O ydyw'n brif yr oeddwn i. Now I'm not just the Av guy for those that don't know me. I do a lot of things. Lots of different things. One of those things is I like dabbling with data and analytics. In particular learning analytics. Which I hope all of you have that word buzzing in your ears and PVCs or asking you questions like what should we be doing about learning analytics? Yn y context yw Mooks, mae'n slyg yn ymwneud o Simon Buckingsham, sy'n dweud i gael y dyfodol arall, y dyfodol ar y dyfodol ar gyfer ymwneud, rwy'n ddweud i'r gwrthwyng yn ymdweud i'r ddaeth, a'r Amythyn yn ymdweud i'r ddaeth. Mae'n ddiddordeb i'r ddweud i'r Ddweud i'r Ddweud i'r ddweud i'r ddaeth, felly mae'n mynd i'r bod yn ymddir iddyn nhw, sydd yn i'ch mynd i ddechrau i'i borders i'r bêl iawn, felly mae'n ddiddordeb sy'n ddiddordeb i'r ddaeth i ddweud wedi'u gwahanol i'r ddweud. Felly oeddwn i'n gobeithio, mae ydych chi'n rhaid i'r ddatganiadau. Mae'r angen o'r eich ddweud o'r adornau yma oedd rhaid i ddechrau Gwylodraeth Adam Cooper. Felly, i wneud eich bod ni'n byw, mae'n cael ei wneud o brifthynau cyffredinol i'r angen o ffasiatus o ffasiatus wedi'i angen o'r angen o rhaid i eu cyfnodol i'r angen o'r angen o'r angen o ffasiatus. Mae'n angen o'r adornau ei ddechrau i'r angen o'r adornau, a'i adornau'r adornau eich rhaid i'r angen o'r adornau. of actual insights. So taking something and producing something that we can do as a result of it. But breaking this down into a very simplistic model, you've got some data, you do some analytic, you get some insight. But when you start looking at this one in more detail, insight, who is this insight for? There's various actors within this, and it was quite nice to see Simon Nelson this morning talking about the self, the learner. So the learner getting some insight about where they should take their own direction of study. Also you have the tutor who quite often at the tutor level might be how do I make this better, how do I make this design better? Where are the things, the points that the students are struggling? The institution, the insight for them might be who are students that we want to convert onto our courses. ac mae'n ddweud o'r llwyddoedd yn ymdillfa ar gyfer y cwmhwyl yn ymdillfa. Felly, o'r cyd-dweud, mae'n ddweud o'r anoleth. Y ddechrau'r top ffasg yma yn y cwmhwyl, mae'n ddweud o'r cyd-dweud o'r anoleth, Ryfbeith y Ffyrtson, a'r Cyngorau Cyngor, o'r 5 o'r anolethau, learning analytics for social. Network analysis, discourse content, disposition content, one of the reasons I'm interested in learning analytics is because it draws in so many different disciplines or so many different ways that you might want to analyse data and I think it's time to keep reminding ourselves that it's not just about learning analytics in this context administration, that is another aspect of analysis that's quite important. And then the data, I was thinking about what data there is and within the current climate of future learn Coursera ed X it's very a platform as a service model so there's data being collected byddai'r platformau. Ond yn y cyfle cyntaf, mae'r cyfan yn cyffredig bwydol, mae'r ddyn nhw'n ddau'r ddau, darlwynt, Facebook, yn y gwaith. Dwi'nSape'r bobl yma, oherwydd mae'n ddau'r dddangos. Yn ysgrifennu, felly hwn yn ymdyn nhw oes eich ddesgwlad yw haes ymddangos ar hyn ar y data, neu yn cael bod ychydig yn oes yn ei ddesgwlad. Cyn ar gwrthu gwneud ymateb ateb o fewn ni'n eu bod yn yr oedd ymdyn nhw oed wedi'i ddweud chi'n gwneud llesu dweud? Y cwrddai'r ddataeth wedi'u ddweud? Mae i'n ffordd, yr unrhyw o'ch ddim sy'n meddwl ac ydw i'r ddweud hefyd i'r analyse, ein ddysgu'r ddataeth a'r aelogau. Ac rydw i ddim yn fwy, rydw i'r dweud nhw'n gwybod y dyfodol y dyfodol o'r cyfnodau ymddangos o'r cyfnodau argymfaen. Felly rydyn ni'n rhaid i'r ffordd am y gweithio a'i'n mynd yn ymddangos yn y cyfnodau, a rydyn ni'n rhaid i'n mynd yn y cyfnodau. Ac rydyn ni'n rhaid i'n mynd yn y cyfnodau, roeddwn i'n ei gwybod y ffordd a'i gydag yw'r cyffredd. Mae'n edrych yn ddechrau. Yna yw'r unrhyw ysgrif yw'r ystod ar unrhyw mewn yng Nghymru. Mae'n ddysgrifio'r hyn o'r fawr ymlaen â'r eich byddoch. Mae'n ddechrau'n dda i ddweud am ddechrau'n ddweud â ddweud. Ddiweddio'r ddechrau'n ddweud am ddweud o'r ddweud o'r ddweud. Mae'r ddweud eich ddweud yn ddweud ar y ddweud. Mae'n ddweud yw'r ddweud yn ddweud o'r eich ddweud. One-off answers where students just had their own particular misunderstanding But for example that big x at the top left is where 2000 students had the exact same wrong answer Now if you have two students in a class of a hundred that have the exact same wrong answer you have never notice But when you have two thousand students, it kind of jumps at you And so Andrew and his DAs went in and looked at that assignment and understood the misconception that lay at the root of it And then they constructed a targeted error message that other students whose answer fell into that particular bucket would get that targeted error message as opposed to just the you're wrong and that gave a much more personalized and useful experience to the students because it put them on the right track in terms of what they needed to fix in order to get the right answer. And so this effectively is a much more personalized experience that you can do by utilizing this large amount of data that we have. Felly, ydych chi'n gweithio. The questions I'd like you to consider are where did that data come from? Was it in a wall log. Obviously Coursera collecting it, so that's the other thing. They have to be collecting this data so that they can query it. Then someone's got to do that, query it and come up with a graph. Do your platforms that you're using for open courses provide that for you? So data access and data shape are all quite important. Can you think quite well about how that's a multiple choice of questions? I mean, if you don't want to try to do a base job where you're not stuck, it's strange that you don't get away with that.  One of the other examples of this was they were actually using it for error detection to find the questions that they had posed incorrectly. So a tutor had created a question and he himself had got the wrong answer. So they're using these techniques to identify bad tutors, bad questions. Which is another way to look at it. It shows a post. It shows that there's a disconnect there. Then you look into the reasons, but by the way it's joining them. And also thinking about, this is a very simple xy plot, but if you were wanting to query that data on demographics, was it gender biased, location, age, how would you do that with your data? This is quite a recent one. I should have probably paid more attention to this before I did the video with Jonathan. It's looking at the length of videos in open courses that are most effective, most effective being people got to the end of it so they didn't drop out. So in the 609 minute range, so these are groupings of video length. I should say this is from edX, this is edX data. And this is the median time spent watching the video. So in the 609 minute you've got a peak of, you know, they watched 60 minutes of it. So the conclusion is six minutes. Make your videos six minutes, not a minute longer, not a minute less, six minutes. Think about the data that's behind this graph. So they've recorded student activity in terms of when did they click the play button, when did they stop the play button. And then they've basically, you know, this is a summary, I think it's about 20,000 videos or 20,000 students. So how would you get to this graph? I have answers for these later if you please to hear. This one's quite interesting. It's getting more advanced. This is Coursera data. It's quite interesting. This analysis is looking at engagement and disengagement patterns within Coursera. So we have, they're using the assignments as basically an indicator of whether or not a student is still active within the course. And they came up with a categorisation, too. A, auditing, so which is, I think we would use kind of like learning. You've got legitimate professional peripheral participation. B, behind, so the art's still doing the assignments, but out of sequence. T's on track and O is out. And this was a summary of, I think, about three or six classes. What's also quite interesting about this analysis is produced by students. Stanford had the Lytex lab where some of their students have been given access to Coursera data and this is what they're coming up with. Interesting analysis, I think. So to come up with these clusterings, they're using K-means, which is a fairly standard analytical tool. And one of the reasons that they were interested in this was they wanted to make comparisons between courses. So was there characteristics of a particular course that suited another one? So by having a way, then, to pull out the data, to analyse it, allow them to start identifying differences. But again, you know, they've had to pull out the data, they've had to do some, you can find the paper online, they've had to do several iterations to come up with this point and there's still work, they say, to be done to refine this further. So I've kind of been prodding you about this whole data thing, data, data, data, which hopefully is the question that you keep asking future learners, Coursera, data, data, data, data. But it has to be the right data. So if you're wanting to do analysis of videos, you need that data. So that data needs to be recorded in your system. It's not just about data, it's getting access to it. Even within an institution, there can be terrible issues with getting access to the data that you need. So even if you're using Blackboard, getting the data out there is a nightmare. Who has permissions to give that data? Google Analytics, which I'll come to in a second, is a fantastic tool for getting data and summarising and analysing data. But usually it's just the web admin that has access to that. How is that being distributed through your institution? So you have to get the data, you have to have the data accessible. The other thing is the shape. I was fortunate to be given access to data from one of the new platforms. I can't say which, I'm under a non-disclosure agreement. It was just my SQL table dump, a database. So I'm talking to people from, is Amy still here? Is that what you get from Coursera? Just a database dump. And you're kind of left to your own devices to work on it. I think you can provide data in more useful means. I quite like CSV. Future Learner said to be my data as a big, scary spreadsheet, but also in a nice, highly pink Ribbon Custodd Plasic platform. For the end data literature, I find that very nice. But I guess when I probably get better at this, they're not asking me questions that I wouldn't want to ask in that form. That's it. I've got the data to interrogate, so that's quite helpful. Which hopefully, if I remember my slides, flows, data flows. So it's creating these recipes of you can get a dump of data out of your provider, but you're able to quickly turn that into something that's useful to you that provides the particular insight that you need. And there's a whole list of tools that you can do to do that. But I think within open courses there's a great opportunity to actually share some of the expertise around whether or not the people that have the dollar signs in their eyes will permit that. I hope they do that. So I just wanted to highlight some kind of quick wins, easy alternatives that you might not be aware of, but might kind of stimulate some of your thoughts in this area. So here we have again the longest video by the global grant. 25,000 students, several weeks work. This is YouTube. Have you done the... I don't know if you can see this. This is the audience retention graph on a YouTube clip. So this is one of my videos. This is about who we are. That's why I gave you this. Well, no one liked it. You know, we've got just about 20% of people got to the end. What was that about? So this is there on YouTube. And YouTube do provide a data export to a degree.  So this video is kind of a nice, fluffy visualisation video. This is a different video. This is an instructional video. Look at these things. We've got people, and this is the nice thing about this. When I play this back, I can see the video. So I can see the point where people are hanging on. So this is an interesting point in terms of cognition, misconception maybe. This video has, I think, 2,000 views. So you've got quite a decent data set there of what's going on. And there's other things within the YouTube analytics about demographics, location, gender, and which might help you. You can specify the date range. Oh, so, yeah. It's not one I've dug deeply into. Don't know, would be the honest answer. And whether or not that information is public is another question, which is another consideration if you were to go down this route. So the MCQ test, what you can do in Google Analytics is you can actually do event tracking. So if you've got a MCQ and you've got Google Analytics, you can track which response people made to that MCQ. You can target is correct or wrong. Within Google Analytics itself, you can get a summary chart. Google Analytics also allows you to do evaluation, so we could do which definitely don't. I could do age, gender. I can run experiments with Google Analytics, AB testing. So I could be tweaking the platform design, running tests and getting data out of Google Analytics. So early I mentioned the issue of quite often Google Analytics is held by your web team and they don't. Can you admin write or out admin access? You can get out is CSV, that's a manual process, but there are automated ways of doing it as well. Basically now Google has a bit code that lets you proxy the data. So you can create a segment or an analysis or a slice of data and then just make that available on a server behind authentication. So you can give people access to the raw data in a controlled way. So the next one, how am I doing on time? So this is Canvas. Have you come across Canvas, VLE made in the States? Canvas have Canvas network, so basically it's a platform to provide open courses. So you can say you have the standard kind of VLE tools, the discussion forums, announcements, assignments. Really nice thing about Canvas is it has an API. APIs? Basically an API is a way for your platform will have lots of data locked away in it or processes or functions and you have something you want to do with that data of processes functions. So you write a bit code that talks to their server and that server gives it back to you. The Canvas one is very well documented. So we can take a discussion forum within Canvas and with a bit of API magic we get into a spreadsheet. This was one I did for the learning analytics open course. Once it's in the spreadsheet you can just play around with the visualization tools in there. This is a Google spreadsheet so I can share it with anyone. So I shared it with the course so we could compare how we were doing performance-wise and tutors could see how we were doing performance-wise. Having access to an API really helps get the data out so that anyone who has access to it can slice it up, dice it, try and find something interesting or useful. There's a link up there for more information about that particular one. You can go further. So this is taking it into social network analysis. So we're looking at the individual discussions. One of the reasons to go into social network analysis is that it provides insight into how the group are doing particular characters within the group. So those were all dealing with other people's platforms. At Alt we've actually experimented with our own platform. We ran Octel, which was an open course that ran early in the year. Not huge numbers, 1,400 students registered. It was in WordPress so we had full control over the platform, which was a godsend because we could create our own data APIs, get the data out that we wanted, which we found useful. So this one, it was a connectivist course, so we wanted to identify all the students who had just created the blog and made their first post and hadn't had any comments as a way to target some insight for the tutor team, the target resource to say, hey, go and give this guy some love and affection. Go and tell him what he's doing, he's good, wrong, right. So it's not an example of just taking the data out and doing something useful. One of the issues within the open context, a truly open context when you're not platform specific is that people have different profiles online and there's a danger that they become analytically closed. But it was quite an eye-opener for me when I came across a site called Full Contact. Basically, you provide a list of email addresses and it goes off and searches those email addresses against various databases and it comes back with a hit rate of, I think, around... So I put in 250 email addresses and it's come back to 178 people.  So even within an open context, you can actually find out quite a lot about someone and then use that data. For good! For good people! And we're going on, at all, to do site with the MOOC Research Initiative to look at some of this open data and analytically closed. And this was the last slide. So this was, from Simon again, this idea of sharing some of these recipes, sharing some of these tools, because the field is very broad and I think we could learn a lot together. Okay, thank you very much.


Okay, very good morning to you. It is Friday the 29th of October. Of course we're going to talk about Facebook becoming meta as Zuckerberg tries to push the idea of the metaverse forward and a rebranding overhaul of the parent company. So we'll have a look at that. We'll also have a look at Apple Amazon earnings. They came out aftermarket yesterday and they were down three and a half percent and four percent each respectively. So we'll have a look at why they reacted so negatively in the overnight set. And then we're going to talk about ECB sources following the hawkish moves that were seen in the euro yesterday, which appreciated quite sharply. A lot of the focus was on inflation, of course. And some of the rhetoric was deemed as her being, or the market perception, her being too timid on the pushback of more aggressive pricing of rate hikes in the European area. And then we're going to talk about Capital Hill updates on Biden's package and then a few things on China from China Evergrande, making a last minute payment again to kind of live another day for the time being and then cold futures, which continues to decline quite rapidly on more verbal intervention on behalf of China to ease some of those commodity price pressures at the moment. So that's what's on the agenda. Before I get stuck into Mark and his metaverse, let's just have a look at the general flavour of how markets are looking at the moment. We had a positive finish across Wall Street last night. The S&P was up a percent, Nadal 7 tenths, the Nasdaq and Outperformer, albeit slightly at 1.4 percent. A bit of a drift lower, though, as you can see in the Nasdaq future, after initially hitting those highs pretty much at the close, we then saw quite a rapid decline on the back of those tech giants coming out. Remember, Apple and Amazon are the second and third biggest companies in the S&P 500. The only company they're behind, of course, is Microsoft. And so those two companies alone account for about 10 percent of the entire Nasdaq 100 index. And hence the reason why that stock index came off quite aggressively overnight session. The move obviously not quite as pronounced, but tracking in a similar fashion for the S&P down 21 this morning. The DAX down about 94 elsewhere. As far as the currency market is concerned, the dollar index this morning is up marginally around 0.14 percent. So cable is pretty flat, Euro is underperforming, but I don't think that comes as much a surprise, given the sharp outperformance yesterday. The gain in the Euro was more than an excessive 1 percent. And the previous 20 day daily range was more akin to a 0.2 percent move. So yesterday was a big move on the upside. So a little bit of a pullback from there wouldn't be too surprising. Gold, a little bit lower this morning down six bucks as to is the US 10 year down five ticks. And then oil is trading absolutely flat at the moment. So that kind of area, a band of support that we were eyeing around these previous resistance support areas in the 81 handle, working out not only yesterday overnight session, but also during the kind of the switch between late European morning and US entrance yesterday and for a pretty, pretty decent pullback up to the 83 mark for the moment where oil resides lack of real news flow right now. Nothing meaningful overnight to mention on that front. But let's get stuck into Mark Zuckerberg and this change. And I don't know about you, but whenever I look at Zuckerberg, particularly in this image, he looks like a combination between a humanoid and an avatar. I'm not sure whether he's trying to do that on purpose, but the way that they dress him up and put the makeup on is slightly disconcerting. But nonetheless, what is Meta's vision? Well, their vision is that people will congregate and communicate by entering virtual environments, whether they're talking to colleagues in a boardroom or hanging out with friends in far flung places all around the world. You name it, they can make it. The new name won't affect the company uses of it or how it shares its data and the corporate structure is not changing apps, including flagship social networks like Instagram, WhatsApp, they all keep exactly the same names and icons and so on that you you've been used to. But leaning harder into the metaverse lets the company appear, of course, to be looking to pursue some kind of diversification at a time when they're facing many different levels of criticism over the years. Obviously, on data privacy, you had the Cambridge Analytica stuff on the political interventions and things like that being used in such such fashion. You've now got this obviously youth issue with the impact is having a mental health on young people. You've got the fact that they don't necessarily fully understand or control their algorithms to stop things like hate speech. And so, yeah, it doesn't come as a surprise, this type of move, whether or not it has longevity to really mask over some of these underlying issues is yet to be to be seen. One of the interesting things also that I read this morning was that how Facebook might find it challenging to try and retain a lot of key staff and if those staff aren't already working on things like AR and VR technology, then trying to maintain employees of a high quality when there's so much competition obviously in the tech space to stay at the company could be a long term challenge that they'll also face. Building up the metaverse, though, will allow meta to reduce its dependency on mobile operating system and browser makers, such as Google, Apple to deliver their services to consumers. So as per the impact that's that's been evident in lights of snap in particular, but other social media names as the new iOS privacy changes have come in on Apple, this as well goes some way to try and circumvent that to attain some power over that user base and generating of their advertising revenues, which obviously is very critical to the business. So, yeah, that's the latest there is change won't come into effect. I don't believe for another month or so anyhow. So it kind of is akin to how in terms of from a technicality, how you still have Google, but the parent company is alphabet. Now you have Facebook, still app, but then you've got the meta that sits above that. Having a look at these aftermarket earnings, then we have Apple. So starting off with the EPS that came in in line revenues amiss eighty three point three six billion below the expected eighty four point seven. Their iPhone revenues were thirty eight point eight seven billion below the expected forty one point six. And CEO Tim Cook said supply chain constraints hurt sales by six billion US dollars, and that was larger than expected. So not often you see numbers that below expectations for Apple, but such as the lofty heights of which the market generally looks at for these numbers to punch, you know, particularly strong numbers that yeah, they just haven't really lived up to that this time round. And the supply chain constraints clearly evident on the firm as suggested by Cook himself. So Apple shares aftermarket down about three and a half percent, as you can see here. And then Amazon, they were even worse. They were down four percent in aftermarket trades. So as you can see here, I just bring it up. So immediately aftermarket, they reported and got hit quite hard and remained lower. The EPS at six dollars twelve street estimate for eight dollars and ninety two. So well below expectations, revenues amiss at one hundred ten point eight billion against one eleven point eight expected. Their outlook, though, was very disconcerting. They see Q4 net sales at one hundred and thirty to one hundred and forty billion dollar range, so one hundred and thirty five. That's below expectations of one hundred and forty one point six on the street. It comes despite AWS putting in another solid performance. Of course, their sales were up thirty nine percent as the division's fastest growth rate of growth since two thousand and nineteen. So overall, then it's for Christmas. They do see the forecast being quite considered to be lower than what they previously had foreseen. The company in itself, though, you know, it was always going to be a challenge to live up to the pandemic, just outperformance that they had. Obviously, year to date, they're pretty flat as we were discussing earlier in the week, compared to companies like Alphabet, which are up, you know, over 50 percent on the year. Amazon is also contending with mounting costs as in expanding its fulfillment networks. Remember, going to the seasonal shift, we tend to see that as well. Not only do they create more business, obviously, as people shop around Christmas, but also it becomes more expensive because you have to hire and retain more people. And earlier this month, the company did say intended to add one hundred and fifty thousand seasonal employees to handle that US holiday period. That's fifty thousand more than it required last year. The additional problem here, of course, is that a tight labor market has prompted the company to have to offer higher than normal wages and sign on bonuses of up to three thousand bucks in some markets. So not only are you hiring more employees, you're having to pay them more or just given the context of how the labor market is looking in the US at the moment. And hence the reason why overall, then, it's a little bit negative for the earnings report. Beyond this three and a half four percent move that was seen after market, quite frankly, I think it doesn't really matter. These earnings, if I'm if I'm being honest, and so it wouldn't shape my perception at all about what I think about these companies in the direction of travel, which they're generally getting higher and bigger all of the time. Don't see that changing despite these slightly softer numbers that we've had overnight. All right, other things just before I move on. Don't forget there is a brand new podcast episode that will be coming out later on today. So whether you're on Spotify or podcast or Apple podcast, Google, so on, just search for Amplify Me Market Maker and you'll get the latest weekly wrap up episode myself and the head of training will be having a chat a bit later on this morning. We'll talk about Metta. We'll talk about Tesla Elon Musk and anything you guys want to hear about. So some of the key topics of the week. Otherwise, quick like elsewhere, you've had ECB sources. This obviously follows that meeting yesterday, which did create a fairly meaningful move in the euro, as I discussed. It's becoming very usual fair now for ECB sources to come out shortly after the press conference. What is a source just to quickly cover this? Well, it's not like it's Bloomberg or Reuters speculating of what they think they've heard from hearsay. An ECB source is very much a person at the central bank using media agencies to under the cover of an anonymous source communicates some more transparency to the market. And so generally speaking, then, this is when the quality of what they're saying is very much to the point. It's very authentic and true. And so the market does listen to these things. And so the sources last night said the guards mild rates of pushback reflected the ECB colleagues view. So this is that divergence at the moment between the hawks and the doves. How do they feel about inflation? They paid heed that it was going to be more sticky than that they previously thought. However, they stuck to their transitory view. And it's the division then between what we're seeing with all central banks at the moment, which is this idea about, you know, is inflation indeed still transitory or not? And do we need to take action time policy at a more rapid pace, which we're seeing in other places like the Bank of Canada, the Bank of England, someone and so forth. The other thing is officials advise not to say the market is wrong. So again, in a way, this is a hawkish source comment because the market is priced quite aggressively. Some would say comparative to the fundamentals in Europe, which for different reasons, perhaps is not at the point of warranting such tightening action. But if your officials are advising you not to say the market is wrong, you're effectively passively saying the market is right to price in a more aggressive rate cycle. So, yeah, this came out, hasn't really had much of an impact, but just kind of plays into the hands of very much what the market takeaway was from yesterday overall. And then other news, Joe Biden called on lawmakers to push ahead with the revised 1.75 trillion tax and spending plan, which has been scaled back, of course, from the previous north of three trillion and the 550 billion infrastructure bill is also still pending. So there's kind of two ways to look at this. Obviously, this thing has dragged out forever and that number is getting smaller as that's the mission to try and achieve a compromise of which Biden himself has said this isn't really what he's wanted, but there goes trying to make a deal and we have to compromise. The other thing, though, is that these things are moving forward and something is better than nothing. So hence the reason why I think the markets at the moment are a little bit fatigued by this whole situation, this whole dialogue happening in Congress at the moment. The impact has been fairly marginal, to be quite honest. And then Evergrande, there was an update overnight. So they've made an interest payment for an offshore bond before a grace period expired today. The amount was forty seven point five billion. If you remember this time last week, pretty similar to the situation with the eighty five million that was due at the time. And so this just kind of avert the trigger point then for a more mass sequence of events that could lead to a more significant default at the firm. So again, they kind of surviving for the time being, keeping their head above water. Probably this is the status quo for the time being. And then in China, you did have coal futures. Now, very rarely I talk about coal futures, but they extended a dramatic decline in overnight trade. I think there's a chart here that you can look at and this came as Chinese government said, there's further room for prices to fall. Essentially, ratcheting up interventions in the market aimed at easing the energy crisis is very evident globally at the moment. So again, it's not what you do is what you say. In this instance, they're saying there's room for prices to fall further. So you're you're kind of intonating towards the fact that look, we'll intervene. This isn't yet price moderated enough. We will continue to do and take action until it comes a little bit lower. What that point is, they've not been specific, but that's the overall takeaway. Chinese authorities pressing coal producers to both raise output and lower prices to help address tight fuel supply. There's prompted widespread shortages, seen power restricted for some heavy industrial users and left many utility generating electricity at a loss. And so hence the reason why that is happening. As far as the calendar is concerned for today, this morning, you've really had the French data out the preliminary Q3 GDP at 3 percent. That was actually above the expected 2.1 percent. You then get the flash German Q3 GDP reading the quarter on quarter expected 2.2, the only expected 2.4 percent and be at 9 a.m. London time. You then get at 10 o'clock one of the main releases of the morning. You get the overall Eurozone GDP flash reading. The quarterly print expected 2 percent year and year 3.5. And you also get the HICP flash year and year reading for October expected 3.7 percent. And with a lot of eyes and attention on inflation, particularly coming on the coattails of the guards commentary from yesterday, that's expected to go up year and year from 3.4 to 3.7 percent. You then in the afternoon, you've got US core PCE price index. You also get Chicago PMI from October in the States and the final University of Michigan reading for the same month as well. Earnings wise, Exxon and Chevron are ones to just be aware of that will be coming out a bit later on today. So that is it. I wish you guys a good session ahead. Have a fantastic weekend and please do remember to check out the podcast as well. All right, take care.


Those of you who don't know, between 1 to 2 p.m. East Coast time, I did a quick show on what is going on in Russia. I covered the events as they were happening and they, right towards the end, they flipped on me. They changed completely. So everything's changed. So let me give you a quick update on where we are right now in terms of what is going on in Russia. As I think everybody knows by now, there was, I don't know any other way to call it, but basically a coup attempt, a coup attempt earlier today by the head of the owner of the Wagner Group, also known as Putin's chef or Putin's cook. The guy started out his career in a catering business and has since purchased himself a private army and that is what he has now. It's called the Wagner Group. The Wagner Group both took a city in Russia, Rostov, with the military command facility for the Russian forces in Ukraine and it headed towards Moscow. It indeed reached 200 kilometers from Moscow. It was quite close. During the day today, it appears that the dictator Abel Agus Lukashenko was on the phone with, well, maybe they were zooming. I don't know how they're communicating. Who knows how they were communicating. Maybe a telegram communicating with the head of the Wagner Group. The Wagner Group free goes in and they got a deal. With Putin's backing and basically the deal is that Prygozin gets what he originally wanted, which was the firing of the Russian Minister of Defense and he gets the firing of some of the generals commanding the Russian forces in Ukraine. He also gets a basically a pardon. All treason accusations against him get dissolved. He also gets his mercenary army gets for protection and in exchange he is turning his forces around and leaving Moscow and leaving Rostov. I just saw a video of Wagner mercenaries leaving Rostov and heading towards back to, I guess, their bases in Donbass in occupied territory that is Ukraine. Also, Rumid is part of this deal because Prygozin will send many of his troops to Africa where he has many troops already and where they control much of the natural resources in Africa. I don't know if that's true, but you think that Putin would require that because otherwise Prygozin has basically a force of 25,000 maybe more troops ready to deploy whenever he doesn't feel right, whenever he doesn't like somebody in Moscow. The bottom line is that Putin is dramatically weakened. Putin is a much, much weaker leader right now in Russia. Prygozin is basically, Putin has capitulated to Prygozin and that doesn't go well generally in an authoritarian state. So how it all plays out, I think there's still a lot still that we don't know and there's a lot that is going to still happen. Who is really in control over there? But it really does seem that whatever happens, Putin has lost a lot of his power, a lot of his influence and Prygozin now to a large extent is calling the shots in Russia. I think it's important to note that it doesn't really matter who's calling the shots.  Russia is a mafia state. It's a state run by bandits, private armies, crooks and criminals. And who exactly is the crook in charge? I think matters little to most of us. Of course. You're on? Yeah, go ahead, Andrew. Would you trust Putin, pardon me? No, I don't think Prygozin can trust anything Putin says. Indeed, I saw a story just from a few minutes ago that was being published, like most of the news now on Telegram, that basically says that the Security Forces FSB are reigning homes of Wagner Group troops all over Russia. So they're taking over their homes. Now they have the family members of the officers and the soldiers. For all we know, this so-called agreement just bought the Russian Security Forces time to be able to go after the families of all the Wagner soldiers and gain leverage over Prygozin.  And we won't know, I don't think, for a while. You know, how it exactly happens.


Hello, good evening everyone. Thanks a lot for coming here. I hope this is the last session for you. So I'm in between your Guinness journey and Hyperledger. So today we have an interesting topic about how we can accelerate Hyperledger Fabric specifically and what is the use case we tried using in this case. So for every, for any blockchain, the business value is very important before going into whether we need to accelerate and what kind of model we need to choose and other things. In AMD, we try to make this journey like five years before when Hyperledger.9 got released. But that time we don't have any strong business use case so we can proceed further. And again, we restarted this journey like a year before. So we'll walk through what we have done. Quick disclaimer and just a quick update about AMD. As you all know, we are a semiconductor company and we have presence in all the possible combinations of acceleration as well as on Nick as well as like standard CPUs and your 3Js. And also our presence is there in all the way from edge to cloud and even beyond like most of the satellites, mass rover and all those areas. And also like AMD presence is there all the way from supercomputers to edge. So with all this presence, it also creates a unique challenge as a company for us. That challenges mainly on this particular area. In the recent COVID situation, you might have heard about a chip shortage which created a huge publicity for chip companies. So that is a constant news at least for past six months. And specifically for automotive, you might have heard many of the countries like people has to wait for somewhere between like two months to two quarters to get a new car. And this is the first time in US history they used to market used car market price cut increased. With all these things, since due to a shortage, we also seeing a different kind of market happening in semiconductor. So that one we generally call as a gray market. So as you point out few areas here, the huge like a revenue loss also because of the gray market. One is like the current estimations around 75 billion dollar loss because of various IP loss as well as like tamper chips and all those things. And it also have a huge impact to the job market itself. So how we can address this kind of things. So we always constantly look for how we can give a proper workable chip to our customer. There is nothing called tampered and they're not buying it from wrong source. It's supposed to do what it's supposed to do. So as part of this we validated and we worked on various solutions. Especially when it comes to supply chain in a blockchain use case. We came out with like a three different options. How we can use a blockchain for this to address this name. So one is like a product provenance as you all know. And we also looked into both tokenization as well as like a POS. POS is more like a point of sales. So what happened between like a distributor to the real client? Are they getting the real product what we're shipping? At the end we started focusing more towards a product provenance where we have like a lot of use cases to address and solve. So when it comes to product provenance just I want to touch base. This is a very, very abstract simplified diagram of semiconductor supply chain market. So we have a die bank where the fab creates our product. Then we have a packaging. Then we have a separate team for testing. And we also have like a finished goods. From there it's getting shipped as well as like it goes to a customer. Sometimes we also have like a contract manufacturer. So it's a huge chain before we are getting that like real customers are getting their finished goods. So in all this whole model what is happening here is previously they aimed is a central party where they receive the data. They will filter out something and they'll send it to us next process. Similarly it is happening throughout and aimed is being a central point for all this data management. Now with blocks in solution what we are looking is how we can make like a mesh so that all the parties can exchange data freely. And again based on a smart contract they will get whatever the data what they want. During this process one more interesting observation what we made is since we are making it as a blockchain the number of transactions obviously going to increase compared to standard database where I can have like a really huge table with multiple columns and people can keep on updating but here everything's based on transactions. So what does it mean is we need like high performance blockchain. So one is like a transaction volume is going to be keep increasing and all my following process those also needs like a higher performance because they are looking like something like a database level that kind of performance where traditional blockchain generally lacks. And also we don't want to create like a very complex architecture with hundreds of nodes. We also make it whole process simplified. So this is the time we are looking to various solutions what is available in the market which can address some of our future needs. So while doing a research what I notice is within aimed itself one of our researcher done some kind of acceleration blockchain use case. So let me invite Harris to proceed with the next steps. Thank you. Hello. Okay. So I'm going to be a bit more technical than Muthu because he was explaining the business use case and why do we want it to go into the blockchain space and the supply chain and product provenance. I'm going to be a little bit more technical. So before I go into the details just to give you a quick recap of how transactions flow through a Hyperledger Fabric Network. So most of you would already know this that there is a client on the left hand side transactions get created and it is sent to endorsing peers after the transactions have been endorsed. There are enough endorsements on a transaction. The client would send that those transactions to the ordering service where the consensus protocol is run. And finally what comes out of there is blocks of transactions. And those blocks are sent to all the peers in the system which is highlighted as yellow here. We call them validator peers. I will come to that later. And these blocks are validated transactions and validated finally committed to the ledger. So what I want to highlight here is that in our observation and experience the validator peers significantly limit the performance of a Hyperledger Fabric Network. And the way these transactions flow through this lifecycle the third party, the external party can only see the data after the blocks have been validated and committed to the ledger. And that's when the transaction is confirmed. So that is why we wanted to look into these validator peers and see what are the performance bottlenecks and how we can improve it and accelerate it for higher transaction volumes near real-time updates. So here's a snapshot of a performance dashboard from one of our validator peers in the network. So what we are trying to show here is that we just took the standard Vanilla Fabric Peer using CouchDB and the performance you get from such a peer is about only 150 transactions per second. That's far from what we wanted to kind of achieve at the end of the day. And we are not the only one to get this sort of performance. There are a lot of other papers which I've highlighted here and those papers have also shown multiple times that validator peers become one of the major bottlenecks in a Hyperledger Fabric Network. Let me switch gears and talk about FPGA cards. So an FPGA card is just like a GPU card. You can bite off the shelf and then you can plug it into the PCI slot of your server. But unlike GPU cards, you can actually program these FPGA cards with custom hardware accelerator which you can build for your particular application. And not only that, you can actually reprogram these FPGA cards for different sorts of applications. So I can program it for a blockchain application. I can program it for a machine learning application and so on. So inside an FPGA card, typically is something called adaptive SOC chip. A simple example is shown on the left-hand side. You can see, you might not be able to read all this, but there are a lot of blocks here. So some of these blocks are ARM processors. There are blocks which do PCI connectivity. There are blocks which do Ethernet connectivity. But the most important part is in the middle, the red block which is called adaptable hardware or programmable hardware. So this is where your programmable custom hardware can go. And then you can reprogram these for different custom hardware. So what we can actually do with an FPGA card is that we can actually program it to do compute, network, storage, acceleration, all together in a single card. And then we can also have it connected to the CPU and we can have a hardware software code design kind of a setup which is much, accelerated for the application you want to implement. So with that in mind, coming back to Hyperledger Fabric and the validator peers becoming bottleneck, our solution to that problem is the accelerator we call Blockchain Machine. So essentially Blockchain Machine is an accelerated validator peer for Hyperledger Fabric. And you can see a picture there. So it's a server, a multi-core server, with an FPGA card or maybe multiple FPGA cards in future. And it's programmed for the accelerator we designed for Hyperledger Fabric validator. It is specifically designed for high performance. So what I mean by that is much higher transaction validation rates, reduced block validation latency and so on. And then it is also designed to be adaptable. So what I mean by adaptability here is that all of you would know that Hyperledger Fabric by design supports smart contracts, right? So you can install a new smart contract. So what if someone installs a new smart contract? We should be able to adapt our program, reprogram our hardware, upgrade it to include the new smart contract. And that's how we can have more smart contracts supported over time. And then finally, the Blockchain Machine peer is compatible with existing nodes in the network. So why that is important is because I can bring up a Fabric network where all the nodes are standard nodes. For example, standard peer order nodes. And then one of the nodes is a Blockchain Machine peer. And that will work with all these existing nodes as well. And that's how you can first evaluate it, see whether it gives the right performance you want. And then later you can upgrade over multiple cycles the rest of the nodes in the system as well. So the current status for our work is that it's available for Fabric v1.4, although I don't think anybody uses that anymore. And it's also available for v2.2. And it has already been open sourced as a Hyperlager Labs project called Fabric Machine. The software code is all open source and the hardware is available on request. So if you wanna try it out, please reach out to us. So let me give a little bit of more details of what Blockchain Machine is. So here's an overview. As I mentioned earlier, we have a server, multi-core CPU here on the right-hand side and an FPGA card plugged into the PCI slot.  So the FPGA card has a network interface. What that means is that all the network traffic comes into the FPGA card and then goes through these modules. And what these modules are trying to do is that they implement certain types of network acceleration, for example, efficient transfer and access of block and transaction data from the Ethernet packets. There's a compute accelerator which accelerates the verification and validation of the transactions. And overall what happens is that data comes in, gets processed and then it's ready for the CPU to be accessed and further processed on the CPU side. So we call this bump in the wire processing. It's a very typical term and it's a very popular paradigm for higher performance computing. So data streams in through the FPGA card goes all the way to the CPU. So a little bit more details. Protocol processor at a very high level. We have a very hardware friendly protocol to send blocks from order to the validator peer and then it will extract all the relevant data that is needed for validation of the block and the transactions. For example, transaction IDs, block IDs, read write set. People who work with the fabric would know this and how you do the endorsement policies and so on. And then the block processor is basically the compute accelerator. We have different types of block and transaction level pipelines here. So what we are trying to achieve is that we want to process as many transactions in parallel and in a pipeline fashion so that we can achieve high throughput. And internally it has a lot of different types of transaction processing engines, validation engines and so on. And finally we have something called a memory map or a register map. So the idea here is that once the data has been processed transactions has been validated. The results are basically written into some sort of a register map which can be accessed by the CPU. So we provide a Go language API which is then used by the fabric software code to basically interact with the hardware, get the data out of the hardware and then combine it with the block on the software side. And this also provides us a way to basically overlap communication, overlap computation that is happening on the CPU side versus the FPGA. So we can have certain operations running on CPU while other operations running on FPGA in parallel as well so we can overlap these computations. So here is the final snapshot of how the whole system looks like in terms of implementation and in terms of how the hardware and software is partitioned. On the left hand side we have the entire system where you can see the CMAX app system without going into details. Essentially a CMAX app system allows you connectivity to the Ethernet port or the network port on the FPGAs or Ethernet packets coming through here. Then on the right hand side we have a QDMS app system basically that provides you connectivity and access to the CPU. So CPU can interact with the accelerator and then in the middle the user logic box is where the accelerator goes. So you can see we have the protocol block processor and the maps and everything in there. On the right hand side here we show how the hardware software basically runs different operations of the validator peer. So in a typical standard vanilla fabric validator peer all of these happen on the CPU side. But then for us we basically offload most of the computationally intensive operations. For example, verification of the block, verification or validation of the transactions, the VSEC, the database, MVCC and so on all on the hardware. And then the CPU is only reading the data, the final data from the FPGA, the hardware accelerator, combining it with the block, writing it to the ledger. So what comes out here, the ledger from this blockchain machine peer is basically exactly the same as any other peer in the system. So now the main question, the billion dollar question, what we were able to achieve with all this. So here is a quick snapshot. We have a lot more results. If you're interested, we have a research paper in ICDCS, the Disability Computing Systems Conference. You can take a look at that. But what I'm trying to highlight here is that on the left hand side is a standard fabric validator peer with multiple CPUs. It can achieve up to about 6,000 transactions per second. And on the right hand side with these blocks in machine, we were able to see up to 70,000 transactions per second as well in validation throughput. So the way we benchmark, this is using HyperDecaliper standard benchmarking tool. We use the small bank benchmark, which is a typical banking application that creates accounts, transfer monies and so on. And then all these peers are using level DB. So now I'll stop here and then hand it back to Muthu who will talk about how we took this research project and then applied it and integrated it into a production level network. Thank you Harris. So Harris was able to convince us like yes, we can get like higher throughput, but as a typical IT like we are not convinced yet, whether it's going to work or we need to do some different type of programming that's compatible. All those questions as any other customers, it exists for us also. So what we done next as part of the process, we want to implement this particular supply chain solution. Initially, we haven't used any of this custom hardware. We just use like assets like a phase one, typical like standard software, written everything for CPU and we are able to make our actual application work first. Then as a phase two, what we done is we introduced this particular blockchain mission to one of the only one orc to see how it works. It was able to work like transparently, specifically like Harris and Steve who's from who's our blockchain SME, they work together and we are able to do everything like transparently without doing any code change from developer or developing point of view. So currently we are in phase three, so we are working on how we can introduce this blockchain mission to multi-blocks. So the overall idea is more like a simplified diagram. So what are the current thought processes? We'll have the standard hyper-login network where AMD already has a blockchain mission and we're going to introduce to couple of customers at later stage and slowly introduce to various vendors and partners whom we are working. So what we done on AMD side is it's not just implementing for sake of blockchain. So here we also introduced like the full application scope as it is. For example, this particular application integrated all the way from our ERP to our client application. So we also took like a modular approach. For example, like we had like a message queues where all the data is getting received from our standard systems and we also created like a client worker, this mall like a parallel workers as a transaction processor. And we also created various rest engines so that it is like a standard interface. Plus all the chain code and other things are running in our accelerator hardware. And we also create like additional half chain databases and we do created like separate client applications also. Especially in this case, it's a mobile application which is using like backend hyper-login fabric. Now I can short a quick demo which is like a recorder. Semiconductor supply chain is a space where significant value can be extracted from enterprise blockchain solutions. AMD supply chain is an interconnection of organizations, activities and resources for transforming raw materials into a finished product for delivery to the end customer. Managing the integrity of products and processes in a multi-stakeholder supply chain environment is a significant challenge. With rising expectations from our customers on end-to-end visibility, establishing reliable provenance, preventing fraud and counter-fitting is crucial for AMD. Now that compute intensive tasks are addressed by the blockchain machine. AMD is able to create a supply chain pilot to showcase a real world implementation using accelerated hyper-ledger fabric. Okay, so this is a quick demo like how we develop the UI, which is like a mobility and also molecular entire process in various steps like various transactions are created with proper transaction code and people can go back and verify instead of blockchain what is happening under particular transactions. So this is the application which we created for some of our external customers and moving up. So we'll also show what exactly happens on the performance aspect. In this video, we show a live demo of our hyper-ledger fabric network with hardware accelerated validator peer. We set up a typical Prometheus and Grafana based that were to look at a few performance metrics of validator peers. For this demo, we only show the most relevant metrics. First, I would like to go through the configuration we used which is shown here in the top row. As you can see, we use fabric V2.2 and we also report the total number of blocks that have been committed to the ledger. So far, the ledger has more than 60,000 transactions. The first set of metrics which I would like to focus on is related to the validation of block and its transactions. We define this as the time it takes to validate all the transactions of a block and commit them to the state database. The top row shows the throughput for a vanilla peer running on a multi-core server while the second row shows the same for an FPGA accelerated peer. We configured a block size of 50 but it can be smaller sometimes depending on the incoming transaction rate as shown here. The software peer is only able to achieve a few hundred transactions per second which we realize is because of this lower database accesses. We use CouchDB as the state database because our application needs to run various queries on the committed data afterwards. The hardware peer, on the other hand, is able to process thousands of transactions per second because all of the bottleneck operations in validation phase are being executed on the FPGA card. This results in a huge speedup which is shown here on the right-hand side. You can also see the throughput over time of both the hardware and software peers plotted in real time as the transactions are received and validated by the peers. The second set of metrics is related to committing of the block to diskless ledger. The reason we separate out this operation is that it always runs on the CPU. Here, what I would like to highlight is that the ledger throughput of both the hardware and the software peer is about the same because the ledger is written by the CPU. The third set of metrics is the commit throughput of the peer which is a combination of the validation and ledger throughputs of the peer shown earlier. Here, you can see that the commit throughput of the software peer is very close to its validation throughput because it is dominated by the database accesses. The hardware peer, on the other hand, delivers at least 10 times more throughput and sometimes much more because of the hardware acceleration. This is shown as the speedup here on the right-hand side. However, a very interesting point to note here is that this throughput is quite a bit lower than the validation throughput of the hardware peer reported at the top. From further analysis, we found out that even after offloading bottleneck operations to hardware, the fabric software running on the CPU is still much slower. We realized that we need to better optimize the software in tandem with the hardware. For example, we can use a better disk to improve ledger throughput and better overlap operations in hardware and software. We are currently working on these optimizations. Thank you. So overall, we are able to achieve better performance like this what we could see during the peak, like the pure validation aspect point of view, we are able to get up to 120X and whereas like the entire operations, we are able to get somewhere between 10 to 20X overall. So all this are happening using this particular LVO C1100 PGA core. So this is like commercially available core plank. So when it comes to implementation of this whole solution, so we follow like a standard IT approach, like all the deployment is happening through CICD because we have to try out like multiple times, like we are like entire desktop automation pipeline creator and all the integration that happened through like a standard rest interface so that as a customer or various application we need to rewrite. And also like from our creature point of view, we decoupled all the aspect in that way there's no tightly coupled and there's no dependency of either hardware or blockchain version or ERP version, all those aspects. And also monitoring is kind of key. We also created some additional monitoring aspect as you've seen previously in Prometheus and other aspect as well. So entirely we created like a end deployment methodology so that it can be ready for production deployment. Some of the insights, I cannot call it as a challenges. The first insight is more about how to do an order adoption. For example, like always there is some kind of dilemma whether I need to put like a new hardware and other things but nowadays in the enterprise data center outside it became like more and more commercial or visible. For example, for many of the training we use a GPU. Similarly, like acceleration also coming into mainstream in many of the enterprise data center itself. And as usual, get your management support so that the whole process can happen smooth. And compliance and legal is very important because here we're talking about a smart contract and other things which are more close to legal rather than like IT code. So we'd be involved with compliance and legal while we're thinking about a blockchain itself and other other concerns and questions, why blockchain, what it cannot do and what it can do, all those aspects. And security also a very tricky portion like as usual like InfoSec has tons of questions about the security of the whole solution as it is. So that also we are able to address. And UI, that is one piece we are, we haven't considered the initial stage. Then later we found out for blockchain we need like a different type of UI so that we can show the full strength of the blockchain. So this one of the overall key takeaways. So what we noticed is we are able to do like a better traceability with the whole model of this blockchain so that all the data is coming straight away to this particular blockchain aspect. And also we are able to prove like there can be the gray market tracking can be happened better. And overall we are able to reduce the overall administration and paperwork because of this otherwise we need to go and scrap some other website to find out whether that is valid and all those things. And overall we are able to find out like better transaction throughput which can be usable once we are going into mainstream with multiple partners and customers. So with this particular solution we are very confident we should be able to get minimum 10X and much, much more beyond using this order acceleration. Thank you. Any questions? Yes, please. Yeah, hi, thanks for this very insightful presentation. I have basically two, maybe three questions. So the first one is if you're using the FPGA how did you get the code into the FPGA that you use some kind of high level description language and then port it over via VHDL what's the tool chain that you used for that? And if you're using FPGA and want to scale it isn't there some point in time where you want to move all this into an ASIC because it's much cheaper if you want to have a lot of them? And last but not least, well this is maybe I don't know if you can comment on that but you mentioned that we have a shortage in the semiconductor supply chain currently but I heard that this is currently reversed because we have in some areas at least a surplus of chips. So how is that going to balance out in the near term? Hopefully, thanks. Maybe I can answer the last question like other two questions Harris can address. Yes. Okay, especially when it comes to shortage just trends are currently reversing but what we notice is whenever there's a shortage of semiconductor the gray market became like more and more visible and many of our customers it's in the CY like they end up buying somewhere in eBay they're not sure like is it a real product they're buying and all the suspect. So that's where we want to have like a product provenant suspect. The other two questions like yes, Harris. Yes, so for the first one the implementation is a combination of a few different setups. So we have used HLS, we have also used VHDL we have also used something else called P4 language which is specifically for network processing. So for example, our protocol processor that's implemented in P4 is a high level language and then you can compile it into VHDL. Some of the parts are in HLS but then the main core of the blog processor like the validation of transactions and things like that those are in VHDL for the best performance you can get out of it. So whatever we found make more sense we use that for the implementation. So it's not like you have to use a certain thing you can implement it in any way if you want as long as you get the performance out of it. For the second, what was the second question? Yes, so yeah, I agree with you. I think at some point we would have to move these into let's say hardened blocks either inside the adaptive associate or have a separate SIG for it once it becomes a more mainstream kind of thing. At this point it's more of still we're exploring we have something which you're trying to move into production but yes I would agree once it matures it should go and become a six or some hardened blocks inside FPGA chips. So the question is when it's gonna be available in the cloud? Cloud a little bit challenges now still we are in a pilot stage. So once it got options coming like much more or mainstream definitely there's a possibility of cloud but there's one chart because we are using network port of the FPGA which is not generally exposed in many of the public clouds but there are in a few specific use cases they were ready to open it up. So once that option became like mainstream definitely we can look into it. Right now it's currently available in some of the co-low clouds not a standard public clouds to try it out. Yeah, so basically the implementation is not specific to an FPGA card in your own premises server like Amazon F1 instances, FFPGA cost this should work there but because the FPGAs are receiving data directly from the network so the FPGA network ports should have network access which is typically not provided by cloud providers. Once that becomes available of course this should be available to try out there too. So one more question from here. So first of all do you really need to support 14,000 transactions per second in your system and also how many third party systems or internal systems you had to integrate to make this a viable solution. I wonder for this particular small client app we end up integrating with around seven to eight different systems for data theme. The transaction per second then many of the data is coming as a buzz to us. So that's why the transaction rate keep increasing is like a sustained like 20,000 transaction throughout the day. So we don't want to cube it like as much as possible we want to complete it faster but still it's currently focused on very very small set of product not for the wider AMD product range. Yeah, just to add for that 14,000 transactions as you said if you have a burst of transactions coming into the system if you know about queuing theory the delay and the latency for transactions will increase exponentially. So what we want is because you want almost near real time updates. So under the peak workload we want to be able to support as much transactions per second as you can just like a visa system where during shopping sessions like a shopping season they typically say that they have to do 65, 70,000 transactions per second but in normal workloads maybe they're only doing 10,000, 15,000 but if the system is not able to support 65 or 70,000 during the shopping season there would be huge delays like minutes, hours for transactions to settle down. So the same thing applies here as well. And that's why we have FPGA card to accelerate, right? So this is maybe a technical question but I was wondering where you got most of the speed up from CPU to FPGA was it the data access or the actual computations themselves? So yeah, if you're more interested you can look at our paper but the summary is if I remember correctly about 10 to 20% of the time is spent on accessing the data because all the data in HyperJet Fabric uses protocol buffers. It's a layered structure. So if you do profiling you will see about 10 to 20% of the time is spent there accessing the transaction, the data the endorsement policy so not policy, the endorsements and so on. The rest of the time is actually spent about 50% in the actual ECDSA verification. So the digital signatures which uses ECDSA scheme that takes about 50 to 60 and in some cases up to 70% of time with the SHA-256 hashing as well. And then the rest 10, 25% is spent in level DB accesses. So we are getting most of the speed up by using multiple of these ECDSA engines in the FPGA so we have like parallel and pipeline transaction processing which efficiently uses these engines to do the verifications as fast as possible. And then because we have in hardware database which is of course gonna be faster than what you will get as go level DB. So that's where the rest of the speed up comes from. I think one last question on from my side the Hedera consensus you can replace with the existing the order is with the Hedera consensus protocol. So that's what I heard. Have you tried that? If you do that then your problem goes away. I'm not aware of the Hedera consensus protocol and it can replace raft but what do you mean by the problem going away? Can you elaborate? I thought that orders will go away once you replace raft with the Hedera then you get this 100,000 transaction per second in hyperlature itself. I'm not very sure. I'm not a techie but the way I understood, I explained it. Okay, I'll look into it later and get back to you. I'm not sure about the outright performance but it is another hyperlature labs project. So you should better take a quick look at it. On the cloud side though there are a couple of like I'm thinking of Equinix metal which used to be packet. I mean it'd be interesting to sort of talk to folks like that possibly Ridge cloud. So in other words, once you get beyond the hyperscale clouds there are some bare metal cloud providers that could be interested in this. Yeah, we are already working with a few bare metal providers to see how we can host. So right now we are planning to host some of the demo environment so that people can come and try it out. So we are working with a few others in just a second. Any other questions? Okay, if not, thank you. Thanks a lot for having the session. We'll all meet you in Guinness. Okay, thank you. Thank you.


अदरनी ए राहुल जी ने कहा, के आज हिंदुस्तान ले अस्ट्रेलिया में अपनी जीद का जंडा गाडा है. सब देश्वासियों को राहुल गान्दीजी की तरव से, कोंगरेस अद्खषाज की तरव से, बहत बहत बड़ाई. देश्खा किसान जो पीडा में है, उसे लेकर आज राहुल गान्दीजी आप से बाद करेंगे. और बहारती राष्चे कोंगरेस की और से, एक किसान की पीडा को लेकर, एक पुस्तिका जो सरकूलेट की जाएगी, में सब से ले आदरनी राहुल गान्दीजी से, और संच्तन, महां सच्च्च्ठ प्रभारी और हमारे कोशा दक्ष्ची से अनुरोगत करूंगा, के पहले वो बुक लेईस कर देएझें और उसके बाद राहुल जी आप से बाद करेंगे. अदरनी ए राहुल जी अन अप से बाद करेंगे. अदरनी है साथिए बगर किसी रिलंप के मैं आनुरोट करूंगा के अदरनी रावल गानदी जी आप से बाध करी हैं compared to how it has in society ... We have seen it .. when it comes to the airports We have seen when it comes to infrastructure We have seen when it comes to power We have seen from telecom We have see it when it comes to retail across the board What we are watching is the development of massive monopolies in this country What do I mean? � Contract ुन्र औब यहूँ यहूँ कोने कातलीश। मैं क्या करक्ट्टियों और आदे। क्या आदान करत्टियों करक्ट्टियों कर्ट्टियों क्या आदाननों। सभибा सब रहा थो आदेतान अस सबबशानकता में दनगर्ये। सर्मटिवागा चाह। और ख़बोराजा एक आदूका। उगर अग्ई आद्झा स्वाचोच जब आपदुयी अस्वाआप। अप्तानिवेंते काऔए विया। खब वी कई कहो सार्म रहिताक। विए बीया विंका। और वो विए आपनबादा अचार। आप आ� 이� भी मेद करिफ, आप जर aosदी अपद की तोच्� chegAnu công 있어요 अगर покупो, छोुब मोन more thanकों आप आप आप, ऐत से बास टी तुछने को n demo orchest मन अपलीा आप आली still सुत आप आप आपुना दे बँ лет work आपक कोग। hump जोज में that the country itself is not able to understand the depth of what is happening so what is going to be the result of this the result of this is going to be 3 or 4 people are going to own India 3 or 4 people are going to own the entire agricultural system farmers will not get the value they deserve the Monday system will be destroyed 3 or 4 businessmen will be able to store millions and millions and millions and millions of tons of wheat, rice, other essential commodities and then my friends in the middle class you will be paying prices that you have never imagined please understand that the rice you buy the wheat you buy comes to you at the rate it comes because of the APMC because of the agricultural system because of the structure so this is not an assault on the farmers this is an assault on the middle class and this is an assault on every single youngster in this country who is not going to be able to get a job so this is what I want to tell the youngsters realize that your independence is being taken away realize that this happened before and we fought this and everything we have today is because of the fight that we fought and the same exact thing is happening to India today this is the simplest way I can put it so why are the farmers of Punjab and the farmers of Haryana standing there who are they they are the patriots of this country who are protecting the livelihood of 60% of this country I 100% support them and I go further every single Indian person should support them because they are fighting for us they are not fighting for themselves they are fighting to prevent this country from falling into the hands of 2 or 3 people that is the heart of what I wanted to say I speak in Hindi if you look at the last 6-7 years then in every industry there is a monopoly of the same 4-5 people look at the power, look at the airports, look at the ports look at the telecom, wherever you look there is a monopoly of the same 4-5 people that means there are 4-5 new owners of this country till today, there was no monopoly in agriculture till today, the benefit of the fields of India was going to the farmers, the workers, the middle class and the poor as such, there was a whole division that used to protect these people there was Mandya, there was Essential Commodities Act there was a legal system these 3 laws are going to be done once again in agriculture before independence in the hands of 4-5 people, the entire agriculture of India is being given by Narendra Modi Ji that is why the farmers are standing outside and the farmers are not protecting themselves and they have to understand the youth of the middle class they are protecting you, they are protecting your food and we all have to support them wholeheartedly this is the reality of India now if you want to ask me any question do ask we will take questions this press conference is for farmers so broadly I will answer you but don't try to distract we will go one by one let's go with Sanjeev from PTI I will come to you madam I have noted it there is no deadlock you have to understand what is going on there is no deadlock the government believes that they can tire out the farmers the government in its arrogance believes that the farmers can be tired the farmers can be fooled the farmers can neither be tired nor can they be fooled the farmers are more aware of the Prime Minister the Prime Minister is more aware of the farmers of India what is happening and what is not happening so that is the reality there will be only one solution these three laws will have to be taken back there will be no other solution we are exerting more pressure we are already exerting pressure this press conference is a means of exerting pressure but more than exerting pressure I think the thing to understand is that this is not about farmers this is about the creation of massive monopolies in this country that are going to take away wealth from the people of India this is the crux of it please tell me one industry in India today just name it for me which is not monopolized by the same 5-6 people that is the real crux of the issue the farmers are this is the first step but you will see after the farmers India will not be able to produce jobs why is it that the Indian economy has gone from the best performance to the worst performance Siddharth from A&I I have noted that this is an attempted distraction this is not a full distraction but okay see what is in their hearts they say about me the government is trying to distract the farmers the government is saying to the farmers see talk to us it has been talked about 10 times it has been talked about 9 times the government is being humiliated so the reality is that the farmers know and who is Rahul Gandhi what does he do every farmer in India knows the farmer knows that Nandaji was not standing I have not seen him land acquisition was not standing Nandaji was not standing Rahul Gandhi was standing so when it was about the land of the farmers Congress was standing there when it came to pardon the farmers Congress was standing there where was Nandaji I have not seen thank you can we go to ABP Ashish and I will tell you one more thing see this is my character I am not scared of Narendra Modi I am not scared of these people I do not care I am a clean man I cannot touch this you can shoot with a bullet but cannot touch and I am a patriot I protect my country okay and I will continue I will stand alone I will stand alone I do not care this is my religion I am more fanatic than them okay and what is happening here for which this country was fighting it is happening again do not listen to me today when you become a slave then listen it is acceptable for me I am not a slave what is this J.P. Nanda is my professor I will answer him who is this is he a professor why should I answer him who is he I will answer him any question I will answer and as far as the reform of the agricultural system yes we have talked about reform but we have not destroyed the agricultural system we have not written we have not written that farmers cannot go to court we have not written that we will destroy the farm we have not written that we will let one or two people destroy the entire India no one has such a law no one has such a law thank you sir can we go to Arun from news 18 I will come to you ma'am in the agricultural system can you confirm anything by destroying the three laws no sorry destroy destruction of Allah you can tell him later I will manage him see this is a process these three laws this is a part of the process what was the process process of land acquisition process of land acquisition process that Narendra Modi Ji said I will not give a bonus I will not give the correct amount of money in MSP gradually Narendra Modi Ji is trying to weaken the farmer once the farmer is weak then he will not be able to fight so these three laws these are a process these are not going to stop these are not going to stop their goal is that they want to destroy the farmer and this entire agricultural system they want to give it to their 3-4 friends this is the reality and our job the farmer of India has to be protected so in this specific instance these three laws will have to be rejected these are the positions of Congress but do not think that if these three laws are rejected Narendra Modi Ji will stop there they will not stop they want to destroy the agricultural system of India and we have to stop them no, that is a distraction one now I will come back I will come back I will come back I will come to you Sanjay Mishra Ji from Jagra I do not want to give a tip on the Supreme Court but the reality of the Supreme Court can be seen all over India thank you sir I will come to you Anand Patel from India today look, the law will take its own course but I have an observation this was top secret information this was not provided to anybody else there were 4-5 people who knew this information Prime Minister of India possibly the Defence Minister of India the Home Minister of India the Home Minister of India the Air Force Chief these would be the 5 people who would know this this is a criminal act giving official secret information to a journalist is a criminal act both on the part of the person who accepted it and on the part of the person who gave it these people call themselves patriots there is nothing patriotic about putting our Air Force at risk there is nothing patriotic about taking political advantage so that our aircraft gets shot down so that the Pakistanis know that our Air Force is coming if Mr. Arnab Goswami knows if it is on his whatsapp I assume the Pakistanis also know it it is not a very big jump so it is a criminal action and that investigation should begin I want to know whether it was the Prime Minister of India who told Arnab Goswami or it was the Defence Minister or the Home Minister or NSA I want to know which one of those people told this man and this is a criminal action so that it should be looked into thank you I will let me go back to Uma Shankarji from NDTV also the distressing thing to me was somebody saying this is going to be very good for us when our CRPF CRPF boys are killed right that is the anti-national act and I don't like that language thank you just one minute see no no we we respect the independence of the farmers we accept that this is the independence of the farmers and we respect their space respect their respect this is the reality this is the press conference to help the farmers I came here yesterday also I said so we are helping we are helping through the press we are helping through the statements but we respect them thank you see I said during Corona in February that a very big problem is going to be created among you people have made fun among you people have said look what Rahul Gandhi is saying word fear mongering I am telling you and you write in your notepad China is seeing the weakness of India and understanding and China has a clear strategic vision they want to shape the world India has no strategic vision India sometimes does this sometimes does that China has tested India twice once in Dhokla and now in Ladakh they have put their troops they are testing and I am telling you that if China has not given clear message if India has not made clear strategy in mind whether it is military strategy whether it is economic strategy whether it is geopolitical strategy then China will not sit quietly China will benefit and when it will benefit then it will be a loss then you will not be able to stop it that is why I am repeating that is why I am repeating I am raising red flag for the government they have come into your land you feel that you can do this by doing tutu maymay you feel that you can manage it through event management you are mistaken China is another thing China wants to dominate and it is systematically working there is no difference from your words whatever you say there is no difference your work is to protect India you are not doing it and as a result you will get to see India you will not be able to stop it that is why I am warning you I am opposition leader I am working and corona is coming economic damage will happen people will die prepare they said you saw the economy is falling the workers are dead the workers are dead because they just keep talking in their mind they don't want to act and the world is going on a dangerous path transition period change period and our national leadership should take it seriously thank you sir can I go back to as I told you that farm law is not an event it is a process first land acquisition process China is not an event China is a process and I study I think and speak I am telling you it is a very dangerous situation thank you I request all my friends that as Rahul Ji said farmers we will keep our principle focus there yes sir look the vipaks they fight with institutional framework vipaks needs free and fair media vipaks needs institutions vipaks needs judiciary vipaks needs parliament so they have captured every institution they control courts they control media they don't let the parliament speak they control the media so the way the English used to run look if it is brown then they can't speak they are running this India they can't speak if they speak then they will hit it is a scary environment you just said that everyone says that Rahul Gandhi is fighting but it is a scary environment you know that you can't see the reality you come to me and say this is the reality and we will fight thank you sir again we are focusing on farmers from today I explained that we want to respect the farmers that is the reason look what was your first question government government government's approach attitude that we know everything and whatever they say that is anti national criminal that is their approach what we are saying is that we can see the problem we saw the problem of corona we said government makes us enemies we said after china government makes us enemies but we are giving warning signal government can't understand that they have taken pressure on the farmers the situation is not going to stop there energy will come out from somewhere energy will come out from somewhere from youth unemployed youngsters so we are warning that you have attacked the farmers of India don't do this you don't recognize their power so this is the reality their approach is wrong they don't want to learn they don't want to think they just want to speak they are taught RSS people teach them they don't want to listen they don't want to understand they just want to speak and speaking is top grade but they don't think and to run the country they need to think more than speaking they need to think more than speaking thank you I'll go one by one Anup Vajpayee from Amarujala we should conclude there is one more question their style is not their style is not their style is not their their other approach what is the benefit of my words I don't believe it so where will the resistance come from it will come from the people of India our job is to highlight what is going on to the people of India we are struggling why because the instrument of transmission is not controlled by us so the press is not controlled by us we don't have the thousands of crores of rupees that Narendra Modi Ji has we cannot do crores of rupees in social media but this is an advantage for us because it makes us forces us to be pure it forces us to be honest it forces us to tell the truth this is good I don't have a problem with it I think broadly I will tell you if you step back a little bit what has happened is that the vision that India had for itself from 90s has collapsed what used to work in the 1990s does not work anymore so India requires a new vision it requires a new imagination and this is all the turbulence that is taking place because India does not have a vision and Congress party's job and its skill isn't providing that vision so you will see that we will provide India with that vision and many millions of people in India who do not accept this nonsense that is going on in the country and they understand that there is a massive failure taking place we used to be pre-eminent economy in the world we are laughing stock we are complete laughing stock we cannot give our youngsters jobs our farmers are sitting outside in the cold I don't know why Narendra Modi Ji thinks that this is something to be proud of this is something to be ashamed of the whole world is looking and saying what is going on, why are lakhs of farmers sitting outside Delhi so he is destroying India's image which the Congress has built slowly slowly slowly slowly Mahatma Gandhi first slowly we have built it was flying means throwing food so that is the reality we need to conclude we were in the government in Bhattaparsal please tell him to read the newspapers and also we paid a lot of money and land acquisition we did not pass in the parliament we were in the government but anyway JP Nadda is not anybody I am answering to thank you very much Samia well I remember the prime minister saying this time Trump is the government I remember in Texas it was that he was holding his hand up and saying sorry I am not Trump I am Joe Biden the point is whether he is Trump or Joe Biden the point is whether he is Trump or Joe Biden as a prime minister of a country you don't do something like that because you are disrespecting the people of the United States by doing this it is their choice not your choice so it is a lack of understanding of fundamentals and you know now of course Trump lost so thank you Supriya see see who he has to book he has to book the journalists or he has to book them who has given the information or both have to book both have to book because the information someone has given or someone has taken see a journalist the sensitive information of the defense the information is given before Balakot the same journalist said after Pulwama that this is very good for us reflection of the mindset of the prime minister his mindset is his see this is very good for us that our 40 people are dead now we will win the elections you gave the information the information was with 4-5 people in such a mission pilots don't have pilots get it in last minute Air chief prime minister Home minister and defense minister NSA among these 5 people this information was given this is criminal action this is criminal action you have to find out who gave the information and you have to go to jail and this process should start but this process should start I will tell you why because the prime minister has given the information so it will not happen maybe later 5 people have you had the information you couldn't give Prime minister knew Air chief knew NSA knew Home minister knew Defense minister knew 5 people knew who gave the information so that is clear cut see this is very good one second sir last question you have to ask the professor why you asking me you ask the professor


I originally applied to and studied politics, psychology and sociology before I made the decision to change into law. It was a decision I took because I'd been talking to a lot of my friends who studied law. I come to Cambridge not really knowing anything about law, hadn't even considered applying to it before I got here because I knew nothing about it. I thought it sounded boring and dull. When I spoke to my friends and we started to sort of have debates on political issues and how that overlapped with legal issues and sort of the breadth of law became apparent to me and I started to think more seriously about whether this was the academic subject that I wished to study. When I considered it for a while and I done a bit more research into the subject, I spoke to the law tutors in my college and they arranged an interview for me. In my interview I was given an extract of a statute and asked to talk through it and then apply it to some problem questions. They also asked me the obvious questions sort of why law do you want to follow this through into career in law or something like that. So it was very similar to the sort of interview you would get if you applied to law straight off. On top of that they specified that I had to get a certain grade in my first year in politics, psychology and sociology. Having done that I successfully moved into being a change over in law. What that basically means is you have a two-year course of ten modules five a year. It seems a little bit odd that you're doing a ten module course when the people who are doing three a course are doing fourteen modules which means that they get a lot more choice than some of their subject options. I think I was actually surprised about how many choices I get. I still get three. This gives quite a big range of subjects for you to choose from as the compulsory ones cover a very large area themselves. You have criminal law, constitutional law, equity, EU. It really does cover a huge amount. Having been a change over and sort of stepping into law, having never done it before, having not really having a background in sort of legal issues or anything. It was quite intimidating to start with. It was quite difficult being taught with the same year students who've had another year of law and it can be a little bit scary to start with just because it feels quite difficult to be around these people. You definitely settle into it very quickly and it's a really challenging and really really rewarding degree. I think the best thing about it is that it's a fully qualifying law degree and you do it in two years so it gives you everything you need but I've had the sort of the background of another subject as well and this isn't something that I think puts off employers at all. I think it's definitely seen as a positive thing. In my personal experience I've sort of talked about the fact that non-law students are quite popular with law firms so I've got a little bit of non-law while still having a qualifying law degree and I think that's something that's really positive and to a certain extent it's let me come into law with a slightly different view of the subject. My experience as a change over has been a very positive one. I've enjoyed getting to know people in different years because you have some of your subjects and lectures with the year below or two years below you and I think it's been really challenging and worthwhile.