Next question is from Sergio Morales-Bustillo. How can I progressively overload the overhead press? I've been stuck at the same weight for several weeks. You don't have to always add weight to progressively overload. What's the episode number, Doug? What number? We just did this. Nine things to, nine things to... Nine ways to progressively overload. Yeah, there's a lot of different ways you could do this. Here's one way is, we just talked about isometrics. Try this.  Or hold it in the bottom position. Don't rest it on your chest, but actually support it for some time. Or put it in the sticking point for time. Or you could take your weight, go lighter, attach a band down. So now you have kind of progressive resistance as you lift. That's another way to do it. You could increase the frequency that you're doing your overhead presses. So rather than training overhead presses maybe twice a week, take that total volume and divide it up over five days where you're just practicing the lift over and over again. Manipulate the tempo. There's a lot of... You have to change the way that you're doing the lift and change your approach to see if you can start making some benefits. Here's a simple one. Maybe your rep range has always been the same. So you're stuck at eight reps with 135 pounds. Okay, see what you can do for 20 reps and then just stick to that for a while. Go back to your eight reps later on and you should notice an improvement. Or maybe you haven't put effort in time in strengthening your stabilizing muscles to keep everything signaling back to the body that it's safe and secure which then allows you to drive more force into that specific exercise which is something I found was a key that unlocked even more potential for my overhead press specifically. So doing any kind of rotational movements and things like progressively adding a little bit of load to these rotations to strengthen and support to bring that up because if we're putting all the emphasis on that one sagittal plane movement then inevitably we're not bringing up the supporting cast to contribute. I actually saw huge gains from this with you, Justin. So I come from more of a bodybuilding style of training and so when I overhead press or for a majority of my career when I overhead press I would do strict overhead presses and I can't remember where I was at weight-wise at that time but I had kind of peaked out and I really hadn't got that much heavier and when I train with Justin, Justin loves to do like push presses where he gets used to lose a little bit of English, right? Use your legs and explore what you could increase the weight quite a bit and so what I would do after I trained that with him and I was like right away I felt myself get stronger is I would do that and then I would hold it over my head for a little bit so I could use a little bit of my legs to explode it up over my heads and then I would get used to stabilizing... Acclimating to a higher amount of load. Yes, I would get used to it and then we all know that you can on the negative or the eccentric portion of the exercise the way down in the overhead press you can handle more load than you can on the way up. So I would put a lot of emphasis on exploding it up over my head stabilizing it up and holding it up there for a little bit and then resisting it on a way down and do like, you know, you talked about somebody who always does eight I was doing like triples. I was doing triples with significantly heavier weight than what I could overhead press with. I was holding it up above my head and stabilizing and then resisting the way down. Three sets or three reps like that and man I saw my overhead press. Something that worked for me years ago was moving away from the barbell and doing only presses with dumbbells or kettlebells and get really good at using kettlebells and dumbbells and then going back to the barbell and feeling stronger or vice versa. So again, there's a lot and I think oftentimes there's a lot you can do. People get stuck oftentimes. Just adding weight to the bars I think is the only way to... Yeah, it's the same thing. It's just I'm not changing anything. Why isn't why isn't anything changing? Well, you got to change things before you can expect them to change. So don't just look at adding weight. Look at changing reps and tempos and technique and different methods. There's a lot of different ways you can get the body to progress.


The race of the students and the students is a very, very important part of this. And I think that we have to look at that as an institution of the kind of decision that has been made for us.


A novel series of chlorine E6 curcumin derivatives were designed and synthesized. These compounds were tested for their photodynamic treatment, PDT, efficacy against human pancreatic cancer cell lines. The compound with the highest activity, 17, showed high cellular uptake and induced apoptosis in the cells. Furthermore, it was found to be effective in reducing tumor growth in melanoma mouse models. This article was authored by Til Bahadur Tapamagar, Ju Suk Lee, Ji Hong Lee, and others.


Hello people of the internet! My name is Johnny and welcome to my review on Willy's Wonderland. As you can see I have my t-shirt right here. This is my terrible attempt at a Nicholas Cage janitor cosplay. I hope you enjoy it. Right off the bat, as you can see there are going to be two sections of this video. A spoiler free section and a spoiler section. So if you have not seen the movie yet, I highly recommend clicking off once I get to that spoiler section of the video. Even though the film has been out for a little while now, I do just want to make sure if people haven't seen it and they still don't want to be spoiled, I'm perfectly fine with that again. I highly recommend clicking off once we get to the spoiler section. So I saw the movie on release date which I believe was the 12th of February and I'm going to be frank, I enjoyed it. I'm not going to lie, I don't think it's a bad movie. Out of 10 I would probably give it maybe like a 7. Would I watch it again? Sure, probably on like special occasions though and like Halloween and especially only with friends. This movie is really meant to not be serious and thankfully it didn't take itself too seriously in the film. I feel like they did a very good job on not taking it too seriously while also, you know, still bringing an enjoyable movie to viewers. It's hard to describe it because it's such an interesting film. They really tried to do a lot here and they, to be fair, they really did succeed in doing a lot, especially with such a small company behind it and that is something I'm still shocked about to this day. Not only were they able to make a pretty banger film, but they also way before release date had a large audience behind the film. Sure, you could chalk that up to all the FNAF fans who are waiting on the FNAF movie getting excited for this movie because, I mean, let's be real here, it's probably the closest thing we have to the FNAF movie before it's being released right now. Before Willys, all we had was the banana splits and my god, I do not want the FNAF movie to be anything like that movie. And I realized I'm going to be jumping from topic to topic because I don't really have a script for this. I just want to talk about the movie because I really do enjoy it. It was funny. Again, don't go into it expecting to be scared. While it is a horror movie, it's not really that scary in my opinion. It's definitely more of an action comedy, um, but, you know, with horror elements in there. But again, even with all those combinations of genres, I feel like they pulled it off pretty well. So giving a quick rundown again, no spoilers just yet, don't worry. Nicholas Cage, who is very clearly me, I am the janitor, I actually starred in this movie. I am. That was me up on screen. He's riding along in his car, it breaks down, and some mysterious dude offers to fix his car if he spends the night at Willys just cleaning the place. And as you see in the trailers, as you see in all the teasers that they release, which by the way, can I just say, great marketing for this movie so far? I say so far. It's out already. They release like new clips, like almost every day leading up to the release of the movie. They did a great job teasing it. And I'm sure you've seen, you can see it in the reflection of my sunglasses right now, the amazing scene between the janitor and Aussie, the ostrich. The action is on point. Really? I couldn't have asked for anything better because it was really good, the action in the movie. Each character had their own section of the movie, and as, okay, I will say, going into the movie, I was a little skeptical about how they were going to show off all of the animatronics, because there's like eight robots in the movie. So I was like, okay, that's a lot. I hope they all get enough screen time. And I was shocked that they all did. They do go one by one killing off the animatronics. So some of them get more screen time than others. The actors were all right. Of course, you know, Nicholas Cage steals the show, even though he doesn't talk throughout the entire movie. He doesn't say a single word. He still kills it with the action. It's amazing. I probably should have gotten a script for this because I'm all over the place. I hope they do a sequel because I think it would be very interesting to explore more of this world. There's a whole back story behind the place, which I'll get into shortly in the spoiler section that I would love to learn more about. So I think that's basically it. I'm about ready to talk spoilers. So again, click off now if you don't want to be spoiled. Yeah, closing thoughts, solid 7 out of 10. Really enjoyed it. It was great. Acting was very, very poggers from everyone all around. Comedy was nice too. But again, if you go into the movie expecting to be scared, you're not going to have a fun time. If you go into it with the right mindset, thinking, all right, this movie is not taking itself too seriously. It's supposed to be a funny action beat them up with Nicholas Cage and robots. If you go into the movie with that mindset, you'll have a fun time. So now hopefully everyone who doesn't want to be spoiled has already clicked off because I'm going to talk spoilers right now. Wow. OK, where where to begin? Let's start with the connections between this movie and FNAF, because that's being talked about a lot right now. And I want to I want to share my thoughts on it. So as we all know, this movie was going to originally be very, very similar to FNAF. There was a Bay Area animatronic. There was five missing kids. There was a killer in a robot suit. There was a Pirates Cove. There was there was so much stuff where I believe Scott and Blumhouse had to reach out to them and say, hey, maybe change the movie up a bit. And they had to change it. Unfortunately, there are still quite a few connections to FNAF, which did kind of it kind of brought me out of the movie just because I was thinking, OK, this is kind of like FNAF. I can see where they maybe didn't change this part of the script. I'm not saying it's a FNAF ripoff. I'm not saying it was inspired by FNAF because I know people get really upset when you say that about Willy's Wonderland. Yeah, there is still quite a few connections, especially the murderer whose name is William, luring kids into the pizzeria with a mascot costume and killing them. And guess how many kids there were? Five. Yeah, when that came up, I was like, oh, my God, you have got to be kidding me. There's quite a few more things like a power outage. And then really, really the weasel appears just like how when you lose power in FNAF one, Freddie will appear. There was also a character who was pretending not to be like the others. I believe it was Cammy Chameleon who was trying to trick one of the teenagers into thinking, oh, I got possessed into this body unwillingly. I didn't want this.  But of course, they turned out to be just as evil as all of the other animatronics, which can I just say the kills in this movie and the way the animatronics set up killing the teenagers, banger, dude, absolutely amazing. I will say I didn't really care much for the teenagers because they just weren't written well. I'll be honest, I didn't really care when one of them died. Though 90 nights kill on one of the teams. Amazing.  Yeah, the kills were great. I'll talk about the sex scene or whatever. It's an R rated movie. It's rated 18 plus. We knew it was going to happen. We saw it in the trailers, went on for probably a bit longer than it should have. But, you know, we were expecting it. It's whatever. I honestly see too many people complaining about it.  I'm going to go see it. Oh, no. What are they doing? Why is why is Audi the alligator just watching them do it? I really enjoyed the kills for the characters as well, especially towards the end. They got pretty good at the end, especially the big fight with Willie. That was amazing. Tito, the turtle, by the way, hilarious character. He spoke in Spanish the entire movie and the way he got killed off was hilarious. Siren Sara got a lot more attention than I thought she would. She was kind of like the final character to be killed off besides Tito. I believe they killed him when they were like driving away from the place when they got out. I honestly don't know if Sara died in the explosion. So I feel like if they were to do a movie where it was a sequel and not a prequel, it would probably start with Sara. The big twist at the end, by the way, was something we kind of saw coming. You know, they set it up pretty clearly at the beginning, where these guys know the backstory or I say these guys, I mean, like the owners and the cops, they know about the backstory of Willie's. So the twist of them feeding them, it didn't shock me because they kind of set it up already. So I saw it coming. Yeah, at the very beginning, you can tell that the person that is renovating the place who wants to reopen it and the person that is fixing. Actually, I don't know why I did this because he did fix cages in his car. So I guess the guy who fixed his car, they were inside of the whole operation. Also, I don't know why Willie killed the police officer. Why did he do that? He was on their side. She was on his side. So I don't know why I did that. That was kind of weird to me when I saw that. I really did enjoy it and I fully support the people making the movie as they move forward with whatever they're doing, whether it be another movie in the Willie's Wonderland franchise or if it's something different, I want to see what they do next. So yeah, that's it. So if I didn't touch upon something that I probably should have, again, maybe I should have written a script for this, but I just wanted to talk about the movie because I enjoyed it. I need to stop saying that, but I enjoyed it. So yeah, that's it. Go watch it. Really. Go watch it. Again, under those circumstances, but go watch it. So that's the end of this mess of a video. Thanks so much for watching and I'll see you all on the flip side. Goodbye.


I'm delighted to be at the E4M Primetime Awards 2018. This is the fourth year of the Primetime Awards and I was extremely fortunate to have been part of the jury and we had a fantastic jury, a lot of debate, a lot of discussion and finally we're awarding the best of the best, the best TV advertising in the country. It was actually very interesting to look at the work across different categories and the way television advertising is kind of keeping pace with the times. We particularly saw very good work in the media and entertainment category. We also saw some good work across auto, e-com and some of these other categories. Earlier television advertising was all about FMCG but I think we moved a long way since then. Congratulations once again to all the winners and thank you to Exchange for Media for organizing this event and having us on the jury.


Hi everyone, hope all of you are doing well in this particular context and submit. I'm Victor Couturier and I work as a Cloud Engineer at Société Générale, one of the oldest and biggest bank in France. And I am here in our new campus in Paris to share with you our journey in OpenStack contribution and how does contributing to Open Source truly increase the delivery of our team. So to begin with a little bit of context, I am part of the Cloud team of Société Générale, which is composed of about 10 devops, and the purpose of the team is to expose cloud services to customers, and these customers are mainly developers of the bank who want to use their own application. So that's all about Private Cloud. This team, six years ago, originally used the VMware stack to propose our services, so it's all about ESXi on a very high suit. So it is about 40,000 VMs who are running today in this environment. But in the past three years, with the growing of the need of infrastructure to host cloud native application, we launched a new OpenStack-based offer, which power, I think, around 5,000 VMs today, and it is growing. So as the context is done, let's begin with presentation with a little story. So go back in 2019, where our OpenStack deployment contains only one availability zone in one data center in Paris region. It is based on SEF, so the storage is a SEF cluster for our bus image on VM, and we just receive our new servers to open a second availability zone in Paris region in a second data center. And all users were very excited by this new availability zone, but there is one last thing to fix, I think, to open this availability zone, because to ensure good storage performance to customer VMs, we really need to have a local SEF cluster in per data center, because nobody wants its VM run half on the local data center and half on another data center. So we need two SEF clusters, and in order to ground these blazing fast provisioning with OpenStack and SEF, this final copy and write, we also need the Glance image to be present on local SEF cluster. So in our case, the Glance image should be deployed on the two storage clusters. And at this time, it was the first version, Glance supports the multi-store, and multi-store in Glance at this time was at the very beginning. So you were able to configure multiple store, but you need to choose only one to send your image. So you weren't able to choose multiple store to send your image. So we have an issue outside, and in order to fix it, we solved about three solutions to deliver finally this second availability zone. So the first solution is to build custom code around the project. So here it is OpenStack Glance actually, and it is actually the standard way of doing it, because when you are using, for example, proprietary software, you don't have access to the code. So it is mandatory to develop code around the proprietary software, and you don't have the choice, in fact. The second solution is to modify the code of the project, but without releasing it and submitting it to the community. So it is kind of working. And the third solution is to do the same, but instead of totally working, submit the code to the community and try to get it merged. So the first solution, I think, is the most acceptable to implement your business-specific logic. For example, in our case, it is inserting OpenStack VM into our CMDB or enforcing security group infrastructure rules, so business rules against our security groups. So that's very convenient, but it will require you, it will require you the biggest amount of work because you will need to develop everything. It is your code, you will need to maintain it forever, and it will be forever long to do it. The second solution is always the worst case, actually, because even if it is easier to develop custom code inside the project, it will be really a pain to upgrade, and it will be so painful that each upgrade will pay ten times the initial investment. So it is a very bad idea to do that. And the third solution is, I think you will love it because it is the last one, and it is the one we implemented as Société Générale, is to actually modify the project, modify OpenStack in our case and submitting it to the community. And at the end, in the long term, it is a solution, it will require the less workload because part of the code you will develop will be supported by the community forever and you will be able to leverage on all the code existing in the project. So that's one of the best solutions we found, and it will also develop a lot of soft skills, like by example a better appropriation of your everyday tools, or maybe you will get a lot of improvement in your development skills because you will develop against the experimented developers, and obviously you will desacralize the open source contribution process. So to emphasize this, let's take a very short example about one of the first contributions we've made, and it was in It. So It is an infrastructure-as-code tool of OpenStack for deploying cloud resources, and internally we are using it basically only for deploying base blocks of the cloud, so flavors, domain, external network, and all that kind of stuff. And in fact, it is very convenient because it is just plainly available and you just have to store your email in Git, then regularly apply your Git configuration to ensure everything is properly set up in all your environment and all your region. So it's very convenient to use it for that. But by example, here is an example of each template for networks. So it is plainly available, it is quite simple. You just describe all your resources in this list, and each resources is described by a list of properties, which describes the resources. So it's kind of simple to store and to write, but the problem is we are relying on the tags attribute in the Neutron network in order to know to which project we will need to share which external network. So we are putting some tags on the project, tags on the network, then we will share the project to the right network, the network to the right project. So it is very convenient to do that, but in fact the tags attribute, despite it is available in Neutron, it is not exposed through it. So it means you can't use the tags attribute as a property of your YAML, it's not allowed, mainly because each resources wasn't updated when the Neutron network implemented the tags framework. So our solution was to look into GitHub, into the heat source code and find if we can understand it, or maybe add a video. And here is what we found. So here is the file we modified. So it is a provider net class in the provider net.py file, and here everything is file. And as you can see, there is only a list of properties, which is all the aload properties in the HTML, mapped to the related attribute in Neutron IPI. So it's quite simple. And later you will find a detailed list of the properties. So as you can see, there is no presence of the tags attribute. So we can't specify tags. So we try to add tags in that list and try if it works. So here is the commit with mail, not much line of code, as you can see, it is only about adding tags to the list, then describe it in the detailed description of the attribute. And we tried that. And guess what? It worked. It worked. So in only a couple of line of codes, we save ourselves a huge amount of custom development to handle tags outside of our heat stack. Because here it is only 10 line of code and it's done. We can just use our tags in our heat stack. And we have plenty of examples like this. Each time, we choose to enhance a bit more the product rather than having to deal with the problem and spend a lot of time to find a workaround. And especially with this commit, in less than one afternoon, the code was pushed upstream and deployed in production in our environment. Because you can still push your modification into your production at the same time it is under review by the community. It's not a problem. And in the same afternoon, we developed that code, pushed it to production, declared and repaired the bug in-storey board, the issue tracker of heat, and on board the people in the team on the contribution process. And guess what? One week later only, it was merged and ready to ship in the next open stack version. So that's a very short example of a quick contribution we saved, which saved us, sorry, a lot of time. And for example, here I have the screenshot of a commit idea I had by preparing this presentation for the summit. So it shows the complexity of these things. It is clearly bugs we just found and we just fixed, like any other bugs we can find in your own internal repository actually. So now let's talk about how we implement that in the cloud team. Because as I said in the beginning, we don't want to add extra workload to the team because we have already so much things to do, especially the devops of the team. So we don't want to add extra workload for the contribution. So we thought about a simple workflow to truly integrate the contribution process into our everyday work. And to begin, we are working in agile with a scrum methodology. So like always in agile, everything begins with a grooming and a sprint planning. And here our product owner, hello, product owner, he will submit us a new task. So we will look at the task and we will ask ourselves a first question. Does it exist in the product? And if the answer is yes, okay, go ahead, we will just use the feature in the product and we will perform the task. And if the answer is no, we will ask us a second question. Does it make sense to add it? So will OpenStack be better if we add this feature we need? And if the answer is yes, we will transform the task to a contribution task. And in fact, in terms of agile, it is just a task, so it's changed nothing. But the point is we will not develop two times the code. So not once for the Société Générale, then transform it to a contribution upstream. So in fact, we will just develop one time the code and at the end of the task, submitting it to the community and use it internally in our production. But it doesn't matter, it is under review by the community. So we will develop only once the code. And another thing is if the answer is no to this question, so it does not make sense to add it to the product, we will ask ourselves, why do we need it? So it is a good way to step back and to think about why do we need this feature, which is not better for the product. So maybe it's a good time to rework it and to sort about how we can fit more easily into the product. So with that workflow, we truly integrate the contribution process without extra workload. And we are able to industriise the process of contributing to open source into our everyday work of the team. And now let's go to what have been achieved today with that workflow. So first, we have two features developed in glance. The first one, the multi-store import, I have talked about it earlier in the presentation was released in usury. So from usury, you are able to deploy an image to multiple store. And the second feature, which will release in Victoria, is the same provisioning capacity of glance. So it will reduce significantly the time of the upload of the image, then the space they will use into your backend. So that's the two main features we developed for glance. And we also developed a new API micro-variation for Nova, which is just about listing stuff, but it is always cool to say that you have developed an API micro-variation on Nova. Then in addition to that, we just developed 26 commits, which are already merged in the community in various projects like Cilometer, Colour, Colour Ansible. I think there is some keystone too. So various projects, in addition to glance and Nova, obviously. And these 26 commits bring us to nearly 4,000 lines of codes contributed by Société Rénérale in the OpenSack community. And it put us at the second place in terms of financial company who contribute to OpenSack. So just behind China and on pay, and I hear that these guys have a lot of OpenSack deployment on quite big. So congratulations guys. But yes, we are proud of this 4,000 line of codes too, contributing to the community. And you can consider this KPI in stack analytics in real time. Everything is extracted from StackVidex. And now let's go to some takeaways of this presentation on what we learn with contribution to an open source project, not only OpenStack, it's applicable for everything. I think we also contribute in Grafana or Prometheus. So the first takeaway will be, do not try to change the world. So you don't have to seek the thing to contribute or willing to do something important. The point is, we are talking about collaborative development. So each thing is important. If you notice something to improve, it is time saved for the next people who will contribute another stuff. And that is collaborative development. Every commit matters. The second takeaway will be to respect the product goal on its roadmap. So if you need to look at it with a neutral eye, so take a step back and do not think specific, but standardize your way of thinking. And when you are working in OpenStack community to publish some code, you are not working for your company anymore. You are working to enhance OpenStack. So you need to take a step back and to think specific, and to think standardize the way. And it is very important when you build a system, we need to go at scale. So that's a good point actually. And the third one, the third one is my favorite by far, is you will gain a lot of kindness in other people's code. And as we appropriate it, we are no longer in a customer-vander relationship where we will blame the product for not working properly and we will waste less time correcting the problem directly than complaining that it isn't working. And I think that's a very good point because you will gain a lot of kindness and you will enhance a lot your development skills with that. Okay guys, so I hope you find this feedback interesting. And if you have any questions, let's answer it live. Bye.


All right, welcome everyone from my voice, obviously. I'm very delighted to have you all here. I know it's late in the afternoon, and the refreshments are waiting for us. This is the last panel, so I will try to keep it very exciting and hopefully not make you sleep. I am Tushar Katarkhi. I am a product manager on the OpenShift team at Red Hat. And this is the panel discussion on AIML, on Kubernetes, and OpenShift. We have, I'm going to say it a couple of times so that it sinks in. We have a new SIG that we are creating in OpenShift Commons that Diane mentioned. So you can go and obviously, I'm going to plug it now and at the end, go and sign up on OpenShift Commons for the SIG if you're interested in this topic, especially towards the end when you heard from this esteemed panel here. So the logistics are, I thought we do some introduction to the topic and to the panelists for about 10, 15 minutes. Then we'll get into the main discussion itself, ask some questions, and then I'll give you an opportunity to ask some questions for, like, say, 10 minutes. And then we'll wrap up with some more plugs. Does that make sense? All right, cool. So a brief introduction to this topic. It's very exciting, right? I mean, AI, artificial intelligence, and machine learning is already touching our lives, be it driverless cars or automobiles, be it personal assistants like Alexa and Siri, or be it Netflix recommending your favorite movie or Pandora, your favorite music, or be it optimizing your optimal energy usage with Nest thermostats, I mean, there is some AI in there. I mean, it's really already there, right? I mean, we are saying it's new what's next, but in some ways it's already there and affecting our lives. So I'm very excited to talk about this today. My personal favorite quote unquote use case really was this thing I was reading that they are using basically pattern recognition and image recognition to reunite families that might have been separated in place like China for 20, 30 years based on photographs.  And now 30 years later, I mean, it's only possible because of AI, I mean, machine learning. And I remember doing pattern recognition 15, 20 years ago in grad school or whatever, and it was so much more rudimentary. And so anyhow, so I'm very excited about this. The one thing is, AI is not easy, right? I mean, AI has been talked about for a long, long time in academia. So why has it not happened so far? It's because we didn't have things like cloud and we didn't have things like data, big data. So it's certainly building upon a couple of huge technology trends, but also those technology trends and those use cases, as you know, are also moving very fast. So AI is building upon that. So it is complicated. It uses a number of languages, and we'll talk about that. It uses a number of frameworks. It uses a number of, it is very computationally intensive, resource hungry, so you really want to optimize the use of it. And it touches a number of different roles. I mean, data scientists are being an obvious new one, right? So we have all this complexity. And what I thought we would do really is the fact that one of the advantages that we have, I mean, we're going to talk about today, but over the next few days, we're going to talk about containers. I mean, simplistically putting, hopefully we can containerize some of this complexity of ways, how I'm looking at it, for some of this complexity that we saw. Containers also are lightweight. They are fast and efficient. And they enable you to, they're portable across a hybrid cloud footprint. So Kubernetes and OpenShift obviously have emerged as really as you saw in Chris Wright's keynote, have really emerged as a very powerful container platform. And in fact, you might call it the de facto platform. So it is great to have to moderate this panel on AIML and OpenShift and Kubernetes. So with that, let me start with the introduction of our panel here. I'll start with David first. David Arancic is a product manager at Google. He is a manager for Google Container Engine and has been shipping software for 20 years in various roles. He has been a founder of three startups and has had strengths at Microsoft, Amazon Chef, and now obviously at Google. David now is focusing on ML on Kubernetes. And he has some very exciting news that he'll share with us in a bit. But David, say hi to everyone and add something that I might have missed. That's 20 years worth of summary right there. So it's pretty good for me. All right. OK, so next we have Chris Oro. He is a product manager at Anaconda. He is a product manager for the data science platform. He has expertise in distributed systems, data engineering, and computational science workflows. He has a PhD in civil engineering from UT Austin. And prior to that, prior to Anaconda, he was at National Institute of Standards and Technology and IST, Southwestern Research Institute and University of Texas at Austin. Chris, say hi. And anything else you want to add? Yeah, hello, everyone. Thanks for having here. It's an honor. And our headquarters of Anaconda is about five blocks over. So this is our home base here. We have about 100 employees. And if you're not familiar with Anaconda, it's leading open source data science distribution. We have just about 5 million unique data science users around the world, Windows Mac Linux. And it's a lot of foundational pieces of data science, machine learning, and gateways for folks to get into things like notebooks and machine learning with TensorFlow. So excited to talk about that here today. 5 million. That's a big number. Very nice. Thanks, Chris, for that. And last but not least, we have Matt Fairley. He's a senior engineer and architect from Red Hat. He is the founding member of a project, Red Analytics, which he's going to talk about in a bit, which is about open source data analytics, ML platform based on Apache Spark on OpenShift and Kubernetes. He was one of the founding members of Sahara. I could say that, right? In OpenStack, which was the OpenStack big data processing project, he was involved in the University of Wisconsin condo project, where some of you who might know that that was like the high performance throughput computing project as kind of early pioneers in distributed cluster computing. Matt, you want to say hi? Hi, everyone. And that was great. Thank you very much. All right, thanks. I've known Matt for several years now, including the University of Wisconsin project. All right, so let's dive right into it, right? This was the introduction. So let's dive right into it so that we have a common understanding. What I'd like to do is, what does AI and ML mean to you? I'll start with you, David, and we'll circle around. You can go in any order. But what does it mean to you? Scope it a little bit. What is it? What is it not? Absolutely. So I always think it's funny and somewhat not great when people joke about AI coming to murder us all. And I am as guilty of that as anyone. But please, there are many, many people in the world who do not understand AI and ML and like to hear it, even as jokes, from the people who do know, not so great. So please, let me plead about that. When I think of ML, I basically think there are three categories of problems that it really unlocks that you've never seen before. The first are where things are hard today, but they're tractable. You could at least potentially do it. So that might be if you had a million pictures identifying what pictures had dogs in them, you could do that today with your standard algorithm. You could do it today with humans and so on and so forth. It would just take a really long time. Then there's the second category, which is there's problems you know how to solve, but effectively, they would be impossible to solve via computers at all. And that would be something like beating go, for example. That's something where we know the rules of go and theoretically you could beat it. But no, you could not use standard computational techniques today to beat go or be better than humans at that problem. And then the third is where we can't even really describe how to solve the problem. We know what a solution looks like or when we've succeeded, but there's no algorithm that we could come up with to solve it. And that would be something like identifying cancer in radiology, for example. We have kind of a generalized heuristic, but if you got 10 doctors together, they might still disagree. Yet, even now you have AI, or excuse me, ML, being able to look at this problem and make an assessment and be better than humans are today. And it's still improving even beyond that. So that's all to say, the three commonalities of those problems and that's where I say the definition of ML is. ML is being able to solve a problem without necessarily understanding exactly the methodology to get there. And that's not great, right? We actually should probably be able to understand it, but that's what we're unlocking today and that's probably the biggest thing that you're seeing. Yeah, when I think of machine learning, if I initially scope that to a library, say in Python or R, it's just a collection of algorithms or statistical algorithms that can be applied to different data sets. So the sort of import a library, run it on some data, that's the beginning of machine learning, but I often think of it on an implementation timeline and how do we make that useful for other people and how do we democratize that? So the following, the next two big stages I think of are you have a library, it has some statistical functions, you do things like model selection, there's dozens of steps beyond that. Once you have something sort of working, it works on my machine, how do I share this and make this useful for the rest of the world to build and improve on to match up with open source philosophy? And I think things like standardized formats, whether it's serializing data or sharing models in efficient ways, allowing other people to build on that modularly is a big deal. And when you start working with larger and larger groups, whether that's a foundation or an enterprise team, things like reproducibility, governance, traceability of those models becomes very important then deploying that out so people don't have to follow dozens of steps to get it up and running, it's very easy to get it up and running in any environment, HBC, cloud, on premise. And then beyond that stage of deployment and usability really comes the consuming of that, right? So we actually want our greatest audience to be able to consume that in a interactive visualization or just a browser, right? So there may be complicated technical stack underlying that and it all starts at the library and infrastructure level. But we think a lot and as I've watched different industries evolve, it's all been about model, about consolidating these models and APIs on a common framework and common tool set to really democratize the audience, the people building and consuming. So to me, machine learning is those libraries plus the reproducibility plus the collaboration at that human scale, like global scale in many different industries. Thanks, those are very interesting answers. I take kind of more of an engineer approach to it. I think of AI as a large body of research that's been ongoing for many, many decades. Within that you have things like knowledge representation and machine learning and then within machine learning you have things like neural networks and deep learning and whatnot. So I kind of think of it from a structured perspective that way. When it comes to the scope or the impact, it's more of how our AI machine learning is giving us ways to interpret the world, interpret all the data that's around us and giving us new ways to interact with the world and interact with other people. To try, you gave some examples of AI machine learning apps that people may interact with on a daily basis if they could buy a Tesla or something like that. But in reality, AI machine learning is really ubiquitous already. Google search is an example of this that's been around for a very long time and is really part of people's lives at this point. On the kind of like, I think you said, what is it not? I like to think that it's not, and I'm going to kind of violate David's comments a little bit, but it's not the destruction of humanity and it's also not the savior of humanity. And really, it's also not a salad dressing, although people might, given the fact that it's so hyped right now, people might say it is.  And I think it's not even that, because just the example of driverless cars, I mean, driverless doesn't mean that you can sleep inside. I mean, for the next 10, 15 years, you still have to pay probably some attention. You know, you may not... We've had many kind of like revolutions of these things that people think are going to destroy humanity or whatnot and spoiler, we're still here and things are for the most part getting better, getting whatnot, so. We'll have the same thing with AI as soon as a lot of the hype settles down and the reality of what people can do with it and how you interact with it in your life actually becomes more clear. I'd like to support that. I actually just want to say one thing on top of that, yes and it, which is, it won't kill us. It won't kill all the jobs or anything like that. But only if we, all the people in this room and the people watching are responsible and think about you are all technology implementers, creators and so on. Please do think as you're doing this stuff. Don't rely on someone else doing the hard work and being aware of that. But yes, I totally agree that it by itself will not do it but we do need responsible implementation. Yeah, ultimately it's a tool and we need to use that tool in good ways. Very good. There's a good introduction to that topic. So next is what I was going to ask each one of you and you can go in any particular order but what is a favorite use case for you? I mean, something that you get excited about, oh, I want to make this work today because it'll solve this problem and so what gets you excited in this? I mean, just one example, maybe multiple examples, it's up to you. I'm sorry, I can start on this at this time. So I think my favorite example and my favorite use case or whatnot is somewhat a selfish one. So I've never been particularly good at foreign languages. I took lots of foreign languages in high school and college but never really kind of like conversed myself in an environment to actually use them, to actually communicate with people and I think the translation capabilities that are coming out right now are really going to make it much easier for people to communicate and much easier for me to communicate with people that I wouldn't otherwise be able to. For me, a little bit of my background is in civil engineering and specifically like life safety systems, building protection systems. So really kept an eye on building systems and building integration as it comes together with many different manufacturers. If you look at a building, inherently it's sort of boring. It's a boxy structure with rooms. But as soon as you start recording and logging information like energy uses, temperature, occupancy, and you put all of that together and aggregate it, you actually get a really beautiful picture of how that building behaves as it interacts with people and people interact with it. And at a city scale, it helps things like emergency responders and it's a nice example of how to bring something that was formerly static, sort of online and something that we can monitor over time and become integrated even with many, many different subsystems of many different types. So to me, the complexity of that and how we wrap that all up into some useful metrics for people is a pretty awesome example of a smart building or a smart city. And to David's earlier point, some of this insights, we don't even know what we're looking for. Right. And I mean, machine learning will help us. David. I spend all my time in ML, so I'm always astounded at everything. I'll try and keep it super brief. I'm gonna, I have a talk tomorrow. I'm gonna give away some of the stuff I'll talk about. But one of them that I love is from Google. Google, as you may know, we have a lot of data centers. We hire some smart data center people. And there's this term in data centers called PUE, power usage efficiency. You want it to be as low as possible. And it means like literally the number of cycles per power. And we hire really good data center engineers that understand this and try and roll it out. And so we looked at it and they're like, oh, all these fans and water and cooling and things like that, they kind of look like signals for ML. And so we hooked it up and the power usage and efficiency went like this. Bam, bam, bam, bam. ML on, down, ML off, bam. Right back up. And literally, and we're very public about this, we save 15% on our power just by using ML against these data centers.  And again, it's not something you could just wire up instantly, but it was really, really important. The second thing I do want to say is one of the most interesting areas of ML today, particularly use case-wise, something called GANs. Is anyone familiar with those? Generative adversarial networks. Basically, imagine the incredibly simple summary is you take two AIs, or excuse me, two ML frameworks, models, and you pit them against each other. So the first one tries to figure out a solution and the second one tries to figure out something that breaks the first solution. You just force them to go against each other. It's just unbelievable and you're getting things and this helped with the translation and it's just, you see this all over the place because for us right now, we're entering this phase where we're actually having trouble coming up with exactly the right data to, you know, face off against the model. And I remain very, very optimistic that this is the area where you'll see the most creativity coming because literally, we can't even think of the ways to break the model in such a way that the model can get better anymore and you're seeing some really interesting stuff there. Very cool, very cool. All right, so what I'll do next is kind of try to get more deeper into it. I talked earlier about how this all sounds very exciting, but it's also complex and it's not easy. So starting with David, one of the things that I was gonna ask is, what are some of the challenges in AI and ML that you see today and how is Kubernetes playing a role in it? And maybe there's a time to talk about one of the things that you wanna talk about. Sure, so generally I see a couple of really big things. The first is really the approachability of ML. Today, if I sat any of you down for any non-ML practitioners and walked you through what the average ML person did, you'd be shocked at how absolutely tribal and back of the envelope and, you know, oh, maybe I'll tweak this number, the way it is right now, which is really, really disturbing for a bunch of CS folks who are like, well, there should be a standard process for going through this and exploring, and I think we'll get a lot better there. But part of it relates to the second big problem, which is real transparency and understanding, being able to prove into a model. So if I run an application today, I can attach to the process and see exactly what's being called, what time, and what's using memory and things like that, and there's not that kind of level of introspection into model and how it's performing right now, which is not great. That's kind of a second major effort. And both of those kind of relate to the third thing, and this is something I'll talk a lot about in a little bit, but it's basically, one of the biggest problems is, is that it's not easy to roll out a standard ML deployment. Everything is very bespoke. You kind of piece together whatever works and how you would like to approach it. And that's not great, because that means that that standard tooling doesn't work anymore. You now need to not only create this stack, but then on the other half of that, create a set of tooling that supports your stack and lets you introspect and lets you understand what's going on, lets you auto-tune and all those various things. That said, that's where I hope that Kubernetes can help us. Today, people generally create their stacks from the bottoms up all the way down. They understand exactly what version of Python, what libraries they're running, what networks, all these various things. And that's just too much for the average data scientist to approach. And the data science shouldn't have to think about that. And that's where Kubernetes has really changed the game. They create this wonderful standard abstraction over the infrastructure that you're running on and not just create an abstraction, but actually create rich objects that allow you to interact with various components of the platform. And to your point, like, help you wire a bunch of services together. So I remain very optimistic and like I said, I'll talk about it in just a little bit. What I think the future looks like for ML and Kubernetes. Definitely. So in terms of data science with Python and R, many of our users, if you sort of climb the stack over the past couple of years with our users, Anaconda solved the problem of I need to get Jupyter up and running with TensorFlow with all of its Fortran and C dependencies as quickly as possible across Windows, Mac and Linux. So that was a good thing. The next question was, now I want to share this analysis or this model or this server or this visualization with my buddy on a different operating system, or so they need to install system packages, allow these things on their firewall, get these other libraries in, all the mismatched version of this. So Docker was a nice addition to something like an Anaconda Python distribution where sort of where the open source Kanda package manager left off in terms of environments, it took over and said, oh, now I can bake everything into an image and it's very portable. And then the next layer was resource management, scalability, orchestration, and that's where Kubernetes came in because what we found about a year ago was that our users were building amazingly different things and amazing things with Anaconda and the last mile was, now what? Now, how do I deploy this thing out? So data science deployment, you can read blog posts of 20 easy steps to data science deployment, right? Spin up a web server, do a reverse proxy, set up your SSL certs, hook it with authentication on and on. But what Kubernetes did for us was to create an ecosystem that was a common interface and a way to take what we had already built with the package manager, with Docker images and containerization and give us that last mile where now someone using Python 2, Python 3, R, Lua, Julia, Fortran, C dependency, any of that doesn't matter, can all play in the same space. They no longer have to worry about things clobbering around another environment. It all just works at that abstraction layer for these people who just want microservice, they just want their model to run and share that run alongside others so they can build on top of it without having to go through that over and over and over. So this has been very important for enterprises adopting things like machine learning and ML, large organizations working together and really democratizing environments, right? The fact that they can deploy to the cloud or on-prem without changing anything is a game changer. That means we don't have to switch APIs every single time we want to deploy somewhere. So really, in the last year, deployment has data science deployment from anywhere from interactive Viz models to machine learning libraries and all of the above has just become that much more pervasive through things like Kubernetes, containerization, isolation and orchestration. So I'll be quick and I'll build a little bit on what was said here, I think. And also, I mean, touch upon data, right? Like one of the things and how data is so important for all this. Well, so quickly, I think Kubernetes has done a tremendous job really providing the API, the interfaces that's expected by operations people, by system ins, by developers. It's really codified a lot of their best practices over many, many decades of experience. There is, with AI machine learning, there is a shift in the way that the systems operate, the way that they're built that Kubernetes is gonna have to adapt to to some extent. It's, there's an understanding that I think is really being formed will be talked about with data later is how data scientists operate and what expectations they have and then what expectations are the things that they build have on the infrastructure that they're running. A concrete example that we usually use is thinking about BitRot. Thinking about BitRot from a developer perspective is you've deployed some piece of code and ROT is something that happens over long periods of time, usually with some sort of dependency changes or some sort of input changes and things fail and things fail in a fairly drastic fashion. And the infrastructure, Kubernetes understands how to deal with systems that do that. AI machine learning systems are inherently more statistical based. They're not, they don't give you that clear things that failed. They just start performing suboptimally and detecting that suboptimal performance, being able to have infrastructure that can respond to it is something that is really gonna be more and more important as we try to take the data science artifacts that are produced from the bespoke systems and put them on to something that an enterprise really understands. It's gonna be a challenge for Kubernetes. God, and it's not available. So Matt, let me start with a kind of a new line of questions in some ways. Is like, tell us a little more about what Red Hat is doing in this space and how there's some of the projects that you are leading. What are you contributing? Do you want to walk us through that? Sure, just really, really quickly from my perspective, truly from my perspective. Really think Red Hat's getting into this conversation about AI machine learning in, I'll say, two important ways, three interacting with our customers, one is that we're really consuming best practices for AI machine learning. We're understanding what it means to build these applications, to use these applications to improve our business and whatnot, so that becomes really important to be a practitioner of these things. The second thing we're doing is we're starting to have the conversation as to how do we influence, how do we give back to what these best practices are. So Chris mentioned the RAD analytics work that's going on, this is an output which is starting to form some of the understandings that we've built up over the last number of years using AI, using machine learning. So I think that's really key. And then the third thing, probably to add into this is we're actually putting out services and software for our customers that don't have a big AI machine learning stamp on them because in the end it's a tool to do something but are actually powered by AI machine learning. Chris mentioned too earlier today, he mentioned the Red Hat Insights, also mentions the work that's happening with OpenShift.io. I'll add a third which is actually kind of like intelligent routing for support or getting customers to solutions more quickly using AI machine learning. Cool. Who wants to talk about some of the work that is happening? I mean, we talked a little earlier about the Kubernetes resource management working group and some of the work that is happening with respect to GPGPUs and enablement of that and some of the work that is happening in Kubernetes. Who wants to talk about that? I'll throw a couple words. So there's work happening with those working groups around figuring out the kind of like hardware technology that is becoming more and more important for these machine learning algorithms and making sure that hardware is exposed and accessible to the algorithms that are actually running on top of OpenShift. There's the, I think it's the performance sensitive pod, application pod work that's happening to really kind of like make sure that Kubernetes has a very solid foundation, not just in the ops APIs, the developer APIs, but then in the hardware support to make it worthwhile to use those APIs. All right, so the next question really was, what I was going to tee off really was this way, right? So if, I mean, it's one of the things that everybody is thinking and like everybody makes these decisions, right? Like why should we care now, right? So can you address that, especially like, you know how data is important and even if you decide to do something today, you know, you might not have collected the data, how difficult data is for an organization to collect internally, but also from outside and what's the dynamics there as well as, you know, how that's important for AI and ML. That's fine. So, I mean, I think that we are awash in data in a way that we've never been before. You know, literally we are collecting data from every movement, right? Every device, Fitbit trackers, you know, the sensors in this room, repeating heat and thermostats and so on and so forth, all the way up to the largest possible, you know, number of queries, user behavior and things like that. So we're at this phase where it's just absolutely transformative relative to data. And I think there will be a really important transformation that goes on, you know, a lot of researchers out there nowadays would argue that actually with all the hardware investments and all the data investments, we have everything that's necessary to make these great decisions. The problem is it's not available in a format or a way for us to consume it or our algorithms are just wrong and too slow and so on and so forth. And I think that's a really valid point. I think probably the biggest problem today and then when you think about your average pipeline and the average number of the amount of time that anyone spends building a model versus everything else that's involved in building out ML, you know, building a model is very, very small versus ingesting, getting rid of outliers, feature engineering, transforming it, moving the data around in a pipeline regular way, let alone after it comes out, you know, are you tracking it? Are you being responsible security wise? All that good stuff. I think a lot of this stuff is gated on the process and the pipelines rather than just, you know, the actual implementation of building a model. I think a couple of interesting things we've observed in the last year happening in the data world, one of them is related to data virtualization, right? The data is going to live in different places. We know that it's gonna live in different formats and that's something that data science both at the machine learning library level and at the operational Kubernetes level that we know as a fact and folks have worked really hard to build in connectivity whether it's Amazon S3, Google Storage, on-prem, HDFS, different connectors are, you know, it's very important that those, that they have high value to be maintained over time, right, that's a lot of hard work that goes into that and things like standard data formats and best practices for things like Apache Parquet or Colomer data stores, these have become, so from the Python and R perspective and data science, right, Python has connectors to just about every data format and data source you can imagine. It's part of the Python data science and the kind of philosophy, right, to just connect to all sorts of remote data and compute sources, but what really happens is we're seeing users exercise, you know, for a given problem, it's best to use Parquet stored in this particular data store for performance reasons, for training reasons, so we're seeing a lot of that as we see these get exercised in different verticals and another thing interesting we've seen in the last year is generating synthetic data when training, right, so just sometimes you just don't have enough data to get started when you need to do model selection on natural language processing or image classification and we've seen really interesting use of generating huge data sets in parallel that can be used for the training iterative process and then you can bring in the real data on a rolling basis, so between those two things, data format, data storage, especially many remote sources, I think that it is hard work and it's something that was recognized up front in Kubernetes and containers and it's going to be hard work to continue to maintain but we're gonna learn the high value connections of things like standardized data formats as the training, whether it's image classification or NLP, orders of magnitude difference in performance when you use the right tool for the right job with the right data format and the right data storage, so that's all starting to come together and I think we're learning a lot to that together even in the open source and cloud activity that's going on. Cool, I was gonna go to the Q&A next but before that I wanted to give you each an opportunity to kind of talk a little bit about something that's happening here at the conference or a plug that you want to do for an announcement that you want to make so David, you want to start? Yeah, absolutely, thank you. Well, my plug is for my talk tomorrow, please come. But I do want to talk about something that we're doing just between everyone in the room and those on Facebook. We're launching something which is designed to solve exactly a lot of the problems that we've talked about here on stage. It's called Kubeflow and the idea is it is a standard ML stack for running ML on top of Kubernetes. It is not about re-implementing all the great hard work that's out there in the world. TensorFlow, XGBoos, Scikit-learn, anything like that or any of the UIs or any of the transformation tools. It is really about much in the same way that Kubernetes didn't go and re-implement a database serving tool or something like that. It just allowed you to take that containerized tool and spin it up in a very elegant way but not just elegant but also portable and very scalable so you could deploy it to your laptop, you could deploy it to a GPU rig, you could deploy it to a cluster all with the same command repeatedly. And that is something that we're very, very happy to get out the door because this is something that we hear so often from customers. Oh geez, I wanted to go down ML but I had to completely re-implement that stack or I had to build it myself or my data scientist had the wrong version of Python and so everything failed. Our goal is with Kubeflow to be this very open framework that the community can come together and help collaborate on. So we're kind of being quiet about it right now, we love your thoughts but the GitHub repo is open and please join in. All right. My plug is for Anaconda, what else? If, I guess how many folks in here have downloaded or used Anaconda? There. So if you haven't, it's a free download, Windows Mac and Linux, it's up to 1,000 libraries for Python and R. Any area you can think of, image classification, natural language processing, NLTK, Gensim, Jupyter Notebooks and machine learning, we've been very busy adding more and more libraries, TensorFlow with GPU support and the nice thing is you just kind of install it, it's all pre-compiled across Windows Mac or Linux, makes it very easy to use, free to use on any of those platforms and then we have Anaconda Enterprise which you can sign up for a 30-day trial of, it's pretty much a manifestation of data science platform with collaboration, authentication, security but it's all powered on the underlying Anaconda distribution and the Kanda package manager. So if you haven't used that and you're tired of living in dependency hell and dealing with Fortran C libraries, system libraries and when doing machine learning, try out Anaconda and let us know what you think. Cool, Matt. So I'm gonna bookend you here with Kubeflow on the other side too. I think one of the really important things that we should be looking at when it comes to something like Kubeflow and what's David's gonna talk more about is there are many, many organizations out there who have been producing bespoke solutions for building these pipelines, building these flows, trying to put them into production, trying to address how data scientists work, trying to address how operations folks work. So there's a lot of engineering work that's gone into this. There's also a lot of engineering product work that's gone into this. You see cloud vendors are producing solutions for these almost on a weekly basis if you look over the last few months or so. What's really missing in the space right now is a place where a community can form to really not just have a single vendor or a single instance position on what this flow should be, what the interfaces should be, how things should fit together. So I'm really hoping, looking forward to Kubeflow as a potential place where that community work can really happen. Cool, and David forgot one, so let me plug it for him. There's a Birds of a Feather, BOF session at 7.30, something like that this evening. Tomorrow at, I think it's early, I think it's 5.30, but it's on the schedule, it's Birds of a Feather, ML on Kubernetes, I'm gonna be there to talk about Kubeflow, but I wanna be clear, it is the organization, the community that comes together and wants to talk about whatever they want, that they take in that direction. All right, thank you. We'll come back to you for one final thought, but we'll do some Q&A now. So I'll open it up, and then I'll. I'm gonna put in one more plug too, if you go to commons.openshift.org, halfway down the page, there is an ML working group that we're starting up on the OpenShift Commons. So if you're interested in this and you wanna get involved and hear more about the best practices and lessons that we're learning around Kubeflow, please sign up there as well. So do we have any questions in the audience? I know it's towards the end of the day. There's one right next here. One might go, but she's still up. Hi, I was just wondering if there was anything in the predictive analytics space in particular that you're excited about that you feel is really interesting? I mean, honest to God, I see something almost every day. I think the thing that I have, I approached this and I'm not at ML PhD by any stretch of the imagination. I came at it like the ML space as being like, well, how hard could it be to take an existing model and transfer it to this new space? And what I've found is that unfortunately today, it is highly, highly bespoke. Like you could have a predictive model that is 98% accurate in the movie theater ticket space that doesn't work in the baseball game ticket space. And it's just kind of crazy, but that is where we are. So I guess the things that I'm most optimistic about are things around transfer learning, really, where you, for those that don't know, basically imagine your model is, I don't know, 100 neuron or 100 layers. You strip off the last five layers and then you retrain on a much smaller set of data. I think that's very exciting to me. And there's a lot to do there to say the least, but that reduces the total amount of data you need by a factor of 100 or more often. And that's quite compelling. Yeah, for me, it's a little bit more rudimentary, but it's exciting to watch our users going through the process of dropping their batch jobs in terms of models that are constantly training, constantly running instead of this daily or weekly thing. That's exciting because there's a lot of wasted time that goes into the iterations and the daily iterations as opposed to just bringing something online and having it run sort of on an ongoing basis. And the other part that's interesting, again, it's not cutting edge, but it's watching our users refactor the way that they work into microservices. So what would have previously been a monolithic image classifier with a UI built onto it with a very specific declarative way of doing something, let's say recognizing images or edges, is now completely different in the way we're seeing our users in the past year sort of build a specialized API that just does the classification and a specialized front end for that that's modular that can swap between the different backends. So actually watching that roll out into the larger masses and not just the developers, the bleeding edge developers is actually really nice to watch. And it lets us sort of focus around the best tool for the best job instead of a monolithic approach to everything. So we're starting to see those projects get deprecated and sometimes broken up to microservices that are actually healthier than the original monolith. So it's exciting to watch that happen as things get adopted more and more across the industry. So just two quick things then. One is I wanna add on to David's comments about transfer learning. People need to watch the space as the vast majority of the complexity that's happening in data science and data engineering work, model design and whatnot and being able to reuse that as a developer is gonna be hugely empowering. So that's really key to watch out for. To your question about something happening in the predictive space, as a user, I'm really excited about more work that's happening around kind of like news curation or really curation in my RSS feeds. There's more and more work happening there. It's just saving lots of time in the morning. Yeah, I mean, at least from a radar perspective to add to that, right? I mean, something that you mentioned, using the digital exhaust like logs and metrics and stuff like that and how do we make our systems much more smarter in terms of scaling or in terms of even a better fault tolerance, et cetera, et cetera is something of a lot of interest for us from a radar perspective. So one more question, all right there, go ahead. Hi, so I've heard a lot of people complain of how when technology advances, humanity regresses. So how as technologists or advocate of technology are we supposed to educate people to use this to our advantage and not the opposite? We really need to become data literate. We need to understand what the sources are. We need to be teaching people to understand what data is, how data is used, and what the potential is. And really, as from a personal perspective, also looking at understanding what our, to use the term, our data exhaust is in the world today. I think if we can teach people, if we can educate people about data, how it's used, how they can use it, and then what they're actually producing, we'll have the building blocks for not regressing. I think a big piece of empowering both the producers and consumers of ML and AI is about transparency, reproducibility of the models and the analyses themselves. So I think a bad example is treating something as a black box, only runs in a certain environment and we don't really know why it works so well, but it works great and saves us money. It's not a good approach. You know, I come from a sort of civil engineering, very hands-on physical engineering. I think ML to me is the same. I often relate to it in a way that when I think about the hyperparameters or distributions that are going through, I want to see those all the way through. I don't ever want to see a step that, I don't really know what happened to that distribution or hyperparameter, but it looks good. You know, that, I think, you know, as producers, we need to be very careful to document and be very open about the stages that are going through the layers and what intentionally each layer was put in for in a given analysis. And then as a consumer, we need to ask those questions of, hey, this is a really nice model. It performs, it performs the others by 10%, but why? Can you reason through why, whether it's a physical model reasoning or a hyperparameter tuning, just having that global picture often lets us not focus so much on one well-performing local model, but really get a big picture of the context, you know, especially depending on the industry you're in, in finance or healthcare, that can have really big impact. So reproducible transparency of the models, I think, are the two key things, as a citizen data scientist, right, to always exercise and either produce or ask of things that are being produced. I think those are both terrific answers. I, you know, I think if I was going to kind of generalize it a little bit, there are two key things that these both factor into. Is anyone familiar with Chaos Monkey? It's the Netflix tool that they use to actively kill machines randomly to tease out issues. Like, technology is not neutral. We need to be aware of that. We are technologists. We need to be aware that it is not neutral. It has a positive or negative effect. And it is up to us to be our own Chaos Monkeys for the technology we roll out. We need to be probing in every possible way and be mindful that, hey, have I checked to make sure that this model that I rolled out doesn't actively bias against a certain population, whatever that might be. Have I checked to make sure that the, you know, what the edge cases look like here? And hey, is this an area? Let's be clear, you are not reinventing anything. I promise you, right? If you develop a loan analysis system for looking at someone's credit report, there is literally 50 years of sociological research showing how minorities and people of color and women were biased against through standard methodologies, like long before ML came along. Go be smart about that area and the ways that it impacts you, even prior to technology, and be thoughtful and mindful about entering that space again. And don't try and be an expert in it either. You will get it wrong. Go find an expert and chat with them and understand how you can be better at it. So that's largely it, but to be honest, we will never educate the population. We need to be doing the educational work on our own and really have a hard line about this. Great answers. Next question. Yeah, I guess I'd follow on. You guys were talking about it from a producer of ML technologies and I'm thinking about it from a consumer of ML technologies in terms of is there development or some kind of transparency guidelines that we can use to figure out, okay, when a ML model makes a certain decision, why is it making that decision? And is there a way that I can tell if I'm consuming Google's version of this algorithm versus Microsoft's version of this algorithm, which one is gonna give me the appropriate transparency and how do I trace it to make sure that the decision is not a biased decision on a case-by-case basis, but also on a software-by-software basis? I'll give a very concrete example of that.   No, you will never know that. Yeah. That's their goal is to, and I say that as a Google employee, but any AI, or any solution, and this is not AI or ML related, they're just gonna experiment. They wanna see whether or not a new thing works. So that's not the problem. I know technology is not the solution or panacea or anything like that. My hope is that, and I know this is my job to pitch my new thing, but my hope is that by creating standardized ML stacks with somewhat standardized reusable components, we will develop standardized reusable transparency tools for that. It is impossible to look at, for example, there are two very, very popular image recognition models right now out there right now, ResNet and ImageNet, they're both very, very successful and they both perform better than human right now. You could not use transparency analysis tools that you built for one with the other. They're just completely different layouts and models and so on. And so my hope is that by building some of these standard tools, you could do it. But let me make a pitch out there. I would love someone to build Chaos Monkey for ML, meaning you don't need to introspect into the model, right? Like you could build this and say, hey, I have a set of multiple different population types as data that I can feed into your model and get the results back on the other end. And it doesn't have to be real humans, they can be totally anonymized, but like if at the end your model comes out and it's biased, then you're like, hey, something bad is happening here. And it doesn't give you the transparency that we all should demand, and literally there are 100 PhDs working on introspecting into models today, but at least then we have some awareness. And so I will pitch that and I will endorse and find Google engineers to help you if you wanna leave that kind of thing. Exactly, but test cases where it's not like where we know what the population source was. The population source is not made available to the model. You just hand these objects in and some results come out the other side and that test on the other side looks for bias against populations. So two quick things. One, going back to my definition of what AI is, what machine learning is and what not, we need to understand that machine learning, there are sub areas in machine learning. Neural networks, deep learning capabilities is one particular area that's being applied a whole lot right now. And it specifically has interpretability concerns associated with it. There are other approaches that are better in some use cases, worse in others like image recognition speech, things like that, but are interpretable. When it does come to neural networks, I think we need to extend your question as to, is it, it's not just the focus on the model, but it gets more to kind of like what David was talking about is it's, it's the focus on the model, the code that was associated with it and the data that was used to drive it. And we'll, we don't know it, we don't have an answer now, but if we can start capturing these things, smart people will figure out how we can get an answer to it. Then one more question. Yeah, David to your point, I think we had chaos monkey like 10 years ago in the financial industry, they all used AI and they never saw it coming, but what I wanted to ask, where do you think will be the main contributions of AI when we talk about things like the self-driving data center? When I listen to your answers, David, I think you're hedging a little bit that you say right now that complexity is too high, we have to focus on abstraction. Will you foresee a future where maybe the scheduling is predictive that you already know, okay, to assure the performance of a workload, this is where I have to put it, I can provision more stuff, or how do you see this play out? Sorry, so if that's what you took away, I apologize, I don't think the complexity is too high at all, right? We have existence proof of us solving that problem. I think in my opinion right now, the problem generally relates to the approachability of using AI or ML for your data center, for your self-driving data center, that is too high. And by that I mean literally the interface between a model and your system is broken, it's highly bespoke, meaning either I have to rewrite my model in some very specific way, or I have to build some crazy feature engineering tool to translate the data that I have into something that's actually usable, or then I have to, like even if I get answers, you know, do I have the correct feedback loop so that as my take action on my answers, it's feeding back in properly, like all that is broken right now. Like it's very approach, or it's very implementable, the problem is that there's no standards. And so what we're, all three of us on the stage are contributing towards this stack. And again, I don't wanna hold this up as the end all be all solution, but my hope is that we're able to develop some standards as an industry around stacks, around ways to ingest data, ways to spit out answers, getting feedback loops, all that kind of stuff, where it doesn't. And to be clear, like I said, we have data centers that we do it at Google, we have internal services that literally self-drive our data centers. So it's absolutely possible. It's just, how do we make that available to everyone? Democratize, that's the word that was used. Everyone's democratizing, I'm done with that. I wanna feudalize our data center. All right, with that, I think this is a great panel discussion I thought. I hope you all agree. Thanks to our panelists. And I hope to see you all over the next few days, including the few sessions that we have.


The mysteries of Islam fascinate us time and time again. This is no different from the life story of the Prophet. Who was Muhammad, sallallahu alaihi wa alihi wasallam? An illiterate dozen merchant who one day stumbled upon amazing Arabic rhetoric? Or was he the creation of Allah's greatest light? Sent down to earth to pull man out of ignorance and bring them to the purest of truths. I Ali Burji, I'm on a journey to discover the real story behind the Prophet, the real story behind our religion, the roots, the beginning, the cradle of civilization. In the name of Allah, the Most Gracious, the Most Merciful. May peace and blessings of Allah be upon our master and beloved Abu Al-Qasim Muhammad al-Mustafa, sallallahu alaihi wa alihi wasallam. And may peace and blessings of Allah be upon those who are gathered from now on to the Day of Judgement. Dr. Al-Aziz, so we've come to the point where Quraysh have abandoned the embargo since the discussion with Abu Talib regarding the document, the treaty which was placed in the Ka'bah. So the Quraysh found the document which was eaten by termites. And only wherever the name of Allah was written, the termites did not chew on it. So now the Quraysh must be very desperate. They don't see any other solution other than eliminating the Holy Prophet sallallahu alaihi wa sallam. And logically I presume that the Holy Prophet as well will be informed about this. So if we can go inshallah into the details regarding the events of the preparation and the scheme, the whole strategy, the plan regarding the assassination of the Holy Prophet and the preparation for the Hajr. Bismillah. I seek refuge with Allah from the accursed Satan. In the name of Allah, the Gracious, the Merciful. And praise be to Allah, the Merciful. And may peace and blessings of Allah be upon those who are gathered from now on to the Day of Judgement. The Quraysh were determined to eliminate the Prophet sallallahu alaihi wa sallam in any way they could. Allah's Messenger. And after the death of his devout and loyal supporter, his uncle Abu Talib, sallallahu alaihi wa sallam, and also the death of his devout and loyal wife, Sayyida Khadija, sallallahu alaihi wa sallam. Art Angel Jibrail, sallallahu alaihi wa sallam, descended upon the Prophet sallallahu alaihi wa sallam. He said, you've lost your supporters and you must migrate to Medina. It is said that the Quraysh came together and they said, we have to do something about him once and for all. Basically, storm his house, and they were thinking how to do it. And they, of course, there was this notion that who's going to do it? It is said that in their meeting that they were sitting, it's reported that an old person appears to them or comes to the door, and he gives them a suggestion. They were not sure how to go about addressing the issue of Prophet Muhammad sallallahu alaihi wa sallam, basically trying to get rid of him. How to deal with it? How to deal with it, trying to get rid of him. And this old man, he said to them, I'll give you a suggestion. He said, you're fearful that if someone from this tribe goes and kills Muhammad sallallahu alaihi wa sallam, then you're fearful of Benny Hashim. They said, yes. He said, my suggestion is that one warrior from every clan is put forward, so that there is a group of warriors attack the house of the Prophet sallallahu alaihi wa sallam and kill the Prophet sallallahu alaihi wa sallam. And because you have a member from every single clan of Quraish, then Benny Hashim will be faced with not a single clan, but the entire Quraish.  If they do, they will be defeated. And they really liked that idea. They thought that was a clever idea. Okay. And who was this old man? And it is reported in our traditions that this old man was Satan himself, Shaitan Iblis. He appeared to them. So Shaitan himself, he intervened. He intervened and he put forward this, if you like, solution to the problem that they were having. And they really liked this idea. And of course Iblis appeared to him, appeared to, when the Khilafah was reserved from Imam alaihi wa sallam, he also appeared there and made allegiance, but inshallah we'll come to that in the future. Okay. With regards to Shaitan, it's quite interesting because this is proof that Shaitan can appear as a human being and walk among us. Is that something he can do today as well? Of course if he could do it then he could do it now, but inshallah we'll go into that at some other stage. So they decided to do this. They decided to put forward one person from every clan, which they did. Forty warriors, if you like, were gathered and it was decided that they will attack the prophet, while in his sleep at the time of dawn. Just before Fajr. And the prophet, who was informed of this plot by Archangel Gabriel alaihi salam, and the prophet, sallallahu alaihi wa sallam, decided to go along with this and leave, migrate at midnight, or at night, for Madinah. He made the arrangements, if you like, and he asked Imam alaihi salam. He told Imam alaihi salam, informed Imam alaihi salam about his plan, and he said to him, will you sleep in my bed instead while I'm leaving? And Imam alaihi salam said to him, will you be safe? He said yes, I will be safe. He said, if you will be safe then I will stay in your place. And again, it comes to show that in the most important phases in the life of the Holy Prophet sallallahu alaihi wa sallam, he goes to no one else but Amir al-Mu'minin alaihi salam, which comes again to prove a statement that the next one in charge, the most trustworthy to Rasulullah, his best friend, his companion, everything is Amir al-Mu'minin, and he goes to none other but him. And of course, Amir al-Mu'minin, you wouldn't expect a lesser response, subhanallah. In fact, Amir al-Mu'minin alaihi salam accepts the most dangerous tasks.  A similar one when he had in a conversation during the embargo, when his father, Abu Talib alaihi salam, said to him, you come to sleep in the place of the Prophet sallallahu alaihi wa alaihi. And he, as the point that I want to make that this will, this could lead to my death, which Abu Talib thought that he was afraid, but he was saying, Imam Ali said to him, I just wanted to show you that I am totally obedient to you. So in here, again, Imam Ali alaihi salam faced imminent deaths, basically killing by 40 warriors, if you like, Quraish warriors. And despite that, he accepted the challenge and accepted that he would sacrifice himself for the safety of the Prophet sallallahu alaihi wa alaihi. And the importance of this is so great that Allah swt revealed a verse, an ayah in the Quran, which Allah declared that there are those who will offer themselves, give up themselves, their souls for the sake of Allah swt. I can, if you like, I can tell you which ayah, which Surah you probably need, I don't know, shall I do it here? It's Surah al-Baqarah ayah number 207. It says, women and nasiman yashri, nafsahu ibtiqaa marzatillah wa allahu raofan bil ibad. And the English translation is, amongst the people is he who sells his life, seeking the pleasure of Allah. Allah is most kind to his servants. Allah swt reveals this ayah about this, about what the Amir al-Muwni alaihi salam did, which agreeing to stay in place of the Prophet sallallahu alaihi wa alaihi so the Prophet had a chance to escape from Mecca and escape the assassination attempt by the warriors of Quraish. So who did he, the Holy Prophet sallallahu alaihi wa alaihi wasallam, who did he flee with from Mecca at night? And where did he had to? They had the assassination to oppose, so the Holy Prophet at night he leaves Amir al-Muwni alaihi salam in his bed, and he flees with whom? There are, the general acceptance is that he, of course, he went to Ghar Thor. Ghar Thor? Yeah. The cave which first ever Al-Qangil Jibrail alaihi salam appeared to him. Yes. No, that's Ghar Haraa. This is closer. Ghar Thor is further away from Mecca. And yes, because when they came, attacked, and they found Amir al-Mu'mineen there, and his first contact was with Khalid ibn Walid, and he was like his father, Khalid ibn Walid. Like his father, they were staunch opponents of the Prophet sallallahu alaihi wa alaihi salam. And Imam alaihi salam threw him to the ground, and they realized that he is not the Prophet, and he's Ali ibn Abi Talib alaihi salam.  And the Prophet headed for and stayed in Ghar Thor. The majority of cases say that he was in that Ghar, in that cave with Abu Bakr. Some say that he wasn't, he was with someone else. But the overwhelming majority among Sunni scholars and Shi'a scholars say that it was Abu Bakr who was with him. And again, just as there was a Quranic verse revealed about concerning Imam alaihi salam staying in the bed of the Prophet sallallahu alaihi wa alaihi, which I just read for you. Another ayah was revealed in the cave concerning the cave and the person who was with him. And that verse says, It's ayah number 40 of Surah Al-Tawbah. It means those two were in the cave, while they were in the cave, he said to his companion, The ayah begins with the fact that you did not support him, the Prophet sallallahu alaihi wa alaihi, and he was forced out. And he was with another person in the cave and he was telling, that is the Prophet sallallahu alaihi wa alaihi, who wasn't supported by the people, he was forced out. The Prophet sallallahu alaihi wa alaihi said to his companion, Do not grieve in Allah and with us. So all of these are talking about two people. Allah brought down his tranquility upon him, not upon them. And this is quite significant. It doesn't say that Allah brought down the sakinah on both of them. Even though all along the ayah it talks about two people. While those two were in the cave, he tells his companion, do not grieve, Allah is with us. Allah revealed or brought down his sakinah, his tranquility upon him. And this is quite significant. If we have a pause in here, if we see the occurrence of sakinah in the Quran, it occurs in five other places. One of them to do with the coffin of Musa and the Jews, and the other four to do with the Muminin and Rasulullah sallallahu alaihi wa alaihi. In short, the other four revealed to us that Allah descends the sakinah on the Muminin and no one else. If you like Allah doesn't bring down the sakinah upon the kuffar. This is one. And whenever Allah brings down the sakinah on Rasulullah, he brings it on the Muminin as well. It says, Allah brings down the sakinah on the Muminin so that their faith enhances, increases. So this is a criteria that only descends brings down the sakinah on the Muminin, those who have faith in their heart. And the purpose of that is to increase their faith, to increase their iman. In other verses, we say, So when there is the Prophet and the Muminin, when the Prophet sallallahu alaihi wa alaihi wa alaihi and the Muminin, he brings down the sakinah on the Prophet and on the Muminin. Not for example on the Muminin only or on the Prophet only, sallallahu alaihi wa alaihi. Because some say, Oh, the Prophet doesn't need the sakinah. The others do. And this is not true because we have the ayah, As I said, we have these two verses. One is that Allah descends the sakinah on the Muminin to increase their iman. The other one is that when he descends it on Rasulullah, he also descends it on the Muminin. But in here, the sakinah was brought down by Allah sallallahu alaihi wa alaihi alone, and not on his companion. Okay, that's quite interesting. So it's like Allah sallallahu alaihi wa alaihi through this ayah is trying to give you a hint. And this hint is basically that that second person, whoever it was, not getting the tranquility that means is not Muminin. That's right. That's quite disturbing. Yes. So from this ayah, we understand this person, Mr. X, doesn't really have much iman in Allah sallallahu alaihi wa alaihi wa alaihi his holy Prophet. Any iman? Yeah. So, okay, from here, from this very important ayah, we take this with us. InshaAllah, further on, we can take a lot more. At the end of the day, we want to uncover the truth and expose the truth, and there's no reason for holding back. Yeah? Now, at that cave, did anything else occur? Any other incident, a miracle, maybe? Because I do remember there's a very famous event that occurred with the cave when the Quraish were looking for Rasulullah sallallahu alaihi wa sallam. With the bird, if you can please share some of your knowledge and enlighten me on regards to that event. They say that they followed the, if you like, the print. The footprints. The footprints or the trace of the Prophet, if you like, these two people, the Prophet and his companion, and they came to near the cave and the guide who was the expert in identifying the footprints or any kind of prints and distinguishes whether this is of what kind of individual or what kind of animal and so on. The guide said, he noticed that there is, if you like, a bird sitting in its nest and also a cobweb on the entrance of the cave. And he said that he couldn't have gone, Mohammed and his companion, they couldn't have gone inside the cave because... Without damaging the cobweb, for example, and the nest. So this is, if you like, what is narrated to us. On the issue of the companion, if I may, as I said, the majority of Shi'a and Sunni scholars say the companion was Abu Bakr. And there was one mention in the book of Sulaym, reporting on the events of the Saqifah, where they, basically it was, if you like, they usabbed the Khilafa from Amir Mu'neen. Which, inshallah, we would like to cover it later on. But the point was that in order to justify that Abu Bakr qualifies for to take over, Omar was saying that you are, he is the one, you are the one who was a companion of the Prophet in the cave. And they forgot the rest of the ayah. Making it sound as if it's some sort of an achievement, where in fact, Allah is quite... They treated it as... The point is that in that debate, he was using that to say that he was Sahib Al-Ghar, he was the companion in the cave. And he was the one, that's Abu Bakr. He was the one who accompanied the Prophet in the cave. So Sahib Al-Ghar was Abu Bakr. And these are the kind of evidence that we have, which show that the companion is Abu Bakr. And that is something which is agreed by the majority of Sunni and Shi'a scholars. Right from the beginning, where the Prophet was at his... If you like, he didn't have all the support that he wanted. When you approach the people and you tell them, I'm not going... I don't expect... I'm not going to coerce anyone. I tell you what my message is. You either listen to it and believe in it and adopt it, or you leave it. It's all up to you. All I want is the protection, the protection from you so that I can convey my message to the people.