35 Burst results for "Mikhail"
A highlight from A 72-Hour NBA Binge With Rob Mahoney, Searching for an NFL Alpha Dog With Peter Schrager, Plus Million-Dollar Picks
"Coming up, basketball, football, million dollar picks. Oh yeah, it's Thursday. Next. It's the Bill Simmons Podcast presented by FanDuel. It's the best time of the year with football in full swing and basketball returning soon. FanDuel, the best place to bet on the action. The app is safe, secure, and easy to use. And when you win, you get paid instantly. Get exclusive offers every day. Jump into the action at any time during the game with quick bets and take home a fast W. Plus check out the explore page for the simplest way to start betting. Download the app today. Bet with America's number one sports book. The Ringer is committed to responsible gaming. Visit theringer .com slash RG to learn more about the resources and help lines available and listen to the end of the episode for additional details. Must be 21 plus and present in select states. Gambling problem, call 1 -800 -GAMBLER or visit theringer .com slash RG. This episode is brought to you by Michelob Ultra. Listen, you work hard. You probably have a job at a house that you have to keep clean and maybe kids and parents you're taking care of. You go to the gym, you play pickup. You still have to mow the lawn. You deserve some time to crack a Michelob Ultra, sit on the couch, and watch some hoops. Hoops is coming back, end of October. You know, come back, long day. Maybe get a little exercise in. Walk around the block a few times. Maybe go to the gym, come back, watch some hoops. Maybe just pop open a nice, nice ice cold Michelob Ultra. Because what tastes better than a beer? Around 9 .30, 10 o 'clock, right when you're starting to get a little sleepy. It's only worth it if you enjoy it. To find out where to order Ultra near you, tap the banner or visit MichelobUltra .com and click Find Product, LDA 21 and up. We're also brought to you by The Ringer Podcast Network. I put up a new rewatchables on Monday night. Did In the Line of Fire. Have a horror movie coming on Monday for rewatchables. So stay tuned for that. Coming up, we're gonna have Rob Mahoney talking after the two Thursday night TNT NBA games. We're gonna react to basically everything we've seen for the last three days. Just things that have jumped out to us. And then Peter Schrager is gonna come on and talk about the NFL. Do we have a best team? What are we noticing through seven weeks? What can we expect in week eight that will lead to million dollar picks? And that is today's podcast. Let's bring in our friends from Pearl Jam. Here we go. All right, we're taping this. It's almost 10 o 'clock on Thursday night, Pacific time. Rob Mahoney is here from The Ringer NBA and showing TheRinger .com. We stayed up late because these were two good games. We've had three straight days of very entertaining basketball and we gotta start with the biggest story. Kelly Oubre in the Sixers. What a signing that was, he looks great. No, we just watched LeBron versus the Suns. LeBron's 29 minute limit I think is out the window. He played the whole fourth quarter. And then made the two big head down just going to the basket plays at the end. But biggest thing that's jumped out to you in the last three days is what? Lakers wise or just in general? In general. I think a lot of these teams that we expect to be really good clearly have some assembly required. And the Lakers are one of those teams. I think we saw that from the Bucks and the Sixers tonight too. We're seeing it certainly with the first days of the Victor Webinama experience. Everyone is getting up to speed into their rhythms, trying to understand how all these new pieces fit together. Not revelatory for the opening days of the season to feel that way, but I think even some of the stuff that personally I thought was going to be seamless, like the Giannis, Dame pick and roll, there's some kinks in it that they're going to have to figure out over time. Lakers I thought were the one that surprised me on that one because I thought they were one of the teams that were going to have the advantage coming in. You think about last year's team compared to this year's team. It doesn't seem like Reeves is involved enough either game that, I don't want to say he's an afterthought, but it just felt like he was more in the mix in the playoffs last year. And I liked what Schroeder did for them last year and he was good on Toronto last night and really fit in with what they did. So they're going to have to figure out that Vincent D 'Lo thing. Wood was playing crunch time, which I was really surprised. Did you think we'd be getting this much Christian Wood? I thought that was like a flyer for them. Guarding Kevin Durant on some possessions, wild stuff. But if nothing else, we can trust that when Christian Wood is out there, he will be Christian Wood. In these uncertain times, we can always fall back on that. He certainly had his fair share of like black hole kind of possessions in this game, but he also does play into the Lakers advantages in terms of their length, right? Their size against a team like Phoenix, they're just going to be able to out muscle, get to rebounds, get to balls that they can't get to. So that part of it paid off, I thought in terms of just like having another big out there and certainly the Anthony Davis experiment continues as far as like, do you want more size with him? Do you want to play small with him? There's always that internal question because he seems a little reluctant to do it on a full -time basis, but I'm sure Christian Wood's going to get his shots. I mean, clearly Jackson Hayes is going to get some shots in the rotation to be a meaningful part of the Lakers, the mix there for the Lakers. So I don't know. I think Darvin Ham has a lot of questions to figure out, including the one you listed with Austin Reeves, which is like, who has the ball? Who's initiating for us? Who is involved on a possession to possession basis? Because this game, this was a lot of D 'Angelo Russell, and it was a lot of a better version of D 'Angelo Russell than maybe we saw the other night, but it still feels like a lot. 33 minutes for him tonight. Yeah, Reeves, seven shots, one assist. And I thought all of his usage stuff was going to go up, but it seems like it drifted Russell's way. The other thing I was surprised, I thought Rui was going to be a bigger part of this team. I only played 12 minutes, but I haven't changed my thought on them. They're just such a big, problematic team. And if you're the Suns and you're feeling good after that Warriors game, right? And the Warriors, no Draymond, they were able to overpower them a little on the boards. The two centers had 22. And then tonight you see the flip side of the use of Nurkic experience, where it's like, you're getting zero room protection and you're getting somebody who's just going to be confused anytime somebody is coming off a pick. Basically Lebron at the end of the game just said, I'm going to go attack that guy. Yeah, I'm going to go attack that guy right there. Durant was better tonight, at least for the first three quarters that he looked on Tuesday night. It was really cool just seeing those guys on a basketball court after all these years. As I get older, I'm older than you, but just think like, man, this goes way back now. We're talking mid 2000s was the first time these two guys played basketball against each other and it's still going on. So that was in a cool way kind of lingering over this game. I was enjoying that one. How do you think Durant looks in terms of being a 35 year old guy who they gave up three first rounders and two swaps and Mikhail Bridges and Cam Johnson for? It feels like a slightly loaded question. Yeah. He's looked good. And certainly as you said, the first three quarters of this game looked more than good enough. I think the problem was just like this version of the Suns like felt very James Harden is hurt and Kyrie Irving won't get the shot. Nets, you know, just like Kevin Durant and a bunch of like - I blocked that net out of my mind. I think a lot of us have tried to, but you know, him with a lot of like serviceable workaday role players can get you so far. But as you saw on this one, against a really good defensive team like the Lakers in the fourth quarter, they can just shut the water off. And this is where, you know, I'm nervous about the Suns for a variety of reasons. I think if it was just the defense or just the depth or just the injury risk of their core guys, I would feel better. But it's all of the above all the time. And that's going to put Durant in some games like this one. It's going to put Yusef Nurkic in positions like this one where all of a sudden he's triggering your offense because you don't really have a default point guard out there. And sometimes the value of having a point guard in your rotation, I don't think it's really going to matter when Beal and Booker and Durant are playing together. Those guys can all handle and play make and do everything they need to do. But in a game like this, where two of those guys are out, sometimes it helps to just be able to run some offense that doesn't have to involve Kevin Durant pounding the rock through pick and roll. Yeah, 28 shots for him today. 13 including, and then 13 free throws. He played 39 minutes and was also playing the five in stretches. And this is game two. They had to basically try to unlock 2007 Texas Longhorns Durant. That's the last guy I want to be throwing miles on, maybe in the entire league other than LeBron. Cause he's, you know. That's going to be true for Booker and Beal too, right? Like when any of these guys are out, those three, whoever's left is going to have to play huge minutes or else you get into Grayson Allen and Drew Eubanks are playing like a massive role in your rotation. And I like those guys. I like Drew Eubanks. Maybe not like tamper and lose a second round pick like Drew Eubanks, like some teams do, but. I'd lose 50K for him. Maybe not a second round pick. It's a little steep. Yeah. The other game, Milwaukee Philly. So no Harden. I wish there was a way to just mute the entire Harden story of all coverage for it. Anything online, anything on Twitter, all conversations. I just don't want to hear it anymore. And I don't think he has any interest in playing. And I just think, just tell us when he gets traded. Their best chance now, now that the, especially the Clippers last night looked great.
"mikhail" Discussed on VUX World
"Yeah, yeah. I definitely agree with that. So the challenge with that though is that Amazon Alexa had the opportunity to do that, had the opportunity to be the personal assistant that lots of people still use Alexa, but far less people are developing skills for it. So it seems as though the moment might have passed. It's not that it won't come back again. And if it implements a more large language model based understanding and becomes more useful in other domains, then fair enough. But it seems like for the time being, at least as far as people's attention, Alexa has kind of died down a bit. But open AI and chat GPT are definitely, as you said, going through that hype cycle. Would you envisage that personal assistant? Because for me, looking at open AI, they have the beginnings of that personal assistant, the trialing plugins through chat GPT, the trialing web scraping through chat GPT, you know, so it's kind of like, it's getting itself into a position where it's trying to lay some foundations that would enable that future to kind of come about. Do you think that's the kind of game plan for open AI is to ultimately create this personal assistant? Or do you think they're just kind of experimenting with various different things? Because they can essentially. I don't know. I think that for personal assistant, you should have two different parts. The first part is technology for very good understanding of intents and context and everything which is happening right now. And on the other hand, you should have like a lot of specific data about this user, because the more data you have about the user, the better predictions you can make and the better service you can provide. And open AI has a lot of technology on side of understanding, but it does not yet have good access to user data. But on the other hand, I think that this personal assistant thing is very, very disruptive. We don't know when. I think that inevitably it will be created by one company or another, but when it will be created, it will just totally change a lot of businesses. For example, it might just make all this web advertisement obsolete. So this is why I think that Google is the company which should have the highest pressure to do something like this. And actually, it has data. It has a lot of personal data about where you were for the last 10 years, what you have done. And also, they had a pretty good, strong technology team, all the DeepMind and the Google Brain and stuff like that. So I think that in terms of potential, Google is much stronger to create something like this. Certainly, they have some legacy with their Google Assistant. Maybe it's harder for them to reinvent themselves, but I think that they have the most chances to create something like this. Well, they have had their fingers burnt a little bit with Google Assistant, haven't they? They've pulled back and sunset conversational actions, which is the third-party component to it. So they've had a stab at creating that personal assistant, didn't quite go exactly how it was hoped. So do you think they will try and have another bite of the cherry or try and just build it all into Google Search? I think that if they will not do this, some other company, Microsoft or OpenAI, if they will do personal assistant on their own and then all the Google Business will die. I don't know when it will happen, maybe in five years, maybe in seven years, maybe in two years, I don't know. But it's definitely, I think that personal assistant, because it's so simple that the more data you have, the better quality of service you can give to your clients. That's it. So if you have personal assistant, this personal assistant has the most data any one system can have about you. And so the first company which will create useful personal assistant will get most data. And when it will get most data, it will get even more better in terms of serving. So it will be very hard to out-compete this company. Interesting. Will that occur? Let's see. Let's see. Cool. Well, I know we're a little bit past the time now, but thank you for sticking with us. So we do have another episode of this podcast with yourself, Mikael, booked next week. So if you're tuning in right now, then Wednesday the 16th, we're going to do this again because we've touched on some stuff regarding large language models, which has been a very fascinating conversation. We're talking about large language models from the point of view of being an enabler and an orchestrator of wider and broader AI programs for businesses when they have multiple different agents doing different functions. We've spoken a little bit about the potential for large language models to enable that personal assistant that we've kind of all been wanting since Amazon Alexa and Google Assistant and Siri. And they've all felt a little bit short of our expectations. Yes. Apple is another competitor, I think. It also has a lot of personal data. Definitely. So much personal data. I mean, Siri should be the best assistant on the planet. It's just that it falls short of actually doing stuff.In fact, most of the questions I ask at these is it sends me Weblink. That's how it seems to come back over. But to get to that kind of place where we've been discussing, which is this more personal assistant and even some of the shortfalls that we've been mentioning here around large language models being less good at classification and entity extraction, there's room for it to improve. And part of that is the ability for it to retain memory, to have wider context, all that kind of stuff. And so Michael is thankfully going to join us again next week. And we're going to get into more detail around the specifics of where large language models need to improve and how for them to be ultimately effective as we believe them to be. So thank you, Michael, for joining us on this one. And we'll see you again next week. Great pleasure. Thank you for inviting me. See you next week, too. See you next week. And for those of you who don't manage to tune in next week, I hope you all do. But if you're not convinced yet about giving Deep Pavlov a try, I'll be surprised if you're not convinced because this conversation has been riveting, as I'm sure next week's will be. But you can try Deep Pavlov at deeppavlov.ai. D-E-E-P-P-A-V-L-O-V.ai. Is there any other places or resources that you would direct people to, Michael, if you want to learn more? No, this one is the best entry point. Perfect. Perfect. All right. Thank you again, and thank you all for tuning in. We'll see you next week on Wednesday. Thank you so much. Bye.
"mikhail" Discussed on VUX World
"So if you follow that thread where you have multiple assistants, multiple agents within the context of a given company performing lots of different tasks. An example I gave previously to someone was, let's imagine that you're a bank and someone comes to your assistant and they say, I've lost my bank card. I was on the train and I think I've left my bag on the train. Now, the assistant in that instance would say, okay, the orchestrator layer using a large language model of some degree knows, okay, this sounds like a lost card. Let's invoke that agent to sort this lost card out. So that agent will say, okay, let's freeze the current card. Let's sort that out. And then let's order you a new card and all that kind of stuff. But then if you go one step beyond just an organization having lots of agents and you go towards a place where lots of other organizations that also have their own agents that are able to interoperate between each other, potentially at very little effort, you could then say to the large language model that's orchestrating this process, okay, the account's been froze and the new card's on its way, but still left over is the fact that there's a bag lost on a train. And if the language model then knows, okay, there's a bag lost on a train, what bots can I reach out to that know or can talk to train operator bots? So then potentially you could kick off something which would be akin to the creation of a new process just for that one customer, which is let's go and clarify with the customer what train line it was. Oh, it was NLER. Okay, now let's figure out what time it was and what date it was. And let's go and talk to the NLER bot and say, on this train, on this time, on this date, was there a bag left behind? Oh, yes, there was. And then let's make a connection between the person and the train companies bot for them to then retrieve their bag. Now that could potentially be orchestrated without needing to be hard coded, providing there is an open interoperable standard that these models and orchestration layers can kind of plug into. Do you think that that's kind of, it sounds feasible, but do you think that's actually where we'll end up, which is that once we've got through solving the problem of orchestrating multiple agents within a given organization, that actually we can start orchestrating multiple agents across multiple organizations to then enable those kind of use cases? Yeah, actually I'm totally with you on that. But I think that I will explain how I think this scenario should work a little bit a little bit different. But in general, I think it's totally feasible. And actually, when even before we created our first repository on GitHub for DeepPavlov, my thoughts were about things which you described. I thought that if we will be able to create personal assistants which understand human very well, understand natural language very well, then these personal assistants might serve as a proxy for the human user and as a proxy for organizations. And then in this case, I think that it's like a qualitative jump in technology. When you have something which allows you to understand natural language in different components of your system, then you can provide this additional layer of interoperability when different components can talk to each other in natural language. And then in this case, you don't need to create hardwired, manually created communication links between agents. You can just create a medium where they can find each other depending on the task. And then because they can communicate in natural language, then they can settle protocol how they exchange data and how they exchange information about some specific task. And it's amazing because then you can add more agents, change what's inside, but still they will be able to be integrated by themselves into the system. And I think that in your example, you've said that this bank agent can connect to this railway service. But I think that in this case, it should be personal agent, not bank agent, because bank agent should handle everything which is related to bank. But if you have a personal agent, then this personal agent can route your request to the railway agent. And the railway agent will ask you all the details. And because this railway agent actually knows everything about trains, about schedule, and maybe if it even knows your route, and if you just provide your ID that it was your, it can identify train and then can send someone to check for your back.
"mikhail" Discussed on VUX World
"And these are different. Yeah, that makes sense. Yeah. So it's large language models for the kind of broad understanding to figure out the situation that users in. More traditional classifiers for delivering specific features, delivering against specific needs, and then rolling back to the orchestrator, the large language model for... In that instance, would that be kind of like a... You get to the end of that initial conversation and then you hand it back over to the orchestrator. Would that be a prompt to a large language model internally, basically, which is not seen by the user, which is this customer has just been through this situation, this is their situation now, what should they do next sort of thing? Is that what you're thinking? Yeah, this totally makes sense because I think also that we can provide like maybe specific like evaluating or guard railing LLM, which just performs sanity check if this response appropriate for the current context. And if the task is already finished, and then we can have like a talk between different models and natural language behind the scenes. It totally makes sense. And I think this will be the next step of the technology. So we will have something like many agents coordinating behind the scenes. Maybe some models, specific models will ask clarification from other models if they have not clear understanding what is happening or if they need some data.
"mikhail" Discussed on VUX World
"So it's inherently like, because large language models, they have trained for so many different tasks. It's their inherent inability to focus narrowly on some specific task. That's so interesting. What do you think then of, you know, part of it is because of just the general hype around this topic, I think, there's a lot of people whose interests are in generating as much attention and revenue as possible in this period. But what do you think then of some of the claims that you see and hear about? Because I see and hear a lot of large language models are better at understanding, they're better at classification, they're better at intent recognition. And although we haven't necessarily used it for that specific stuff internally, we do tend to find that for certain specific things that are of highly high consequence, like, you know, debt collection and government and stuff like that, we do a lot of work with, then the more deterministic models tend to perform better. But even just in the studio of playing around and feeding this sample utterances, this is not not skilled, like data, this is just, you know, qualitative observations, essentially, put some example utterances in and having to try and extract entities. I found that it's decent enough, but some obvious ones you would expect it misses. So what do you think then of, you know, some of the, you know, information and claims that are out there regarding how good these are at specifically classification and intent recognition? Because there's quite a bit of it out there. Yeah, I think that, actually, what we have here is an example for these Gartner hype cycle. Then first we have this inflated expectations. But you see, everyone was just mind blown because no one expected that these chat GPT will produce such a good common sense understanding, such good open domain dialogue. Because, for example, Amazon Alexa Challenge was like the whole competition targeted for open domain dialogue. And no one team, like every year, people from 10 universities try to solve this problem. No one was able to address this to this extent as chat GPT did. And we see that, wow, right now we have like the model with this almost human-like common sense, and it can pass some, I don't know, exams, tests, and it can switch topics. It can mimic different styles. It can provide you with very good advice. And so it was great for creating quick demos just to write down clever prompt and you have almost functioning system. You have input data, some natural language dialogue, and then you have output JSON, which is like a parse from this dialogue data. I think it was amazing. But then people started to study it more carefully, compare with the existing systems. And now we see more and more evidence and more and more data produced by our team, as well as by many, many other research teams in the world that for specific problems like content classification, question answering for specific domains over the text, named recognition, they are pretty good. LLM is pretty good, but they are worse than specific models, which means that we need just to combine this technology because I think that what we had before all these Bird-based, Roberta-based models and LLMs, they are just complementing each other very nicely. And we should use both. LLMs are pricey, but they have very good understanding of text in general, and they can summarize very well. But on the other hand, we have very good models for named entity recognition, which are much better than LLMs in terms of quality. Interesting. So could you provide an example, even if it's a hypothetical one, of a kind of a setup for a conversational agent that utilizes the best of both worlds? Let's give it a hypothetical example of the insurance example that we were given there. The insurance company, they want to provide the ability for people to get a quote and then decide if they want to take out the policy and then actually take out the large language model would fare better. And then also where a more traditional NLP model would perform better in that kind of context. Just trying to understand how the best of both worlds kind of plays out in your understanding. So certainly then you have some incoming client and you need some onboarding, some understanding what is the problem of the client or what the client wants. First, you should start with this large language model for example, a person wants to understand what are prices for different insurance products for cars. And you can understand this intent with a large language model and then switch to, I don't know, like a sales agent, which can then the sales agent will use named entity recognition to recognize the name of the person or all the requests about price, numbers, dates, time periods. And certainly you can do this actually in parallel. You can try to parse them with the large language model as well with the specialized model, like something like assembling them together to get even better quality. And then the specialized agent, for example, can find the best product for the client and then hand it back to the large language model, to the orchestrator, like this warping model. And then it will switch to the particular agent, which will go through the steps for creating contract for filling in specific details and stuff like that. And in this specific agent will have its own vocabulary of entities, which is relevant for filling contract. And it will have specific clarifying questions, which are usually arise. Maybe it will have its specific FAQ related to like filling in contract, because FAQs for selecting product and for filling in like insurance contract, they will be different. And if you have understanding that you are currently in one or in another situation, you just improve quality of your responses because you know that now you need to consider only specific subset of answers.
"mikhail" Discussed on VUX World
"And so, at some point of time, maybe it was 2015, I understand that today to do something really, really impactful, you need to combine some machine learning, deep learning techniques like reinforcement learning with conversational systems, because usually if you apply reinforcement learning to games, it's not a real task. It's more like toy problems. But if you want to apply AI to real problems, you might think first of all about robotics, but it's hard to get data. And then I understood that actually the sweet spot here is conversational AI, because you have a lot of live interactions with the real humans and they are held complex as a real world. So, they have a very, very complicated behavior, unpredictable, and building a system which will learn from humans, it should be amazing. We are still not there yet, but I think that we are going in the right direction. So, this was a start for my interest in conversational AI. And also, first of all, I understood that we need a data, we need to build technology which will be useful. So, we started this deep Pavlov open source conversational AI framework. And already at this moment, there are many attempts by large companies building their personal AI systems. So, the idea was can we build something like operating system for personal AI assistant, like conversational personal AI assistant. And we had a lot of examples like Siri, like Google Assistant, and Amazon Alexa, and Microsoft Cortana. And actually, on the other hand, I had a background in AI and thinking about different concepts of how we can build smart systems. For example, Marvin Minsky's theory of society of mind that we should create something like multi-agent, that human brain is like society of smart cognitive agents interacting with each other solving problems. And then, at that moment, I've seen a paper by Microsoft describing like major architecture of Cortana. And you see all these like different skills carry over from one skill to another. And so basic pipeline for like traditional personal assistants today. So, we decided that we need to implement this as a conversational AI framework so everyone can take and deploy its own Amazon Alexa or Microsoft Cortana. So, this is how we started Deep Pavlov. Nice. And at the time, what year was this? So, we started this project, I think, in the first ideas was about around 2015, 2016. But we actually started this like repo on 2017. So, that was right when Amazon Alexa was really starting to take off and Cortana was releasing smart speakers and all that kind of stuff. And so, was that the kind of like the main sort of goal? Because the way that they're constructed is that they are assistants, well bots if you like, that perform very specific functions. They all kind of sit behind this kind of orchestration piece, which then receives the utterance from the user and then determines like which one of these sub-bots is the one that needs to respond. Was that the primary goal of starting Deep Pavlov was to do that orchestration piece, was it? So, we actually we had not recognized clearly which part of this stack we should build. So, we started actually first on the lower part. So, to provide some deep learning models for building like agents or skills. And then as a next step, we added this orchestration part which we call DP agent, Deep Pavlov agent, which sits on top and then manages all this, orchestrates all these skills. So, right now we have like two major parts in our framework. The first one, which is Deep Pavlov library, which is mainly something which allows you to build pipelines out of NLP models, like combining like named entity recognition, intent recognition, and some generative models, plugging in Hugging Face or some other models into your pipeline. And this part is mainly on like skills level. So, you can provide some specific tasks on this level and build specific systems, maybe some of them with scripted dialogues. But then another part, it's just orchestration. When you have some specific workspace for all your skills to send data to each other and to orchestrator and all your components of your system, like integrating, for example, you can have some pre-processing, like extracting some data, putting them in a shared workspace, and then run some skills, some conversational agents, which will then access this shared workspace, read data they need, and then generate their responses. And then you have some kind of response selection, guardrails to understand which response is the best match for the specific context of the dialogue. Right, interesting. And so, would I be correct in assuming then that Deep Pavlov as a framework is a combination of something a bit like a raza, where you have some degree of dialogue management and control over conversations, but then also is, I'm trying to think of a comparable now, which is essentially like an environment where you can pull in any kind of models that you need to, whether that's through Hugging Face or whether it's through OpenAI or whether it's through Dialogflow or whatever, any kind of model that you need to, you kind of pull in and then you can utilize that or different models within different kind of like use cases within the same framework. Is that understanding that correctly? Yes. So this Deep Pavlov agent, it's mainly Orchestrator, which integrates all these different boards via API. So you can have one skill made with a raza, another skill made with the Deep Pavlov, then another skill build, I don't know, Microsoft Bot Framework on dialogue or data between them. Right. Okay. I'm with you. Yeah. So in that case then, how do you kind of, or is there a requirement for, you mentioned they're switched between skills. I always envisioned like you've got certain agents that perform certain functions that are kind of like defined if you like. And then you would imagine that, let's say for example, somebody wants to do something like change their address with their insurance company. They go into that skill, they complete that task. Maybe they have another followup task, which is, okay, now that I've changed my address, I also want to add another car onto my insurance where that will take it to a different kind of agent. But what you mentioned, they're switching between them. Do you find that that's a common practice, which is that when people are in the middle of one activity with one agent, they need to be able to move into a different agent? Is it a case of like the orchestration layer gives you the ability to bounce along these different agents and keep the context so you can get back to where you were coming from and all that kind of stuff? Or is it a case of when someone's fulfilled one function within one agent, they're then passed on to another at the relevant time? Does that make sense? Actually, it's a matter of implementation of this skill selector part of your system. So you can either like delegate all control to your specific agent and wait until it has finished. It's like performing some function towards specific goal and then like switching to another agent. Or you may try to like orchestrate it on the fly. So constantly monitoring context. And if you see that the user switched from one domain to another, maybe you would need to save this current state for one specific skill and then run another skill related to the context the user switched to. Right, I'm with you. We're getting to a place where I think businesses are beginning to get to this point where they understand the value of having their kind of assistance set up in that kind of way, where you've got specific agents that deliver specific capabilities, but then you've got this kind of layer on top that kind of coordinates them all. That's how Alexa and Cortana was working, as you said. As businesses get more and more skills, more and more content across a broader breadth of use cases, then having that single entry point becomes kind of important. What are the sort of main things to consider when doing that? Let's say that one company has a chatbot with Nuance and they've built another something or other with Deep Pavlov and they're looking to try and integrate and get some synergy between it all. What are the things that they all need to consider or things that they'll need to do in order to kind of make that happen successfully? I think, first of all, it's very important. We also see many examples when companies have already many different boards built by different parts of the company, because usually you have something like, you have support function, you have sales function, you have different functions related to your product, and different departments might set up their own boards. But then user experience might be not very coherent because they are developed independently and so on. And so in this case, I think that it's good practice to set up something like some team in charge of integrating all these activities into one system. And this team or person will be responsible for understanding the whole user experience than interacting with all the conversational systems of a specific company. And it will set up scenarios for these skill selections. And then also work with the skill selection in some specific user experience, making it holistic for the user. And then, of course, these teams should then communicate with all the developers of the specific systems. So I think that it's important that specific agents should be developed by the people who are solving particular problems. So this is why I think that actually there should be some separation between the people who are doing this integration and people who are doing agents, which are like more focused on specific tasks and specific features of the products, because they feel better all the pains of the users and they can better implement all what they need from these parts. But then all these boards, they should have some specific standardization for how to exchange data. And maybe their deployment can be optimized because if they, I don't know, run some services twice, so that you might have one dialogue, but then different systems will pre-process it twice with the same named entity recognition. But if you have one entry point, then you can only pre-process it once and then all the downstream boards can then read this information and use it to produce an answer. And one of the challenges that Amazon Alexa had was that it was doing a lot of stuff, like first-party stuff, play music, set timers, you know, set me an alarm, do all that kind of stuff. But then it also had this kind of developer ecosystem where you could build skills for it, which are these like third-party applications. And one of the biggest stumbling blocks of that platform, if you want to call it that, was Amazon didn't quite figure out a way to figure out what skills were the right skills for a given request from a user. Whether that was a case of not being able to route it correctly or whether it was a case of not having enough understanding of what those skills capabilities were, I don't know. But I'm thinking of like, let's say, as enterprises start to deploy more and more agents, you've got generative AI agents in the mix, you've got kind of more intent-based deterministic kind of agents that all start to do different things. One of them's working with this business division on sales and lead generation, another one's working on support, another one's working on something else. How do you kind of, how do you sort of like make sure or ensure against the kind of like the front end not knowing which agent to send things to, for what the better phrase. How do you make sure that the request goes to the right agent is the question, I suppose. I think that that's a very, very important question. And this is actually why I think that Amazon was not very successful in switching between skills on the fly and still you would need to use all these invocation words, like names of the skills and stuff like that. But I think that right now we have a hope that we might solve this problem with the large language models. Because the problem here with this orchestration is just like having very good common sense understanding of the current context. And before large language models, we just had no technology to have good understanding. So we were able to train model to understand some intents, but still, even if you can predict intent for some specific utterance, I don't know, some specific turn in your dialogue, you still, it's very hard to place it in the context, but the context matters a lot here. So what skills should be called? But I think that right now large language models will allow us to solve this problem because they have a very good understanding of the context. So, and here is like a clear solution that you implement these warper or skill selector part with the LLM. And then what you need, you need just to describe functions for specific skills, just the nature of language, what this particular agent is doing for your customer or inside your system. And then ask your skill selector, LLM selector, you set up correct prompt to direct the user to the correct skill. This is how I think this can be solved because the problem which we had like a year ago was that every skill, it can, it can has a good understanding inside some domain. So it can understand what user wants if it's related to some particular narrow domain. But if user just step out of this domain, then the system cannot understand that the user is already out of this domain. But I think that large language models, they can understand these situations and then they can handle it. Interesting. So let me see if I'm understanding this correctly. So what you're saying there is that if you are to, if you were to describe in plain English the features that each of your agents fulfills, you would roll that up into a prompt, pass in the user utterance, and then you would construct a prompt basically akin to something like this is a customer talking to this bot in this kind of customer support or lead generation context. We have these agents that do these things, which one is the best one to delegate it to, get the answer and use that to then make the decision. Is that, am I understanding that correctly? Yeah. And also on top of them to improve quality, you can use traditional intent recognition. So it's like that you have these, what you have just described as well as some like ranking of all the options provided by your model. And I think that combination of two will be, will give you like best quality, which you can obtain today. Right. I'm with you. So it's almost like, it's almost like when you have, so some organizations when they begin with contact center call center automation, some will begin from the point of view of build an agent that solves a problem and they'll put it behind the IVR system. So you press one, press two, press three, and then you're talking to the bot, right. Others will approach it from the point of view of, we've lost Miguel, he's back again. Are you back? Good, good to go. So what I was saying there is that some contact centers, for example, if they're going to launch, could be either chat or voice, the kind of place to start is either they'll create an agent that solves a specific problem. So you'll go to the IVR, you'll press one for this, press two for that, and then press three, and then you talk to the agent. Others though, and then what they'll do is they'll create one agent, then another agent, then another, until eventually every item on your IVR goes to an agent. And then they'll put a wrapper on top of that, which is just tell me what you want. And I'll put you right through. The other opposite way of doing it is that you begin with the routing, intent-based routing, tell me what you want, and I'll route you to the right live agent. And then over time, we'll then supplement those live agents with automated agents. And therefore, we achieve the same result. We just do it a slightly different way. What you're describing here really is almost a version of that, isn't it? Where instead of routing to the live agent based on the intent, really you're just routing to a separate automated agent based on the customer. Is that right? Yes. Yeah. Okay. That's interesting. And not only at the beginning of the dialogue, it also supports all the dialogue. So when one agent does its function, then these orchestrators can ask the user, for example, maybe you want to have, as you've said, maybe you want to have an insurance, maybe you have another car, and do you need insurance for your another car? Or maybe do you want to travel and we can provide you with a travel insurance. And then if a user agrees, then you can route it to the travel agent for creating travel insurance. Yeah, that makes sense. That makes sense. And so in that instance, you basically, one skill comes to the end of its conversation, and then the orchestration layer comes back in and then takes over with, it knows the context of the conversation. So it'll then just re-prompt you and say, what do you want to do next? Or do you want to try this because that's related to this or whatever. What about switching within the context of an agent? So in the middle of having this conversation, I decide that that's not what I want to do anymore. And in fact, I want to do something else. Or perhaps an example of that would be, so we're working with a government organization right now. And you can call up, you've been outside, hasn't been collected, so you'll call up, you say, I want to report my missed bin collection. So that conversation is one agent that's having a conversation, that's fine. Sometimes people will end up in that conversation and they'll say, hey, you were supposed to pick my fridge freezer up yesterday, you didn't pick it up. Now that's not, that's a paid for service, it's a different thing, it's a different agent. So within that conversation, when that is picked up, we'll need to then send that into another agent. So do you recommend doing that at the intent level within each individual agent? And just, you have to basically then be, from a design perspective, recognize that at this point in the conversation, they might want to switch into this agent over there. Or does the orchestration layer play a role somehow in at the agent level, where when it picks up an intent that's not destined for that agent, it then takes over and makes the connection? Does that make sense? Yeah, I think that a solution with orchestration is much more scalable. Because if you have many components, and then you add more and more different agents, different skills, different functions, then if you have this direct routing from one agent directly to another, you will need to update these routing tables. And then you, so I think it's more complicated in terms of engineering. But if you have just one point where you have a registry of all your available skills, and you have a function of monitoring context, then you can do it better. And then it should be cheaper as well. Because if you run an agent, and when you have orchestration, you can have one smart orchestrator and pay for inference or for the large language model. And then you can have not that smart skills, which might be much more cheaper and run on your own infrastructure, no large language models, something rule-based. But if you want to switch from one skill to another, then you need better understanding inside every skill, every agent. And in this case, to get this understanding, you will need to run LLMs for every agent, which will be costly. Right. So are you saying then that the way to, architecturally, the way to do this is that the input, let's say that it determines that this agent is the right agent to solve this problem. Are you saying that then you won't actually just kind of hand off into that agent? What you would do is you would keep the orchestration layer always processing what's coming in, and then sending that request into the individual agent level and then bringing it back up to the orchestration level. Is that what you're saying? Yes. So actually we provide, maybe we can say this orchestrator is a proxy. So it fully isolates user from the skill. So all the input and output from specific skill goes through the orchestrator. So after some specific skill or specific bot produced its own answer, first of all, this answer goes to the orchestration and then orchestrator decides if this like response appropriate or not, maybe it should be guardrailed or maybe it should be post-processed. For example, some names I added, I don't know. And only then it goes to the user. So this orchestration layer is constantly monitoring what is going on and decides if, for example, we need to switch to some another skill. Right. I'm with you. I'm with you. What are some of the things that you've seen go wrong when people try and do that kind of orchestration piece? Actually, we had not that much experience with this yet. So we're just starting to experiment. So I cannot share with you nothing in terms of, I mean, nothing consistent or reproducing because you see right now we have this explosion of stuff like AutoGPT and agents and so on. And basically what we are talking about in our DP agent architecture is not that experimental because in AutoGPT you can generate plans and you can generate agents on the fly and then run them. Here, of course, for this automatic generation, you have a very low reliability with the current models. So it can usually produce errors and cannot call properly some functions. What we propose is that it should be much more robust because every specific skill is developed by developers to solve a specific problem. And we are solving only this orchestration part, which requires broad understanding. But all the fine-grained details which are required for specific requests, they might be rule-based or may be recognized by specifically trained models, which has much better quality than large language models. Because right now what we see is that if we test large language models for traditional NLP tasks like entailment recognition or intent classification or named entity recognition, they actually had performance like 20, maybe 15 percent worse than specific models for these tasks. Which means that LLMs, they are very good for common sense understanding of some context in general sense, but they are not as good for targeting specific information inside natural language input. And so to have a system with a good quality of responses, you need to combine both LLMs to understand overall context and specific models to dig in to specific domain and have very good understanding inside the domain. So LLM might have no precise understanding what should be done in some specific situation like related to car insurance, but it perfectly understands that the client is talking about car insurance right now. And then it just hands on to a specific model. That's so interesting. So I do want to just reiterate that to make sure that I've understood it and also it's worth reiterating. So what you're saying there is the studies that you've done around utilizing large language models for intent classification and entity extraction, the large language models are performing worse than a specifically tuned classifier for that specific task. Is that correct? Yeah, sure. Even more, actually we can find that if we task LLM with producing question answering over text, even if we can put all this text inside the context, so we don't need any vector databases, we don't need retrieval. So we have all the information in the text, which is in the context of the model. And we ask question regarding this information. Still, it will have performance about, I don't know, 55, some F measure, F1 measure in terms of response, but then specific model trained for this data set will have 75 F measure of response, which means that even if there is all information in the input, the model still can produce errors.
"mikhail" Discussed on VUX World
"All right. Hello there, ladies and gentlemen, boys and girls, welcome to VUx world on the build up to the voice and AI summit. If you're at the voice summit last year, hopefully you would have seen the VUx world stage. Hopefully you participated in the VUx world stage. This year, we are back again at the voice and AI summit, and we will be doing another stage full of epic, epic content. We have a whole host of amazing companies there, with lots of case studies from enterprise, who are implementing conversational AI, generative AI, the whole nine yards. So I hope you will be at the voice and AI summit. If you have not got your ticket, go to voiceand.ai, and you can find out more about the agenda. You can see what's going down and you can get your tickets there. So please do that. And we'll look forward to seeing you in September from the fifth to the seventh. Our day is going to be on the sixth and it's going to be absolutely amazing. So there you go. Next, I'd like to give a shout out to Tideo. Tideo for sponsoring the VUx World Podcast. Tideo is a customer experience platform geared more towards small to medium sized online businesses. If you think about the retailers and e-commerce platforms and stores that are out there, Tideo is what you want to be looking at. If you are looking to enhance your customer experience, maybe automate some of those kind of routine questions that your customers might have. It's already got out of the box, the ability to answer things like product availability, shipping questions, order status, returns, all that kind of stuff comes out of the box. And the track record of Tideo is that it's answering four out of five customer questions successfully. So if you want to increase sales with personalized shopping experiences, and you want to dip your toe into the world of AI, then you should check out Tideo. And if you do that by visiting tideo.com forward slash VUx, then you will save 20% if you were to go ahead and deploy that on your e-commerce website. That is T-I-D-I-O.com forward slash V-U-X. Tideo.com forward slash V-U-X. Thank you to the team at Tideo for sponsoring VUx World. Now, our guest today is an incredibly knowledgeable and very kind of, how can I describe it, someone who's been doing this stuff for a long time, and so much so that has actually created one of the most popular open source chatbot frameworks, Deep Pavlov. It is Mikhail Birstev, and he has got an immense world of experience. He's currently a fellow at Landau AI. He's the founder of Deep Pavlov, as I mentioned. He's also the chief science officer at a stealth startup, so we don't quite know what's going on there. It's a little bit cryptic. Whether we'll get any insight into that during this conversation, I don't know, but Mikhail has a whole host of experience that I cannot wait to dive into right now. Mikhail, welcome to VUx World, my friend. Hello, everyone. Hello, Cain. It's just a pleasure and honor to be on your podcast, and thank you for your kind words and for using me. No problem, no problem. The pleasure is all mine. Really appreciate you spending some time with us. So, as I mentioned in the brief intro there, you have a wealth of experience in this space. Incredibly smart guy. Tell us where it kind of all began with you. Were you working with AI before Deep Pavlov? Where did the interest in AI come from? So, first of all, it all started with my academic interest in building systems which can learn on their own, actually.
A highlight from Mikhail Burstev, Founder of Deep Pavlov, talks AI orchestration and LLMs
"All right. Hello there, ladies and gentlemen, boys and girls, welcome to VUx world on the build up to the voice and AI summit. If you're at the voice summit last year, hopefully you would have seen the VUx world stage. Hopefully you participated in the VUx world stage. This year, we are back again at the voice and AI summit, and we will be doing another stage full of epic, epic content. We have a whole host of amazing companies there, with lots of case studies from enterprise, who are implementing conversational AI, generative AI, the whole nine yards. So I hope you will be at the voice and AI summit. If you have not got your ticket, go to voiceand .ai, and you can find out more about the agenda. You can see what's going down and you can get your tickets there. So please do that. And we'll look forward to seeing you in September from the fifth to the seventh. Our day is going to be on the sixth and it's going to be absolutely amazing. So there you go. Next, I'd like to give a shout out to Tideo. Tideo for sponsoring the VUx World Podcast. Tideo is a customer experience platform geared more towards small to medium sized online businesses. If you think about the retailers and e -commerce platforms and stores that are out there, Tideo is what you want to be looking at. If you are looking to enhance your customer experience, maybe automate some of those kind of routine questions that your customers might have. It's already got out of the box, the ability to answer things like product availability, shipping questions, order status, returns, all that kind of stuff comes out of the box. And the track record of Tideo is that it's answering four out of five customer questions successfully. So if you want to increase sales with personalized shopping experiences, and you want to dip your toe into the world of AI, then you should check out Tideo. And if you do that by visiting tideo .com forward slash VUx, then you will save 20 % if you were to go ahead and deploy that on your e -commerce website. That is T -I -D -I -O .com forward slash V -U -X. Tideo .com forward slash V -U -X. Thank you to the team at Tideo for sponsoring VUx World. Now, our guest today is an incredibly knowledgeable and very kind of, how can I describe it, someone who's been doing this stuff for a long time, and so much so that has actually created one of the most popular open source chatbot frameworks, Deep Pavlov. It is Mikhail Birstev, and he has got an immense world of experience. He's currently a fellow at Landau AI. He's the founder of Deep Pavlov, as I mentioned. He's also the chief science officer at a stealth startup, so we don't quite know what's going on there. It's a little bit cryptic. Whether we'll get any insight into that during this conversation, I don't know, but Mikhail has a whole host of experience that I cannot wait to dive into right now. Mikhail, welcome to VUx World, my friend. Hello, everyone. Hello, Cain. It's just a pleasure and honor to be on your podcast, and thank you for your kind words and for using me. No problem, no problem. The pleasure is all mine. Really appreciate you spending some time with us. So, as I mentioned in the brief intro there, you have a wealth of experience in this space. Incredibly smart guy. Tell us where it kind of all began with you. Were you working with AI before Deep Pavlov? Where did the interest in AI come from? So, first of all, it all started with my academic interest in building systems which can learn on their own, actually.
Harden scores 23 as 76ers cruise past Nets 121-101 in Game 1
"James Harden notches a double double with 23 points and 13 assists to lead the 76ers past the nets one 21 one O one Hardin drilled 7 of the sixers 21 three pointers to help Philadelphia take game one of the best of 7 series. Something that I was giving us, you know what I mean? They double Joe the whole game basically, you know what I mean? Tried their best to not let him get going. He stood at 20 something, but I think he did a really good job of just making the easy passes and, you know, we knock shots down. Joel embiid had a team high 26 points shooting a perfect 11 of 11 from the free throw line. Mikhail bridges scored a game high 30 points for an ex team that turned the ball over 20 times, leading to 31 sixers points. I'm Denny cop.
Lightning beat Islanders 5-0, earn playoff berth
"The lightning have wrapped up their 6th consecutive playoff birth with a 5 nothing route of the islanders. Andrei vasilevskiy stopped 38 shots in his second shutout in three games. Nikita kucherov and Steven stamkos put Tampa Bay in control by scoring one 48 apart midway through the second period. Mikhail sergachev, Tanner janell and brayden point also scored for the bolts. Isles net minor Ilya siroc and was pulled after allowing four goals on 21 shots. New York now holds a two point lead for the first wild card in the east. I'm Dave ferry.
"mikhail" Discussed on Bloomberg Radio New York
"The box office. I'm Brad speaker. And now this Bloomberg sports update in the NBA, the nets continue to play well. Actually improving in the standing since the trades of KD and Kyrie as a coast to a one 29 100 win in Miami, all 5 starters reached double figures Mikhail bridges at a game high 27. They're the 6th seed, a half game ahead of Miami, which is why it was such an important win because they're jockeying for the 6 and 7th playoff spots. The 7th through ten seas play that dreaded playoff round to reach the top 6 seeds. NHL all three local teams dropped the puck Saturday overall a successful day. Rangers picked up a four three win at Florida Patrick Kane broke a two two tie with a goal early in the third period. Yaroslav halak stopped 31 shots for the blue shirts. Rangers are the fourth seed having won 8 of their last 9. Devil's a 5 three home win over Ottawa 5 Devils lit the lamp including Jack Hughes with his 40th New Jersey's a third C just two points behind Carolina. Islanders not as fortunate shout out to nothing on Long Island by the Sabres, former islander Kyle okposo had the game winner for buffalo, New York so 7 seed, so still in the postseason pitcher, a point ahead of the penguins. The first two tickets punched to the men's final four in Houston, the east and west regions have cut down the nets. Florida Atlantic the 9th seed reaches a promised land for the first time in school history with a 79 76 win at MSG for the 5 starters for the owls scored in double figures led by Elijah Martin with 17. There are 35 and three. Meanwhile, the west sought Yukon lead the majority of the game. They took down Gonzaga 82 to 54 Jordan Hawkins. He had 20 points, including 18 from deep. Baseball, spring training concludes their final weekend from the grapefruit league, Yankees and 8 three winner over the Phillies. They opened the year hosting the Giants on Thursday. Manson Cardinals play to a four all tie, New York is in Miami on Thursday in soccer. The Red Bulls in Charlotte play to a one one tie and why FC lost one zero to Houston with your Bloomberg sports update. I'm rob bushka. This is a Bloomberg money minute, the gender pay gap in women's sports is narrowing. Some of the debate and closing the wage gap or the pay gap in sports between men's and women's is to increase the eyeballs on women's works, increasing distribution, which has a chance to increase viewership, which increases the revenue. And so women's sports could catch up. That's Blake Lawrence cofounder and CEO of open doors, the nation's largest technology provider in the athlete endorsement industry. He says one key factor helping close that gap is the success female athletes have over men with Instagram and other social media sites, building their brands, endorsing products, monetizing, especially good at it, members of the U.S. women's national soccer team. And women's college basketball players who are competing right now in the NCAA tournament. And that means big changes in the future. I think the next two decades will see the pay gap close a little bit because women are putting their forts into the spotlight on their own. Tom busby, Bloomberg radio. This is masters in business with Barry red holes on Bloomberg radio. I'm Barry results, you're listening to masters and business on Bloomberg radio, my extra special guest this week is Dominic mill, she is the author of damsel in distressed, the first hedge fund memoir written by a woman, she was a partner in a senior portfolio manager at canyon capital, where she worked for over 20 years. They run about $25 billion. She has been named one of the 50 leading women in hedge funds by hedge fund journal and Ernst and young, she played key roles in complicated bankruptcies, serving as the lead of the creditors community for Puerto Rico and as a restructuring committee member for U.S. airlines post September 11th, she is a director and audit committee chair for the PG&E bankruptcy, Dominic mill, welcome to Bloomberg. Thank you for having me, Barry. My pleasure. First of all, before we get started, I very much enjoyed the book. You have a very wicked sense of humor, which comes through in the pages, starting with the title, damsel in distress, what made you decide to write a memoir about your decades in the hedge fund industry? Well, it started with an article that I wrote as a hobby about my experience as a woman at Lehman Brothers. And it was picked up by Business Insider and I realized a couple things. One was that I really enjoy writing. And to was that I had never really taken time to think about the lack of women in the business and that there really wasn't a voice to tell the story of female investors. And so that's when I thought there might be a hole in the market. Identifying an inefficiency, so to speak. So to speak, except that there's really no money in writing a book. Well, it's best described as a branding exercise, and a way to sort of get things off your chest. But let's roll back a little bit. You get an MBA at Stanford. How do you end up in finance? Was that what you planned to go? Because a lot of the Stanford MBA graduates tend to find their way into technology, not finance. Right. Well, by the time I got to Stanford, I pretty much knew I wanted to be in finance, but where I started was at Lehman Brothers in New York before Stanford, and that was completely, that was serendipity, really. I wanted a job that would take me away from Paris. I wanted to see the world and whether it was investment banking or basket weaving really had absolutely no bearing on my decision. So I ended up as an investment banker, which like every analyst I hated after a few years. And so the MBA was sort of a way out of their job, branching into hopefully what I thought were better, better pastures, but still in finance. What would you do with Lehman Brothers that you grew to hate? Where you just a spreadsheet jockey? Of course I was and I was it was particularly ruthless because I was in a group called fig financial. Financially institutions group at the time they were still a lot of savings and loan institution, the thrift, lots of mergers. So what I did basically was model the mergers of any combination you could think of. I mean, I remember it got so bad that there was a spreadsheet with all the different institutions vertically and horizontally and I had to model them in each. Sounds tedious. Queer. And I thought, what happened? What happens to the diagonal? Do they merge with themselves? You want me to model that too, but that was kind of the. Almost mindless brute force job that I was doing. So you do win a couple of accolades at Stanford when you're getting your MBA. What brought you to the attention of canyon partners? How did you find your way there? Well, first, I sort of zoomed in on the fact that I wanted to work for a hedge fund that I wanted to go to the buy side. That was first. And that was sort of Really. brought to me through a couple of classes that were incredibly illuminating. One taught by Bill Sharpe and one by Darryl
Jokic, Porter, Murray power Nuggets to 108-102 win over Nets
"Nikola Jokić Michael Porter junior and Jamal Murray led the nuggets to a one O 8 one O two victory over the nets. Yokochi had 22 points, 17 rebounds in ten assists in his 28th triple double of the season. Porter finished with 28 points and 9 rebounds for the nuggets who saw a 20 point fourth quarter lead cut to 193 with three 44 remaining. Murray scored 20 of his 25 points in the first quarter and helped Denver shoot 63% in the first half. Mikhail bridges had a team high 23 points in Brooklyn's third consecutive loss. I'm Dave ferry.
Sacramento Kings clinch first winning season since 2005-06
"The kings of clinched their first winning season in 17 years by downing the nets in Brooklyn, one O one 96, de Monte sabonis had 24 points and 21 rebounds for the kings, kings coach Mike Brown. Domes with 14 defensive rebounds. He was obviously amazing. Another double double for him. They improved to ten and two since the all star break as Deandre Fox chipped in 18.6 rebounds and 5 assists. The nets are just one and a half games ahead of 7th place Miami as Brooklyn tries to avoid the play in tournament. Mikhail bridges had a team high 23 points for the nets.
Bridges, Nets withstand Jokic's triple-double, beat Nuggets
"The nets picked up a very impressive road win by downing the west leading nuggets one 22 one 20. Mikhail bridges delivered 25 points in Brooklyn withstood another triple double by Nikola Jokić in winning for the 5th time in 6 games. Nick claxton had 20 points for the nets who outscored Denver by 19 in the third quarter to take an 11 point lead. The lead grew to 15 in the fourth, but the nuggets closed on a 26 13 run. Jokic had 35 points, 14 rebounds at 11 assists in his league high 27th triple double, but the nuggets dropped their third in a row. I'm Dave ferry.
Bridges' perfect 1st quarter sends Nets past Hornets, 102-86
"Mikhail bridges had the hot hand in the first quarter of the nets one O two 86 win against the hornets. Bridges went 9 for 9 in 19 point first period and finished with 33 points. My teammates just kind of finding me as I just kept making shots they just kept looking for me. Coach John a place. His hot start helped Brooklyn go ahead by as many as 37. Spencer didn't what he added 24 points for the nets who were coming off the largest comeback one of the NBA season Friday in Boston. Charlotte made it interesting with a 22 two run in the second half. I'm Dave ferry.
Mikal Bridges helps Nets rally to stun Celtics, 115-105
"The net storm back from a 28 point deficit to beat the Celtics, one 15 one O 5. Brooklyn trailed 51 23, four and a half minutes into the second quarter before closing the half on a 32 13 run. Mikhail bridges scored 38 points and had ten rebounds in 43 minutes. This defense, you know, we got stops and we just stayed together, just, you know, possession by possession. You can't get all back in one place, so you just kept fighting and just stay together. Cam Johnson finished with 20 points and Dorian Finney Smith added 17. Jalen Brown paced the Celtics with 35 points and Jason Tatum added 22 with 13 rebounds on this 25th birthday. I'm Dave ferry.
Ukraine Prepones Pilot Launch of Digital Hryvnia To 2023
"7 p.m. Sunday February 19th, 2023. Ukraine prepon's pilot launch of digital arrhythmia to 2023. Mikhail fedorov, the minister in charge of Ukraine's digital transformation, said in an interview that he plans to speed up.
Vasilevskiy ends 84-game shutout drought, Lightning beat Avs
"Andrei vasilevskiy posted his first shout out to the season at Brandon Hagel collected three points in the lightning's 5 zero thumping of the avalanche. It was the 29th career whitewash for vasilevsky and his first in 85 regular season starts. Ego had two goals that an assist and a rematch of last year's Stanley Cup final, which was won by Colorado. I think tonight we played very well. It's obviously a matchup that we circle at the beginning of the year with everything that happened last year, and for us now going on the road that was a huge game for us to kind of start and get more into going the right way. Corey Perry, Braden point, Mikhail sergachev also scored in Tampa Bay's first one in three games. I'm Dave fairy.
"mikhail" Discussed on Bloomberg Radio New York
"You're listening to the best of Bloomberg opinion I'm vani Quinn. Just more than 6 months after the start of Russia's war on Ukraine, the man who presided over the end of the Soviet Union died. President Reagan and I signed the treaty on the elimination of intermediate and shorter range missiles. Mikhail Gorbachev won the Nobel Peace Prize in 1990 for, quote, the leading role he played in the radical changes in east west relations. In the scheme of history, however, his was a complicated role. I spoke with Bloomberg opinions Clara Ferrera Marquez about the complex legacy of the man who ended the Cold War. Tara, what for you is the lasting legacy of Mikhail Gorbachev. I think it's a really interesting and far more difficult question than I think a lot of people realize and particularly in the west where we have a very particular view of Gorbachev and what he did and what he represents for many of us is the end of the evil empire. It's in reality a lot more complicated. So he was clearly a man of the system and man who wanted to work within the system. Obviously he did not start out to collapse the Soviet Union. That was unintended consequence largely of economic and reform. He changed the world. He was able to end the Soviet Union largely peacefully and that is remarkable. But also that a lot of the ill conceived economic reforms that he brought in the chaos that he left in his wake allowed kleptocracy to take root and in many ways he is responsible for where Russia is today, Russia is two, of course, Putin and many of us in the west who supported the system, but the economic plundering that was possible was to a degree because of Gorbachev. Right, there's a little bit more of a complicated legacy than just he presided over the end of the USSR. And think about him as a bit of a political Rorschach test. To us in the west, he's the man that Margaret Thatcher said she could do business with. For many in the former Soviet states, he demanded the brought down the wall. But for Russians and I think in particular what matters for us as we look at this today is really what he means for the Putin regime. And for them, he is the man who lost an empire, the man who brought national humiliation to a great nation. And I think that's really very, very important in understanding where we are today. So just to go back to perestroika in Glasgow's those reforms were life-changing events, countries, state changing events, were there any remnants of what Gorbachev had introduced? Well, that's a difficult one. I'd say almost no. So if you think about the three things that he really wanted to bring, he really wanted a thriving economy. He wanted openness and he wanted democracy. And under Putin all three have been undone. But the concept of posturing the concept of Glasgow slightly different. And they also self reinforcing means transparency clarity. It was about openness and really that began very strongly after Chernobyl, which was a failure of the system that kept so many secrets. And prior story was the reconstruction. So that's what destroyed means to reconstruct. And what happened in fact is so relevant to today because when he started to unpick it, he found that the Soviet empire was sort of nothing it was built on violence. It was built on lies and really that's what we will find with the Putin regime. Archie Brown in The Guardian said Gorbachev was asked a couple of years ago what is epitaph should be any replied we tried. He was devastated apparently by the war, and at the same time, Clara, he must have seen this coming in some ways. I don't want to compare it to other rises and falls of other regimes and so on, but in some ways, these things are visible in advance, right? And certainly in the case of Russia, this was potentially extraordinarily visible, especially to a statesman like Gorbachev, who then handed off to yeltsin, who then handed off to Putin. I think in terms of thinking about the collapse of the Soviet system, two things are important. One is that they themselves think about this. Beijing thinks that the Beijing spends an awful lot of time studying perestroika began to go back to the Rorschach test it really says everything about fishing and not much about Mikhail Gorbachev. The second thing is just in terms of the visibility. So when we look at the subject collapse most important thing is really to think that what was obvious was that it would come to an end. It wasn't at all obvious how it would come to an end or when. And I think the same is true today. We have an extremely brittle system and a system that is hollowing itself out stagnant economy and impossible plundering of resources at the top, predicting when that can end. I mean, that is almost impossible. You mentioned that Beijing studied perestroika, what lessons did Beijing learn from this study, what so called errors of perestroika would Beijing seek to try and avoid. I mean, interestingly, I would argue they take all the wrong lessons from this. They look at Gorbachev. I mean, obviously, there were violent incidents. But by and large, he was averse to violence and Beijing sees certain, I think, Xi Jinping once made a comment about the iron grip that Gorbachev did not have. They really see this as a sort of demonstration that forced its required if somebody wants to pull out of your empire, you pull them back in. I force and obviously that's what's happened, for example, with Hong Kong. The other thing they think about it as economic and political reform, which comes first. And they really see as a problem that would happen in Russia was that political reform was done so. So that there was openness. There was an ability to discuss the errors and everything was out in the open and they'd see that. The fundamental, but it's a really interesting study because it has changed over time. So Kara, you would have seen this happening when you were a youngster in school and so on, but you did arrive in Russia, not that long after some of these changes were enacted, what was it like? Was it a free and open society where there was a view towards the market economics and so on, or was the yeltsin era already showing signs of strain? I think what the yields are near a really showed was that we were heading towards the sort of personalist and collected democratic system and at the time it perhaps wasn't so obvious we saw a different direction of travel. So I arrived in Russia in August 97. So just before the financial crisis, the year after. And it was a time that was extremely chaotic, extremely painful, economically. And also quite violent to be clear, this was not an easy time at all. But it was a hopeful time in the sense that people did see something better down the line. They were sort of living through this period, even during the 98 crisis, which is absolutely catastrophic. I'd say a lot of us has been reversed the hope in particular, but also this idea that we could escape stagnation, take the auto industry, or take the aviation industry, for example. Clara, Putin, how would Vladimir Putin have been shaped by the events that Mikhail Gorbachev oversaw? I think there are two very important events I think important to understand where Putin is today and the mythical man that he is. One is effectively 1989. He was the young KGB officer in Dresden in Germany. And there's an absolutely he's written about. He was at the case of your headquarters and there was a mob approaching and he was desperate to preserve what was inside and he called the Red Army and he asked for reinforcements. And they said, we haven't got a pro Moscow. So you can't do anything. And then they said something that stayed with him, which as they said, Moscow was silent. And this particular phrase for him was really a sort of demonstration of powerlessness. It was a humiliation. He felt the country no longer existed and he wanted to reverse this destruction of an empire. He said later that the thousand years of our work was undone. The second important moment is that he did consider the role that a collapse in the economy played. So for him and macroeconomic stability was and remains absolutely crucial and he very often positions himself in contrast with the chaos of the 1990s. Obviously that's very ironic given where we are today with the Russian economy where he himself has pushed the economy back to pretty much that period. Yeah. How is Gorbachev seen by the majority of Russians if there is a majority opinion on Gorbachev? Well, Russians opinions on complicated and it depends to some extent what age you are. But I think for a long time, he was actually completely ignored. He was a fringe figure. He complained about the first and regime, though I would say that he saw Ukraine in Russia's orbit the way that Putin does. That doesn't mean he advocated an invasion. In fact, he clearly spoke up against it. But it didn't have a radically different view. I think it's important to understand the role that political deaths play and the regime like this. So political death funeral, the eulogy, the whole pageantry around that is not about the
"mikhail" Discussed on Bloomberg Radio New York
"Opinion, I'm vani Quinn. Just more than 6 months after the start of Russia's war on Ukraine, the man who presided over the end of the Soviet Union died. President Reagan and I signed the treaty on the elimination of intermediate and shorter range missiles. Mikhail Gorbachev won the Nobel Peace Prize in 1990 for, quote, the leading role he played in the radical changes in east west relations. In the scheme of history, however, his was a complicated role. I spoke with Bloomberg opinions Clara fre Marquez about the complex legacy of the man who ended the Cold War. Tara, what for you is the lasting legacy of Mikhail Gorbachev. I think it's a really interesting and far more difficult question. And I think a lot of people realize and particularly in the west where we have a very particular view of Gorbachev and what he did and what he represents for many of us is the end of the evil empire. It's in reality a lot more complicated. So he was clearly a man of the system, a man who wanted to work within the system. Obviously he did not start out to collapse the Soviet Union. That was unintended consequence largely of economic and this co reform. He changed the world. He was able to end the Soviet Union largely peacefully and that is remarkable. But also that a lot of the ill conceived economic reforms that he brought in the chaos that he left in his wake allowed kleptocracy to take root and in many ways he is responsible for where Russia is today, Russia is two, of course, Putin, many of us in the west who supported the system, but the economic plundering that was possible was to a degree because of Gorbachev. Right, there's a little bit more of a complicated legacy than just he presided over the end of the USSR. Think about him as a bit of a political Rorschach test to us in the west he's the man that Margaret Thatcher said you could do business with. For many in the former Soviet states, he demanded the brought down the wall, but for Russians and I think in particular what matters for us as we look at this today is really what he means for the Putin regime. And for them, he is the man who lost an empire, the man who brought national humiliation to a great nation. And I think that's really very, very important in understanding where we are today. So just to go back to perestroika in Glasgow's those reforms were life-changing events, countries changing events. Were there any remnants of what Gorbachev had introduced? Well, that's a difficult one. I'd say almost no. So if you think about the three things that he really wanted to bring, he really wanted a thriving economy. He wanted openness and he wanted democracy. And under Putin all three have been undone. But the concept of posturing the concept of glasnost as slightly different. And they also self reinforcing means transparency clarity. It was about openness and really that began very strongly after Chernobyl, which was a failure of the system that kept so many secrets. And prior story was the reconstruction. So that's what destroyed means to reconstruct. And what happened in fact is so relevant to today because when he started to unpick it, he found that the Soviet empire was built on nothing it was built on violence. It was built on live and really that's what we will find with the Putin regime. Archie Brown in The Guardian said Gorbachev was asked a couple of years ago what his epitaph should be any replied we tried. He was devastated apparently by the war, and at the same time, Clara, he must have seen this coming in some ways. I don't want to compare it to otherwise and folds of other regimes and so on, but in some ways, these things are visible in advance, right? And certainly in the case of Russia, this was potentially extraordinarily visible, especially to a statesman like Gorbachev, who then handed off to yeltsin, who then handed off to Putin. I think in terms of thinking about the collapse of the Soviet system, two things are important. One is that they themselves think about this. Beijing thinks about this. So Beijing spends an awful lot of time studying perestroika began to go back to the Rorschach test it really says everything about fishing and not much about Mikhail Gorbachev. The second thing is just in terms of the visibility. So when we look at the subject collapse, most important thing is really to think that what was obvious was that it would come to an end. It wasn't at all obvious how it would come to an end or when. And I think the same is true today. We have an extremely brittle system and a system that is hollowing itself out stagnant economy and impossible plundering of resources at the top, predicting when that can end. I mean, that is almost impossible. You mentioned that Beijing studied perestroika, what lessons did Beijing learn from this study? What so called errors of perestroika would Beijing seek to try and avoid? I mean, interestingly, I would argue they take all the wrong lessons from this. They look at Gorbachev. I mean, obviously there were violent incidents. But by and large, he was averse to violence and Beijing sees that and I think Xi Jinping once made a comment about the iron grip that Gorbachev did not have. They really see this as a sort of demonstration that required if somebody wants to pull out of your empire, you pull them back in. I force and obviously that's what's happened, for example, with Hong Kong. The other thing they think about it as economic and political reform, which comes first. And they really see as a problem that would happen in Russia was that political reform was done first. So that there was openness. There was an ability to discuss the errors and everything was out in the open and they'd see that. It's fundamental, but it's a really interesting study because it has changed over time. So Kara, you would have seen this happening when you were a youngster in school and so on. But you did arrive in Russia, not that long after some of these changes were enacted, what was it like? Was it a free and open society where there was a view towards market economics and so on, or was the yeltsin era already showing signs of strain? I think what the yields are nearer really showed was that we were heading towards the sort of personalist collective democratic system and at the time it perhaps wasn't so obvious we saw a different direction of travel. So I arrived in Russia in August 97. So just before the financial crisis, the year after. And it was a time that was extremely chaotic, extremely painful, economically. And also quite violent to be clear, this was not an easy time at all. But it was a hopeful time in the sense that people did see something better down the line. They were sort of living through this period, even during the 98 crisis. Which is absolutely catastrophic. I'd say a lot of that has been reversed the hope in particular, but also this idea that we could escape stagnation, take the auto industry, or take the aviation industry, for example. Clara, Putin, how would Vladimir Putin have been shaped by the events that Mikhail Gorbachev oversaw? I think there are two very important events. I think important to understand where Putin is today and this command that he is. One is effectively 1989. He was the young KGB officer in Dresden in Germany. And there's an excellent he's written about. He was at the case of your headquarters and there was a mob approaching and he was desperate to preserve what was inside
"mikhail" Discussed on Bloomberg Radio New York
"Air traffic controllers and just think about how essential air travel is in Russia. I mean, it's the world's largest country. Clara, Putin, how would Vladimir Putin have been shaped by the events that Mikhail Gorbachev oversaw? I think there are two very important events I think are important to understand where Putin is today and mister man that he is. One is effectively 1989. He was the young so KGB officer in Dresden in Germany. And there's an absolutely he's written about. He was at the case of your headquarters and there was a mob approaching and he was desperate to preserve what was inside and he called the Red Army and he asked for reinforcements. And they said, we haven't got this from Moscow. So you can't do anything. And then they said something that stayed with him, which as they said, Moscow is silent. And this particular phrase for him was really a sort of demonstration of powerlessness. It was a humiliation. He felt the country no longer existed and he wanted to reverse this destruction of an empire. He said later that the thousand years of our work was undone. The second important moment to think about when we think about that segment is that he did consider the role that a collapse in the economy plays. So for him and macroeconomic stability was and remains absolutely crucial and he very often positions himself in contrast with the chaos of the 1990s. Obviously that's very ironic given where we are today with the Russian economy where he himself has pushed the economy back to pretty much that period. Yeah. You wrote a column in which you said that Putin will actually exploit Gorbachev's death. He explained that a little bit more to us, how is Gorbachev seen by the majority of Russians if there is a majority opinion on Gorbachev and how will Putin exploit the death? Well, Russians opinions on Gorbachev. And it depends to something what age you are. But I think if you remember, he did that absolutely awful Pizza Hut that in the 90s. And the hawks into a restaurant, there's a family there and the family start arguing. He brought economic chaos and then the younger person in the family and the son says something like he brought his freedom. Oh no, he brought us political instability. This sort of debate is a real debate for many Russians. But I think for a long time, he was actually completely ignored. He was a fringe figure. He complained about the prison regime, though I would say that he saw Ukraine in
"mikhail" Discussed on Bloomberg Radio New York
"It's three 48 on Wall Street. The following is an editorial from Bloomberg opinion. This editorial was written by the Bloomberg editorial board. Mikhail Gorbachev, who died this week at age 91, defied convention throughout some of the most tumultuous years of the Cold War. When he assumed leadership of the Soviet Union in 1985, he inherited a country that was economically moribund, stubbornly corrupt, and suffering through the final spasms of ideological failure. A lesser leader might have tried to ignore such challenges, Gorbachev took many of them head on. Perhaps his greatest achievement was overseeing the largely bloodless dissolution of his own empire, millions of people across central and Eastern Europe gained freedom as a result. Gorbachev's legacy is complex to be sure. The final leader of the Soviet Union failed at most of his ambitions, but the world was better for them all the same. This editorial was written by the Bloomberg editorial board for more Bloomberg opinion, please go to Bloomberg dot com slash opinion or PIN go on the Bloomberg terminal. This has been Bloomberg opinion. I turn on the radio. Yeah, but you let me drive. Oh, no. No, no, no. Honey, please. I'll do the driving drive on. Excuse me, I want to drive. It's a good question for the drivers. This is the drive to the clubs. On Bloomberg radio. All right everybody, just about ten and a half minutes left in the trading
"mikhail" Discussed on Bloomberg Radio New York
"2700 Bloomberg journalists and analysts are working on this morning around the world. It's 5 39 on Wall Street. The following is an editorial from Bloomberg opinion. This editorial was written by the Bloomberg editorial board. Mikhail Gorbachev, who died this week at age 91, defied convention throughout some of the most tumultuous years of the Cold War. When he assumed leadership of the Soviet Union in 1985, he inherited a country that was economically moribund, stubbornly corrupt, and suffering through the final spasms of ideological failure. A lesser leader might have tried to ignore such challenges, Gorbachev took many of them head on. Perhaps his greatest achievement was overseeing the largely bloodless dissolution of his own empire, millions of people across central and Eastern Europe gained freedom as a result. Gorbachev's legacy is complex to be sure. The final leader of the Soviet Union failed at most of his ambitions, but the world was better for them all the same. The editorial was written by the Bloomberg editorial board for more Bloomberg opinion, please go to Bloomberg dot com slash opinion or OPI N go on the Bloomberg terminal. This has been Bloomberg opinion. Bloomberg opinion editorials can be heard every weekday at this time and terminal customers can read more at op IN go. S&P futures down 30 points Dow futures down 175
"mikhail" Discussed on Bloomberg Radio New York
"Thanks there in 6 O 7 on Wall Street 70° in Central Park at a car fire eastbound LIE at exit 43 will get the update in traffic shortly first Michael Barr with what else is going on in New York and around the world. Good morning, Michael. Good morning, Nathan, the earth moved in New Jersey last evening. According to the U.S. geological survey, two earthquakes hit northern New Jersey. The first to 2.3 magnitude earthquake was just before 5 15 p.m. about 6 miles northwest of Moore's planes. At 1.7 magnitude aftershock was then reported shortly after 6 30 p.m., also in Morris county. The last earthquake in New Jersey was in freehold in 2020, recorded at a 3.1 magnitude. One Texas official says migrants crossing into El Paso want to come to New York City. El Paso has been housing asylum seekers at welcome centers there to assist the homeless population. Texas has been busing migrants from the opportunity center to where they want to go. Speaking at ABC's K VIA in El Paso, John Martin, the deputy director of the center says up until now, it has been working out well. I've never seen individuals run so quickly to take a shower so that they could get on a bus and be able to go to where they wanted to go. Martin says, though, it appears to have stopped a bus schedule to leave Monday from the center was postponed. Former Soviet president Mikhail Gorbachev has died, Russian news reports say it happened at the central clinical hospital where he was undergoing unspecified treatments after a long illness. Mikhail Gorbachev was 91. Texas is announcing the first confirmed fatality from monkeypox in the U.S.. Doctor Jennifer mcquiston says the patient had other underlying health conditions. It's important to focus that we have mitigation measures in place to prevent monkeypox. Get vaccinated. If you're sick, go to a doctor, get tested. And if you have severe illness, there are treatments that are available. Doctor mcquiston says, though, only a handful of monkeypox fatalities have been reported globally. President Biden announced his safer America program. He proclaimed in Wilkes Barry Pennsylvania, the nation had to fund the police, then criticized GOP members of Congress for not supporting his plan. Guess what? Every single Republican member of Congress. Every single one in this state, every single one voted against the support for law enforcement. They talk about how much they love it. They voted against the funding. President Biden, global news, 24 hours a day on air and on Bloomberg quicktake powered by more than 2700 journalists and analysts in more than a 120 countries. I'm Michael Barr. This is Bloomberg Nathan. Thanks, Michael
"mikhail" Discussed on Bloomberg Radio New York
"To cut carbon by procuring wind, solar power, wind and solar are some of the most some of the cheapest forms of generation today, but when 2005, 2010 time frame, they were much more expensive. And so in setting these goals, the utilities were forced to go out and contract for really expensive power that had financial implications for all of the utilities. But when it came to making sure that the utilities made good on these targets, that was left to the California public utilities commission. And frankly, that was the sexy place to work within the agency. It was the place had the most cache overseeing the procurement of these contracts, whereas the safety division historically was pretty understaffed. They simply didn't have the manpower to make sure that the utilities were doing enough to maintain their infrastructure and that was particularly problematic within PG&E. Sit tight, we're speaking with Catherine blood, she's renewable energy and utilities reporter for The Wall Street Journal. She's the author of the brand new book. It's out today. It's called California burning, the fall of Pacific gas and electric, and what it means for America's power grid. She joins us this afternoon via Zoom from San Francisco. We're going to do some news Catherine. We're going to take a quick break and then we're going to come back with much more. On the other side, what I'm interested in hearing about specifically is what PG&E is doing on the other side of bankruptcy. What the company is doing in its plans to prevent tragedies like the campfire from ever happening again. Its efforts to put power lines underground and tap more into renewables over the next few years. You're listening to a Bloomberg businessweek on Bloomberg radio. Let's get to world of national news with Nancy Lyon. Hey Nancy. Thanks, Tim. We're getting some light breaking news here. There are reports coming from Russian news media that former Soviet president Mikhail Gorbachev has died. He was the last president of the USSR and he led his country out of the Cold War era. Again, we're hearing that former Soviet president Mikhail Gorbachev has died. President Biden says he is determined to see military style assault weapons banned in the U.S., speaking in Pennsylvania Biden said they are the weapons of choice for mass shooters. We have to act for those families in Buffalo. Yuval
"mikhail" Discussed on Game Theory Podcast
"Rim protection, anything that you need from draymond green, deflections, getting his team organized in transition. Draymond green has all of it. I'm taking draymond green as my is the captain of my defense is my number one pick here. Number two overall. Yeah, I mean, if he'd come back, I don't know, a month earlier and had been at the same level. This is defensive player of the year this year. I don't even think it's close. Yeah, he was running away with it when he got hurt. And honestly, I'll be honest, I just forgot about him. He wasn't on my list. Well, yeah, it's like, look, because he missed out. Frankly, it doesn't change my pick at all, but it is funny that I just, he fell off my list because there were a lot of really good defenders this year and when he didn't come back and then when he came back wasn't at the same level, easy for me to kind of leave him off. Yeah, definitely, look, I think I would have draymond green first team all defense this year. And the reason is for me, I care more about performance on the all defense team than I do about missed games or anything. In terms of the defensive player of the year or the year award, I'm a little bit more willing to consider, okay, Mikhail bridges played well over a thousand minutes more than draymond green this year. He should be ahead of that in the most valuable defensive player of the year voting this year. But in terms of the all defense teams, I like to award a little bit more based on performance and make it a little bit more fungible in terms of games played. And for my money, there was no better defender on a permanent basis this year than draymond green. That's my pick. I don't hate it. I'm assuming Alex Caruso will be coming soon for you. Yeah, I'm gonna go bam out of bio..
Biden's Nuclear Negotiator Robert Malley Weighs Lifting Iran Sanctions
"Andrew we got. I mean, there is this negotiation that's happening. I mean, we touched on it before, but there is basically Robert malley, who is the U.S. special envoy to Iran, is currently in Vienna, and apparently the negotiation is going so poorly about the. So he negotiated the JCPOA in 2015. So he is the guy that basically was responsible for the document that Trump came in ripped up and got the United States out of the Iran nuclear deal, right? It was an unbelievably bad dude. Yeah, it was an unbelievably bad deal. Well, guess what? He's at it again. So when we talk about our elites failing us, when we talk about how America deserves better, at least, at least we like to think we deserve better elites. He's at it again and he's in Vienna and basically there's a substack from Melanie Phillips. She published it 21 hours ago. She basically says that it's going so poorly that she was, you know, she felt obligated to write this and that she had permission from some people on the ground. This is all happening while we're worried about Putin launching nuclear weapons. Like, oh, let's go give another authoritarian nuclear weapons in the Middle East. Yeah. And so she highlights a Twitter thread from Gabrielle norona that says one, my former career at the State Department NSC and EU colleagues are so concerned with the concessions being made by rob malley in Vienna that they've allowed me to publish some details of the coming deal in hopes that Congress will act to stop capitulation. What's happening in Vienna is a total disaster, one warned, the entire negotiations have been filtered and essentially run by Russian diplomat, Mikhail ulyanov, the concessions and other misguided policies have led three members of the U.S. negotiating team to leave. They're basically stripping away different sanctions of all of these leaders within Iran, including the Iranian military that has murdered people all around the world, apparently, according to this thread, we're about to be not only in a geopolitical cluster in eastern Ukraine and Russia, but we're also going to have Taiwan coming on board. And now we've got basically JCPOA number two. So
Malley Allowing Sanctions to Be Lifted Over Terrorists in Iran Deal
"He goes on These people have told him what's happening in Vienna is a total disaster one warned The entire negotiations had been filtered and essentially run by the Russians Russian diplomat Mikhail yola nav The concessions and other misguided policies have led three members of the U.S. negotiating team to leave In other words to resign This is a long and technical threat he says but here's what you should know The deal being negotiating at Vienna is dangerous to our own national security It is illegal It is a legitimate and in no way serves U.S. interests in either the short or long term Here's why Led by Robert malley that's the name The U.S. has promised to lift sanctions on some of the regime's worst terrorists and torturers Leading officials in the regimes weapons of mass destruction infrastructure and is currently trying to lift sanctions on the Republican guard itself He said now let's dive in first Biden's team is preparing to rescind the supreme leader's office executive order as soon as this coming Monday It lifts sanctions on nearly every one of the 112 people in entity sanctioned under it even if their sanctioned under other legal authorities We sanctioned some of the worst people you can possibly imagine under this authority Like motion razor who was involved in the 1994 AMI a bombing that killed 85 people in Argentina He'll be able to live free of sanctions next week if Robert malley proceeds
Switzerland Breaks Neutrality, Adopting Sanctions Against Russia
"One of the latest and most notable reactions to the invasion is the fact that Switzerland has chosen a side On Monday the famously neutral country said it would adopt European Union sanctions against Moscow and freeze Russian assets located in the spanx Governor adopted sanctioned specifically against Putin prime minister Mikhail mushin and foreign minister Sergei Lavrov Good for them That's a good thing Even the Finnish are getting involved The two nations of border Russia were threatened with serious military political consequences by Moscow if they were to join NATO but both of them brushed off the threats Finished foreign minister Pikachu said we've heard this before and don't think that it calls for a military threat Finland's 830 mile border with Russia's the longest of any European Union country I want to be extremely clear It is Sweden that itself and independently decides on our security policy line added the Swedish prime minister Magdalena Anderson's she said in a statement
"mikhail" Discussed on Blindfold Chess
"Three. They should be seven rook to see one queen. B eight ninety four castle. Kingside bishop g. three ninety two seven four five five luke. Eight queen c. Four d five night takes far bishops. Five check king age one rookus. Six queen takes bishops. Eighty five night takes ninety. Five route takes ninety five. He takes f four ninety seven check king. F eight rotates ninety seven. Bishop takes roque de seven. Fish takes is six lack raisons..
Instagram DM Automation Is Here
"Hey mike welcome back to the econ- crew podcast. Hey there thanks for having me. Yeah absolutely i. it's funny. We were just talking before hitting record. And i swore that we had chatted before on the podcast after four hundred episodes. It's hard to keep them all straight but it was under a different name. So i was looking for mikey on and it wasn't showing up then i You mentioned it. It could be under mikhail so looked under the under that name popped up episode one eighty six. So we've more than doubled the number of episodes and she so it's been too long. My friend nice nice I'm glad to be back. And i think last time we talked about messenger and now we have some exciting news to share so glad that we could make absolutely so before we talk about the new stuff and even the old stuff. Maybe if you could just because it has been so long since you've done the podcast maybe tell people a little bit about who you are and in. What many shadows sure. My name is mike. Von i'm the ceo and co founder of many chats and many chapters a chat marketing platform. We're the biggest marketing platform in the world. We started on facebook. Messenger then added new channels lake sms and email and now since june second this year so basically two weeks ago. We added instagram automation. Which is a huge new channel. Excellent and for those of you who are new to chat marketing or messenger marketing. Basically what we do as a platform as we help you automate conversations on messaging platforms. So think about like. If you're using messenger you could run ads directly to messenger and automate the conversation to qualify lead to nurture leads and then to actually convert that person that leads into a paying customer and we basically integrate with messaging channels like facebook messenger. Instagram and whatsapp is in the works. And basically we have this visual flow builder that allows you to create these chats marketing campaigns without doing any coding. It's very visual. It's just of like a choose your own adventure type of thing. You just set up the text the buttons and you can create these automations
Pfizer to Seek OK for 3rd Vaccine Dose; Shots Still Protect
"An official with drug maker Pfizer says the company will ask federal regulators next month to approve the administration of a third dose of its covert vaccine Fizer says early data suggests that if people receive a third dose of its vaccination there antibody levels will jump five to ten times higher than what they had with the second dose chief scientific officer Dr Mikhail Dalston says that could also help neutralize the delta variant but even if the FDA approves of third Kobe does that doesn't mean people will get boosters a vaccine expert at Vanderbilt University Dr William Schaffner says public health officials might prioritize getting protection to those who haven't gotten any shots he says the current two duo system has been effective for keeping people out of the hospital and tamping down on the variance I'm Jackie Quinn
"mikhail" Discussed on WMAL 630AM
"Leader Mikhail Gorbachev on a tour of Red Square and the spring of 1988. Behind the boy stands a blond man dressed in tourist garb with a camera around his neck who bears an unmistakable resemblance to young poutine. And so, Pete Souza put this thing out. And sure enough, it really does look like now Putin was KGB at the time. And in fact, at this time he was assigned to the Stasi, These German secret police, murderers and torturers. At that time he was assigned to Dresden. I believe in East Germany, but he could certainly have made because President Reagan was coming and they needed all hands on deck and, according to intelligence officials, everybody gathered for this photo op with Putin out there in Red Square. There were KGB and KGB families, right? As they staged all this stuff. They're communists. They lie about everything so one agent said, according to the story said, Yeah, these are all KGB families out here. And it looks like poutine slipping into the frame with Reagan and Gorbachev, you know, and he looks a little nervous, but it looks like it's him. The pointy nose face kind of thing. Strong guy, you know, for killing people more effectively and things like that. That's good stuff. That's very good stuff there. It is back to a Democrats and racism because God knows they're Racists Chapman University Chapman universities and is that a four year school Chapman University hosts racially segregated quote..
Biden to Meet With Putin on June 16 in Switzerland
"Now. The russian leader vladimir putin and his us counterpart biden will meet in geneva on june the sixteenth off. The back of biden's european tour nuclear arms control be high on the agenda echoing meeting held in the city in one thousand nine hundred eighty five when ronald reagan and mikhail gorbachev met talk about arms control and at which they developed a personal rapport. Joining me from burn is editorial director. Tyler relay and from our zero studio. Wanna call security correspondent. Ben is log Good morning to you. Tyler moore ingredient now. There are no huge expectations for the outcome of this meeting but the one country that comes out as a winner will be switzerland. Why is it important to geneva to host these tools jersey and this is a story which has been circulating for a while. it's no great secret of course Prog within in the running for the summit a couple of other cities along the way but it's been circulating in the swiss press geneva wanted us. They wanted it very badly into your question. Geneva has been slightly dented. The city sees that its reputation home of multi-lateralism somewhat scarred Because of course it doesn't really control the narrative from many of the organization. It hosts so if you look at of course the past year and a half an organization like the who for example which sits in geneva of course has been in the headlines almost daily. I'm often for for the right reasons so even like listening this morning to the mayor of geneva certainly other politicians from the city. They see this as a very important reset moment the city. How does the city reestablish itself as a home of multi-lateralism and of course getting to bed to go with
The Institute of Illegal Images
"According to mark mcleod the origins of blotter art come from the criminalization of lsd. Lsd was originally distributed at liquid before becoming illegal in all fifty states after nineteen sixty. Six underground drug dealers. Started using something called blotter sheets. They dip these paper sheets and lsd and let them dry then they cut it up and sell it that way pretty soon. They were printing artwork onto these blotter sheets and eventually artists were designing images specifically for the broader sheets. They were fun. Little codes clues as to where the blotter might have come from and who might have made it. And it's all this blotter. Art mark mcleod collects and displays in the blotter barn. The barn is just the a skeletal formation of the entire history of blotter which has a very small history from november nineteen. Sixty eight to return and i tried to get examples with sprint across. I put the. I fit up there. A judge Have a skeletal formation. That you can hang more flesh on each blotter. Sheet is divided into perforated taps or pits and each tab about a quarter of an inch across sometimes a portrait spread out among several tabs on the same sheet like a mosaic or a multi piece puzzle other times. An entire image fits within one tiny tab and it's best seen beneath a magnifying glass. Either way the images from many different artists are impressively detailed and precise. There's a portrait of the pharaoh. Mikhail gorbachev in nate picture of alice peeking through the looking glass. These are just some of the tens of thousands of images on display. Mark doesn't even have a full count