The floor may not be in your old box. It was a couple days before Christmas, and realizing that I haven't gotten my grandmother a gift, I asked my mom if we could go somewhere like an antique store, because she liked that kind of stuff. It was about a 30 minute drive to the place. It looked like it was in horrible shape on the outside. But on the inside, it was beautiful. There was a marble floor, newly furnished shelves, glass display windows, and it was quite large. I strolled, sighing, realizing how long it would take me to find something in all of this. Before I knew it, anxiousness turned into relief, when I saw an old painting. The framing was beautiful, and the picture was even more stunning. It was a cabin, surrounded by huge trees, and there seemed to be a man, looking out the window. I knew she'd like it. Hell, she'd like anything I got her. I grabbed it, and what I thought was the owner was standing right behind me, as I turned around. Startled, I jumped back and saw an innocent old woman. She told me to follow her, and she took me to the counter, where I paid for it. I thanked her, then sighed once again. I had to start yet another journey to find my mother. After about 15 minutes, I found her, and we left shortly after. Luckily, my grandmother wasn't home when we got to the house, so wrapping and putting it under the tree wasn't a big deal. Come Christmas Day, she opened it. Simultaneously, we smiled. She thanked me, and she hung it above her bed later that day. Something seemed off. The man went from standing at the window to walking out the door. I just brushed it off as nothing. A week passed and I looked at the painting again. He was now walking down the stairs. I was getting a little creeped out, but reassuring myself by saying that it was probably a cool feature about the painting. Well, another week passed, and I took another peek at it. The man seemed to be moving closer, as if there was a camera, and he was moving closer to the person taking the picture. I told my grandmother about it, and she agreed with me. Now seeing that the man was moving, reassuring ourselves, we almost forgot about the picture and the man altogether. Probably because she took it off the hinge and placed it behind her bed. Well, weeks turned two months. And for old times' sake, we pulled the picture out from behind her bed. We were mortified. We could see the man's face, clear as day. He had dark bags under his eyes. It looked like he was trying to smile, but was missing many teeth. But what stood out most of all is that his left arm didn't fit the rest of his body. It seemed too feminine, and blood seemed to stream down it. He had long silky hair, and seemed injured in a sick, twisted way. Well, another week passed, and we were planning for my grandmother's funeral. The people who did the autopsy said that she died from a heart attack. I know better though, because the man. He is nowhere to be seen in the picture. I know that he is loose now. I know that I am already gone.


As we record this the next expansion to the Elite Dangerous Universe Odyssey goes into public alpha testing in the next few days. In this video we're going to go through every major feature that we know about so far that is coming to Elite Dangerous Odyssey. If you enjoyed this video hit like and subscribe and remember to click the little bell icon and select all notifications as that stuff really helps the channel and if you'd like to help further support our work you'll also find us on Patreon, links to everything you need are in the description below. So our first headliner then is the new planetary rendering technology that covers all landable planets even extending to the current landables for players who don't own Odyssey. The tech is designed to offer finer details up close as well as being much more realistic overall than that currently shown and where appropriate it incorporates the inclusion of multiple separate geological feature rich zones on a planet what the team refers to as geomes. Odyssey players will gain access to billions of new planetary surfaces that feature tenuous atmospheres as well as new organic lifeform POIs featuring new simple biological entities that are somewhat akin to plants. And the biological discovery and sampling will play into a new biologist elite rank coming to the game.  Where the local conditions such as gravity or temperature don't allow for movement or survivability you will be prevented from leaving your ship or SRV but bear in mind that you're on a planet in motion in a simulated star system so those temperature conditions can vary wildly between day and night for example. Get caught without a ship or SRV when the wrong sun comes up at the wrong time and well you could end up deep fried in your space suit. Talking of spacesuits, to help you survive outside your ship and SRV you'll now have access to three types of role specific spacesuit alongside the current in-ship suit that we're used to seeing. Those suits are the Artemis which is geared towards exploration, the Dominator which is geared towards combat and the Maverick which is geared towards scavenging. The suits are complemented by a range of weapons, equipment and consumables that will allow you to further specifically tailor your characters functions and utility. The suits, their weapons and equipment can be upgraded in a similar fashion to the way engineers are used currently to upgrade ships and you can now own as many of the suits as you'd like and they can be equipped whilst in a ship or SRV as needed. We know that suits are fitted with a functioning flashlight and that a night vision system can be added as a modification somehow. I mentioned weapons, there are three distinct types that closely mirror what we're used to dealing with in ships, those are kinetic, energy and plasma. Kinetic weapons like machine guns and pistols are good for damaging suits, armour and the fleshy squishy bits enclosed within. Energy weapons like lasers are good for damaging the shields that the suits come equipped with and plasma weapons are good at damaging both but the outgoing rounds are slower moving so their use requires more skill or indeed less range to be effective, after all someone's not going to dodge your red hot glowing ball of plasma if you unload it directly into their eyeball. We also know that there are three types of grenade arriving with the new expansion, one is a traditional explosive grenade to inflict physical damage, then there's an EMP grenade that primarily damages shields and then a slightly more unusual grenade that deploys a static energy shield around itself when thrown that players can use to shelter inside from incoming fire. There's a new starport based social hub and communal area where you can pick up missions, interact with NPCs and meet other players, at least some of these social hubs will have a real time view of the rest of the station interior where you'll see player ships arriving and departing and landing badly, a nice extra layer of pressure there in knowing that the whole bar in the space station is watching your cutter slide about while looking for the sweet spot where the landing clamps will engage. The starport social hubs contain a number of services and shops that help equip you and your newly discovered bipedal locomotion, those services are inter-astra which acts in the same capacity as the current shipyard system in stations allowing you to buy and equip ships. Here's supplies, this is essentially a shop where you'll buy all your suits, weapons and consumable items. Vista genomics, where you'll hand in your collected biological genetic data in a similar fashion to how explorers use interstellar cartographics currently, patrons are politely encouraged to please wear gloves when delivering their gathered genetic material. Inside you'll also find Apex Interstellar which acts as a kind of space taxi service taking commanders who don't own a spaceship or don't want to use their spaceship somewhere that they want to go and Frontline Solutions which is a service you can sign up to to become a kind of gun for higher mercenary in ground combat scenarios including what Frontier have described as large scale battles. All new on foot missions will be available for commanders to take on and as with the current game they'll range in nature from assassination and combat heavy missions to courier and stealth missions. The missions are often approachable in a number of different ways and it's down to commanders to decide what methodology to best use to complete a mission in a fashion that best suits their play style and the kit that they have available. To help complete the various tasks available commanders can use a variety of tools and equipment including the energy link which can transfer power in two modes siphon and overload. We've seen this device being used to overload a door mechanism forcing it to open and siphon mode can be used to transfer energy into a player's suit battery system for use. The arc cutter delivers a powerful cutting beam that can be used to remove panels to gain access to a mechanism's internal workings, a profile analyser, a device capable of scanning an individual and cloning their access control credentials for use in more nefarious scenarios. The handheld genetic sampler, how this device specifically works as of yet is unknown however we do know it's used in the gathering of genetic material from the new planetary organic sites. Alongside these handheld tools commanders also have access to a number of consumable items including a medical kit for topping up player health and a device known as an e-breach. An e-breach is a very expensive one and done illegal consumable item that allows players to hack, override or otherwise gain access to a computerised system or mechanism that they shouldn't really have access to. The new on foot missions will predominantly centre around permanent planetary settlements all of which have accessible building interiors and feature NPC civilians and guards. There are a number of different layouts to these planetary settlements but they will feature fairly clearly marked buildings with specific functionality applied to each building so you should have a fairly good idea where you're going when you get there. Whether you're allowed to go there or how the residents react to you will be driven by your own criminality, your personal standing with any given faction and the background simulations inherent conditions at the time you visit. The NPCs at settlement sites react to outside stimulus for example they don't take kindly to finding a cut access panel on the floor but importantly they don't possess a hive mind that's to say if one becomes alerted in an isolated part of a settlement they'll need to raise the alarm to tell the rest of the settlement that something is up giving you a valuable few seconds in which to let's call it persuade them otherwise. Elsewhere we also know that physical multi-crew is now a thing come odyssey you'll be able to let other commanders into your ship, fly them somewhere and drop them off and whilst in your ship if you allow it they'll be able to avail themselves of any SRVs and SLFs you own. Autolanding on planetary surfaces is about to be a thing we're presuming with the aid of the existing docking computer rather than a new module but either way Frontier have confirmed that if you so desire your ship will find a spot and land on a planetary surface for you. We know that there is some sort of melee attack available but there are no melee specific weapons and we know that Guardian and Thargoid ground installations will be accessible to on foot gameplay. The community manager Bruce Garrido confirmed on the system chat podcast this week that the upcoming alpha test will be what he called a quote staged unquote affair. That likely means that the alpha won't see odyssey's full feature set available right from the start but similar to the games initial public beta test some 7 or 8 years ago now players will be slowly given access to more and more of these features as the test progresses. Alpha has previously mentioned that he believes the alpha test for odyssey will be around 6 weeks long how soon after that process ends odyssey then launches we don't yet know. So that's it the odyssey alpha launches on March the 29th and the face of Elite Dangerous will never be quite the same again. What bit about odyssey has you most excited or what bit do you want to know more about let us know in the comments below. Thanks very much for watching if you enjoyed this video consider subscribing to the channel and take a look at one of our other videos which is linked on screen right now.


Over the last 40 years, more than any other organization, the Foundation, or FI, as it is known to its friends, has acted as a first source, an introduction to the philosophy of freedom. Since the beginning, FI has worked to present younger generations with the moral and economic case for free society. For 70 years, we've hosted incredible speakers and life-changing student seminars. We've created educational resources used in high schools and college classrooms around the country, and we regularly publish some of the most respected original content in the non-profit world. But why stop there? FI has always been the premier organization connecting younger audiences with powerful ideas. But now, with the support of our generous donors, we're bringing a new approach to outreach for the 21st century. We started by redesigning FI.org, where you'll now find an easily searchable archive of our incredible legacy, featuring speeches, downloadable e-books, and every single issue of the Freeman Magazine. Plus, you get access to our latest original content, with new material coming out every day. We're also growing our social media presence and raising the bar for creative media online. And we're working with other creators across the country to help us reach more young people than ever before. We're even building new innovative measurement tools to track our effectiveness and share what we learn with everyone in our network. Together, with your support, we're making the ideas of a free society familiar and credible to the rising generation. And our brightest days are yet to come. We invite you to get involved with us today. Visit FI.org.


We're bombarded, okay, by people that look, they look like they are more beautiful, they have more money, they have a better life, they have a better relationship.  Like we all know that social media is someone's highlight reel. We know logically that they may not be any better off than we are in whatever scenario. But the dumb part about it is, it doesn't stop us from comparing ourselves to them.


Which one do you think hits the highest note? This one? Or this one? How about this one? Maybe this one? How can I forget about this one? Let me know in the comments below. I'm trying to see something.


A couple of warnings. I don't know whether it's a warning or not. This talk is a bit more technical. I presented the same topic in my department seminar, and I didn't change anything because I'm pretty lazy, so. So you might find this bit technical. As I said, it might not be warning for you.  So OK, let's start. My background is I'm doing a PhD in AI in National University of Singapore right now. Before that, I worked in Flipkart for three and a half years. Before that, I was studying in another university in India. So anyway, this work has been done jointly by some of my colleagues in data science team in Flipkart. OK, let's start with the motivation first. So this title is very cryptic, as I understand. So we slowly understand the meaning of it. So motivation, why do I really need to do forecasting? Well, I'm not going to talk a lot about the business reasons. Forecasting is very important for any commerce company. It's not some fancy cool stuff they do. It's kind of bread and butter for them. If they don't do it correctly, then they're quite screwed.  It's very important for the business. So that's obviously the most important motivation. But why am I here? Why do you really descend to me? That's the thing I... So ideally, people don't use deep learning. By the way, this talk is also on deep learning. We use deep learning to do the forecasting. So a key takeaway could be from this talk is how you can use deep learning in forecasting. So if you have some similar problem that you're facing in forecasting or doing some sequential data prediction, then probably this talk might come handy for you. And third thing is normally deep learning doesn't get used in structured data. Deep learning really shines when the data is unstructured because the other methods don't really work well. In structured data, other methods work quite well. So you can argue why you are using deep learning when this is a structured data problem. So again, I don't have a theory to back it up. But as we have seen, the results is much better. So that's a very loose way of saying things. As I come from academia, I really shouldn't talk like this way. But anyway, if I show you the results, it works much better. We really tried hard using other methods also. They work reasonably well. But with deep learning, we got more success out of it. So that could be another motivation. So let's start with the problem statement first. So this is the primary objective. We want to forecast daily sales of all the products that Clipkart sends, which is roughly in the order of, I think, 1 million or something. So the data set you have is for each of these products, we have all the history, like how much it has sold, what was the price on a particular day, what was the offer we ran, what are the discount we had for this product, many other things, actually, whether it was displayed in the home page, whether it got a big banner or small relisted in the home page. So all these things, actually, we have. All these data captured. So given the entire history, now we have to predict the sales of each of these products 30 days ahead. Normally, it's really forecast, but sometimes, depending on the use cases, we do weekly also. So we aggregate the forecast at a weekly level and then forecast. And all these problems are sort of interrelated. So if you solve one problem quite well, then you make good progress in the solution of other problems. As you can see, the price elasticity is something that you have to predict the sales function of price. So that is nothing but forecasting as well. And the gold sick problem, which is the inverse problem of price elasticity, that is you have the sale target with you. You want to know what price you should charge the customer. And the fourth one is predict the sale of new product. So new product is something which we haven't seen before, completely new, like the iPhone, which is going to launch in a couple of days. And we want to predict the sale of that. So it's a very hard problem. Since you don't have any history, there is a very high chance you're committing some behavior. So we are not going to touch the fourth one that much, but I'll give you some motivations on how you can go about solving that also. So what affects demand? This is a high level overview of all the factors. There are many other factors probably you can come up from your own intuition. But I think we have reasonably captured most of the things that affect demand. By demand, I mean the sales. Actually, demand is not exactly sales. If some of you have come from MBA background, you probably recognize. So demand means that actual demand, the maximum amount of sale that can happen. And sale is the actual sale that has happened. Well, I would just highlight two columns here. One is price and visibility. These are the most important drivers of sales or demand. Of course, price is also combined with the discount also, so actual price and discounted price and all these things. So we have many different types of price. In fact, we vary the price over a different time point of day. So you can have some sort of statistics of price, like median of price or maximum price, maximum price over a day, minimum price over a day, all these things. Well, when we started, we tried to do some feature selection engineering, which is a traditional way of tackling this problem. We have all these features, many, many features. And we intelligently come up with different types of engineer features and try to see whether that works or not. And that works reasonably well, as I said. And why is Q forecasting is tough? Because just, see, look at this graph. Sorry, you can't. OK. So in the x-axis, you have date. And the y-axis, you have the sale. So you can see the variability in the data, right? It's extremely noisy. You can't come up with anything which would predict what is going to happen in the x-axis. If you just use this data. Well, I'll just summarize what I just mentioned. So we first started with some time series based model, which is this so-called Arima model, auto-regressive integrated moving average method. So this is the equation. Basically, it says that today's sale depends upon last previous five periods of sales. So time series forecasting is pretty rich area in statistical learning. So there's a lot of literature in that. So you can go through all these things. But I don't have much time. So I'll skip all these things. Quickly go through all of them. Then we started with Cubist, which is another model. So what happened? We cannot do a good job using just one time series and then predicting. We can understand that this is a hopeless case. So we really have to understand what caused that spike in the sale or what caused the trough in the sale. So for that, you need to have some kind of causal model where you have six features. And basically, you train a model using those features as your independent variable and your sale as dependent variable. And I tried many methods. We tried many methods, like Exhiboost, all these popular methods in Kegel community. So you can run all these methods in a for loop and see which one does best and pick that one. It's very easy. I would say very mind numbing work. So all these things work reasonably well. Cubist don't go by them. It's a very simple model. It's a variant of decision tree-based model. The paper was published in 1993, I guess. It's quite old, in that sense. You can think of Qubis as random forest. Just replace Qubis with random forest. Same thing holds. Now we come back, come to the main topic of our discussion, which is deep learning model. So I'll be fairly quick, but also I'll try to give you, I'll spend more time on subsequent slides. But still, if you have some questions, you can go through our paper, which is there in the archive. So you can just download the paper and you can read through it, if you want. Or unless I'm around here, for most of the times, you can catch me and ask questions also. So main contribution of this paper is we propose an RNN architecture. So how many of you know about RNN? By RNN, I mean recurrent neural network. Quite a few of you. Who don't know anything about RNN? OK, I see. I'll give you some slight bit of idea of what RNN is. So how many of you know basic stuff of deep learning?  Quite a few of you. Likelihood maximization? OK, cool. So some of you are able to get more out of this talk. Those who don't understand everything, then you can just go through these things first. These are more basic things. You shouldn't be in this talk if you don't understand this. You're very honest. OK, so our model doesn't give you a point forecast. This model gives a probabilistic forecasting. By that, I mean normally, ideally, the model gives expectation of a distribution, which is a single number. Here, we are giving a spread. So we are saying that the prediction lies between y1 and y2 with some probability. So how it makes probabilistic forecast? So in our model, we find out the estimated distribution of sales. And actually, we find out the actual density function of the sale. And when you have the actual density function, you can draw Monte Carlo samples from that distribution. You can use MCMC, Markov chain Monte Carlo, some other methods, variational inference, lot of other methods are there. If you want to sample from a high dimensional distribution, you can do all those things. And it can get all those samples. And using those samples, you can, when you have all those samples, you can easily calculate mean. You can calculate 90% quantile. You can calculate 10% quantile, everything. And this model is trained on a cohort of products. It is not just trained on only a single product. So because of that, what happens? Some of the products doesn't have much history. So the product, which is just launched, but by the way, we are not covering the case when the product has no history. We are only covering the case when the product has some amount of history, say 10 days at least. So if you have a product which is fairly recently launched, then you have very less history. And then if you just do a time series prediction, then it won't work well because you have very less history. Of course, we just told you that time series prediction doesn't work well, but suppose you do a time series regression, then also it doesn't work well because you just 10 days data. So because of that, we need to leverage the history of other products which are similar to them and which have sufficient history and use that somehow to make the prediction right. So our model does that. This is the technical. So our approach can generalize for non-Gaussian noise. So that means we actually assume that the distribution follows Gaussian distribution. In fact, a mixture of Gaussian. So here I'm saying that you can assume that you can model the data saying that my data comes from a Poisson distribution because that makes sense because sense is always positive. Poisson has support only on the positive points. So for x greater than or equal to zero, it doesn't put any probability mass on x less than zero. So you can say that my data comes from Poisson or negative binomial. So if you do that, you can perfectly adapt your likelihood to this model and everything works in a similar way. Less manual feature engineering work, the stress is on the less. You might expect that deep learning will do all the heavy lifting like into all the future engineering for your use case, but it doesn't really work that way. So it sounds really good that deep learning does automatic future engineering everything but in real life in the wild, it doesn't really work that way. So we had to do some amount of future engineering, not much but some. Okay, now I'm going to talk about a little bit about how recurrent neural network works. So those of you who haven't heard of recurrent neural network, so I hope you understand a little bit of linear algebra as well. Otherwise subsequent notation is hard to understand. So here, so this x is our price, this x is our price. Say that we have just one feature in our model, which is price. Then this x is not a vector anymore, it's just a scalar. So each of this x is a, so this is a price on 0th day, this is a price on first day, it's like that. And this h naught is called state vector, so internal state vector. So we take, so come here, so we take x two, so this arrow means we are doing some operation, normally linear operation. So linear operation I mean is we do a cross product, sorry do a dot product with a weight vector, right? So we are taking this and, so we are taking a dot product here and then we have ht minus one. So ht minus one is the state vector from previous state. And you combine them together, you get your ht, current state vector. So if you expand this recursive formula, you'll see that ht depends on ht minus one. ht minus one depends on ht minus two and xt minus one. And so on and so forth, actually it depends on your entire history, right? And your zt is basically the cell, by z i means cell, the cell is just the function of this. And I have put the color as not i, actually this has been taken from a block by Crisola. So I think some of you have come across it, it's very popular. So by green means that these weights are shared. So these weights are not changing. So whwx, bh, these things are remaining same over the time. So that way you don't have a lot of parameters, you don't have to learn a parameter for each time step, right? That way you can scale over a very long vector. This is just vanilla RNN, this is not the RNN people use nowadays, people use LSTM, which I'm not going to go into much more complicated but the idea is similar. Okay, now let's come back to our model. Well, let me explain the notation first. So as I said, j is our cell, i means the ith product by the way. So we have say 10,000 products and i is the ith product and t0 is the current time, t0 is the current time today. And capital T is t0 plus 30. If you are predicting for next 30 days, right? And so we are trying to model this density function, right? Where this notation means all the cells from t0 to t, t0 to capital T. So from today to 30 years later, right? So this is concatenated into one vector and similarly, I mean if you follow this notation, this means from first day to previous day. This is the same of first day to previous day. And this x is first day to, so you can see that for x, we have one to capital T. So that means you're assuming that we'll have all the features data in the future also, which is slightly unrealistic, I know, because if you ask business at what is going to be the price for after 10 days, at least in Clipkart we found it hard to convince them to give the data to us. So, but this is the assumption we have to make. We cannot predict without knowing what is going to be the price. If you suddenly decrease the price, of course, it is going to be higher. If you don't tell me the price, it's impossible to do the forecast. So that's why you have taken from one to capital T, which is the future. T is the t0 plus 30. And note that I have written x as a vector. So it may have price and other elements also. Okay, now I'm writing the likelihood function. So likelihood can be written in this way. So you have these things. So basically, we are writing likelihood as this joint density only. So likelihood is not this actually. We have to take log of this, right? We are talking log likelihood here. So then, sorry, no, this is not log likelihood. This is likelihood, sorry. Anyway, so this is just this probability, I think. I should have used the same notation. Anyway, so here you are taking the product from, so given all these things, these things we are assuming, this is an assumption that these are independent. So that's why we are taking a product. This means product. So from t0 to capital T, you are taking the products. This is called conditional independence. And okay, so yeah, so next thing is that this, so this whole thing, this whole thing has been summarizing to these. So this whole information in the past, and plus the x of the future, everything. I think I should skip the notation. If you have understood the notation, it's really straightforward. So all these things have been summarizing to these. So it's just a new notation for that. Now this thing, this HIT is modeled as LSTM. So this HIT is, as you can see, it exactly has this form of LSTM. So it HIT depends on HIT minus one and ZIT minus one and XIT. Okay, now, given that we have this model and we optimize this model using stochastic gradient descent or whatever, then we can, we'll know this value of theta, right? This is currently unknown, but when we have optimized our model, then we know the value of theta, and then we can, we know this density now. Once we know the density, we can get Monte Carlo samples using NCNC or the techniques, and then we can do all those things. We can say what is the mean value of the prediction, what is standard deviation of the prediction, what is one time, all these things. Okay, I'll give you an example to understand what I just mentioned. So suppose you are using Gaussian likelihood, right? So in Gaussian likelihood, you have two parameters, right? You have mu and sigma. This is a density function of Gaussian density, right? So in this case, you have two parameters, mu and sigma. So our neural network will give these two as output. So this is coming from this LSTM model. This HIT is coming from our LSTM model, right? So HIT, and notice that for sigma, this is actually sigma square, by the way. So for sigma square has to be positive. So we have to take a transformation like this. So it's called soft plus transformation. This transformation makes sure that your sigma square is greater than zero, right? Because log of anything, log of anything which is greater than one is greater than zero, right? So, okay, now this model, we first try it with this model, but this has some problems. Problem is that flipkart data is very sparse. So it's called 80-20 rule. So it means that 20% of the product drives 80% of sales in the category. Because of that, most products actually don't sell much. So you have a lot of zeros in the sale, right? For most of the products. So basically what I'm trying to motivate is, your data is multimodal. You don't have a single mode. But if you just model using a Gaussian, it has single mode, that's one mode, right? Here we are saying that our data has multiple mode. So first we try it with this model when you just one Gaussian model, one Gaussian, then it didn't work because it assumed that it has one mode and product we sell a lot and product sometimes sells a lot on some days and sales not much some days. They all club together and become concentrated on the mode and it gave a very average of prediction. It didn't capture the dynamics of the sales versus price and other things. So we had to assume that our data comes from multimodal distribution and simplest way to do that is using a Gaussian mixture likelihood. So mixture likelihood is typically has multiple modes. So these modes are mu one, mu two, all these things, right? But in this case, we have more parameters. As you can see, if you take two Gaussian, then you have two mu one, two sigma on square. So four of them. And then you have P one and P two. But P one and P two are one parameter actually because P one plus P two equal to one. So basically you have, if you have two Gaussian, you have five parameters, right? So, of course, you increase the number of parameters and next thing you need to take care of because the P is your probability. So you have to do some softmax to make it so that it sounds to one, right? So this is just a pictorial thing, a pictorial evidence of how Gaussian looks like. This is two Gaussian here actually. So you can see this is one Gaussian, another Gaussian. So it has two modes, one mode here, another mode here. Okay, this is the final architecture. So I'll just quickly tell you what it is. So as I said that you have all the same, we right now we have three features which is price at time t, offer at time t and this P is basically product embedding. I'll come to that. So product embedding. So the problem we face, I mean sort of problem, I mean should be straightforward to understand. So most of the, so if you just don't consider which product it is because you are training the model on all 10,000 products, say in the category, then if you don't somehow give this information that this is this product because each product has its own characteristics of sales, right? Then it doesn't work. So how can you take product ID as a feature? Then becomes a problem because if you have 10,000 product you one hot them, it becomes 10,000 cross one vector. And if you are in book category, then you have one million cross one vector. So which is problematic. So that's why we had to do some embedding which is very similar how what to heck works. So you have this product ID and we have, this dimension is much lower than 10,000. Here it's a vector also. This dimension is like, say if it's 10,000 this is like 100 or something. It gives you a much dense representation of this thing. This very sparse here is much dense. So, yeah, right. So as you can see here, so this is your feature. This goes into this. So this is the dense. So this is also some categorical. There's some categorical features here. So all these things you can think of as continuous features. There are many other embeddings also which I didn't put here because it'll be too long. It won't look good here. So, and this becomes your XT. And XT minus one also has all these things which I haven't written here. So instead of T, you'll have T minus one, T minus one, T minus one here. And XT plus one also will have these things. Now it's going here and then here and then from this HT you are predicting all these things. I'm assuming that you are talking about two Gaussian mixture. So you can see that this is the parameter. Okay, now how we train this model is very straightforward. As I said, I mean some product might not have much history. So if you just train model on one single product, you have very few data points. Say product has 10 days of history then we have just 10 days of data which is impossible to train anything in fact. And also you won't be able to learn from other products what it's going to have. So that's why you are training in a homogeneous cohort of products. Of course you don't want to mix mains clothing with say mobile phone. That would be ridiculous thing to do, right? So you have a homogeneous set of products and you are training the model on that, right? Actually Flipkart and any e-commerce company in fact, Amazon also, they have a hierarchy of products. Like you start from lowest level which is product ID and you go up. Like after that I think it's vertical and then category, super category and then you have the entire Flipkart. So you can actually build a model at each hierarchy and then after training the model then you can fine-tune on each of these. You understand what fine-tune means, right? So by fine-tune I mean you reach a parameter and then from that parameter as a starting point you do your super security lesson. That is also possible actually, you do that too. So this model training is fairly straightforward when you have the likelihood which is our Gaussian mixture you take the negative of that log likelihood and you want to maximize your likelihood so you have to minimize your log likelihood. So that is your loss function, right? Just like classification, in classification we assume that the data is coming from Bernoulli and then take the log likelihood that that becomes machine learning community gives a fancy name to it which they call it cross entropy loss. It's nothing but log likelihood actually. Anyway, actually no actually cross entropy has some connection with information theory also. Anyway, okay so optimizer that we have used is Adam Osterkosti gradient descent or batch gradient descent with momentum of course. You need to use momentum otherwise it won't work. This is highly non-convex problem. So this is the experimental evaluation. Quickly go through this, don't have much time. So as you can see that this green line is actual sales and this blue line is a Cubist model. Can you see from the back? I don't think so. Just believe what I'm saying then.  So as you can see there is a sudden jump in the sales and our model can capture that thing really well. By the way, this is not an ideal way to look at how your model performs because you're just looking at a few examples. This is some good scenarios that I have captured so that the paper gets published. So but actually you should look at this, right? So in the summary of performance, so you are taking WMAP. So in the WMAP you see that everywhere our model, so NM is neural network, the ARD MDN model. That model works much better than the other models. Other models being Cubist here. Basically Cubist is the best model we have seen amongst other models. Now this is the category-wise performance. Again, don't try to figure out what is going on here. Let me just say that our model works better. If you want, you can come here and get a closer look of what is going on. Please believe me, it's fine. So next I'll just conclude by saying a few things and some future work that can be done. Okay, so one thing we didn't take into consideration is cannibalization, which is very important for certain categories like electronic accessories where cross-cell or up-cell is the norm. Like if suddenly iPhone cell becomes much higher then you'd expect the iPhone case will also go up, right? So our basic assumption fails here. So if you look at, so you can see that I'm summing over i equal to one to n. So that means I'm saying that our data is independent. Our sales is independent, it doesn't depend. So one i doesn't affect the other i prime, right? So that's erroneous, that's not correct. So, but anyway, we cannot really model the actual thing. We try to approximate it as much as possible. So that could be the good direction of research, how we can incorporate some correlation between different products. In fact, it will really help for fashion categories also where there is a lot of cannibalization, right? Red shirt, blue shirt, green shirt, they all cannibalize each other, right? And certain categories are really hard to solve, namely fashion, again, I pick fashion, yep. Yeah, okay, yeah, so fashion is the biggest pain point for us and in fact, Amazon also is, Amazon has like a team of 100 data scientists who only work some fashion for this. It's a very hard problem to solve because of very sparse data and extreme cannibalization going on all the time. So, we don't know what to do here actually. We have to do some research about it, yep. Next is how can we predict the sale of new product? This, I didn't touch, but I guess this shouldn't be much of a problem because for new product, you have to take the product attributes as your feature and then that product attribute would be a very sparse feature so you have to learn some embedding and in the embedding space, you need to do some kind of similarity metrics or some distance or something. I am not sure how to go about it, but I think it's really much difficult. Next is like the Utopia, which can happen or won't happen ever, I guess, I don't know, but this leverage forecasting to automate planning, buying, pricing, everything. I guess this is too hard right now. I mean, I don't see this happening in the next 10 years. Unless India becomes as matured as, India market becomes as matured as US or Europe market, where Amazon has reasonable success in that they have like made 90% things automated, but not everything. But in India, it's very wild. A lot of human intervention, manual tweaking, all these things goes on and it doesn't really work that well. Okay, so the conclude, yeah. Yeah, yeah, we tried, we tried, we tried that too, but it didn't really work that well. Like, yeah, yeah, yeah, we tried that also. So those also, those all come, the state-based model you're talking about, that all comes in, we call that dynamic linear model, DLM. So we tried that. So there is a paper we found out so, but that uses Kalman filtering to estimate the parameters and all this stuff. But that didn't really work as good as this. Cubis is the best, as I said, I tried really hard, yeah. Well, those things, it's not in our hand because we can't ask Amazon, say, hey, give us the price of products for next 30 days. Yeah, yeah, of course, of course, of course. Those all goes into the error term. That's why it's a probability distribution. The variance of the error is quite high, I'd say, but we can't do anything about it. Exactly, exactly, exactly, yeah. So you can see that as we go along more and more weeks in the future, you'll see the error is going higher and higher because of that problem on, yeah. Actually, it's a good question because this is the biggest pain point to deploy this model because you need all these future inputs. And it's very hard to get from the business because at least in Flipkart it's all manually driven right now. The business guys don't easily give these data to us. So because of that, I would say right now, that's probably the reason why it's not deployed yet. I don't know what's the situation now. I did it like a year back. But of course, and this model assumes that you all have all the future inputs. You have all the future attributes, right? Future features, yeah. Yeah, yeah, of course, yeah. So those also called event embedding, which I didn't show here. I just showed three things, price, offer, and product ID. So I could have included that also, yeah. Well, no, no, no, train the modeling house. So at that time I used TensorFlow, but if you ask me right now, I would use PyTorch, definitely. Yeah, yeah. No, no, no, no, no. Any other questions and comments? Yeah, yeah, yeah. Every feature is indexed by time t. So as you vary time t, your feature changes because today's price is not the same as tomorrow's price, right? So it's a function of time t, right? Of course. No, no, no, no, no, no, no, no, no, no, this, this. Yeah, I understand, but this one doesn't have that. So here the set of features is fixed beforehand. If you have new set of features, problem is suddenly new set of features comes, you won't have much data to train a model on that, right? Yeah, anything else? I'm around here, but you don't have much time, so I'm leaving the stage for the next speaker.


The immigration department, located in the Lamar building on Bridge Street Casteries has revised its operations to ensure occupational health and safety concerns are adequately addressed while simultaneously transitioning to the technologically advanced and more secure e-passport system. Immigration department operations have been temporarily relocated to ensure continuity of service. Limited workstation because of space and we have some issues with air quality in certain parts of the building that we are not occupied presently because of health and safety reasons. Technical challenges have affected the e-passport transition which has resulted in delayed processing times. Assistant superintendent of the immigration department, Sean Alexander, has acknowledged the inconveniences the service disruptions have caused the public. We acknowledge that there are some inconvenience and we do apologise for that but it's way way beyond our control so we advise again that no need to come to the office if you don't have any emergency or you're not travelling in the immediate future. And despite the limitation, the department is working and yes we are working to put measures in place where we can streamline things and you know, persons can have a smoother entrance into the office to apply for a passport and to get out as quickly as possible. The immigration department in the interim currently processes a maximum of 50 e-passport applications on Mondays, Wednesdays and Fridays. Collection days are reserved for Tuesdays and Thursdays. Although the new e-passport facility has gone live, the immigration department is reminding the public the current machine readable passports are still valid for travel. That means your machine readable passport will still be accepted at air and seaports around the world. I would like to advise the public that the machine readable passports that they currently carry, the passport is valid until the expiration date that is on it. So if you're not travelling or you don't have a medical emergency or anything of the sort, no need to come to change your passport. You can use that passport until up to the expiration date. So no need to come to the office. So we will only look at persons who are travelling and they have emergencies and medical emergencies to process now. These persons can wait. If you have a few months on your passport or even a year or two, no need to come to the office, these passports are still valid. You can travel any part of the world with it. There is no plans to get rid of the machine readable until it expires. So if you have a machine readable now, at the end of it life, then you will receive a new passport which is an e-passport. So gradually, eventually, the machine readable will be fixed. Just to make it clear, if my machine readable doesn't expire until 2026, I don't need to come to apply. Do I need to come to apply for it? If I need to come to apply, I can give an example myself. My passport is a machine readable. It's valid until 2026 and I would not be renewing mine. It's not necessary. The cost to obtain an e-passport is $250. An additional $50 charge is applied to express or expedited e-passport applications. From the Office of the Prime Minister, Rihanny Isidou.


Y cwmhwyng yw'r gwaith o'r llangwydau a'r wneud o'r dillogl yn ddweud. Mae'n gweithio o'r ddweud o'r ystod a nid oed o'r gwneud. Mae'n gweithio i'r rhannu i ni'n gwneud o'r ddweud o'r ddweud o'r sgwmpidd. Mae'n ddweud o'r ddweud o'r ddweud o'r ddweud. Mae'n ddweud o'r ddweud o'r ddweud o'r ddweud o'r ddweud o'r ddweud. Hi ymatech. Felly dyna'r cyfnod o'r cyfnodd. Mae'n gweithio i chi'n cael ei ddweud o'r cyfrinod. Fyddwch chi'n gyfarfod o'r hyn o'n iawn o'n rhan o'r gweithio, mae'n gwybod o'r ddatblygu i chi ddweud. Felly gallai'n cyfrinod o'r ddweud o'i ddweud o'r cyfrinod? Mae'r ddweud o'r ddweud o'r cyfrinod o'r ddweud o'r cyfrinod, ac mae'n ddweud o'r ddweud o'r cyfrinod. I want to come, except I didn't want to do the exercise class, which I was busy. But I'm confused at why you would be offended by that. It's just us trying to support you more. I just wanted you to understand. But I text you all night, I text you in the morning, you still ignored us. So then how can we ever understand if you're not letting us understand? I don't know how else I can go around saying you didn't attend the class, so instead we're going to do work out. How, Melissa, how do you want me to say that in a sentence, which isn't going to upset you?  I'm not saying at all, but taking it, you don't know how it feels. The theme of partner with Melissa is that for some reason she thinks that the whole world's against that and everyone's trying to do something nasty to her. Just be best friend. If anything, I want to come on this journey with her and I want to help her for the good of her health, for the good of her well-being, for our mind. Why would we want to do anything nasty to Melissa? Me and Lauren have been very sensitive around the whole of this subject. We know it's been hard for you. That's why we've walked on eggshells around at this whole time and we have said, because look what's happened, that's why, because I can't do right for doing wrong. This has been such a big thing for me to speak about. This is something that is really important to us and they might think that the support on us, but really they're just digging us out for not attending a class. In my eyes, there's nothing left to see at Charlotte. I can't come to South Africa because you're still not getting a Charlotte. What does she mean she's not coming to South Africa?   Why are you throwing this all away? Why would I want to go to South Africa now? They've been so insensitive. I do hope they'll have a lovely holiday, but I won't be joining them. She's been stubborn. So what? I've never said anything personal to you in the slightest. I just give up. It's got the point where I give up. She's making us feel like a terrible friend constantly and I honestly don't believe I am a terrible friend. She just makes it so difficult and it's not fair. She has to change the way she is until I'm going to be able to be friends with her again.


Then we have the limit when loans exceed the fair market value of the home now This shouldn't happen normally because normally the bank The bank obviously doesn't want to have a situation where they're giving out a loan and the loan amount is higher Then the value of the home because the whole point of having the home as collateral is to try to make sure that the Person that's borrowing doesn't default on the home So you would expect something unusual happened if the loan balance is greater than the balance of The home but if that unusual situation comes up you could take a look at publication 936 You would think under normal conditions They would want to have like 20% down unless things get weird like they did in the you know the mortgage Crisis and you know the great recession or whatever But and and then the the value of the home would have to go down fairly substantially in a normal condition To for the loan balance to be higher than the value of the home But if the total amount of all mortgages is more than the fair market value of the home You can see publication 936 and then we have line 8 a Enter online 8 a mortgage interest and points reported to you on form 1098 that's the form you'll typically get from the financial institution Unless one or more of the limits on the home mortgage interest apply to you for more information about these limits You could see limits for the home mortgage interest We saw earlier home mortgage interest limited if your home mortgage interest deduction is limited see publication 936 to figure the amount of mortgage interest and points reported to you on form 1098 so that's where you're gonna go if you hit those limits again that are deductible only enter online 8 a the deductible mortgage interest and points that were reported to you on form 1098 Now remember that the IRS is gonna have that form 1098 just like they do with the 1099s and the W2s and so on and the 1098 is reporting something that possibly could be Deductible to you so you would expect then that the iris would want you to report the amount on the 1099 1098 or something lower right because if you put something lower than the amount on the 1098 the iris is fine with that because then you're gonna pay They're not gonna come after you for reporting less interest than possibly you could have but and so But if you report something higher than the amount that's given to you on the 1098 You're getting a deduction higher than the amount of the form that would indicate you get the deduction That's when you would think the iris would be more likely to question that position, right? So refund or overpaid interest if your form 1098 shows any refund or overpaid interest Don't reduce your deduction by refund instead see the instructions for schedule 1 form 1040 line 8z so Same situation with like the state sales tax for example Where we said if if you got to deduct the state sales tax, I mean sorry the state sales tax and the oftentimes the state income tax If you got to deduct the state income tax in the prior year and then you got a refund Do you have to go back and amend the prior year tax return or do you just record it as income in the current year? similar situation here although far less common if you deducted interest in the prior year and then they Refunded that interest in the current year. Do I have to go back and fix the prior year or more easily? It would be just to include it as income in the current year Then we got more than more than one borrower So if you and at least one other person other than your spouse if filing a joint return because your thought of is one Taxable entity if you're married for the most part. We're liable for and pay interest on a mortgage that was your home You can only deduct your share of the interest So you obviously you might get a 1098 for an amount that really isn't applied to you because you have multiple people That are purchased in the home. You can only deduct the amount applicable to you shared interest reported on your form 1098 If the shared interest was reported on the form 1098 You've received deduct only your share of the interest online 8a let each other borrowers know what their share is You got to tell you got to tell the people The other borrowers their share so that they possibly could deduct it if it's an deduct deductible component possibly as like a business expense or whatever their Interest is to them so shared interest reported on someone else's form 1098 So now someone else got the form 1098 and we think that part of that interest is reportable to us Now this is where it gets messy because remember that we're talking about a benefit here in the deduction So the iris is going to be skeptical if we deduct something more than a 1099 1098 That we received and now someone else received the 1098 and we're trying to report the deduction That would be an unusual situation Typically you would want the 1099 1098 to be assigned to the person that's going to be taken the deduction if at all Possible but some loan structures get get messy So if the shared interest was reported on the other person's form 1098 Report your share of the interest online 8b as explained in line 8b later So form 1098 doesn't show all interest paid So if you paid more interest to the recipient then is showing on form 1098 Include the larger deductible amount online 8a and explain the difference So now you're gonna have to tell the iris because the iris is gonna say hey Look you've reported more than it's on the 1098 and you're gonna have to tell them why so if you are filling out a paper return You can explain the difference by attaching a statement to your paper return and printing C attached to the right of line 8a so you would hopefully not want to have to do that But if you had to do that then you could do that


Everybody forced here ripping a box of 2022 Prism World Cup soccer breakaway for Nick N. Thanks, Nick and good luck, man Base pack Gabi base Who base packs? Cody Gokpo base out of yummy Farin Torres breakaway prism red breakaways Jairdan Shaqiri Chicago fire legends 62 of 99 of our the all rookie Nuno Mendes Foden Tiazvinia Bono Uno Kingsley Coman Saliba silver rookie Fajardo Almuahlan Suchich rookie silver scores club Raheem Sterling Couple more packs. Come on. Bonzo Davies. William Alvarez breakaway Memphis and last pack as a purple honey ball rookie Honey ball Mabry numbered eight out of 35 and that is gonna do it for the box. Thank you again, Nick We'll get these out to you shortly