Good to be here. I'm excited to be here. I'm Ronendar. I'm the CTO and co-founder of Ron AI. And I'll be speaking about training large language models on Kubernetes. And I think one of my goals in this talk will be to convince you that scheduling is a critical component when it comes to operating AI clusters. We saw quite a few talks on scheduling today and actually also in previous KubeCons, previous AI days. So I think there are a lot of people who are already convinced in that. But I'll try to convince those that are not convinced. But let's start. So OpenAI, right? A year ago, they did this amazing thing and launched ChagGPT to the entire world. And the amazing, powerful capabilities of language models suddenly became available to everyone, essentially. And that was amazing. And that created a lot of excitement today around LLMs, around generative AI. We all feel it, right? It's amazing days to be in the AI space right now. So amazing times, right? OpenAI, they launched ChagGPT and shortly after, they raised $10 billion from Microsoft. $10 billion, that's a huge number. And in Microsoft's announcement, they spoke about the goals of the investments. So three main goals. The first is to build a supercomputer for OpenAI. The second is to use OpenAI's models in Microsoft products. And then the third is to be the exclusive cloud provider for OpenAI. So $10 billion mostly almost entirely goes to compute, just to compute power that OpenAI needs to operate, to train models, to deploy models, to run their operations. And since then, a lot of startups raised billions of dollars in inflection AI, raised $1.3 billion. Anthropic just recently, $4 billion from Amazon and $2 billion from Google. Crazy numbers. It all goes, mostly goes to compute power. And we're seeing this trend in the last 10 years that there is explosive demand for compute power in AI. This graph shows the computing power that is required to train state-of-the-art models versus the time in which those models were published. So you can see the blue region, right, the blue part and the red part are those the parts were in the deep learning era and the large language era. So you see that since 2012, 2013, huge increase in the computational power requirements to train state-of-the-art models. In the last decade, we saw eight orders of magnitude increase in the computing power. Eight orders of magnitude. That's 100 million times increase in computing power to train state-of-the-art models. And with large language right now, we're seeing again this big jump. The large language models are those red dots. So two orders of magnitude more computing power to train LLMs with respect to training traditional deep learning models. So a lot of computing power. So there is a big promise for AI and LLMs, but also a huge problem when it comes to computing power. And what we see right now is companies struggling with compute for AI. Sam Atman tweeted about that. That's like a famous tweet about the compute cost that are there. They are eye-watering compute cost, right? Microsoft warned investors in their previous financial reports that they might not reach their goals because they won't have enough chips, enough GPUs, essentially, to get to their goals. So the big companies are struggling, also small companies, right? The GPU shortage, we saw that in the last year. So companies are struggling with just getting access to compute. Sometimes you'll get into your cloud provider and you won't find a GPU to spin up for days. When it comes to high-end GPUs, H100, you can wait for days until you're just getting access to one GPU. That's crazy. So compute, access to compute is a big problem. And what we're seeing right now is a shift in how companies are consuming cloud resources. So companies are just securing from their access to GPUs, to AI compute. And there is a shift from on-demand spinning up resources whenever you need them to just reserving more and more instances, more GPUs, reserving blocks of GPUs and having a capacity for your own use. And when it comes to managing compute capacity for your AI workloads and just managing it, operating it, providing access to your compute capacity is key. It's key to your AI development, to your AI initiatives, because when it's done wrongly, what we see time after time when we're working with customers is that a few things can happen when it's not being managed correctly. So first of all, GPU utilization. I think we are all hearing about it. GPU utilization can typically be much lower than 20%. So you have a very expensive GPUs and less than a fifth of it is actually being utilized. But more importantly, even when companies are secure for the such GPU access, time after time, people are still feeling that the GPU is a bottleneck, but just getting access to GPU is still a bottleneck and there are long waiting times to get access to compute. And that means that productivity of teams is degraded, poor productivity. And that means also that the quality of AI degraded. So managing and operating compute capacity and access to compute capacity is key. And what are the solutions that we see right now and how people, our companies are managing access to compute? So we see a few solutions. I want to go over them quickly because one thing is, and that's like straightforward approach is just giving static allocations of compute resources to users. For example, each user is getting a whole GPU machine in the cloud. It's for them used, for their use only, it's always available for them. So that's the good thing. But then what we see is that static allocations are somewhat of a problem because when it comes to AI, AI requires this dynamic access to compute. Most of the time, people need just fractions of GPUs to run their notebook, to build and debug their code. And just occasionally, they need a lot of computing power to fine-tune LLMs to just run multiple experiments. So we see this, this is like profile usage of our own users and the way they access GPUs around the workloads across time. And we see, for example, user two, user two for a few days running a big workload on 16 GPUs, just fine-tuning a model on two nodes of GPUs, and then idle, almost idle for weeks. And then user four, idle for weeks, and then launching a lot of jobs in parallel using more than 20 GPUs, right, for a few days, for like 10 days. So the access to compute is very dynamic and it's burstable. So when giving static allocations, that's not that efficient, right, because either the allocation is too high and then you're getting a lot of GPUs idle most of the time, or the allocation is too low and users are being limited by the quota that was given to them. For example, if user two had gotten just 8 GPUs, right, those workloads could not run. And if user four will get an allocation of 8 GPUs, most of those 8 GPUs will be idle for a long time, but then those workloads wouldn't be able to run or they will run for longer time, right, so time to get results becomes longer when allocations are too low. So static allocations, that's a problem, right, to pick squatters and you're getting these idle GPUs, people are not getting access to as much compute they need whenever they need it. And so there's a need of this dynamic quotas, right, dynamic quotas that adjust to the needs of users at every point of time. The users could share their GPUs, share their idle GPUs. And the second solution that we see is teams trying to share GPU machines between themselves, right, in a manual way with Excel sheets, or through Slack channel, just, you know, coordinating between themselves. And right, doing that is not fun. First of all, doing like manual education. But then we also see this problem that we call it GPU hugging. So people don't want to let go their GPUs because if they would, then they would need to fight to get back their GPUs because someone else would take it. So we're seeing this absurd situation where people are hugging their GPUs, they're not actually using it. And while other people in the team are just waiting for their GPUs, they're waiting to get access to GPUs. So that's also not the way to go, right? But there is a sense of giving a guaranteed quota to users, just giving them guaranteed access to compute whenever they need it. And last thing that we see is trying to share a GPU cluster, right, a cluster of GPUs with an orchestrator, with a scheduler. And then, right, we have HPC schedulers, high performance, scheduler from the high performance computing world that were developed more than 10 years ago, like Slalom, right? So as we see it, they are really, they provide some of the good solution when it comes to running batch workloads and running training batch workloads. But AI also requires these interactive sessions for users to use their Jupyter Notebooks or any other IDE tool. And there is also, right, a need to deploy models as services, as applications. And HPC schedulers were not built to that. So when it comes to interactive and inference, they provide some more limited solution. So HPC schedulers are great for solving part of the problem as we see it. On the other hand, Kubernetes provides a somewhat like a mill image to that, right? So Kubernetes was built for running micro services, running applications, right? So it's a perfect fit. You're getting a lot of tools like load balancing, all those scaling, a lot of tools that you need when you're deploying services. And it's also good, right, for running interactive sessions. But when it comes to batch scheduling, we all know it, right? Like we saw talks about it. So there are limitations. There are some gaps when it comes to the different Kubernetes. But the good news is that solutions exist. Solutions exist today so people can build and can use and build a management cluster, management AI clusters with Kubernetes and just solve the whole problem. So solution exists. So let's recap, for example, so what's needed when it comes to AI and with scheduling in specific? So there's a need to run, to schedule interactive sessions, the batch jobs inference. Kubernetes can do all of that, right? But there are gaps when it comes to dynamic quotas. So with the default schedulers, static allocations, there's a need to get like current guaranteed quotas and sharing resources in a fair way with fair share algorithms, hierarchical queues, advanced queuing, gang scheduling for distributed workloads. So all of those, there are gaps in Kubernetes. And when it comes to GPU provisioning, same thing, there are gaps. GPUs can be provisioned in a dynamic way in Kubernetes, but it's the solution for just provisioning fractions of GPUs in Kubernetes that's somewhat limited, right? And over provisioning GPUs, that's also very limited, right? Why not over provisioning GPUs as you over provision CPUs, right? But as I said, the good news is that there are solutions today and the community did this amazing, amazing work. And when it comes to scheduling, right, those tools, Volcano, Unicorn schedulers that cover those gaps, Q, we heard about that, Q together with the scheduler framework, so they're providing them a way to overcome those gaps. When it comes to GPU provisioning, advanced GPU provisioning, we heard about the dynamic resource allocation today, so another framework by the community that can solve those gaps. And we are trying AI, we're doing that, right? So we built our own scheduler and we're providing this layer of advanced GPU provisioning with fractional GPUs with a way to over provision GPUs inside of Kubernetes. So we worked on that in the last five years, we did a lot of work there, and we essentially, we have an enterprise product, right? An enterprise product built on Kubernetes with providing the GPU scheduler, the layer for GPU provisioning, and all the tools that cluster admins and researchers and data scientists need to easily train and deploy models on top of Kubernetes with all the good stuff under the hood, the scheduler and everything that is needed. So that's around AI, right? An enterprise product. I won't speak a lot about us, but I will invite you to visit our booth. That's our booth. Our people are there. And we love every KubeCon we're here. So please come and visit us. In the second part of this presentation, I want to talk about the community work that we're doing. We have a few projects going on. I want to speak about one of them. And this project is about just making it very easy for the community to train LLM, train transformers on Kubernetes with all the data preparation, data preprocessing that is needed. So it's a joint work with NVIDIA, with the NEMO framework by NVIDIA. We're working on it together and we're providing, in our repository, an optimized script with optimized configurations that people can use to launch training jobs, LLMs, on big workloads, on multi-nodes very easily. So everything is optimized. You just need to run it. So that's the goal with the how to guys and everything. So we have our repository. All of it is also under the NEMO framework and the docs as well. So this is a work by our engineer, Omer. He's our lead engineer. He's amazing. He did all of this work. He was supposed to be here, but he got stuck in Israel, so he couldn't join me. But I wanted to give him the recognition. He did a good, amazing work. So we also did this benchmarking work on training performance on Kubernetes. And we had this setup with DGXs, 4 GPU machines with A100, with 80 gigabytes of GPU memory, overall 32 GPUs, with InfiniBand. And we used this software stack on top of Kubernetes, the NVIDIA GPU operator, the network operator, which the network operator simplifies a lot of the installation and setup that is required to run distributed training in an optimized way. And the Kubeflow training operator, which we use our customers using a great tool that we highly recommend. And so we did this work and we trained GPT-3 with five billions of parameters with Kubernetes and without Kubernetes on bare metal. And we measured the throughput in terms of number of tokens being processed per second on one node, two nodes, and four nodes. And this is the results that we got. You can see here the difference between the throughput with and without Kubernetes, right? We know the software layer that comes with Kubernetes. And you see that overhead is really small, less than 2% in this case. And you can see here also the scaling, throughput versus the number of GPUs. So with and without Kubernetes, very close to each other. And the results are very close to perfect linear scale with the number of GPUs. We also trained the smaller model and GPT-3 with 126 million parameters. And again, with and without Kubernetes and with MPI. And the results again are the difference is very small. And in terms of scaling, also scaling, it's harder to scale smaller models with a number of GPUs, right? So we see here somewhat of a degradation in terms of how close the results are to perfect linear scale. And that's it. Those are the, because you can get access to those scripts, to those frameworks that we did in our repository and in NVIDIA and NemoDocs. And that's our booth. And that's it. Thank you very much. Thank you. So we actually have quite a lot of time for questions. So the microphone is in the middle. Get open. Bring it on. I can kick things out. You had a reference in the on-premises and cloud resources in one of your slides, I think, on the previous one. So I have two questions on, eh, here, down after. Yeah. So I had a couple of questions, two questions here. One is how do you solve the challenge of, like, merging on-premises resources with cloud resources? And do you see a challenge there, not only for multicluster on-premises, but when you start crossing boundaries like this? The second one is you mentioned ASICs. And can you talk about a little bit more about how do you integrate those with Kubernetes and any challenges that might be there? Yeah. Okay. So that's a great question. So two great questions, actually. So one is about multicluster, right? Multicluster scheduling, essentially. You can have one cluster on-premises and another cluster in the cloud. And scheduling across clusters, that could be beneficial, right? And when resources are finished in one cluster, you want to maybe migrate them to another cluster, so doing all of that. So we're actually, eh, we're providing a control plane on top, a multicluster control plane, right? So you can orchestrate and view and monitor all your resources, workloads across clusters from a single location. And you can set policies and quotas for each cluster and from a single plane. In terms of multicluster scheduling, that's interesting. That's a hard thing to do. There are projects like that. So Armada by Geo Research is an amazing project. They're working hard on that. That's a great work there. And yeah, I would love to see, like, how this, this topic is being moving forward by the community for sure. And you spoke also about, what was the second question? I guess especially for, yeah, especially for inference and how you manage that. That's a great question, right? So, and of course, most of our customers are using NVIDIA GPUs, right? Like, most of the world is using NVIDIA GPUs. And in terms of A6 and integrating A6 into Kubernetes and scheduling workloads on top of A6. So that's an interesting topic. We're working on those things. So we're working, of course, AMD are there, TPUs are there and more, right? And more A6 are there. I think we'll see more and more from them in the future. And I think that today it's relatively easy to integrate A6 into a Kubernetes cluster and schedule workloads on that, on those A6. Maybe we, you know, there are challenges around monitoring, you know, providing, like, metrics in a simple way. But also those are, like, not big issues. So from what we see is something that is doable in a relatively short time. I think things like fractionalizing A6, like you can fractionalize GPUs, that's harder. That's more difficult. But in terms of scheduling workloads on A6, I think it's, in Kubernetes right now, it's in a good state. I don't see any, okay. I don't see any additional questions. So thanks again for a great talk. Thank you. Oh, there's one. Save. I'm sorry if I came in a little bit late, but you mentioned fractionalizing GPUs. Do you do anything beyond MPS or MIG or those things that come with the operators that NVIDIA provides? Do I think with MIGs, can you repeat that question? I didn't. Yeah. So I'm assuming fractionalizing a GPU means that you carve it into little pieces which NVIDIA does with MIG or MPS. Are you doing anything else or you're kind of orchestrating those things? Okay. So we're doing a few things. So as you said, there are a few ways to fractionalize GPUs. One way is with MIG and that would provide you a hardware isolation. You'll get hardware isolated slices of the GPU with MIG and that's great. And that might be less flexible with hardware. When you do things with hardware, it's less flexible when you're doing things with software. And there are also approaches of slicing GPUs which are software only. So MPS is one of them. What we're doing, we're doing few things. So first of all, fractionalizing GPUs is a problem that needs to be solved at the Kubernetes cluster scheduling layer. That's scheduling workloads on fractions of GPUs. That's something that the default Kubernetes doesn't support out of the box. So that's something that needs to be solved. That's one thing. And then the second thing that we do and I think is unique for us, we're sitting at the CUDA layer and we're intercepting CUDA calls and we manage the access to the GPU and we allow multiple workloads to share that GPU in a controlled way. So with software, you can be much more flexible in terms of how those workloads are accessing the GPU. Because for example, if you're running two workloads on a GPU, when it runs with MIG, then those two workloads at every point of time, they're getting just the MIG slices that were provisioned to them. But when you're running with software, you can essentially give the entire GPU to one of the workloads if the other workload is idle. So that flexibility you can give just with software. So that's one of the advantages of using software to fractionalize and functionalizing GPUs. Then of course, there are questions of what happens when the workloads are colliding. So how do you control that? So we're sitting at the CUDA level to control what happens when workloads are colliding. So how they are colliding in a controlled way. Awesome. Then let's thank Ronan again and switch to the next talk. Thanks very much. Thank you again.


They give it to Henry running to the right. He turns the quarter. He is the king. He will be the king Henry the ball at the goal line did he get in? Yes, he did touch down the monster. It's Peter


That's correct, correct. That's why we're having slaves. Inventured servants. Exactly. You want a roof over your head? Go shovel the snow. Love it. I'm taking notes here. Great. Great. Great. Great. Parental advice. Yeah. Love that. So, well, folks, I guess we'll see you on Tuesday morning again. Thanks for showing up. Have a look at what's happening. News will be very important about how people are feeling this, I think, because the overall sentiment will change or just be increased over the weekend. So that's one thing. But 007, you used to say something about Buffett. You want sharing that with us again? Oh, yeah. I also wanted to ask Byron, too, because he's been really good at picking beats and stuff. I was going to ask him, you know, what would he pick in this environment?  Or last month. So we're now at a point where everybody's like, oh, no, the sky is falling. But I think at the same time, if you know what stocks you like, you know what values there are, you might find values here. And I'll leave that to Byron to see if he has some gems for us. You know, it's funny that you actually say that because it's a cloudy with a chance of meatballs is what I like to say, because it's a good show. But as far as leaps go, believe it or not, yesterday when Piton dropped drastically, I purchased some 30 calls for January 2023 and I'm currently up $170. Wow. So to me, Piton, yeah, it was more like a pandemic stock, but you have to like the big grand scheme of things like, you know, exercise bikes or treadmills or whatever. They're not going anywhere. You know, they could always come up with a version two that's cheaper to offset it. You know, like look at Apple for an example, like you have your Apple iPad Pro, then you have like a mid tier and then you have a lower tier so I can see them ramping up production to kind of doing something mid tier to help, you know, bring more people in into the company to have more Piton Peloton bikes, whatever, into people's houses. But I also feel like down the road, I know that there's little in a little Twitter, social media concepts. A lot of people have even talked about the potential of Nike, Apple and even Lulu lemon potentially buying them out, which would be interesting to see if that would actually come true. But that wasn't one of the like driven factors of me wanting to actually get into the leap. Personally, I feel like there's tons of value still within the stock. And I feel like there's going to be a lot of wealthy individuals that are going to start bringing this back up like we like to stock. But that is one that I've definitely been watching PFE, Pfizer. I don't know if you look at any of like the bio FDA approval stuff, but they have something coming out on the 24th for HIV that I'm quite interested to see if it's going to be fully approved or not. If that happens with along with all the COVID news and stuff that could potentially come out, you know, that's it's just going to go up from there. So those are two that I that I am personally watching. So I'm in Peton, but I'm watching Pfizer. I like that. Remember, folks, Byron is extremely extremely good at finding the trades of the lost cause whenever something is completely below where everyone is usually expecting. He's the one that looks at it. And usually he's pretty good at finding all these over extended plays. And then we've talked a lot about the meal play that you you called last year.  So thanks for sharing that with us. So what are you looking at the January 20, 2023? Yeah, so I have the 2023 for for January, a 30 call for for Peton. The other stock that I've been watching nonstop. I know I've pissed guru off with it is snow. I always tell them when it's the good and bad news, like this week I played some snow puts just yesterday, paid $160 sold it this morning for almost 2000 piece. Just ran hard. But that's one, one ticker I want to, you know, get with JTW one of these days to fine tune some NTS on it. Because I feel like there's a lot of potential because a lot of the growth on that ticker itself each day presents a both bullish and bearish run. So you can play both sides daily on it. So yeah, I've got some answers to send you in your I'll light up your DMs with some stuff like a Christmas tree. If you look at the five minute chart on that one is toppy at 297. And I guess you're right. Every time you reach the top of the channel, you can you can go for puts. But then again, it looks like guru is playing snow better than I am with the real snow.  Okay, folks. Well, I'll do that. I'll wish you a very, very good weekend. Unfortunately, this time is not a long weekend. Stay safe. Stay warm. And I guess I'll catch you on Tuesday morning. Absolutely. Everybody have a great day. Have a good weekend. Likewise. Take care. Thank you guys. Thanks for hopping in, folks. What's up, beefy? What's going on? Let's see. Catch back to the comments. Yeah, we're a couple of magic. Amazon blew through, huh? Today was nuts. I'm trying to find to see if we can find some value in some of the tickers. Got salary cuts from stocks. It was a fantastic day. Oh man, this guy must have been shorting like crazy. Good afternoon, Mario. How you doing?  Hmm. I don't think my Apple hit my stop loss yet. I remember putting a stop loss, a trailing stop loss a while back. And the last time I checked, it was somewhere around like 135, 140s area. I'm not sure if we're at a point where I'm probably still in the Apple trade. Maybe I'll manually cut it, but I'm just going to let the stop loss do his things. You know, if it hits the stop loss and it breaks under, you know, the 147 level, then so be it. I might never own Apple for the next few years. Who knows. But the market's definitely moving at a very interesting pace. We're seeing some, some things that could potentially be at great bottoms, but at the same time. Hey, they were the same thing that we thought were at great bottoms. The week before, right, like look at Microsoft. I remember looking at Microsoft last week and, and man. Or maybe two weeks ago, we were looking at Microsoft and we're like, oh man, Microsoft's been so strong, so resilient. They, there's a probably good chance they were bounce off this, this possible bottom here. And they just absolutely cut through. So once they cut through here at the next level that we're waiting for is just, you know, coming back down to here. To maybe two nineties area before we get another bounce. The trend line though is actually at 286. So that could mean that we have more room to go on some of these big tickers. Another one I was looking at that I thought was pretty interesting was Starbucks. We almost came down pretty hard. Starbucks had a Starbucks had a triple top ejection over here. Thought maybe it had a chance to pick up the trend line there. It didn't happen. There was also another trend line here on the bottom side. That also didn't get picked up. So we absolutely just cut through both of them. And, and now we're over here. Now we're over here. We're almost Starbucks is almost back to 2020 levels. Would you guys pick up Starbucks at 2020 levels? What do you guys think? Let me see how Starbucks is doing. You know what? Starbucks actually made with pretty decent money last year. They actually finished off 2021 pretty good. Let me see here. Not right now. Waiting for a nice dip on Ulta Beauty. Oh, I haven't looked at this ticker in a long time. The white girl index tickers. Not with an environment that look and find inflation. Top or bottom, same with wages. Hey, what's up, man? How's the martini today shaking the stir? I'll be honest with you. I actually got, I actually got smacked pretty hard today. With the way the market went. So earlier this week, I posted the market outlooks because usually every Sunday as a trader, every Sunday I tried to be prepared for the market and try to kind of figure out, you know, where the direction of the market was going. So this Sunday, I knew financial was a short. I, you know, I knew we were looking for that 3960 area. We talked about the possibility of this dollar index moving rate. It's a kind of weak cap on that. We talked about possibility of dollar index moving up and the cues on this downtrend. However, I didn't think cues was going to break this downtrend line here. I thought we were going to potentially bounce off a little bit that cat bounces on point, you know, to this 380 area. But we didn't, that didn't happen. And then, you know, we have to spy S&P 500 over here. This is a risk off week possible double bottom setup. If we're looking for bullish scenarios and need to stay above 457 to stay bullish, there it's a nibble longs position for one month out, but don't go heavy. Right. So that was my expectation coming into this, this week. And you can kind of see how the XOF play out, you know, XOF we're looking for that down move and XOF play out perfectly. So the short on the XOF this week worked out for me. But spy, you know, I didn't anticipate us breaking down that pattern so quickly and so fast. The original probability was possibly doing a small bounce here. But now we're completely going to bearish territory on both the cues and the spies.  I thought we were going to chop for a little bit. So I had overexposed myself to positions on this other ticker, SGH, that was having a split soon. You know, overexposed myself thinking the same double bottom was going to play out on this as it was playing out on spy. And because of that overexposure, regardless of how much hedge I have, I couldn't have hedge against this drop here. So that kind of messed me up for this week. Actually, probably one of the few weeks, no, that's probably one of the first weeks in a while where I'm going to close on the net negative side. Where I usually end up closing net positive. So it's going to be pretty interesting, pretty interesting. Let me catch up on the comments. So I'm definitely, I was definitely pretty messed up today. I was definitely stirred.  Fiat-Wero says, I have, I had had 445 puts expiring this upcoming Wednesday. So 75% of them perched 50 spy to 18 per 70s. Was that today? You're expecting to bounce, right? That makes sense. Let me think about Peloton that cat bounced a reversal. That's a great question. Let's take a look at that. I think I covered a little bit about Peloton yesterday. You know, we talked about how what their business model was and stuff and and how if you've read the initial reports from the IPO, you would know that there they were actually built and designed for community gyms and things like that. But hotels and whatnot, but not necessary directly, you know, marketing to consumers, but their, their plan accelerated five years forward. Just simply due to COVID, you know, so the stock price went five year forward as well. And now it's coming to the end of that supposed five year cycle on their original business plan because they, they expanded so fast. And if any, if, you know, if you guys have ran the business before, you know that it's almost near impossible to estimate the supply and demand issue because when you're, when you have a company and you're getting crazy pre-orders, you know, the next thing you're trying to do is trying to fulfill those free orders as fast as you can, right? Because you think these pre-orders are going to keep coming. So you're going to keep trying to fulfill these pre-orders. But initially, you're not going to, you know, try to double your production, you're just going to try to fulfill them. But then as you see the pre-orders come in and back to back quarter after quarter for the two, three quarters. Now at this point, you're going to be like, you know what, let me try to get ahead of these pre-orders because these pre-orders are coming nonstop.  And, and, you know, it was a tragic event.  So things went real quick for them on that side. They went from expanding company to a contracting company. Now we're interest rate and everything going up. They can't borrow as much money to keep their loans and everything going. They're in real trouble. So the other thing is keep in mind, they were never profitable. That's the other thing they had. They were never profitable despite how expensive they were selling their bikes at. They never got to profitability. And they should have to. Oh, they almost did. They almost did these three quarters. They were profitable, but they couldn't finish the year profitable. And now they're going back to how they were losing money in 2019. So at this point that kept bounce the reverse. So I think that's going to depend a lot on what they're, you know, what, um, what, what the situation with the interest rate is, if they're going to really continue to raise the interest rate, then it's going to be tough for them to reverse from here. Even though they're trading at very low price of sales ratios. So they look at Peloton, they're trading at 1.7 price of sales. You know, are these sales going to keep going up? Are these sales contracting? If these sales are contracting, then this number is going to get higher and higher on multiple than the stock price that needs to get lower. So those are, those are, you know, things that you have to wait out. So at the point before, you know, they were having 120% growth, revenue growth in one year estimate that that could come down to, you know, a lot less. So with that said, I could see us dead cat bounce maybe to the 40s area as possibly let's look at a half day chart. Maybe even to, yeah, probably 40, $40 area. I could see them come back to that. They can't bounce to that. Maybe they have to clear $31 first and then maybe 40. And then after that, it might just come back down again. I think even when they IPO, they IPO pretty high too.  Holding for two years. I'm not sure if I need to cut here any suggestions. Are we talking about, hey, what's up CP? Are we talking about a few hundred shares or just a few shares? If you have a few shares, I would do the same thing that I am doing with Apple. So let's take a look at Amazon. I'll show you what to do. So if you're looking at Amazon and you have a few big shares and stuff and you're not sure what to do, you know, and this is at a point where you need to put stop losses. So anytime I have a stock in my portfolio that I really, really like and I don't want to lose, I usually put a trailing 30% stop loss, which means like if let's say it hit like, you know, this top over here, 3,700. Every time the stock goes up, it moves the stop loss 30% up, right? So once it peaks like 37, what is this? 37.64. It would put a stop loss around 26.30s. So I would say that's probably where your next level. Because look, Amazon is already at the max support that could be at right here on the daily chart. I mean, I can't really see it. I can't really fathom it, you know, moving much below that. So obviously we're at the point where if we drop any further, you know, what's the next big level on the weekly chart? 2400. If we drop that, if we drop under this, you know, 200 average here, which is 2400. Where else can it go? Right? Where else can it go? So, I mean, you got a 2000, you got a 1900, you know, you got to ask yourself, where would you be willing to go? Put a trailing stop loss. And if it drops another 10%, then, you know, things are probably not going to come back as well. And we all know that Amazon is not going to have a great earnings quarter either. They literally told us that themselves. They literally told us that. S-O-X-L, big loss. Damn.  So SGH, next big support. Next area of support is the yellow line here, MOB at $55. $55.50. The area after that is $48.49. So that's probably where I would buy. I mean, me personally, I'm going to hold it till close to the split day because usually something would get manipulated at that point or another and they could possibly run. But if you guys already got stopped out of that trade, then just wait. Wait till next support to pick it up and just play accordingly to the trade. You know, overall at the end of the day, I knew getting into that trade. I liked the company. I knew they were making good money. They didn't have much production issues. You know, and they were continuing to make good money. So I knew going into that trade fundamentally, you know, I was confident in owning that stock. I wouldn't mind owning it.  So they've been having good quarters. They continue to make money. And I just don't see them slowing down. They're having some of the best quarters ever. And I think like I think the whole semiconductor industry is going to be pretty resilient. But we do have some that are huge fly high flyers, you know, like like things like NVIDIA and where people got too excited about what they could do with the possibility of of AI. And then people kind of traded NVIDIA like they traded Tesla to crazy high valuations. So I'm not necessarily dipping. I'm going to probably keep adding on the way, but depending on how your portfolio and what your cash position is, you know, diversifying play accordingly. But I did have some some put spreads that expire. It's going to expire worthless. I mean, Peloton, there's a rumor doing around that Apple may buy it. Nothing back to it yet. I mean, yeah, anybody could buy it. Hey, even Facebook could buy it. You know, imagine Oculus 2, Oculus Quest 2 plus Peloton. Yo, you're not just on a bike anymore. You're on a bike in VR. What's good? Can you imagine? That'd be pretty cool. I mean, look, one of you guys was telling me about VR. It was a lightsaber, lightsaber. The game you guys told me last week totally convinced me. I went out and bought a quest to last weekend. What's up hazard? You know, hazard with the with the crypto yesterday. He came on the stream. He was like, he or she, I'm not even sure actually. He's got a nice little cute cat pick. But hazard came on the stream last night and it was like the general market is dropping. Crypto market must be dropping as well. Right. And we sat there tall for a minute. I was like, hell yeah, man. Went and shorted some SI bank and some Coinbase and saved my account on that drop, man. And after hours toward the end of the stream, crypto market literally started flooding. So we are at a key level here. We have a trend line support here. Just like the same trend line support that we kind of have on spy, I guess. If we break that support around 38,000, it will easily liquidate down here to the 29,000. It will liquidate real quick because the thing with crypto market is there's so many people trading on margins. When it goes, it goes, it literally goes down so hard. I bought them at close looking for a bounce purchase point five each. I think we can still go down a little bit more, but not by much. 3,000 pounds. I'm looking for 100%. I agree. I think we're going to chop. 13. Oh, fiat world saying they're worthless. Hey, yo, just because you guys are pretty bearish on Peloton, don't try to short it too much. Because, you know, just because everything looks like doom and gloom for them, you got to be careful. Sometimes they can sure squeeze as well. A lot of serious support broken across the market. What's up? Actual tick? You're completely right. There was a lot of support. Supports got broken across big tickers as well. You know, Amazon, Netflix, right? You think Facebook that has support right now that can potentially get broken? Yeah, Facebook is at that. Another major sport that can broken. Microsoft had the support broken already. What about Apple? Apple not yet. Apple, you know, still has room to go. That's why to a certain extent I was thinking to myself, I was like, you know, I will feel confident. Usually when markets pull back, these big tickers usually all pull back to the moving average support together. But for some reason, Microsoft has started to get disconnected. They just kept running weird. And then Apple took its time getting disconnected from the market. You know, not pulling back and whatever doing this weird thing. And then Google is just completely pooping the bed right now at this point. So Facebook, a whole other thing too, right? Facebook kind of lost their while back and never really recovered. Facebook technically went into bearish territory while back in September when Snapchat lost the bearish territory. Like went into bearish territory. So it's kind of interesting to see how things play out. Yeah. When market is very, when interest rate environment starts going up, market has no patience for these negative cash flow companies. Same thing with the cruises that I was talking about yesterday. TXN. Ouch. Thoughts on cure. Is cure a stock? Oh. Not sure if you've even followed what Biden is talking about. It's kind of a little bit scary. Biden is actually, he could, they could potentially attack healthcare because sometimes they want to like make healthcare cheaper or whatever. And that could be bad for healthcare. Yeah. If you want to add more to SGH, wait for that further level. Honestly, wouldn't be surprised to see Peloton a single ditch in the year too. Well, if you guys look at IPOs, right? Of 2018, 2017, 2016, you will see a lot of IPOs that do things like that. I mean, some stuff I can think of are maybe like GoPro where they, the IPO pretty high.  GoPro is the best, right? But then everybody came out with a knockoff and things like that. And then boom, look at it now. You know, the company was trading equivalent to almost $100 at some point, high as $60 at some other point in 2015. Now nobody even wanted for $9 at this point. So let's do is that thing. One of the other ones, blue apron. Let's see where that now. It was like, it was like a grocery home delivery service.   Everybody is working so much now. Nobody has time to make their food. So this company ships you all the pre pre prepared ingredients like together and they give you the recipe and you can make food yourself. You know, then they thought it was good idea. People liked the idea for a while. They had, you know, some customer growth, but then just completely end up dying across the board. So a lot of things, man, people don't, people don't like, people always get too excited on IPOs. And, and I think we're going to see that on the 2020 IPO. So for some of you guys that that think like some of these IPOs are never going to go down, be very careful. Even things like like unity, right? Like unity, this was a great IPO. The stock went up. It is great. You know, it's a great company. They do a lot of things. But when we look at these, these, these earnings, they aren't really like making the money that they should even talent here. That was a huge IPO. A lot of people love it. You know, when they open, they literally said, Hey, you know, we think the company's value around $10 based on the amount of money we're making and everything. And people drove it up to $30, $40. But things are finally coming back to the real valuation. Even AI. What a cool ticker, right? Crazy stuff. DKNG. Netflix back to 200. I'm not sure. Actually, let me show you Netflix. So I actually did, I actually, I actually did do something dive into this because I asked myself, where would I buy Netflix? What, what I think their value is. And to answer that, I kind of look at the balance sheet, see what the current amount of assets they have, how much money and revenue they're making. Right. So we're looking at Netflix 2015. We're going to use 2015 as the baseline. Okay. So at the, at the end of 2015 toward the end, the company was trading around $130, right? Give or take 130 and trading as low as let's say 80, 82, right? Or 85, 130 and 85. So 130 and 85. And if you look at their revenue and like assets and stuff. So go to the financials. Oops. So it goes to the assets from 2015 to now. So 2015, about $10 billion in assets right now they have about $40 billion give or take. Or well, actually 2022 now, 2021 would have finished now. So they have more than that. They have about $42 billion in asset, right? So about 4.1, 4.2 times. And then, you know, look at shareholder equity, equity to see if there was much change there. So that was about five times look at revenue and profit. So from three to about 15. So four to five times, right? So say four to five times we're talking about $85 to $85 to like let's say 120s, right? So on the lower baseline, 85 times 4x would put it around 340s, 120 times 4x would have put it around 480s. But who's to say at that time it wasn't overvalued as well. So if we were saying at that time it was overvalued as well, we'll look at the lowest multiple. I mean, we'll look at the lower side of that area, which is 85 times four. That would put it around 340. So to say that it would kind of go under, what do you say, $200? I don't know. That's going to be hard. I mean, could the selling pressure take it there? Yeah, it could take it there. You know, all the way down here, right? 230s, some support there. Could the selling pressure take it there? Yeah, for sure. The selling pressure could take it there.  But what I think the true value is, I think it's somewhere between 300 to 400s based on that quick dive that we just took a look at. But the other thing that a lot of people don't think about when we're talking about fundamentals and PEs and stuff as well, is that when you're looking at PEs, you're always constantly looking at the sector and the current situation. Now, if currently people are okay with 100 price earnings or 30 price earnings for the stock, then people don't mind the stock trading at 100x because they're forward looking. They don't mind it at 45, 39 or whatever. But if people start, if economies start going into recession, people won't be as forward looking. They're going to be starting to look for real value. So instead of stocks trading at 40 or 50x, they're going to be looking at stocks that are only trading at 10x or 5x at this point. Because that's what naturally they should be looking at. How could you see a stock trading at 40x? They would have to have ridiculous revenue growth year over year to be predicting them trading at 40x. Because how many years would it take them to make 40x that current revenue? That's another thing that I think a lot of people have to put some thoughts behind. So hopefully you guys put that into thoughts when you're investing and looking back at some of your long-term portfolio. Because that long-term port is going to be very important. MasterCard broke 360 support today and closed back into the old channel for the first time since it broke out three weeks ago. Let's see. Real Estate Bowl 3x. So you know the real estate sector, the ETF. There's a few ETFs, right? One of the real estate, real estate ETF hasn't really recovered despite real estate flying all-time high. And also home-building ETF was also moving really high was because they knew that real estate is being propped up. Because there's the eviction moratorium situation that they keep kicking further and further down the line. So now that they're raising an interest rate, people are really finally going to get squeezed in the mortgage. And people that bought houses last year, when they have no freaking reasons to be buying a house because they just didn't have enough reserve cash left over, are going to start seeing the pain, especially if they're trying to rent it out to people. But people don't want to rent it or something like that. You know, or if their expenses across the board went up in terms of food prices and things because of inflation, but they can't collect as much rent, you know, so it's going to be a tricky situation here. But that's looking further and long-term, though. So MasterCard 2020 levels was around 346. Wow. In November, it was trading under, it came back, went over. They have earnings coming up, so they said Estimus 5.17 EPS is 2.21. How does that compare to the last quarters? More. How does that compare to the previous quarters? A lot more. How does that compare to last year? A lot more, so. So this earning is supposed to be a good earnings for them, but at the same time, we also need to see the guidance as well. And if interest rates are going up, people might not want to spend as much. So I think that's going to be the key here, just trying to see what Powell is going to say. You know, the narratives have definitely changed to interest rate is going up, and when interest rate goes up, it means the economy is going to slow down. I think that's the big, key narrative that we got to start looking at. Hey, what's up? Any thoughts on Tesla or did you cover that already? Tesla is a really tricky one. We talked a little bit about that on the podcast. As far as technical levels, we'll take a look at Tesla on the technical levels, right? So as far as technical levels, Tesla is at the MOB line here, which is right here. If it were to bounce, this would be a good area for the bounce. And we know Tesla, we talked about Tesla last week as well. And we have said that, you know, Tesla is like creamer in science. You know, it can do whatever it wants. It can either break up or break down really quick. And when it does it, it's going to explode. So if we get the, like Fiat world said before, like we, you know, we thought that the market is going to have a dead cat bounce, right? If we think the market is going to have a dead cat bounce, I would not be surprised to see Tesla participate in that dead cat bounce. So where do we think it could bounce to if it does participate in this bounce? Probably back to this line here. This red line here at $1,000. Predicting their earnings is easy, almost I think, because they have released so much numbers. Tesla is actually one of the only companies, one of the, I mean, one of the few companies that literally tells you what their earnings is going to be. They give you enough data sets that you can figure that out. So I think we can all price in for an earnings beat. The only problem is we have no clue what the guidance is going to be, you know, just like how we knew for a fact that Netflix was going to price in for earnings beat, right? But we had no clue that they were going to miss that guidance so far off and just so bad, right? So that's what I think is going to happen to Tesla. We know they're going to beat. They said they had really good numbers. So I'll show you. I'll show you what I'm talking about. Tesla delivery numbers. So this is a website called Electric. They have really good, they keep really, really good track of Tesla. Amazing track record for Tesla. You can find really good data from them if you want to look up data on Tesla. Tommy says four billion dark footprint on spy. Which side? Which direction? In some ways, isn't it just that a narrative? I've been alive long enough to remember 7%, 8% for mortgages. I know my parents, they were far higher than that. Oh, yeah, for sure. There was a point where interest rate, I think was like over 10 plus percent. And because of that, you know, mortgage interest rates are always pricing higher, right? So if natural, regular interest rates are going high, mortgage interest rates are going higher than that. So even right now, I think mortgage interest rate was at 3.6 at the low, probably at 4.4 to 4.5 right now. But you know, these things go up and down.  But at the same time, we also have to kind of listen to the narrative and play the narrative as well. Because if they're going to push the general narrative to the market that, hey, guys, interest rate is going to go up. You know, we need to stop spending and things like that. Then we've got to be like, oh snap. What they're telling us here is that the market, the economy is going to contract and slow down. We need to listen to that narrative and play the market accordingly. We can't continue to buy the dip anymore. We need to look for to short the rips at this point. So that's something that we have to kind of remember as well.  Yes, that does look like a hidden shoulder in Tesla. 2 to 3% market, just what we have for like a decade, 0% financing for up to 2 or 3%. Come on, we had 10% of financial effort because in the 80s, 90s, people survived some reason. Yeah, for sure. But the market now moves big on narrative, especially with so many retails in the market. And everybody listening to like CNBC and Bloomberg. But actually, I think interest rate on financing is actually pretty bad on vehicles right now. I know some people are paying like 17 to 20% because they have bad interest rates. I mean, if it's bad credit scores, I think there's only a few of us that's really fortunate and we have like excellent credit scores where we're not paying that much. Let me see what these links you guys are sending me. Nancy is over here. Yeah, guys, you got to buy the dip. You want me to tell Pop-Up Howl to not raise the interest rate? Okay, I will tell this bozo. 441, 440 is the major supporting gamma level and also 200 SPX. So it breaks, we're going lower. That is true. I'm pretty sure Guru probably covered this already because we talked a little bit about that as well. How we're at the 200 SMA on the spy and SPX. There's a lot of fear. I agree. What do you got over here? Pacific snow. What ticker is this? The SMP on the weekly.  Fatter candles too. Watch that over here. That's also something you got to be mindful for. It could either be one of these or one of these, but I'll show you something even better. So talking about SPX on that tweet. So SPX really hits the 200. Usually when it does hit the 200, it does bounce pretty well. So we had that over here. So that's where it's heading right now. Also a big area for point of control as well. So the other two time we hit it was over here and here. And we also bounce off it. So we rarely lose the 200. Even when we do lose it, we typically come back to back test too. So that's a normal thing. And we bounced off this other time over here. So the 200 is a pretty essential level. We rarely ever lose it. Usually when we come off the cliff long enough, big enough to hit the 200, we usually do bounce. So I do think we are going to get a bounce today. Let me catch up on the comments. Nice. What's up MassApil? Which name should it look in? I see Cowermax, DayDI, TSWBY. You know what? That's a good question. We'll take a look at Market Bubble today. Oh yeah, for sure. They got to make money. Yeah, but what if people default though? Yeah, we know the direction of that dark pool now I think. In a way.  Maybe. I think the thing is she said too much. And I mean, you got to at least respect her in a way. Because at least Kathy doesn't flip flop, right? She says she believe in technology. She thought they were the future. And you know, and she stayed true to that. She just kept buying the dip. The crypto was the dip. She bought the dip. You know, she thought these bio, whatever things. Jill, Jill stuff was the dip. She bought them. So at least she stayed true to her narrative. So I respect that man. But is her fun always buying at all time highs? Yeah, she's a grandma, bro. She's a grandma. She don't know no TA man. When she sees RSI 100, she's like this stock is showing relatively strength today. Let me buy because the stock is showing relative strength. They love high RSI.  Because that means their strength in the stock. Now I didn't see this. You welcome to throw it here if you like. Let's look at the market bubble. Let's see if we can find some opportunities. Let's look at the map and see what is the worst performance stocks in one month. So we thought we saw a big drop today, right? But did we really see a big drop? Because we're looking at the one month performance. Let's see the greatest one for this month. DGX dropout 20%. MCO MSCI. Some financials and stock exchange. Oh my gosh. Every single one of Cathy's top holdings are dead right now. Hopefully she's buying more. Oh yeah, you're completely right. I think Cathy was actually... I said this before too. I think she's a genius. I thought that she was a genius because she found way to preach to the market. She was one of the few hedge fund managers that was able to return 100% plus return in 2021, when every other hedge fund manager would be lucky to make more than 30%. So she did a phenomenal job last year, man. But hey, like I have also told you guys before, just because somebody like a star player, whether it's like Jordan of Curry or LeBron, just because these guys are one of the best players in the game right now doesn't mean they're going to consistently be the best player in the game every year. They will eventually get weak, they will eventually slow down, they will eventually get old, and they will eventually make the wrong decisions. So that's just what's happening to Cathy right now. Obviously she had a such a great year, it's going to be extremely hard for her to outperform her previous years. But yeah, Kip is also right in this too. Most people miss Cathy, she's only buying these things because she believes in the long term, she thinks of the future, and as long as she's looking at their financials correctly and she's really buying the stocks based on how well their financials and how much they can perform, and she has the inside information on that, she will probably be okay. What's up SBJugs? Always a good place to find out what to short next. That's true, they could be attacking her too. The crypto just liquidated. Oh, some Mac. Oh snap. Fiat world, I don't think we're getting that bounce. Your boy is about to add some more to this crypto short. Damn, I should have added the sh...  It's over, Tommy. That's so true, SBJugs. Everyone is a genius in the bull market. I remember there were a lot of people that outperformed me in 2020 and 2021 when things were just going to the moon. Like, as so many people just outperform me, they were making 100%, 200% of the year, and I was just like, damn, I'm sitting here barely struggling to make 50%. You know, like in the long term portfolio, but people were just crazy outperforming because it was just too easy.  Yeah, a lot of hitch funds, they play both directions, right? They play strangles. So when they play strangles a lot, it's very, very hard for them to pull more than 30% profits. Oh wow. Imagine this coming to $3. I'm sorry Fiat world, man. It's over for you, bro. It's over. Your lungs just got done, though. Got this COVID cough, man. That's still lingering me. Modano's will not hire any individual that have health, trade it or sell cryptocurrency. If you overlay crypto site, they move similar. Yeah, 100%. We said this on stream too about this because once they made the futures for BTC available in the CBOE market, that pretty much gave hitch funds access to crypto and then manipulated the same way. They're manipulating the market. So we're actually having this double top move and we're coming down. We just broke that line here. So our next area of support is going to be around that 29,000 to 31,000 area. And if that doesn't hold, oh my gosh, man. We would die, brother.  I would prefer trading BTC instead of monster. Monster trades an interest in value of the BTC. Oh yeah, of course. I mean, I'm only shorting the stock tickers because I'm trying to hedge my stock account. I don't want to log into my crypto account right now and then try to short. Yeah, they're always three months behind. Yeah, I mean, I've always traded like fundamentally and it really made me question myself that, you know, like the last year or two, I really had to question myself like, man, fundamental doesn't make sense right now. I need to just straight up be technical and not even care about fundamentals. But now we're at a point where you got to care about both again. So it's good. It's good to have that back a little bit. Sorry for the long-term portfolio. I was just messing around, man. Madonna labor shortage should become real. You think so? Madonna's already did such a good job with the laborers though. They already essentially replaced half the workers with the cashiers, with the robot cashiers. Have a good weekend, Kepp. Take care, man. We pray for you. I thought about that too last night. Last night when we came down to this level and, you know, an S&P 500 broke it, right? Because you know how we went into bearishness over here, right, in that spy? So once we broke that level on spy over here, I was looking and I was like, man, you know what? Like I don't mind holding some of my crypto, but we're also at such pivotal levels in spy that if crypto drops, the next area is going to be here in crypto. So we hit that area yesterday. I sold off like over here around that $40,000 area. And then once we hit it over 38, I wasn't even confident buying because look at the signals that we're getting in the RSI site. We're getting the crossover here. You know, that can't be ignored. We're still pretty weak on the over here. So which means if we replicate this move, we replicate the same move that we have here. We're probably going to grind down a little bit further to here, which is the $30,000. So that's the next area I'd be confident are okay to spine. But as of right now, I'm not. I think that's where we're going to hit next. I mean, I mean, you can still, this is probably an area where you can probably still add some SPXS as well. You know, if you want to hedge those spy costs, SPXS, which the very TF could help out, especially with the direction crypto is going. Is there a crypto ETF that we can show it? Sure. Is there like an inverse crypto ETF? If only it was so easy. Oh, there's got to be a, there's got to be a micro futures. Maybe there's a micro futures for Bitcoin. Ooh. There is a micro futures all is 50 o'clock. That's because people know, people know that people are hedging. That's why I thought about going long on SPXS. But then I'm not really sure if that's the right move. Because it's already moved up so much. Let me see what's the hottest ticker this week. It's not enough yet. So there were a few anomalies, LVX, the EA and take two. How green was TXN? Take two interactive is at the moving average here. I think it was just I think it was just opening really low from last day. It's not really green. It's fake green. Something is going to happen over the weekend at this point, huh? Coronavilles already hit the first move here. Next move is 18s. Yeah, I'm going to take some shorts on this. Ha ha. Oh my god, your sister is still back holding that.  How could you do that to her? Let me see if I can add, I'm going to add some shorts on this actually. Because I need more hedge. Yes. Did you see how many cruises got canceled in the last few days? What is that? It's 43.74. Oh, I have a video for her. Send her this video. Where's that video? Yo, here you go man. Send her this video. Oh snap. I think I'm going to end the stream here guys. I got to make some moves on the positions based on this crypto move. Because lately the crypto has been moving with the market and this is a pretty significant move here. Breaking under that 37,000 area. So unless we can we claim it over the weekend, you know, I don't know it's going to be pretty scary. But typically crypto has been pretty bearish over the weekend. If people are getting liquidated at the point right now, knowing crypto the liquidation is going to get even worse and it's probably going to carry people down here because crypto liquidation is way worse than stock liquidations. Because in stock market people can't 10x but in crypto they can use 10x the buying power when they shouldn't. So good luck guys. Have a good weekend. Hopefully you guys will be okay over the weekend but you know just remember that if the market is bad sometimes the best move is no move sometimes the best move is on hand as well. Take care guys. Have a good night.


going to discuss the topic problem as a state space search. In AI we have seen that we will represent a problem to be solved in the form of a state space search. So at the end of the session you will be able to define a real world problem as a state space search. What are the components to build a system to solve problems? Here we see we define a problem precisely looking into all the micro level aspects of the problem. We analyze the problem, we isolate and represent the necessary tasks of the knowledge and not the total knowledge which is available with us. So we choose the best problem solving technique then and we apply it so that we get a solution to our particular problem. Knowing the components to solve a problem we now try to find out the structure that will support us to solve it easily. As we define a problem as a state space search we go through three criteria that have to be fulfilled. We have to define and identify a starting position then we have to define rules that are legal moves that take us from this starting position through intermediate states and finally we have to define a goal. When doing all these things we also make explicit previously implicit goals of not only legal moves but sometimes to a win. Let us consider the two jug problem. The problem is given a four gallon bucket and three gallon bucket which are jugs. How can we measure exactly two gallons into one bucket? There are no markings on the bucket and you must fill each bucket completely. For the two jug problem we see that the first criteria to be solved is to identify initial states. These initial states are the bucket is empty which is represented by a tuple zero zero the first zero for the first bucket and the second zero for the second bucket. Then we identify the goal state one of the bucket has two gallons of water in it which is represented by either if the second bucket has two gallons then x2 if the first bucket has two gallons then two x and the path cost have to be found out which is considered here to be one per unit step. For a two jug problem there are various actions that are intermediate steps that might happen so the action and successor functions or intermediate steps is filling the bucket where x is for the first bucket and y is for the second bucket it may be three where x has a value three that first bucket has three gallons and y may have a value four in the second case where the second bucket has four gallons. You may empty the bucket so the first bucket if it is empty you will have x equal to zero and y is equal to zero if the second bucket is empty you pour the contents of one bucket into the other by adding and subtracting from the values of x's and y's. Then we represent all these different states into which the system was from the start state where both the buckets were empty zero till the goal state where either one of the bucket is if the first bucket is having two gallons in it then it is two zero and the second bucket is having two gallons in it it is zero two so we could draw such a graph that traverses right from the start node through intermediate nodes to the goal node. The second problem we would like to see is the eight puzzle problem where we have states descriptions of the eight tiles and the location of the blank tile. We have successor functions or intermediate states wherein we generate all the legal states from trying the four functions left right up and down movement of the various tiles. Then we go for a goals test which checks whether the state machines and the goal configurations are satisfied as per our requirement and we set the path cost to be one. The next problem we deal with is the eight queen problem. Here we place eight queens on the chess board such that no queen can attack one another. So the queens should be apart from each other and we should also see that no path cost because only a final state count is of importance here. We have incremental formulations that have to be done and we have a complete state formulations so that we achieve our particular goal. So here we see that we can define the states which are arrangement of zero to eight queens on the board. Zero indicating there are no queens on the board and eight indicating all the eight queens are on the board. The initial state no queens are on the board then we have successor functions where we add a queen to the empty square and then finally we see the goal states that are existing which are eight queens on the board and none are attacked. So you will have 64 into 63 into 5th dot dot dot dot up to 57 for all the eight queens that is 1.8 into 10 raised to 14 possible sequences and one of them or a few of them might be the goals that we want to achieve. Ouch! This would be too much and probably you might consider to be a combinatorial explosion. So you would have the arrangement of n queens instead of eight queens now and generalize the particular problem. One per column in the left most n columns with no queens attacking each other in their states and the successor function to add a queen to any square in the left most empty column such that it is not attacked by any other queen. So you have 2057 total sequences to investigate. We go for another problem that is planning for touring cities using a map. Here we see the different cities are connected with the help of weights that are leading from one city to the other. These weights may be distances between the cities or costs to reach the different other city. So the methodology is I want to have an initial state so I set it up to be at error and a successor function that takes me from there ahead to my goal which is a set of action state pairs going from error to zerind and going from zerind and reaching there itself. But my goal test was at butcherist. So I would try to find out going across this particular path with a dot dot dot till I reach butcherist which is some of the distances traveled. Now I have given you some time to think about having formulated some problems. How do we solve them? Now you know already you have to search through a state space. You have to use a search tree that is generated with the initial state the successor functions and we define the state space. So take some time to think now. So we have a state which is representation of a physical configuration a node which is a data structure constituting the part of the search tree that includes in the children depth path cost and the states do not have children or depth or the path cost and we expand the functions to create new nodes now and this is done by filling the various fields such as the successor function expand the successor to go to the next node and you create the corresponding states. So the methodology is to identify the states and to point to its parent and to the next state keeping an account of the depth and keeping an account of where we are at present. So you might define different functions like a function which is using a strategy and returns a solution or a failure looping on till you get your particular output or your goal has been reached and then returning the state which is your goal state. You would require a function that returns a solution or a failure and you would improve on it inserting all with an expansion of the nodes at various levels. You would also modify to find out and returning the nodes of a particular solution further. So having this particular methodologies we now have a state space search and looking through the states and going across with the help of the arcs we are going from the start state through intermediate states to the goal state representing our search as a state space for our references we have used the textbooks. Thank you.


Welcome back to another screencast about functions. So in the last video, we introduced a lot of terminology and definitions that makes rigorous the notion of a function. In the next few videos, we're going to be instantiating those definitions by building examples and non-examples. This is the most important thing you can do when confronted with new terminology and definitions. It's just to build examples and make them concrete. I'll be doing several here in the videos, but it's important that you do several many more than I do on your own. Just to make sure these basic concepts are very, very well understood in your brain. So in these next few videos, we're going to do it like this. I'm going to be presenting several processes here, one for each video. And we're going to return to the same six basic questions here that we ask about these things. First of all, is this process a function at all? And it turns out there are several questions that are sub-questions of this one right here that we'll have to ask. Secondly, if the process is indeed a function, what's its domain? Third, what is its co-domain? Then we're going to pick some points out of the domain and see what their images are. Then we're going to pick some points out of the co-domain and see if they have pre-images, and if so, what are they? And then finally, we're going to ask what's the range? And I encourage you to take these same six questions and apply them to all sorts of different processes of your own making here. So in this video, we're going to work with a real simple sort of non-mathematical example. We're going to take the set A to be the set of all full names that will be first, middle, and last names of all human beings that have ever lived or are currently living. Now, I know that not all human beings have just a first, a middle, or a last name. Some people don't have any middle names. My two daughters have two middle names and so on. But let's just keep it simple and assume that each person has a first, a middle, and a last name. And then we're going to let B be the set of all three-letter combinations of the English alphabet, like R, M, S. And we're going to define a process called initials. That is a process that takes things from A and turns them into things in B. It takes names and turns them into three-letter combinations. You can probably, by the name of the process, tell what it's going to do. The process is, I want to change a person's full name into their initials. So let's talk about the process here and see what we can do with it. First of all, is this really a function? Well, remember those five basic ingredients to being a function. Let's tick through those. Is the input set specified? Well, yeah, it definitely is. That would be the set N of all full names of people. I'm sorry, we call that A. The set of all full names of people who are living or ever live. And that's certainly a well-defined set. I don't think I could list everything in that set, but it's certainly a well-defined set. Is the output set specified? Yes, I said this process is going to send things into the set of all three-letter English alphabet letter combinations. So that's there. Is the process specified? Yes, indeed. I have all the directions I need to carry out the process. There's nothing ambiguous or incomplete about the process that I've described here. Now, the two important properties, we definitely need to check. Does every valid input have an output? I think the answer there is yes. Because if I have someone's three word name, if I have someone's full name, I definitely can create a three-letter combination out of it. There's nobody's full name that would not get an output at all. And secondly, or fifth in the list here, importantly, does every valid input have only one output? And I think the answer to that would be certainly yes. A single person with a first and middle and the last name is not going to have two different sets of initials. So this is really a function because it satisfies all five of these properties and especially the last two. So now that it's a function, we can start talking about things like its domain and co-domain. Well, the domain of this function is definitely A, the set of all first, middle, last names of people. And the co-domain is just the set B, the set of all three-letter combinations. So we don't have to think too hard about domain and co-domain here. In fact, if we page back and just look at the way that the process was set up, the domain and the co-domain are essentially given to us in the definition of the process.  So let's take a point in the domain. A point here would just be someone's name. So let's say we had John Joseph Alexander, for example. That person's name, what would their image be? Well, that would be the output of the function. If I take this person's name and evaluate it into my initials function, I'm going to get the result J, J, A.  And we can see that it's very easy to take a point in the domain and find its image. So J, J, A is the image of this input here. Now what about pre-image as a point in the co-domain? And so pick an element out of the co-domain, like let's say RNT. And ask yourself, could I put something into this initials function? To get that as an output. Does RNT actually have a pre-image at all? If it does, then I should be able to fill into this blank here the name that produces that three letter combination. It may have more than one co-domain. You might, John Joseph Alexander, for example, would be a pre-image of J, J, A. But there could be another person like Jessica Jenna Austin, for example, I don't know, that has the same three letter initials. You see that quite often in real life. Now in fact, RNT does have one particular point in the co-domain. It's my name. My name is Robert Nathan Talbert. And that would be so I would be a pre-image of this particular three letter combination. And I'm sure there are a million people out there at least with the same three letter combination. So this is another example of where you could have many different inputs getting sent to the same output, for example. But if I just use my name alone, I'm not going to get sent to three, two or three different sets of initials. So that's pre-images of a single point in the co-domain. Now let's think about the range here. What is the set of all actual outputs of the function? Now that we've played with inputs and outputs, what's the set of all actual outputs of the function? Well, let's first of all keep in mind what the co-domain is, okay? The co-domain B is the set of all three letter combinations from the English alphabet, three letter combinations. Now the set of all actual outputs certainly seems like it's going to be a subset of this co-domain here. And this is something we're going to prove a little bit later on. That the range of my function, the range of the initials function will definitely be a subset of B, okay? But it may not be equal. We don't know if those two sets are equal or not. So the question here is, are there any three letter combinations in the co-domain that do not appear as actual outputs of the function? I don't really know this for a fact, but I think not. I think that every three letter combination that you could possibly make would be somebody's initials. And so I would have to go through and, I don't know how I would check that, ask the linguists about this. Are there any three letter combinations like Z, Z, Z, or something like that, who were nobody ever lived that had those three initials? It seems unlikely. It seems like there has to be somebody out there whose initials are these. So I think the range here is going to be B. I think the range is going to be the entire co-domain in this particular situation. That's not always the case. Functions, ranges do not necessarily have to equal their co-domains. But I think they do in this case. I think that every point in the co-domain does actually appear as the output of this initials function at some point. Okay, so there's our first instantiation of all the definitions here. And again, you'll see more in future videos, but don't wait for me. Go out and do some of this on your own and you will achieve a very high level of understanding. Thanks for watching.


internal sources of change. So in this session, we are going to highlight various sources of change that play important and critical role at internal level, like the shifting of population. Shifting of population means that when population tends greatly affects social structure and social organization, the increase in population size, the migration of masses, then it can cause the towards the social change. Population in any place is considered very important in any area. When the population is growing much and much, then definitely the growing population, the traditional trends of society, almost every society, they are under change. Because as the population increases, new trends, new demands are born in the society. Now to adjust the new population, new numbers are made, new populations are made, new people migrate from one place to another. To settle those people, when new colonies migrate to new cities, then social change is the component part of this whole development. Then technological innovation. Now along with time, the more technological innovation comes, now it is not necessary that the duty will meet each other, then we will meet. Now through technological advancement, through technological innovation, the link that is connected to each other, which is communication, changes are taking place in it, then the impact of that is definitely coming over social change. We have seen a very clear example of this, that when the world had to face COVID, then under this pandemic situation, the method of getting education, the traditional method, the virtual learning was focused on it. And because of this virtual learning, the world was getting in contact with each other. When Sir Hadeh was formed, TravelBand was formed, and other resources of the movement were formed, transportation was formed, so people were getting in contact with each other and learning was also going on. Then new ideas and cultural values, definitely the cultural values with the help of the new ideas, it is an important source of change. We all know that in the developed world, they are trying to introduce their cultural supremacy to the developing world, the cultural supremacy in the name of their brands, in the name of their clothing, even in the food items. Now, it is not that there is one thing in the world, and the developed countries, if they have economic resources, then they are not in their excess. The influence of culture is spreading so rapidly that you can order everything online and you can order everything online. Another source of change is Diffusion. Diffusion refers to the process by which change comes about as culture spreads from one society to another through direct or indirect. Now, the concept of Diffusion is directly linked with the cultural exchange. Now, cultural exchange happens through planning, and Diffusion is indirectly affecting our minds, and what affects our minds definitely brings the change. For example, we go to buy things in the market. Now, from many days, we are looking at its advertisement on the media. Otherwise, our relationship with it would not be established. Now, the advertisement campaign is repeatedly on the media. After watching it, our mind necessarily forces our hands to pick up these things. So, what is this indirect cultural diffusion that the developed countries, in terms of planning, the countries that are not very advanced yet, are convincing people to create their own market there, and people are becoming a part of them, including their culture, their market, and their economy.