menel And Brian Reynolds seven out of 27 Pittsburgh Pirates going to Ricky J We got a little Vladimir Garo Jr. Five out of 27 and then spot five going to Matt L and Seth Brown 54 to 75 for the athletics All right, three more boxes guys summer after this Take a quick little lunch break Come back. We'll go through some orders If anything else sold out, we'll break it. I know Whitewell or booklet or relic autographs, so You got to think that should be coming soon and if if there isn't any I Have seen plenty cases on Instagram and actually here where there's actually two or more case hits in one inner so Something to think about for the next one if you guys want to do the next one Go to an Alvarez 96 out of one 99 and Reese Hoskins Ricky Anderson and Ronald Acuna Jr. And you've been attendee 16 out of 18 Boston Red Sox Chris Walker and Trent Grisham for the Padres 38 out of 50 So 38 spot eight going to Matt Think you have a nice one right there We got Nolan Aronato and wow seven out of nine. How about this all Atlanta Braves from Matt Medlin? Seven out of nine that is Dale Murphy Andrew Jones and Ronald Acuna, Jr Seven out of nine very nice I feel like I've got you some really nice hits in triple threads as of late Matt. There you go Two more to go guys All right, Chris Bryan and Nico Horner three out of 50 32 so spot two Matthew Nielsen three When a Roger, I don't know that was pretty close Derek Jeter 160 Cyril going to Daniel. Oh, yeah, he's safe CC's about the 27 or 17 out of 27 spots seven. That's going to Matt L CC 3k Matt Dias for the Angels 26 out of 75 Such a huge ass top John Smoles 1 out of 18 for the Braves nice break for the Braves slider Another one for the Braves Ferdy Freeman 24 out of 36 All right guys last box. Good luck So again the second half of triple threads is in the store already And like I said the last time I checked on it It was down to single digits I want to say we're at 10 left or something just a quick glance to see how we're doing on some breaks Looks like triple threads is the one that has been moved. It's down to six left now So when I come back for my lunch break, I'm pretty sure we'll get that triple dress knocked out And if anything we'll post a little R&B some of the bigger teams just to remain Kahluas and Nolan Aronato Reggie Jackson Justin Verlander for the Astros Nico AJ puck 89 out of 99 Chris Davis and Francisco Lindor 31 out of 36 and Tend it off little Nico Horner 33 out of 99 Three gonna Deborah What's up, man? How's it going man? All right, there you go guys So I'm sure I missed a few but there'll be unnumbered base cards These are the rookies of course for the random number blocks. So we'll randomize the customer names For both lists and any unnumbered cards base or whatever we'll go to the customer that gets at the top spot But there was some really nice stuff in here guys got a little John Smoltz One out of ten got a triple Raleigh here out of nine Murphy Jones and Ronald Acuni jr Some very nice stuff. So no wide way on this side. So there should be on the next side guys So the next ones are already down to six. It's gonna get the next one rolling or actually down to four So once I come back to my lunch break We should be able to knock off the next one So let's quickly get the dice roller rolling. Let's list list So this would be for the 11 team customers Any unnumbered cards will go to the customer that gets at number one And then the second one will be for these customers here Whoever comes in at number one gets all the unnumbered cards as well So good luck guys Use the same dice roll. Let's roll it and we got ourselves a two and a five seven times. Good luck one two three four five six seven Mark L. There you go. Congratulations, man. And seven times out on this list. One two three four five six seven Going to Matt L. So there you go guys. That was triple threads picker team number 17 and my 18 is in the store We're gonna get that going tonight


First, thank you all for being here. This lecture we're giving today is the result of some of the ODI's innovation program, for which the first year ended in March this year. And we're presenting one of the projects that we've worked on on AI, part of a broader program looking at emerging technology. And I wanted to thank two of our colleagues who worked on this. And this was a team effort from the whole of the ODI team. But I wanted to single out Jamie Fawcett and Jared Keller, who co-authored the report that this talk is based on. I'm giving the floor to Lysiana. OK, cool. Thank you, Olivier. So recently, there's been a lot of excitement around artificial intelligence. And large companies have made huge investments on this technology, such as IBM, which has invested 15 billion pounds alone on their cognitive system, Watson. And from PWC, who estimated that artificial intelligence could add $15.7 trillion to the global GDP by 2030. And so all these big numbers on all this bold pronouncement have shown that AI actually has a huge potential in improving our lives. But first of all, why AI and what is it? So more and more businesses have been using AI to help them make decisions, for example, where to invest, what route to take, or even what movie to watch. So that really shows how AI is being more and more present in our daily life. But first of all, how does it really work? So think of it as a black box. Where you put lots of data in it, such as pictures, and also taste, as well as your social interactions. And from all this, this returns to you with decisions, recommendations, as well as pattern recognition. There's many type of AI systems, as you can see. And it's very confusing to actually think of what actually AI is. So here's what we think it is. Artificial intelligence systems are a combination of clever statistical and mathematical techniques, an understanding of the world, and lots of data. So the current trend within AI businesses is to treat large sets of data that we fit into AI systems that we use to train. And then we sidle that data. So instead of making it public, or share it. And if that continues, this will actually lead to negative effects, such as oligopoly, which means that that would stop innovation. And thinking that AI systems use data to train their algorithm, if they can't have access to that data, it means that fewer businesses or startups can actually make their own new AI systems. And this is actually a natural tendency that researchers have found, have noticed. And this is partly due to the fact that this is a self-feeding data ecosystem, which means that those who have the most data can train the best AI systems, and obviously the ones that have the best AI systems collect the most data. So that goes round and round. And so one of the most famous examples could be to take giant Facebook and its famous algorithm, which I'll hand to Olivia for that. Thank you. So it's worth thinking for a moment. We've been talking about artificial intelligence systems and data, data, data, data. Typically, in our kind of collective narrative, when we talk about artificial intelligence systems, we hear a lot that word, algorithm. And I wanted to spend a few minutes kind of unpicking what that word means in the context of AI and why we still think that data is a more important word and one that needs to come as an equal in the conversation about AI. Now, caveat. AI is a moving target. AI is using a lot of Bud's words. And AI is really badly defined. One of the experts we talked to as part of our research said something that I really like, that we need different words for AI. Now, for the time being, we're kind of stuck with the ones that we've got, but know that it is a moving target and that the definitions and the word that we're using are going to change. For one thing, one of the really good definitions that I've heard about AI is AI is basically the field of computer science of stuff that doesn't work yet. Once it works, we just call it computer science. So keep that in mind. But let's have a look a little bit at what we mean by algorithms is when we talk about artificial intelligence. So as we see, we're saying a typical analogy for AI systems is a black box. But if we open the black box typically, and it's important to remember that typically doesn't mean in every case because there is a really, really broad set of techniques, a really, really broad set of applications that we lump under that term AI. But typically, what happens is that inside that box you've got a lot of data, typically training data. Although it's worth noting that in some cases, such as if you've heard of GANs like the system that powers alpha zero, you don't actually have training data. But in the overwhelming majority of cases, an artificial intelligence system will have been trained with a lot of data. This icon here is my feeble attempt at conveying that there's a lot of statistics happening and a lot of methods to deal with this data.  You're using statistical techniques to optimize something. And that something is in some cases a neural network, in some cases other things. But let's look at this as a model of the world that has been trained on testing data with statistical techniques. So far so good, you take some data, you use it to train a model using statistical methods. Then you take this model that you've just trained and you give it input data and you get stuff, insights, recommendations and so on and so forth. So, we see I talked earlier about taking our taste and so on and so forth. So in here, our taste, our history of watching stuff, reading stuff, et cetera, et cetera, plus some statistics gives us a model of what we like. Then we say, today I want something and it gives us recommendations. For instance, what's interesting when we look at what typically is called the algorithm and there's a lot of the talk of the algorithm when talking about Facebook, is that we talk about this. The algorithm is the result. The algorithm is what is shown to us as output to all our input. But when you talk to data scientists that work on that black box, what they call algorithms is the set of techniques that they use to train the artificial intelligence system. So, already algorithm is a bit of a complex thing. What's interesting, however, is whatever definition of algorithm you use, a lot of the time, these algorithms are open. That is the techniques that are being used to train artificial intelligence systems. The software being used to use the model that you have trained tend to be open source. There's a lot, lot, lot of the software being used to train and then use the models that are open source. And they're all based on mathematics that have been published in, say, the 70s and 80s. There is, of course, quite a lot of research and kind of cutting edge AI algorithms that are being published right now, but the overwhelming majority of the algorithms being used in AI are open. And the reason behind that is that we have now a relatively mature open source movement that is able to be convincingly making the case that open source for those algorithms create better transparency, that you have more scrutiny over the code that is being used to train and then power those models, and collaboration. You don't have to reinvent the wheel because you can reuse software that's somewhere else built and you can collaborate in it and make it better. What's interesting then is looking at how the perception and the behavior of organizations that use AI today do with regards to data. And I'm going to give the floor back to Lucia with this quote that much of the new hype on AI is based on better hardware to run these algorithms that are not particularly new and a lot, lot, lot of data. So let's look at how those organizations deal with data. Thank you, Olivia. So let's have a closer look at data. So providing access to data and to the algorithm is not a binary choice, it's not just a yes or no, it's more of, so obviously yes or no do you want to share and what do you want to share, with whom you want to share and how you want to share it. And to describe this access to data, we're here at the ODI like to use this ODI data spectrum, which you can see from the very left-hand side, it shows where organizations choose not to share anything at all with any external organizations and to the very right. This is actually the other extreme is publishing as open data with a license that enables anyone to share access and use data. And in between, you can see that there's also very different ways of sharing. It's either you can share with someone in particular or a selected group and also you can also share publicly but with a restrictive license. And as for algorithms, we have found that in our research that it works pretty much the same way. That's why we came up with this quadrant and we have broadly defined archetypes and trends that we think that businesses using AI are using to approach data, the access to data and the access to algorithms. So you can see that the very top is open algorithm and the very end is closed algorithm and then at the very left is closed data and then open data here. So this is by no means categorizing any products or services. It's just to give a broad idea of how businesses approach the access to data and the access to algorithm in order to create a competitive advantage. And we have so seen a trend here that goes towards the top left-hand side of this quadrant. So the trend is leaning towards open algorithm and closed data. And the reasons for that is that they want to open up their algorithm not for other companies to use it and to build on it so that they can identify any potential or existing issues alongside with any potential benefits. And then a second reason for opening up their algorithm is to speed up adoption of new methods or new ways of operating and this can be seen in many interviews that we've led is that a lot of businesses have the desire to help the AI community progress. But then why do we keep data closed? So I'm going to hand this over to Olivier. So the first reason we heard when exploring with roughly the 20 organisations we interviewed in this research of why they were relatively convinced by open source is not very good news to those of us who advocate for open data. We'll look in a little bit at why they don't typically want to open the data that they do hold but there was actually some hostility to using open data at all in their AI systems. This is exemplified by this quote from Anvali Bak from a VC organisation, a pan-European VC funder who says that in the organisations that she talks to they barely use open data because they think that if they use open data it might not have the relevance and the quality that they really need to train the algorithm. And that's bad news because if you work in data science you know that 80-90% of your work is actually dealing with data that is messy, dirty, not 100% relevant. So in the open data movement we have work to do to counter the expectation that open data should be better than this. Either it means that we need to get the message out there that there is high quality open data available to train AI systems or actually fix them so that they are better at being used for AI systems. But it's not all a negative reaction to open data. There's actually quite a lot of arguments being presented by those organisations we talk to that we're not about saying we don't like open data, we don't use it, but we hold data and we want to keep it closed for a number of reasons. I'm going to let Lucia talk you through some of those reasons we heard. Thank you. So we have talked to a couple of businesses and we've asked them why they were reluctant in sharing their data, open up their data and they came up with two reasons. So first of all is personal data. So data itself is already a sensitive topic and if we add personal data this adds one more layer of difficulty because of privacy reasons. And we have Roma from Frosha who expressed it quite succinctly when we asked him whether they would benefit from data that was siloed whether that would help them create or innovate. And this is what he says. Of course we would be able to develop more business models out of that but as a citizen I would probably object to that. It's with good right, there are some silos. And this approach is quite common when we talk about data about people within businesses. Obviously there are access to data that should be restricted because of personal reasons or privacy reasons unless they have given an explicit consent to that. And besides privacy issues there's also business reasons for keeping that data limited or closed and that's due to trust so that would retain trust from the customers and also avoid some potential regulatory issues. However obviously this business reason is justifiable but many companies use this as an excuse to keep the data they have as proprietary data. So proprietary data is the second reason why businesses are reluctant to share their data. And this proprietary data is a form of companies IP with intellectual property. They see it as their market advantage and they don't want to disclose that. Obviously that's also a valid reason but only if those data have anything to do with profit or their loss. So that would be understandable that they don't want to share this data. And obviously that would mean that if less data is shared then there would be less innovation as well and knowing that AI systems need loads of data to train itself it also means that there is a limited... with this limited data they could lead to a lot of bias and as Sandra says so if we talk to machine learning people and if you don't have a rich data set then you will actually start discriminating people because there is bias in the data set and I'll hand over to Olivier to give you two interesting examples. I'll stay with this quote for a moment because there's a double whammy here. One is silos lead to lack of diversity in training data sets which indeed means that bias can creep in but also worth remembering is that analogy of the black box that we talked about a few times. Most artificial intelligence systems at the moment are black boxes. AI systems are very bad at explaining why they came to decisions that they came to partly because of the way they're built you've got a set of weights in a neural network it doesn't really tell you, I did this because of that. Now there's a lot of research going on at the moment to figure out ways for AI systems to be self-explanatory but at the moment the only scrutiny we have on AI systems is whatever is available to us to query it so we can use an AI system and go yep there's a problem here but at the moment mostly what we've got is the code which as we said a few times earlier tends to be open so the code that is used to train AI tends to be open but the data is closed and therefore the scrutiny on what the data that has been used to train an AI and therefore what bias might have been in that data because they are typically held in silos and held as property IP or personal data they are not, you can't go and look at that and look at it for bias which leads to some pretty egregious discriminatory systems so those are a couple of examples that are a few years old you've got a few more that are a little more subtle but in order to make my case I think those are good examples this is an example of what happened when Google holder of quite a lot of data and quite a lot of resources to make sure that their systems are not massively racist released a feature in their photo software that was very conveniently sorting out photos based on topics and probably because it had been trained with mostly photos of white people as the exemplar of what a person is it then classified photos of this person's friend so Jackie Elsin was the person who raised the issue with Google this person's friend with dark skin were classified by the algorithm as gorillas now you can be pretty sure that Google was not intending to do that so that's not bias that is intended sometimes you do have a view of the world and you're trying for it to be reflected in your AI system in this case that was not the case and that is largely due to the fact that there was no way to test for that kind of bias until someone was able to put their photos in the system and see that another example that is tragically funny is from 2010 Nikon multinational company but you know Japan based released a new camera with a really nice feature that helps you take better photos and what it does is it's trained on facial features and tells you when someone's blinking and this person, Jos Wang on the picture here kept taking photos of herself and her family and the camera kept saying did someone blink up to the point that her brother had to basically test how badly he had to like artificially open his eyes for the camera to stop saying that and that again is a pretty egregious case of a training set with some inherent bias in this case the camera application was trained on feature mostly from Caucasian faces and therefore did not take into account that actually across the world there are many more types of faces and types of shapes of eyes that might be mistaken for someone blinking So what then? We've talked you through the fact that we talked to quite a lot of organizations and told you that most of them tend to go to the top left of the quadrant using open source software to build their AI systems and power those AI systems but sticking with close data I could be mean and say if you want to know what we recommend as next steps, read the report it's good but let's very quickly go through some of the things that we think are the right next step The first one is a note of optimism While a lot of the popular imagination around AI revolves around tech giants the ghouls, the facebook, the apples of this world actually most of the unlocking of value through AI is probably not going to be done by those organizations but by organizations that sit on a lot of data and don't know what to do with Those are the organizations that we think have the opportunity to test other models than the closed data as personal or proprietary IP parameter There are ways for a company holding a lot of data to not say we're going to build an AI on that but maybe they're going to partner with others Maybe they're going to work with experts in the field to create systems with that data out of their hold But that means a few things That means that bit of optimism much of the data is yet to be unlocked But that means that we need better data sharing and access while retaining trust If you look at the recent scandals or problems around AI both the Facebook and Cambridge Analytica scandal and a little earlier the issues that DeepMind had with their data sharing agreement with the Royal Free Hospital Those were well-meaning data sharing agreements that backfired spectacularly and we think that the problem is not with the concept of sharing or giving access to data but the problem is that we don't We're at the infancy of figuring out data sharing agreements and data sharing models So we've started a project a few months ago trying to categorise, classify, understand and explain data sharing and data access models so that companies and organisations can pick and mix and pick and choose the ones that work for them while retaining trust while not ending up with, yeah, lawyer's say it's okay and then two years down the line you end up with a massive scandal and really bad publicity And in order to unlock data that people think well there might be some personal data so we're going to keep it closed because we don't want to take the risk We acknowledge that, that is a fact that there is a risk and especially now with the UK's implementation of GDPR the data protection bill that is going to make willful re-identification of people when using data crime, felony, misdemeanor and the lawyer, I'm definitely not a lawyer but a bad thing then we need to help organisations deal with that risk rather than go binary, no, we're not going to do this because there might be a risk of re-identification and I don't want to end up in jail because someone somewhere re-identified data that I shared because there was some personally identifiable data in there So we've started this second project to help organisations better manage risk when dealing with data sharing, data access and in some cases, opening data That's it from us, thank you Hannah, I'm supposed to give you the microphone so I'm going to do that right now Are there any questions, any comments, any concern anything that wasn't clear Hi, thank you, that was really great My question is, at the start especially with that sort of matrix of open algorithms and open data you're actually talking about open data but by the end you're talking about risk management and access to personal data and that makes sense to me because most of the applications for AI are with personal data I'm wondering if you have examples where open data is truly regardless of whether a venture capitalist thinks that it's relevant examples where algorithms can and should be trained on open data and because I feel like I'm being muddied between personal data and open data in the narrative There's a few examples out there of successful data sites being used to train AI I'll mention just two One is OpenCV, which is used for computer vision Pretty big data set of things and categorizing things in computer vision is really, really hard but at the same time done it's a solved problem There are good data sets for that There's good data sets for kind of linguistics, computer linguistics One of the examples that we have in the report is a project by Mozilla to open the data set they used to train their voice recognition So it can be done but you'll see there that what's interesting is that those are data sets that are specifically made to be used to train AI and not just random open data sets that happen to be somewhere Yes, we start at the beginning with let's say some kind of box data box called like you... What do you mean by data box? My question is when it comes to the question you have the data you are running this AI but of course you have to run in some kind of infrastructure, some support because I think in this box you have big data you have AI so where do they run in other words that box I figure myself like a data cube where you can have a lot of data and analyze it so this means AI is not just analytics, data but it's also infrastructure this means all together I see AI that's my observation Wait, we can share Okay, so I'm also proud of this so the black box is a metaphor to explain why it's so hard to understand at the moment what AI typically does the way to explain this metaphor is that you can if you've got a dog you kind of understand why the dog does what they do there's food there so they're salivating you kind of understand that but you can actually not really understand that by just opening their head and looking into it you can put electrodes and perhaps figure some patterns that relate to seeing food and being hungry but we're not in the same way as neuroscience doesn't really have a grasp what is happening and why we create systems in AI that we can't really understand we know what comes in so training data we know what comes out decision, pattern recognition and so on and so forth but there's an aura of magic because it's so hard to exactly explain the why it's very, very hard to create a system that says yes, I gave you this response because of that the answer typically is I gave you this response you're trying to bring me with some data, I don't know but the second part you answered, the infrastructure do you want to answer that? Thank you very much for mentioning the infrastructure so I think from our research actually this infrastructure is basically the fact that we have more and more data we are able to have access to loads of data nowadays 21st century compared to the past because the AI system it's nothing new it's just that right now we have more and more data to fit in and that's why we're able to develop more to have better trained AI systems so hope this answers your questions here You are right I think this black box will be eliminated once you are able to combine infrastructure for example, IBM you mentioned 15 million they spend 15 billion this is mainly to link infrastructure with AI because if you have this type of I see different type of teams that it goes from infrastructure to the data scientist to the developer, to monitoring to go back etc otherwise this is already with Cognos Cognos does the same of things so this means it's the same idea of Cognos just in different way, it's more complex that's where I see AI and the utility of AI so from my perspective I think these challenges of getting the companies who are setting on this data to open up are probably almost insurmountable firstly the competitive advantage of this data for most of these companies this is the main thing they have is that they have the data like you said the algorithms are known and they're not going to have as good engineers as Google, Facebook and the others so this is all they have as a way of competing and it's also a barrier to other people from competing with them so I work for a search engine company and we would not be able to so with some initial data to start with you just cannot even start and you're so far behind Google you're never going to catch up so there's a massive disincentive to open this data then on the other aspect the risk this is also extremely high so I work on the privacy aspect of it and even data sets that companies think are de-anonymized are not a classic example is the Netflix data challenge where they published a data set that they thought they'd anonymized to see if people could come up with better recommendations and researchers proceeded to de-anonymize the people and these are people who know this the best there's many many data sets and also the more public data sets that are the easier it gets to de-anonymize because these can be cross-referenced so I wonder if you have any other insights about if this is any possible Lucia can talk in more details about the organizations that we've seen that do actually manage to find ways to find other models than the closed data open algorithms but what I'll say to begin our answer is it's better to again think not just as a binary choice of open versus close but more on a spectrum and a lot of answer is going to be it's not a binary thing so full full proof total infinite forever anonymization is nearly impossible unless you go and you know mangle the data to the point that it's unusable we get that but you know managing risk is not about binary full full no risk and full risk of you know what the risk you are that you're taking and then you make your choices accordingly and likewise opening we're not saying through this that all the AI should be based on completely open data and all organizations should open data entirely but we're saying that the model of closed data open algorithms that is preferred might not be the best for everyone Lucia did you have examples of successful organizations outside of this top left quadrant well there's which we mentioned a couple of times so this is a Dutch startup based in Amsterdam and they collect they collect actually personal data and this new GDPR law is they say that it was actually beneficial to them so what they do is that they collect those personal data then they anonymize them and then they give it to they sell it to companies so they have a open algorithm but sort of shared exactly sort of shared data so that's why at some point in the quadrant we have a shared model in the very middle of the quadrant which is where we see a lot of companies that is that actually tends to move towards this shared model because they see that collaboration and partnerships is actually easier for them to open their data because there's more trust building during this whole process I noticed a trend in enriching web pages with structured data to enable natural language processing in search what trend how you see this coming and where can people find more information to enhance their web pages in that way your websites downsides I'm going to try and answer but I think your neighbor behind you working on search engines might have more knowledge so don't throw a bottle at me if my answer is wrong I'm going to answer somewhat broadly the sense that if you count the web as data that is publicly available most of the web is dirty and messy and any effort to increase the structure of it will not automatically but will very be very likely to help people then use that structured information is that essentially what you're going beyond the semantic perfectly everything marked up but then you enhance it with structured data this process is quite complex complicated for many organizations we've got 99.7% SMEs in this country and they have absolutely no clue about that so how can they approach this topic otherwise they won't be in search soon I mean we've got quite a lot of it working already there natural language processing techniques are again not entirely new what's important is that to realize that they are useful only to some extent in my previous role before joining the ODI my team was actually building natural language processing to understand the web better and the joke was that we surprisingly failed time and time again to build algorithms that would detect sarcasm not sure why but we were still able to automate quite a lot of the understanding of content out there through fairly effective machine learning techniques so the key is to understand what it is usable for and what it is not usable for so understand where the the efficacy and the accuracy of those methods are okay so have you found a question here yeah thanks for the lecture it was very interesting I think something that maybe also interesting to us is the fact that when you're talking about data and the fact that they can that you have to be sure on the kind of data that you plug in actually the machine learning apart from having like good quality data something that is also like warfare I think is the biases towards the kind of people that actually like plugging in that data because like a big thing on that on this topic is also about having more diverse team working on that data thing so like open source is actually like a very good thing to kind of avoid having I mean kind of shaping actually and unbiased future a bias future but I think something that is also very important adding is working on creating a device a diverse team to avoid that don't know if you answer that but other than nodding yeah thank you thanks for the questions as you can see on the screen here the R&D project funded by the UK government is still ongoing research is still happening so if you'd like to collaborate with the team find out more about what we're doing please do get in touch info at theodii.org can we please give Olivier and Lucie a big round of applause


Bearder! Garnham get wrecked dummy first off. No, I am not legendary actor Sam Elliott But thank you the moustache is actually for Movember I do it every year in memory of my dad the raised money for positive men's health this year I'm rocking the top lip people pleaser to help work towards suicide prevention So if you can spare even a couple of dollars I would really appreciate it if you went to the description below and checked out the link there And hey, if you can't afford it, that is completely fine. No worries at all I know it's a hard time of year for a lot of people I know the pandemics really knocked a lot of us on our ass So there's no need to donate if you can't afford it All I would ask is simply that you go down and you have a look at the link It's all about awareness and at the very least I would love it If you could see all of the great work that this foundation is really doing Dark alleys the mariana trench facebook comment sections What do all of these things have in common horror this they're scary. It's hot The theme is horror if you've been around for a while first of all Thanks, but second of all, you may already know this if you're new though Also, thanks, but I have written quite a bit of horror recently especially in the last year in the last year alone I have written a psychological horror novel called welcome descent and have short stories in both local haunts and We're not home both really great anthologies with 100% of profits going to charity Today though, I would like to talk a little bit about how I approached writing the horror short stories for these books I'm no Stephen King or anything. I'm no professor, but hey Maybe it'll help and the story that I'll be using as an example today for how I write a horror short story from scratch Is my new horror short story in a new anthology called served cold launching on December 11th That's right, baby another one served cold is the next anthology in line after local haunts and it is an absolute beast With 22 short stories from a huge list of authors Including yours truly my story and served cold is called red albums and the reason I'm using it as an example today Is because it's a bit different to the stories I've written for the other anthologies My other two short stories were pretty brief and mostly fun I'd probably classify them as like campfire horror my story in served cold However, is much longer and much more or intended to be much more emotional and dark I am super proud of it and if I can be so bold I think it's literally some of the best writing I've done, but you know, that's not for me to say It's for other people to say about me So coming up with an idea for a story is pretty subjective Almost any writer you ask will have a different explanation for how they started It could be a dream. It could be based on something from your own life But for me, the important thing is that I start off with one theme That's all you really need to light the fire So I've described it in this way in videos a few times already But I'm going to do it again because I think it's I think it's a good analogy But the way I would think of it is like a building block Just take one theme. It could be hotels, neighbors, fear of heights In this case, it was cold Just one central point or plot device or it could be a character that you can build your story around And once you have that building block, all you need to do is start piling them up Piece by piece Imagine it's a game of Jenga and you're the sucker that got stuck with the job of setting it up My building block was cold. So I thought cold I could do snow I could set my story in the snow. Maybe a snowy valley. What is scary about a snowy valley? Well, maybe it could be isolated from other people. Just like that. Boom. I have my setting It doesn't have to be a setting that you end up with first. It could be a character or a bad guy slash monster It doesn't matter where you end up first. All that matters is that it came from that one building block Most times for me though, I will start with a setting That's just kind of where my train of thought goes and if I'm starting with a setting I always try to make sure I have something I can add to that that makes it scary or dangerous So again a snowy valley that is far away from anyone that could help if something goes wrong When it comes to settings and horror most of the time it is going to be isolation that makes it scary But maybe it could be that the setting is haunted Maybe it could be that the setting is in a very high up place with danger of falling to your death There are so many different ways you could go literally just right now and I'm not even taking the piss I was thinking to myself in saying to myself high up with risk of falling to your death I thought what about a snowy cliffside and the story is about someone who is stuck there With danger of sliding and falling to their death and the story is about them trying to get to safety That would be a pretty good horror story Maybe they're on the side of a cliff a bit of a snowy slope And the only safety they have is at the top of that slope But at the top of the slope is a pack of hungry wolves I just I literally just came up with a story idea right now. That's how easy it is You know what you guys can have that one Obviously a lot of this will come down to your creative ability as a writer But once I figure out a character that we can experience the story with I'll try to do something that is very tricky But very important So usually I try to make sure that the horror in the story scares the character for a very personal reason Rather than just this thing is scary And the way I think to do this is to give your character a bit of a backstory In horror that will usually take the shape of a past trauma I know what you're thinking already that sounds like a lot for a short story But trust me you can give a character a backstory even in the really short short stories For example for my story in local haunts. It was called alone among the gum trees, which was a bit of a If you're Australian, you'll you'll know that story is a really quick and simple campfire story about a group of tourists in the Australian woods Quite literally sitting around a campfire But the main character is a young woman who is still dealing with the paranoia from leaving an abusive and predatory ex-boyfriend. You don't learn that from an info dump You quite literally learn it just from a few sentences of dialogue from another character and the way that she acts Around other people ultimately that abusive past does play a part in her experience in this horror short story Spoiler alert. It's not that her ex-boyfriend comes back or anything like that. It's not directly relevant But the feeling of being preyed on it all comes back to that her backstory is barely mentioned But it's relevant to what is happening now the point here is and this is gonna sound a bit weird So bear with me, but you don't actually need to tell us the story of their backstory You can allude to it. In fact, I think it's better if you leave most of it unspoken Let the reader fill in the gaps and they will so in my story red albums for served cold Our character is a young man who must go back to his childhood home in a snowy valley He's going there to care for his sick father, but he hoped to never see this home again It is a constant reminder to his time as a drug addict and the guilt from him being an addict Because of the strain that it put on his father at the time I obviously won't spoil anything, but his past plays a part in the horror of the story His past as an addict is relevant to his decision making in caring for his sick Father and it all starts coming together when he starts seeing the sickness for what it truly is I hope all of that made sense Obviously, I have to be kind of cryptic when I'm referring to my own stuff to avoid spoilers But the main takeaway here should be that you can say a lot about a character's past without saying too much at all And personally, I think the less you say the better hint to their past Tell us about that past in the way they act around other people It makes the characters more relatable and it makes the reader care more about their well-being And if the reader cares more about their well-being if the character is scared the reader probably will be too Now as far as outlining goes what I normally do when I write like a novel Is I'll do a small paragraph outline for each chapter that I intend to be in the book What happens in those chapters obviously can and often do change But the point is that it helps me to know whereabouts in the book the story should rise and fall In regards to like tension and conflict and exciting moments It just helps with the structure as for short stories though You usually won't have chapters I'll often still create like little character profiles listing their physical attributes Like eye color and physical quirks For example, if they clench their jaw when they're angry But I don't really do much more outlining when it comes to short stories I will have maybe a few paragraphs giving a small description of what happens and the order I would like those things to happen in but really that's about it One of my favorite things about horror is that the ending really can go anyway There's no shame in killing off the protagonist in a horror story Especially a horror short story or in giving them a happily ever after I've done both But what it usually comes down to is this the feeling that I want the reader to be left with In almost all cases the last page of the story is what the reader is going to be thinking about for a fair while after they finish Do you want the reader to be happy and have some catharsis in this character Rising above their fear and winning or do you want to leave the reader scared or maybe even sad with an ending where the protagonist Loses both is fine. In fact, I think happy endings are super underrated in horror But that's a big discussion for another day The thing you need to keep in mind though is that the ending of the story should have the character In some kind of different place than where they started or in a different state of mind to where they started The one thing you don't want is an ending where nothing truly important happened or changed Anyway, like I said, I'm not a professor. I'm not an expert I really wouldn't want you to think that I'm claiming to be this video is more just me talking Kind of at myself telling myself what my thought process is when I'm writing a horror short story And if it happens to help you great And if it doesn't You know what go ahead dislike the video load the video on your phone put the phone on the ground and fart on it I don't I don't care. Actually, don't do that. I just made it sound like I have some kind of weird like fart That should do it. But here's what I want you to do I want you to go down to the comments and let me know what your thought process is when you're writing horror Namely, what's important to you as always? Thanks for watching especially for watching through the whole video You are objectively the best You're the best out of all of the people who've watched this video if you watch the whole thing through You're the best. That's it for now though. So Go right. Don't think I forgot. Nanorima isn't over yet Get to work. Oh, and hey, if you could spare a couple of dollars again The move ember link is in the description below you would be helping out a really great course, but that's it See you in the next one. Catch you