So, then after discussing the perfect competitive market and monopoly market, we will move into a new kind of market which is in between the perfect competitive and monopoly market structure. And this is the form of imperfect competitive, imperfect competition or this is a part of a imperfect market structure. So, imperfect competition refers to those market structure that fall between the perfect competition and pure monopoly. So, it is a market structure that is strictly between the perfect competition and the pure monopoly market that have some feature of competition and some feature of monopoly. It has taken some feature of a perfect competition and some feature of the monopoly. And this concept of monopolistic competition generally comes from the seminal work of professor Chambal in theory of monopolistic competition. And the basic assumption of the monopolistic competition is same as perfect competition except the homogeneity of product. So, if you look at it more close to the perfect competitive market structure, because the basic assumption of the monopolistic competition is same as the perfect competition except typically the homogeneous product, homogeneous nature of the product. So, let us discuss the features of the monopolistic competition and then we will see whether how it is different from a perfect competitive market structure or a monopoly market structure. So, there are large number of firms each satisfying a small but not microscopic share of the market and share of market demand for a similar not identical because there is not a homogeneous product. The share of the market demand is for a similar product not for the identical product. There are large number of firm each satisfying a small but not a microscopic share means large number of where they share a small but it is not very insignificant also. Products are close substitute but they are not perfectly substitute each product is different from the other in term of some of the component. Seller of each product group can be considered competing firm within the industry. So, seller of each product group that is substitute product can be considered as the considered competing firm within the industry. Market is monopolistic product differentiation created degree of the market power it is why we call it is a monopolistic market because each product is different from the other product. So, when you are when you are doing that independently each product is a single product with no close substitute and that product differentiation created degree of the market power. Market is competitive there are because there are large number of firms and there is easy entry into the market. One of the important feature of the monopolistic competition is the product differentiation and how this is how this product differentiation makes it is different. Each firm produce a product that is at least slightly different from those of other firms rather than being price taker each firm faces a downwards looping demand curve. So, each firm produce a product that is at least slightly different from those of other firms and we will discuss about the parameter on what basis the product has to be different from the others and they are not the price taker each firm follow a downwards looping demand curve. So, whenever they have to increase the output they have to reduce the price. The firm in the market do not consider the reaction of the rivals when choosing their product price or annual sales target. It means the price and output decision of the firm is not getting influenced by the reaction of the rivals. There is a relative freedom of entry and exit there is no barrier as such apart from product differentiation creating as a barrier. Neither a opportunity nor the incentive exist for the firms in the market to cooperate in any way that decrease the competition and the number of firms in the market adjust until the economic profit are 0. Now, since we are talking about a feature product differentiation and that makes the market different from the other market structure. Now, we will see how the product differentiation is being practiced. The product has to be different from each other either on the basis of the product quality or in the basis of the services or in the basis of the location or in the basis of advertising and packaging. So, when it comes to product quality may be you can take a case of a toothpaste or you can take a case of a soap. You get a soap which ranges from may be 500 rupees to 5 rupees. How they are different from each other? They are different from each other in term of the quality or you can take the case of detergent powder it ranges from 200 to 2 rupees and how they are different from each other? They are different from each other in term of the quality the raw material that is used and also the usefulness of the product. Similarly, services like if you take the case of a typically this is the after sale service if you take the case of a grocery store or you take a case of a supermarket. Now, what is the service? Some supermarket they do a home delivery, some supermarket they will the their assistant will help you to put your grocery in your vehicle, some super store they may be they will take your typical coupon or all these things. So, these are what these are the services extended to the consumer and the product is different from each other in term of the service associated with the store service associated with the product. Like you take the after sale service of a vehicle after sale service of a electronics product. For someone it is free for someone some for a typically if you are sending your vehicle for a servicing in some store they come and pick you pick your vehicle.  So, these are all small small activities small small service given by the given by the store or given by the specific product and that makes the product different from one to another. Location the location decides how they are different from each other may be the same brand shop when it is in a mall or when it is from the outskirt area that makes the difference and that creates the product differentiation. Like if you take a grocery store near to the petrol pub in the highway and the grocery store near in the nearest locality that makes the difference between the products. Advertising and packaging again may be if you look at for someone the focus is more on the packaging. So, packaging has to be neat packaging has to be attractive. So, that makes the difference if the packaging is good you are buying the product and advertising who is doing a massive scale of advertising and who is not doing it. So, generally if you look at the brand loyalty also comes from the fact is that who is advertising for the product and that somehow influence the product has to be different from the other product. Because if it is a well known celebrity you always feel that whatever the endorse that is has to be a good product because the celebrity is endorsing the product rather than endorsing the same product by someone else there the comes the product differentiation because that takes out the consumers perception about the product. If it is a celebrity it has to be good rather than the other one who is advertising for the product. Then we will talk about the price and output determination in case of a monopolistic competition. This is similar to the monopoly as it face a downward sloping demand curve which is because of a strong preference of section of consumer for the product and quasi monopoly of the seller over the supply. So, pricing and output decision is similar to monopoly it face a downward sloping demand function because it is a strong preference of the section for consumer for the product or typically the brand loyalty and there is also a quasi monopoly of the seller over the supply. Brand loyalty or strong preference of the consumer gives the seller the opportunity to raise the price and yet retain some consumer. So, brand loyalty or the strong preference of the consumer generally allow the seller to increase the price but steal the consumer they buy it because there is a brand loyalty or the strong preference for it. Since, each product is substitute for the other the firm can attract the consumer of other product by lowering the price also and that is why we get a downward sloping demand curve. So, in case of short run again the monopolistic firm may get a super normal profit or they may get a normal profit or they incur a loss. So, we will analyze where they get a super normal profit where they get a loss and where they just get a normal profit and if you look at this is always on the basis of the average cost and average revenue. So, this is average revenue this is marginal revenue here we will take Q here we will take P average revenue and marginal revenue. We have a average cost we have the marginal cost we will take the point marginal cost marginal revenue we will identify the price and we will identify the cost at that same level. So, this is the cost and if you look at in this case since average revenue is greater than average cost for corresponding level of profit maximizing level of output here the firm is getting super normal profit. Then we will take the case of normal profit when the generally the firm just get the normal profit. So, here again we will take average revenue we will take the marginal revenue we will take the average cost we will take the marginal cost corresponding to marginal cost and marginal revenue we get the equilibrium price we get the equilibrium quantity. And in this case is average revenue is equal to average revenue the firm is just getting the normal profit. Similarly, in case of loss the possibility is that in the short term the monopolistic firm also can get the loss. So, in this case this is the average revenue curve this is the marginal revenue curve this is the average cost curve this is the marginal cost curve following the marginal cost and marginal revenue will get the equilibrium quantity and equilibrium equilibrium price this is the cost. So, this is the amount of the loss what the firm is getting because average cost is more than average revenue increase in this case. So, in the short run the firm may incur loss the firm may just get the normal profit or the firm get also the super normal profit. Next we will see what before going into the short run we will see what happens when the firms get super normal profit or when the firm gets loss. So, short run economic profit encourage the new firms to enter the market increases the number of product offer reduces the demand faced by the firms already in the market and increment firms demand curve shift to the left demand for the increment firm product fall and their profit decreases. So, whenever there is a super normal profit that incentive for the new firms to enter the market that increases the number of products being offer because the new firms will again produce a differentiated product reduces the demand for the existing firm that reduces the profit of the existing firm and finally, they again land into a situation where they just get the normal profit and the entry into the market continues till the time all the existing firm they are not getting the normal profit. If they are getting super normal profit again it is an incentive for the firms to get into the market and produce a differentiated product. Then we will take a case when all the some of the firms they are getting the economic loss or the the firms they are getting economic loss in the short run. So, short run economic loss encourage the firm to exit the market at least for few of them which decrease the number of product offer increase the demand faced by the remaining firm with the exist of few of the firms shift the remaining firms demand curve to the right and increasing the remaining firms profit. So, again it is a transition from the loss to the normal profit and this exit from the market of those firm who are making loss will continue till the time the firms they are not getting the normal profit in the market. Next we will see the price and output determination in the long run. So, the long again in the long run again we follow the same principle we take the marginal cost marginal revenue as the profit to find out the profit maximizing level of output and price and after finding that we find out the price on the basis of the demand curve we find out the cost and the difference between the cost and revenue that gives us the profit loss or whatever may be the outcome. So, in the long run if you look at the super normal profit in the long run attracts the new firm to the industry that leads to loss of market share and the normal profit. So, increasing the number of firm that intensify the price competition between them price competition increases existing firm cut down price to retain or regain the market share new firms lower to penetrate the market and demand curve becomes more elastic. So, let us understand this long run equilibrium that how the demand curve is more elastic when there is a super normal profit in the market. This is marginal cost marginal revenue. This is the price and if you look at at this price the firm is getting the super normal profit because at corresponding to this this is the average cost and this is the average revenue and since average revenue is more than average cost the firm is getting super normal profit. Now, what will happen this super normal profit will attract the new firm into the industry which will reduce the loss of market share and that becomes the normal profit. And to normal profit what we need to find out we need to find out a new set of average revenue curve and the marginal revenue curve. So, this is average revenue one and this is marginal revenue one. So, increase in the number of firm intensify the price competition between them price competition increases the existing firm cut down the price to retain their market power and new firm lower the price to get the profit. So, at this case if you look at corresponding to this marginal cost and marginal revenue all of them they are just getting the normal profit because the average cost is equal to average revenue at this point. So, in the super normal profit so how the generally the sequence of event. So, average revenue marginal revenue is there on that basis they get a super normal profit. Super normal profit attracts the new firm into the market existing firm demand decreases market share decreases and then the new we get a new average revenue curve this is average revenue one and correspondingly we get the marginal revenue curve one. At this with the help of this average revenue and marginal revenue cost function remains same. We get a demand curve we get a profit price level which is again lower than the previous price level and also at this point the average cost is equal to average revenue. So, this leads to the fact that this average cost average revenue brings down the equilibrium level brings down the profit level and the all the firm they just get the just get the normal profit and there is also one more point to note here is that if you look at between the previous average revenue curve and this average revenue curve this average revenue curve is more elastic because all the firms they are following the all the firms they are lowering the price in order to increase the profit existing firm lowering the price to return their market share and the new firm lowering the price to lowering the price to at least penetrate in the market and that overall leads to a lower price in the market. Then there is a again a significant feature of this monopolistic competition is there is some excess capacity is always there in the monopolistic competition as compared to the perfect competition. So, if you look at in case of perfect competitive market structure the firm is equilibrium at the minimum point of the average cost curve whereas, in case of monopolistic competition the firm is equilibrium at the falling portion of the average cost curve and there that brings the excess capacity. So, this if you look at each firm will be equilibrium at the falling portion of the average cost not at the minimum point and this is because of the excess capacity theorem and what is this excess capacity theorem says there is a excess capacity with each firm more output can be produced at a lower cost but generally firm they are producing the output which is less than the full capacity or less than the minimum point. So, we will check this graphically how the monopolistic firm becomes the have some amount of the excess capacity. This is our average revenue curve this is the marginal revenue curve this is the long run average cost curve this is the long run marginal cost curve corresponding to this we get the level of output. So, whether you call it monopolistic output or whether you call it perfect competitive whether you call it monopoly output this is the same profit maximizing level of output whether it is monopoly or monopolistic if you look at they operate at the decreasing portion of the average cost curve. But ideally if you look at always the perfect competitive output is produced at the point where minimum point of the long run average cost curve. So, the difference between these two is generally known as the excess capacity because this much amount of output more could have been produced. But the monopoly market and monopolistic market since they are operating at the decreasing portion of the average cost curve they are not coming to the full or they are not coming to the minimum point of the average cost or they are not coming to the full utilization of the economies of scale or advantage of economies of scale. And they are just stopping their production here that leads to some excess capacity because there is no full utilization and that is why this difference between this QM and QC generally known as the excess capacity in case of the excess capacity in case of the monopolistic and monopoly market structure which is not there at least specifically in case of when it comes to the perfect competitive market structure. And this feature if you will find out it is more in the monopoly and monopolistic rather than any form of the market structure. Then we will talk about since product differences is in their advertising is mandatory the firm has to tell the consumer the firm has to give the awareness to the consumer that how the product is different from the other products in the market because their product is different from the other product. So, the product differentiation is inherent in monopolistic competition and leads to use of advertising and the brand names. Critics argue that firms use advertising and brand names to take advantage of consumer irrationality and to reduce competition. However, the defender argue that the firms use advertising and brand names to inform consumer and to compete more vigorously on price and product quality. So, there is always a two school of thought that whether advertising is positive or advertising is negative, but if you took at the real sense the advertising on to a limiting extent is good till the time it is not maybe not not getting use unnecessarily the advertising in order to attract a market share or in order to use a power. So, when it comes to advertising we know that advertising is necessary if the product is differentiated, but what is the impact of advertising and other cost of production and selling. So, this type of cost incur to sell thus advertising cost and other cost of production selling like selling cost giving free samples, R and D doing a market research all these expenses incur to sell more of a product without reducing the price must be added to the production cost to compute the average cost and contribute to higher price. Till the time all these costs are not getting added in the production cost this cannot be accounted for the calculation of price. So, if this is not getting added the cost of production will increase a price is maintained at the same level the firm is not going to make some the firm is not going to rather they are going to lose some profit. So, this leads to increase in the average cost consumer buys more of the advertised goods however resources are diverted from the production of other goods to provide the advertising. So, ideally when this added into the production cost this also leads to increase in the average cost and consumer buys more of the advertised goods because that is fresh in the other memory or they know that if it is coming as a part of A1S they will see this product in through the different media the they generally buy more of this advertised goods, but the resources are diverted generally from production of word goods to provide the advertising. That is the again the precondition that why generally the cost to be added in the production cost because they are incurring a good amount a large amount of in money on this. So, if you look at till the time the what is the form of competition in the monopolistic market because they are not the price taker they are they have they can influence the price to a bit because their product is different from the other product. On what basis they should compete with each other maybe someone can charge a higher price and always add a quality to it and that can be also sold in the market and someone will charge a lower price, but it is not qualitatively good and that is the reason he survives with a lower price, but apart from this price competition what may be the other factor on what basis such a price is getting this price typical price typical non-price competition takes place in the monopolistic competition. So, two common form of non-price competition one product innovation second one is advertisement. Both go simultaneously the product innovation and the advertisement and cost incur on this selling cost whatever the cost whether it is innovation or it is the product advertisement both add some selling cost. So, there is increase in the selling cost. So, average selling cost initially decreases, but ultimately increases and that is why the average selling cost is used like the average cost. Non-price competition through selling cost list all the firms to an almost similar equilibrium and they are adding the selling cost we can again check what is the firm's group equilibrium when the production cost also consists of the selling cost. So, we will understand the group equilibrium and what is the criticism to chambalize this theory form of monopolistic competition and we will talk about the oligopoly market structure what are the different models in our next session.


Hi everyone, Joe for jasmizcasebreaks.com. Happy Saturday, coming at you with Donner's, 2020 Donner's Optic Baseball Choice Edition. Random team break number three with six baseball mixer spots being given away. Right there. So we'll do the break first as always and at the end we'll re-randomize everybody's names and the top six we'll get into that baseball mixer. Thank you to all of these folks. Did I grab the right list? I think so. Yeah, thank you everyone here for getting in. Appreciate it. All the baseball teams are in. Let's roll it. Let's randomize it. Two and a three, five times. One, two, three, four, and five. We got Brian down to Zach. We'll do a different dice roll for the giveaway part. All right, two and a three, five times for the teams. One, two, three, four, and fifth and final time. After five, we got the Phillies down to the Twins. All right, let's get all this here. So I'm not gonna read all these names off right here for this little filler break, but there's all the teams right there. First half of the list and the second half of the list. Right here. I think White Sox is pretty good for 2020, Raymond. We have a lot of good stuff here. All right, let's get all this on one list. Let's sort by column B. And I think we just need to do one more of these. And then some full spots out of the mixer, and that should get us to Brakesville. Yeah, one more filler break, which's already loaded up. So this is break number three. Break number four will help sell out that Fishing for Trout baseball mixer. Once again, here's the final printout. Hot off the presses. Let's pop this open. There's a few autographs in the middle right there. All right, a lot of nice stuff here. We got a Yordon Alvarez. We got a 76 out of 45. We got a Rico Garcia, red and white to 45. Yordon goes to Houston. That'll be for Anthony. Rico goes to San Francisco. That'll be for Kevin. We've got the green dragon scale, skin print to 84. Eugenio Suarez, Cincinnati, Chris Phelps. We got a Miguel Cabrera for Detroit. That'll be for Adam, 39 out of 84. Francisco Lendor, still Cleveland edition here. Cleveland, this is for you. 59 out of 84. And that'll be for Barry Roberts. Behind Josh Bell is a rated rookie, Jonathan Daza, rated rookie, Hollow. Chris Phelps with Colorado. And behind Josh Bell are your autographs. We've got Davey Garcia, rated prospect autograph for the Bronx bombers, rich with the Yankees. 73 out of 99. We got the Catman, Tony Gonsolin, rated rookie autograph for my Dodgers. That'll be for Josh, 39 out of 49. He likes cats. And behind Josh Bell is a yord on Alvarez autograph. Nice, rated rookie auto for Anthony and the Astros. All right, 79 out of 99. Not a bad break at all. The Josh Bell, Hollow will go to Adam and the Pirates. Now, let's flip back to this list here. Let's gather everyone's names from Wade down to Jonathan and everybody in between. Let's get all those names in here. Let's randomize that list 11 times, six and a five. It's gonna be top six after 11. Good luck. One, two, three, 10 and 11. Then final time, good luck everybody. Now, from seven on down to 30 and everyone in between there, thank you for trying. I appreciate it. We'll get them next time. In fact, there is a next time if you wanna try again, jasmuscasewrakes.com. Top six, big congrats to you. Wade, Barry, Charles, Adam, Kevin and Jonathan. There you go, top six. You're in the mixer. You're in the baseball mixer. In the mix, a chance to win a Mike Trout as well in addition to whatever we pull in the mixer itself. I'm Joe, I'll see you next time for the next break.


So, hi guys my name is Gayan, I work for a marketing automation startup in Singapore. I am Arjun from Sri Lanka, today we will be talking about how we adapted serverless for certain aspects of the application. So to start with I am pretty much sure that all of you guys are aware of the serverless offering at AWS which is Lambda, but there is just more than one service that does serverless. So today we will be talking about AWS Lambda itself. So in general our application is a PHP application, it is a monolithic application which follows general design patterns and MSI architecture and all that stuff. So what you find here is an average structure that we had earlier. So you get the ELB, the cache services, the database services and application servers. So one issue we had with this was, if you are close to look at this, most of the applications today use offloading tasks to workers which actually reduces the load on the application servers. So your web servers actually perform the way you want. The traditional way of doing this was to use a messaging queue where you actually push up a message and just continue with the general web service, web service actions. So then there is another EC2 instance or it could be just any application server or worker server which will process those individual messages. So the problem with this is, imagine you have about 100,000 messages pushing into the queue every day. So you need to make sure that these get processed immediately as soon as possible. The other thing is you need to spin up a number of workers to make sure that it gets processed as soon as possible. So the downside of this is you spend money on spinning up the instances which is going to be running for a while. The other thing is there is actually a delay in processing. So if your queue has about 100,000 items, it's how soon the worker can process an item is the speed of the completion. So in general, this is what we actually had the problems. At least one worker was running to make sure that we don't spend time spinning up one instance whenever there's an item in the queue. The other problem was making sure that we manage configurations for all instances. So when you have multi-tenant application, you need to make sure that a worker is configured for all tenants. Then the other problem is making sure that you sync all those configurations across. There are a lot of tools that you can use for server orchestration, but still for all, it's a pain point where you need to actually have someone do that for you. Then the other problem is parallel processing is expensive.  This is arguable because the cost is not that bad these days because the spot instance offerings and all, the cost is actually minimum. It's more or less of the overhead that you spend with the staff and the resources allocated for this. So what we changed, this use case is basically, we have used a lambda for several use cases. Our application, if you talk about the application itself so that to add context to the diagram, this is a marketing automation tool where you have connected mobile applications for each customer. So each of these customers, the most simplest explanation is analytics where whenever a customer visits, let's say, for example, if you have been to Harry's here, so Harry's is one of our clients. So the mobile application, whenever you visit a campaign on the mobile app, we actually record those views and how much time they spent, how many redemptions were done for a particular campaign or coupon. So all this tracking is done early. It was just a rest in point where the mobile actually pushes the data and we actually record it. But now when you offload it to lambda, what happens is the mobile directly pushes it to a topic that is pre-configured on SNS. And we would actually have one function taking care of the data collection. So the advantage is when you have 100,000 users pushing data simultaneously, it's quite fast and since we don't actually have to worry about the resourcing, the functions get triggered off quite quickly. So at an average, if you compare this to a traditional structure where we push all these analytics messages to a queue, then make workers process them. 100,000 users multiplied by the number of times they visit the app. It's a lot of messages. Processing it in this manner, we make sure that whenever something goes and hits the SNS topic, it gets processed immediately because you can actually make sure that the lambda function is subscribed to the SNS topic. So that's actually fast processing for this. The other thing is what we use this for, the SNS gateways. So in general implementations, regardless whether you use PHP or Java, for most SNS gate integrations, for now we have integrated with Clickatel and Twilio. So whenever we need to add a new driver, we need to actually implement the driver and make sure that the code supports it. For now, once you've changed it, what actually happens is this, for each driver, there's a separate topic which we actually send a message using the AWS DK. So nothing changes except for the topic. So each function is a standalone function on lambda, which sits for each SNS driver. So this is actually an isolated implementation of the SNS driver. So we don't actually depend on third-party libraries or anything on the application itself. So this can be managed individually. If something actually is stopped working, or if we have a bug, we have an alternative fallback. We could just quickly switch the function without actually deploying the whole application to fix that issue. So this is just the standard architecture that we use. Our plan is to basically move as much as functions possible because, when I started this, what we call as adopting serverless ones, because we have a monolithic structure right now, but we wanted to use serverless to offload certain aspects of the application. We couldn't rewrite it for microservice, although it's a hot topic these days. You can actually rewrite an application overnight. When you spend two and a half years trying to shape the product, you can actually go and convince your management that you need to rewrite to support microservices, just because it's a hot topic. So what happened was we started offloading certain functions so that we could just make sure that we decouple whatever we can, and the code remains the same. So a few advantages we had from this was one, analytics data didn't actually hit our servers directly. It was just going through AWS SDK to SNS and just data collectors were pushing it to our DBs. The SMS drivers as a service made quite easy for us to add additional SMS drivers on the run. And quickly switched between them using just, we just need to deploy the configuration because the topics changed and the configs actually changed. Right now we also use it for push note processing and processing bulk emails. Our idea is to actually move as much as jobs to serverless in the future. So, thanks. Yes, but the thing is, right now we offload to Lambda. The processing that is quite less than that. We don't actually run for functions more than 100 milliseconds right now. So, large or long processing at Cronjabs is not actually a fit right now. Although we are processing push notes, it's quite different because we have a queue which holds the items. And then there's a function that runs for five minutes. Yeah, then it continues, run, yeah, anything else? Thank you.


They're saying that Asian women have a 80% decrease from their middle-tier representation to their top-tier representation. I believe Asian women tend to be more hyper-sexualized in all industries. I'd say my biggest piece of advice is the company. You would want to enter the top of the pyramid, but I guess statistically, this study is showing that it's unlikely. But how does that make you feel? I feel that for our generation, it's more important to let our assertive personalities shine through and maybe tone down the looks a little bit. They feel like they were put in their sort as decoration. I was in the South, hated my life there, hated where I was, hated my job. And I was... There's some nice people in the South, by the way. Dang, I can't believe you said that in the bottle, bro.


Hello and welcome to your Q&A here in Tetro. Okay well we've got loads of questions and the man beside me, yes he's the man leading at the moment, Leon Madson. So first of all how you doing enjoying the sunshine? No, not today. It's really, really hot today. Yeah if you're wondering it's literally boiling here. It is so, so hot. Okay so thank you so much for all your Twitter, Instagram, Facebook questions. We've got loads coming in. Liam would like to know that who is your best friend? I don't know whether he's like offering to be your best friend or he just wants to know who's your best friend between the Speedway riders? I have a lot of friends so not any particular best friend maybe back home. Yeah okay good choice, good choice, all right. Steven on Twitter says, so what would you do if you did not ride in Speedway? I haven't thought too much about that because I always wanted to race Speedway so but it's a good question you know. I think if I didn't race Speedway I probably would be sitting in an Airbus 280 now and flying around the world. Okay so you'd be a pilot? Yeah it's one of my big dreams as well and maybe I will do it one day when I stop my Speedway carrier. Interesting, interesting, all right. Elliot says, who's got the best haircut in the Speedway Grand Prix series? I said Freddie Lindgren, who did you say? Myself. I'm just hiding it. It's amazing under there. Okay Dan says, did you ever expect to be in this position at this time of year in your first season? I mean we probably ask you that about 50 times a weekend don't we? Shall I just ask are you getting sick of that question? No no I'm so surprised. Yeah I don't know it's as I said beginning of the season that I am here to fight for the championship and when I said that in winter time I know a few people they were maybe a little bit laughing at me like oh you know take it easy it's your first season but I knew what I'm capable of and I am here now and I'm in a good position but it's a long way to go. Okay favorite exercise to do to train for Speedway? I can say. Okay great. Do you like hot or cold? Hot for sure. Day or night? Both. Pizza or curry? Pizza. Yeah good choice, good choice. All right. With pineapple. Oh now many people get angry. That's just wrong. Okay yeah yeah okay why are you always so happy and energetic and go on Facebook once to know? Why are you always so happy? Because I'm just enjoying my life you know I'm healthy, have a healthy family and I'm here now in a GP. I try to get in here for so many years and now finally I'm here so of course I'm happy. What is not to enjoy? Yeah exactly. Okay Anders on Facebook says what goes through your mind when you're out in front and you've got three bikes behind you? Thinking how it is in the back. No you don't. You're thinking how many laps to go? You're not really thinking too much in the track that's a really good question to be honest because if you start to think on the track it sounds maybe a little bit wrong but if you start to think too much it's not good.  Yeah for sure. At the start line do you start thinking then or is that just literally the point where you switch off? Well that's probably the place where you have to not think. In that moment when the green light turns on you have to be in your own focus and ready to drop the clutch when the tapes go up and I tend to do that really fast sometimes. Do you ever think about that moment before you go to sleep? Is that in your dreams at night? That what? At the gates. Do you think about that? Do you dream about that? Is that in your mind off the speedway track? You know I'm thinking about speedway very much and I'm preparing for a meeting like today much more than people might think. I study a lot like because I haven't been on this track before in total so I'm watching a lot of videos and I'm studying the track a lot you know how where to race, where to be on the track and etc you know so much more than people might think. Okay all right last question because we might melt out here. What is your favorite speedway league? My favorite speedway league it has to be Poland. It's the best in the world at the moment and it's so intense and there's always full stadiums and I enjoy to race there. All right thank you so much. Okay that question from Jimmy that was your Q&A with Leon Madsen. Ciao!


the visualization team in the BlueBrain and HumanBrain project today. And the second part is more forward-looking and connects right to Thomas' talk about interactive supercomputing and what our bigger vision is and how the current work actually fits into that bigger vision. I mentioned BlueBrain and HumanBrain project, not everybody might know about it, so the BlueBrain project is a project that is funded by the Swiss government and that started in 2005. And in 2011, initially it started as a small, typical research project. And in 2011, they realized that doing this large goal of simulating the human brain needs more solid infrastructure than just one small research project and they started to hire more engineering-related people. This is also the time when I came on board and a few other people in my team. And this work then eventually led to the HumanBrain project which started in 2013, which is funded by the European Union and really runs alongside the BlueBrain project. So the two are having the same goals but different funding and complementary funding. In the visualization team, we are in total 10 people, mostly software engineering people. We also have one designer which helps us a lot with the media design for communications and things like that. And the interface internally in the project with different teams in the software engineering field as well as in the neuroscience research. And then outside, we of course have collaborations with different groups in the human brain project and other external collaborators such as KAUST or the University of Zurich. Our mission in the visualization team is really what somebody else characterized as the 3D of visualization which is debugging, dissemination and discovery. Most of the time visualization gets put into this box of dissemination, you just do pretty pictures and that sells the project. Dissemination is actually as well a scientific goal. So we do quite a bit of work to produce figures which then go into scientific publications which help to show a certain scientific background in a very clear way. But more importantly, and that rarely gets published is the debugging part. So building a brain is inherently or building an infillico brain is inherently a 3D problem and you can capture quite a lot of the algorithms you develop there and test them automatically. But at the end of the day, you still need to look at the data to see if that makes sense because we regularly have the case that people get access to a new type of visualization. They see the data for the first time and they actually realize that they have a bucket or where and then of course that never gets published because you don't publish what bugs you had along the process but it's a very important part of visualization. And the last one is discovery and that's really the hardest where we aim to give tools to researchers to find things they didn't expect in their data but that requires very mature tools and flexible tools to make that happen. Our customers are, as I already mentioned of course the neuroscientists but also the other software engineering tools who for example build the high performance computing tools to build these models and then have to make sure that they work correctly. On the tool side that's the applications I will show you a few pictures and explain what we are doing. We have software infrastructure to run on large scale type display walls and then different visualization tools for geometrical rendering, volume rendering and ray tracing. And of course, oh dear that doesn't come over on the trajectory at all. The point of that slide is really to show you why we have these high level applications. A lot of the work we do actually is to build smaller components which then we reuse in different tools and also are reused not in visualization related to it. So the green areas really circle the application areas and then the yellow ones are lower level libraries and components we maintain and develop which do things like data access, data models, just general facilities for synchronizing distributed applications and various other things. Of course, we also have some hardware. So the first one I will talk about is a high resolution type display wall which we currently drive from a single machine. The point of that is really to say this type of thing has become very affordable to previously and also relatively easy to maintain. I will show you how that looks like on the next slide and we also have a large scale stereo display which we use for various things. And all that is of course driven locally in Geneva where we have these systems installed and then our main facility is at the Swiss Supercomputing Center in Lugano which is a few hundred kilometers away and there we really have a heavy lifting analysis cluster which is tightly integrated with our supercomputer and data storage facilities. Then on display cluster which is the software which drives our tile display wall so in the upper corner you see a video running from one of the presentations we did. We really use this tile display wall as a collaborative workspace and it enables very new ways of working together with data. So for once, simply due to the size is much easier to do group work in front of it than in front of a computer. You all have been there I'm sure if you have to work on a certain problem in front of a monitor you can get two or three people and then it gets really crowded and it doesn't work anymore. In front of that wall you can really put 20, 30 people and they can all see what's going on understand what's going on and a few of them contribute concurrently to it. So you can really think of that wall as a large screen iPad type of interface that has a touch screen which has also shown very successful because it lowers the barrier to entry for people. They, everybody knows how to use these devices and it works really very similarly and people are simply not afraid to use it. And then of course it enables you to integrate all kinds of media. So just starting with higher resolution images. One great feature of that is if you load up megapixel or gigapixel image you can actually zoom in, see all the detail and while you step out you're not losing the context very rare but you really can stay in the data and have what we call focus and context so you are aware where you have been in the image you've zoomed in but you can go up close, see a certain detail without losing where that exactly is located for example in a brain region. And then we have of course more interactive integration as well there's a desktop streamer application which just allows you to stream your laptop screen into a window on that wall or and that's based on a library which everybody can use and integrate into their applications to stream them directly to the display wall and receive events back to have an interactive application running there. We are currently extending this type of infrastructure with similar sized walls in Geneva and other institutions in the human brain project have installed them as well as well as KAUST is using the same software on their existing tile display walls. And as the next step we are planning to build similar installations in the spirit that we are we want to use consumer hardware which is affordable to build hybrid systems. So really what cave two has begun that you have a system which you can use as a 2D collaborative workspace but also as a single user or a few user hybrid immersive system where you can really go in stereo and your data have that tracking and really immerse yourself in the data. Then onto the applications we have so Artineuron is our oldest and workhorse application which we use to generate a lot of the images for visualizing smaller in silico circuits. So the image you see on top is it really comes not well across on that projector but is a single neocortical circuit with simulation data. In the middle you have a video which we actually rendered out in 4K and if you display that on the 4K screen or on the display wall it really comes out beautifully of about 7,000 neurons and having a simulation running there which you can barely see on this video now. And the bottom image is for example one of these scientific illustrations where we simply take a single neuron and show all the sign ups on that neuron and how they are distributed on the morphological skeleton. The second tool is a volume rendering tool which we developed in collaboration with the University of Zurich. This uses an out of core rendering or a data storage engine which really allows us to use unlimited resolution. So for imaging data we have visualized over four terabytes of data with that engine but the really cool thing about it is we also use it to do visualization of differently structured input data. So we have one data source where we can just load measures in and then produce solid voxelization of these measures. That's the middle image here and that's really more for artistic purposes. And the bottom video which is playing there is a more interesting one that's a recent visualization we did of the 3 million neuron simulation and that just shows the spike density in a given time window of that simulation. And the new thing about that is and that really leads then to interactive supercomputing is that the volume which is rendered there is not something we pre-compute, store on disk and render it but we really take the simulation data which is just 3D events in space and then compute the volume on the fly and we compute it in such a way that for smaller circuits than that that you can do that really interactively. It's like a Google Earth of things when you're looking at the volume from the outside you just compute the low resolution and then you zoom in these filters, the drone where they will actually re-compute the volume at a higher resolution and you get all the detail you need. And then the last part where we started investigating recently is interactive ray tracing. On one hand that allows us to do physically more correct rendering. For the middle image you see there is from a publication we did recently where we did an in-silico fluorescent light-sheet microscope where we use ray tracing to reproduce images of our in-silico data to make them comparable to biological in-silico light-sheet microscopy and comparable in a way that we really do a physical validation of these images to make sure that the energy going in and out is correct, that the fluorescence spectrum is correct and all these type of things. That allows us then to, and this type of work will allow us then to do more validation of the in-silico models against biological experiments. And on the other hand, the other reason we are looking into ray tracing is not only the physically correctness but also that due to the nature of the algorithm it scales much better with large data sizes and as Thomas has said before we will have petabytes and exabytes of data to visualize. So that's why we really think this is one of the more promising visualization algorithms in the future. Now to the forward-looking things of interactive supercomputing. So it's really motivated by the limited amount of storage bandwidth and size which is really becoming more and more of a problem in interactive supercomputing. We had more floor earlier today. The thing which is really mentioned there is that more floor brings the compute power on an exponential growth curve which doubles roughly every 18 months. Nowadays it's a bit longer but still it has been 18 months for a few decades now. But the bandwidth you get to disk storage or main memory actually doubles only every 45 months and that's one of the problems by main memory is now becoming so expensive for supercomputers and it's even worse when you look at the storage systems. So a lot of the computation of the HPC machines today they actually have to very selectively decide beforehand what type of data of the simulation they want to write out of the simulation to disk for later analyzers. And that really puts computer science a little bit in versus existing paradigms is that the computation and that's a little bit of a provocative statement I'm doing here but the computation of things is going to become free. It's gonna be much cheaper just relaunching a simulation, recompute your results, then do in situ your data extraction for the concrete problem you're trying to look at and then stream out the data to a visualization node to render that instead of pre-deciding what type of data could be potentially interesting done that to disk and then do your analyzers the day later. Which brings us, which brings me to the second point why we really think that interactive supercomputing is the way to go. High performance computing is one of the last domains in computing which is still doing batch driven work for what is essentially an interactive exploration problem and when we actually managed to bring interactive supercomputing to HPC we will see a hugely improved time to solution because suddenly scientists can do their experiments and their workflows and work iterations and hours instead of days today. The big challenge of it is of course that we have a very complex system you saw Thomas talk with all the different memory integrations, accelerators, different chip architectures. So there will be very many components on a very heterogeneous environment and that has then to work in a way that it's robust. So meaning that if there is a bug in my volume rendering engine that it doesn't bring it down the simulation but that keeps running. It has to be interactive of course. So the latencies and the way you interact with the system has very different requirements from HPC software today. And one of the additional goals we have is that it also has to be usable by non computer scientist people. The good news is we have a couple of years time to attack these problems. How are we seeing to get there? So one thing which I touched already in the current talk is really we really see due to the heterogeneity of a lot of the problems we have is that we need to push this idea of competentizing or modularizing applications much further and then have to find a way of dynamically connecting all these components. So for example, you have a running simulation. I first dynamically connect some type of visualization to it which gives me an overview of what's going on in the simulation. And then I see that there's a certain problem which I want to analyze further and then I maybe want to connect a Python shell to it to run some statistical program on a certain region of that simulation. One programming model which has shown very successful in the visualization field and tools like PyroView and Visit is really this programming model of data flow programming or event driven. So all these components are just little filters which can accept certain type of input data. When we get an update, they perform your computation and they push that in a different form out for an output port to a downstream filter and then you can connect this filter to either run on the same system if you don't want to move the data or you configure it at runtime that they run on different system. For example, if you want to have the visualization closer to the user for increased interactivity. One key insight is there which is often forgotten. For us, the simulation is also just a filter. So the output of the simulation filter would be the simulation results and there can be different type of structured output data. For example, one I'm going to show next, just the spike events or voltages if you have a more detailed neuron simulation or concentrations of various things. And the inputs of that simulation would be what is classically called a steering of simulation. So in that respect, the simulation would just behave as one filter in that whole system. And I'm going to give you one example which we will show at the end of September of the HPP Summit, we are preparing that right now. And it's very hard to read again. So on the yellow box up there, you have a nest simulation which will run at not interactive rates, but fast enough that steering actually makes sense. And that nest simulation produces spikes. And to get access to that data in this dynamic and interactive fashion I mentioned earlier, we are using a library called music which is MPI based and allows to interface different simulators like Nest, Neuron, and the third one which I forgot now. But music currently uses MPI. So MPI in its nature requires you to statically configure your setup when you launch your application. And to work around that limitation from our point of view, we wrote a proxy application which interfaces with Nest through music. And then to the outside speaks a more dynamic protocol which is based on zero MQ, which is an industry standard library and some higher level extractions libraries which allow us to get that data out more easily. And then you see on the up level, the first output thing is that you stream spikes out to any number of visualization applications which we can dynamically connect to the running nest simulation at any given time. And then they can visualize it like in the volume rendering movie you saw. Then to actually control that whole, on some level we will have a specialized, very likely user interface application where you can for example, change the transfer function of this volume rendering application. Or if you also use the RT Neuron measure rendering application that will use the same transfer function as well. So you have one user interface, you change the transfer function and that changes both your measure rendering and your volume rendering. This user interface will also get input data from information visualization type of application which is called Neuroscheme, where I have a screenshot here which allows you to view the Neuron circuit in a 2D environment in a very graphical interface. So what you see here for example, is a graphical representation of the whole circuit. And you can see a pyramidal cells on the left with the little pyramid symbol, the different layers and you can just select different layers and then this selection is communicated back to the user interface. You can also on this user interface, for example, zoom in and see all the mini columns making up the in silico circuit and then just select a few mini columns and then the selection is communicated with user interface. The user interface can then query the music proxy, what type of stimulus for example, the Nest simulation can provide and then you can take that selection, the stimulus and tell Nest, now please apply that stimulus to the selection I just made in that user interface and change the simulation that way. And with that, I'm going to wrap up. I can go into way more detail on the interactive supercomputing and how all the technical components work together and how we see that going forward if you're interested. But what we're really trying to do in the BlueBrain and HumanBrain project is to create production-ready tools for non-computer scientist usage from different components. We have different rendering engines of course because we are a visualization team after all. We provide these volume rendering engines and of course other type of visualizations and 2D renderings and stuff like that through high fidelity installations which we build from commodity hardware. Since this whole problem of interactive supercomputing is not just for neuroscientists in our project, we have a very aggressive open source roadmap for the volume renderers available on GitHub. Various other components are available and other things which are not yet available, we will make available over the next month. The point of that really is to have a lightweight way of collaborating with other teams both in the neuroscience field but also outside of that field to really establish these tools as one of the ways of doing this interactive supercomputing or when it doesn't work to use other tools and then integrate them in our workflow. And we have a long-term version for interactive supercomputing which the most challenging part really is it's a disruptive change for high performance computing because it changes the way you work with a supercomputer, it changes the way you already starting, you launch jobs on an interactive supercomputer, you launch jobs on a supercomputer to be interactive and that for example has been seen in the PCP process where we are working with the industry to make these type of things possible. The interesting part is that neuroscience and to my awareness, the human brain project of one of the first project in neuroscience which tries to bring the simulation driven approach to that field, neuroscience is relatively new to HPC so we have an opportunity here to try out and establish a lot of these interactive supercomputing ideas in a new field and not in an established field like material sciences where the simulation engines are already well-established and all the workflows are well-established. And then of course we try to implement that interactive supercomputing vision through collaborations throughout the field both in neuroscience and of course in the HPC field for other domains. And with that, I conclude my talk with the GitHub URL where we have all the open source software available and my email address in case you have follow-up questions later. Okay, as we get ready for the next speaker we've probably got time for one really quick question. Hi, Stefan, here. So with the volume renderations I was just wondering if it's very consistent with data set to data set. Is it just because of the power of the simulation you're able to achieve that? Or is it because of the availability of the quality of the data that decides the volume renderations? I'm not sure if I, I try to answer a question when you tell me if I answer it correctly. So the reason we use volume rendering there is that the simulation was three million neurons and we are currently actually scaling up that by two orders of magnitude and there mesh rendering doesn't make any sense anymore because you only have two million or on a big screen 24 million pixels. So having aggregate visual representations of that from the simulation data into volume rendering makes sense for us because then we have a good way of having a scalable way of visualizing this data and it's qualitatively exactly the data being simulated. So we had their spike density so we can actually say that red means we had in the given time window in that region of space five spike events if we need to do that. Okay, thank you. Thanks again.


Africa has seen considerable growth over the past two decades, but the number of jobs created has been very far from what is needed. And because of that, poverty has fallen much less than we would have hoped for. And we've been asking, why is there so little industry in Africa, and how can policymakers manufacture economic transformation? We've identified three key drivers of firm productivity. The first is exports, the second is agglomeration, grouping activities together, and the third are the capabilities of these firms. All too often, these dimensions are lacking in Africa. Four lessons stand out. The first is the commitment from the highest level of government is required to ensure coherence and accountability. The second is about the need for focus, such that actions are taken to resolve concrete constraints for private sector development. Third, the need for experimentation and learning, experimentation both on the side of the private sector and on the side of government, such that successes can be scaled up and that areas where interventions don't work are avoided. And then finally, the need for feedback from the private sector to government, such that lessons can be learned for the future.


Hey everybody, welcome back to Magic Orthodoxy. My name is David and this is a Magic Review. Hey today, we're gonna look at Numbric from Taiwan, Ben. What is Numbric? Basically, it is a 5x5x5 speed cube, rouges cube, if you've ever seen the speed cubes, and you're able to hand this out and then each one of the colored squares, as you can see, has a little number on it. You can tell the spectator to select any number they want. You're able to take the cube back, and then through just a series of questions, you're able to tell the spectator exactly what number they selected. You want to see what the routine looks like? This is it. All right, so that was Taiwan, Ben. This trick, Numbric, it's self-released, self-published. So this is coming to you from Taiwan, Ben. And you might remember I've done a couple of other Taiwan, Ben trick reviews in the past. This one, so far, this one's my favorite. This is my favorite Taiwan, Ben trick that I have reviewed. All right, so a couple questions we always ask about all of our Magic Reviews, and the first of which is what is in the box? What's in the box?  Is it what I thought? You know what first? I really didn't know what to think. I didn't know what to think. I saw the review and just was, wow, if the trick lives up to the hype, I don't know what to think. And it's one of those things where they fool you with something that's so old it's new again. So it was a pleasant surprise. How are the angles? There are no angles. Can it be inspected? Absolutely. In fact, you have to take this cube and hand it to the spectator to begin with, and really there's nothing else to find. So it's a beautiful self-contained effect. What's the overall quality and production value of the DVD? You got a moving camera and microphone. You got multiple angles. You got background music. Lots of different shots. You're getting the shots where he's doing the trick for the spectator. He talks to the camera. There's an over-the-shoulder table shot. There's a chest level high shot. There's over-the-shoulder shots while he's doing writing and you can watch what he's doing. The camera is constantly moving. It's HD. It's beautiful. You could sit down and just watch this DVD like you would a movie. It's beautifully shot. It's got great music. He does have an accent, of course, and it is a thick accent, but it wasn't an issue for me. Okay, so it wasn't an issue. Is it well made? Is the 5x5 speed cube well made? Absolutely. And if you don't already know, speed cubes are coming back. It's just a thing now. The kids, the kids today, they're into these things now. You can look them up on Amazon. There's tons of them. So this is a 5x5x5, okay? And it's high quality.  I know how to make, you know, the checkerboard. That's all I know how to do, or that's all I remember how to do. But I did that and, yeah, it moves, it glides, it doesn't lock up. It's a great, great product. How much practice does it require? The practice that it requires is math, okay? It's a math trick. And so you learn the secret pretty much in the first five minutes of the DVD. The DVD is 32 minutes long, but you're going to learn the trick really in five minutes. And you could almost just start doing it right there if you were that good. I mean, I am not good with math, but he is teaching you multiple routines on the same principle. So if one routine doesn't work for you, he's got another option. You know, if math is difficult for you, then he's going to show you, okay, well, here's another option. If memorizing the order, the sequence, whatever, if that's difficult for you, he's got another option. So he's giving you lots of outs.  He's going to walk you through it. He's going to relax you. He's going to make this comfortable for you. And it would involve a few more props, you know, like maybe a pen and paper, but it's angle-proof. You know, he's got it down to where he's kind of worked all the kinks out. That said, I did say that it was an old-school trick and it is very old-school. And it's so old-school, you've probably forgotten about it. My kid, a couple years back, he got the Chris Angel Magic set for Christmas and this trick is in that. That's all I'm saying. Okay, how much setup and reset is there? It depends. Like if you are good with the speed cube, like if you can figure it out, it depends. I mean you could do the trick without messing the cube up and just do it that way. Or you could mess the cube up, hand it out, bring it back, reset it, show them the cube and then mess it up again. So it's up to you. It's up to your comfort level. Like I can mess it up, quote-unquote, you know, I can mess it up to a logical sequence that I can undo. And if you can just learn how to do that, I think the setup is instantaneous. There really is no setup or reset. All right, so what are the positives? Positives are you are able to take this classic of magic into the 21st century. It's a trick that a lot of us have forgotten and just given it a wonderful new facelift with the idea of using a speed cube. Something that is actually becoming relevant again. I think anytime you can take old-school magic and make it relevant again, I think it's wonderful. Anytime you can take something and reintroduce it to your audience and magicians, I think, is brilliant. And I love the idea of recycling these old tricks. And so I think what Taiwan Ben has done is wonderful, really. Another thing is on the DVD itself, there's a PDF and he walks you through, if you have to, how to take it apart so that you can put it back together. Like, if you get it and you're like, oh, I can't solve it, don't worry. He'll show you how to take it apart so that you can put it back together again. In numberic, Taiwan Ben has made a wonderful piece of magic that'll be instantly familiar to a lot of veteran magicians, but also surprisingly new to a lot of younger magicians. And I think taking this piece of magic and bringing it forward to the 21st century is wonderful and the DVD is awesome. He's giving you so much. This is a great, high-quality trick. All right, so what are the negatives? You know, like we say around here, there's negatives to everything. These don't have to be the negatives that you have found. They're just the negatives that I see. So the negatives for numberic is math. I don't like math. And that's all I got. Math, just adding. I get nervous and flustered. When I have to do math in my head on the fly in front of people, I just get flustered. Even if I'm writing it down, it doesn't matter. I'll just second guess myself and I won't be confident because I am not good at math. There, I said it. I'm not good at math. All right, so what is it with your money? It's 50 bucks, 50 dollars. So that might be like, oh, you think, oh, that's a lot of money. It is a lot of money, but you're getting this wonderful DVD. You know, you're getting 32 minutes of information. You're getting at least four or five routines where he's helping you and saying, look, if this one's not your speed, then try this one. And the speed cube itself, like I said, it's beyond description. It is a beautiful, high quality piece of magic. And put this in your magic box and take it with you. I think it's, you're definitely getting something that's worth your money. I mean, and the speed cube is large enough that, you know, in a parlor setting, they could see it. So I think there's so many different opportunities for this, even if you were a high school or a college age person, you feel this in your backpack. Nobody's going to think, you know, why do you have a speed cube in your backpack? It's a thing that people carry now. And so I think it's totally worth your money. Who would like this? Anybody that has parlor, anybody that has stage, anybody that has corporate magic, walk around. Like I said, the list goes on and on and on. There's so many different opportunities for this. It is mentalism. Okay, I will give you that. It is mentalism. So know your character. Know your character. If you're a card magician and you do cards, it might not make any sense if you whip out a speed cube and say, I can do mentalism. So just know your character and just ask, is this product right for you? But I love it. Okay. So that is Numbrick from Taiwan Ben. And I want to thank Murphy's Magic for providing this for me so that I could review it for you. And if you would like to purchase it for yourself, you can find it at your favorite Murphy's Magic retailer. Thanks. Bye.