I am very happy this afternoon that we have five excellent speakers here with us that will explain us all we need to know about machine learning in this connected world. Summer Simar has been an angel investor and managing director of Modrona's Ventures Group through his work he has years of experience in machine learning and other emerging technologies. Mr. Gareth Keane is an investor in that field and an investment manager at Qualcon Ventures and mostly he invests in hardware and robotics companies. Carly Agneema is the CEO and co-founder of Neutronomy. Out of his research here at MIT he has built one of the world's leading developers of self-driving car software. And also second day is the co-founder and CEO of Guardhat. Guardhat develops wearables that improves the safety of workers in heavy manufacturing industries. And Ari will be leading the discussion this afternoon. She's the director of emerging technologies experiences at IBM and spends her time identifying and enhancing scientific breakthroughs. I'm extremely excited about this panel. I hope you are as well. And I will ask our five speakers to take the stage. Thank you. Thank you. Hi, welcome everybody and good afternoon. So I'm also excited with the panel today. I think I'm going to start by asking our panelists, given that machine learning is really everywhere. So that's almost a commodity. How do you help or in your own jobs or help others understand what intelligence means in this context? Who would like to take that? Hard question. So am I going to start? Great. So whenever I think about intelligent applications, the thing that comes from my mind is, you know, it's a way that application developers or applications think about how to take advantage of both historical data as well as real-time data, whether it is structured data or semi-structured data or unstructured data, and then be able to process the data to help make decisions and predictions that is going to enable people to be able to deliver personalized, rich, adaptive experiences for users, no matter whether you're a consumer or an enterprise user. Cool. I think from an investor perspective, we look at it almost from the lens of what problem am I, this company, this entrepreneur, going to go solve for an enterprise or a consumer. Being an investor for a large technology corporation, we also have the advantage of a lot of very smart people internally, a lot of whom are working on either hardware implementations of classic machine learning models or the software stack that sits on top of those. So in particular, application feels like computer vision, sensor fusion, where these software pieces on the machine learning side add a lot of value. We do have some internal competency that we can use to sort of peel the onion and understand if a team truly has something unique and differentiated, or if it's a, you know, vaporware type approach to things. Well, if I were to leave the stage right now, the average IQ of the stage would increase. So given that my background is in steel and mining and metals, you know, I often find machine learning, artificial intelligence, and deep learning to be words where I think people confuse each other and trip up quite a bit. And often enough, at least from my end user perspective, it's hard enough to teach human beings, you know, think about teaching machines. But let me get back to the sense of machine learning itself. I think there's so much talk and so much hype around this term now, you know, artificial intelligence has been here for at least 60 years. Machine learning is a portion of artificial intelligence to be precise. It's a part of it. And there are elements of which we can apply today, the elements we can apply in 10 years or 20 years from now, and the elements that you could never potentially apply. So a lot of what my background being from one of the larger steel companies in North America in a former life is to distill what's going on in academia and in technologies looking for use cases is to come at it from the other side where, you know, what's the use case and what's the right way in which you address that use case. And that's where I think that's my perspective of machine learning. Right. Yeah. So, you know, we also think about machine learning from a practical user perspective. You know, how can we deploy it in our system? And I guess a useful discriminator for us is we ask ourselves the problem that we're facing, whether that's, you know, to help a car detect a pedestrian on a sidewalk or to help a car decide when to, you know, travel through a yellow light. We ask ourselves, can we model this using other tools? Can we write down equations that precisely describe this phenomena? If we can, that's usually a good way to approach the problem.  If we can, it's often because the phenomena that we're talking about is so complex or rich or inherently unmodelable, that is, there's just no good, you know, way to simply write down a closed form expression that describes that phenomena. In that case, often a useful way to tackle the problem is by looking at data and to use methods like machine learning that are often data-driven and can, through some magical process, generate these complex models that can help us explain our phenomena. So we, again, think about it more from the end user phenomena. And, you know, I come at learning methods with an air of skepticism because at the end of the day I don't understand what this thing is, but I do know that it works. Sometimes that's good enough. Right. That's great. So, I think that, right, from the user perspective, that's the interesting situation because it could be what we call it, it doesn't really matter anymore to the user, but what matters is the interaction and what they get out of it. So how do you see the current trends or machine learning in particular? How is that affecting how users interact with machines? That's a great question. You know, I think we track a couple of things very, very closely and there's some interesting developments we've seen in sort of both consumer and enterprise in the last 18, 24, 36 months that we think are indicators of how technologies like this are starting to percolate into, you know, stuff that we all touch and interact with every day. I think self-driving cars is a perfect example, right? If you talk to a computer science professor here at MIT 10, 15 years ago, they would have told you that problem is probably intractable and never will be solved. But we've seen companies like Neutonomy and others make great strides and, you know, Uber and you guys are driving autonomous cars in various jurisdictions now. So I think that's one place we'll all see a lot of interaction going forward is just with the way we interact with machinery in general, whether it's a very tactile physical thing like transportation or something like Voice UX UI. You know, Amazon has stolen a march on a lot of people with this amazing Alexa platform. They sort of pulled out of their hat in the last two years and it's fascinating to think about how that ability to interact in a very seamless, natural way with technology becomes this ubiquitous, magical technology layer. It's pretty exciting. I would say when you interact, when you go to sort of amazon.com, for example, or when you go to Netflix or you go to a dating website, right?  That's one place where we all encounter day-to-day. I think, you know, autonomous car is a great example, but probably it's going to be another three, four, five, six years before a lot of us here are going to get a chance to be able to directly experience the benefit of that. But if you take an enterprise scenario right now, hey, I flew Alaskan Airlines to come to Boston. I like Alaskan Airlines. But whenever they sort of surface offers for me, I want them to be smarter about knowing my travel patterns, knowing my likes and dislikes, and give me offers that matter. There's a lot of machine learning that goes into them having to deliver that to me. So those are like, you know, sort of day-to-day experiences that we are all starting to see what the power of machine learning could deliver for us. From a, that's a great point so much, from a user perspective, as I see the adoption of machine learning in different technologies or different use cases, the way I kind of try to distill it into something that, you know, somebody like, even as dumb as me, can understand, simplistically, is on one axis, if you think about, you know, the downside risk of things going wrong. Is somebody going to lose their life, or am I going to get a pepperoni pizza versus cheese? Right? On one axis, the downside risk of machine learning going wrong. On the other axis, on the vertical axis, is the element of human control on that decision-making, which means, is it giving me a recommendation of the five people, you're young Sloan folks, I'm sure you're all probably dating at this point, of the five people I could go out on a date with, whereas you have the ability to control that decision that's saying, this is the person I choose, versus the other extreme, I'm sorry, making it really absurd. Like, this is the person you have to marry in your code date for getting married is tonight. So that's the other extreme where you don't have control on the system, just give you a sense. If you distill it, distill every application in those two kind of parameters, or those two axes, machine learning becomes extremely easy to understand where the adoption's going to happen and where it's not going to happen. So I'll give you a simple example. I know, you know, Carl's here and Carl's the expert in autonomous driving. I'll take my personal example. If I were to be driven in a driverless car, I would think twice, if my kids are being driven in a driverless car, I would think twice of stepping in that car because the downside risk is pretty high for me having an accident and maybe, you know, whatever is the downside from there. But having said that, you know, think about a city like Bombay or Manila or Mombasa for that, a chronic parking issues, right? So people keep drivers in these cities or employed drivers because they want to get off of their location and let the driver drive itself to a parking lot and then call the driver, say, bring the car back, I'm done with my appointment. For something like that, you have the control on the system or an autonomous and yet the risk is low in terms of the downside, at least to yourself. Forget about the societal part of it. Now that's the most apparent way as I see it. So as you can map what application you're trying to write on that matrix and map it against, is my user ready to adopt it in that same matrix, you get to see where you can actually get user adoption for machine learning type applications. And that's the apparent universe. The invisible universe is think about technologies like fly by wire or autopilot. These technologies have been existing for a long time. The only problem is or only difference between a car being driven autonomously versus a plane is you know in a plane when you get in, you're out of control. The pilot is up there, he's guiding the plane and he's got a door between you and him and if you try to get into that door, you land up in Guantanamo, right? On the other side of that door. So the difference is since you've lost that control anyways, maybe that's some place where adoption has become easier versus not. So think about as you've, what I would encourage the students to think about is as you think about applying ML or AI or any deep thinking or neural network or genetic algorithms or whatever you're thinking about applying, think about where you fit in those squares and whether it's applicable, whether it's usable and what's the lack of control or the sense of control you give to the end users. So you're bringing a great point that we've discussed earlier where the technology might be ready but are we ready to use it in a way that we feel safe and that enhances our experience as opposed to freaks us out, right? And self-driving cars is a great example. What are other really good use cases that you see that are just perfect for the current state of the art of machine learning? Well, I mean this is, it's a really I think important point. Yeah, I don't know that I could make it any better. I mean, we, I can just go back to that for one second. You know, we have competitors in this space of automated cars that would argue that you can solve the entire problem through a machine learning approach. You can basically go from sensor inputs through some learning process to some control outputs. And you know, I strongly believe the real fallacy there is that if something does go wrong and this is, I would say, one of the weaknesses of machine learning is that it's very difficult to interrogate that system to understand why it made a particular decision. The end result of this machine learning process is typically a black box. And so it gave you an answer. Well, why did it give you an answer? Well, it just did, right? It's hard to know why. And in many application domains, that may be insufficient. So to, you know, kind of paint with a broad brush, safety critical domains and machine learning methods, I think it's probably a fairly complex and delicate marriage there. For these non-safety or critical applications, convenience applications, applications that require adaptation, customization for the end user, you know, those are potentially fantastic applications. So that's where I mentally draw a distinction. I kind of hinted at the outset that I'm inherently suspicious. And this is one of the reasons why is because I think about the prospect of deploying machine learning at the heart of our driving stack and I get very nervous. We don't do that today. Now do we deploy it in other parts of the stack where we can tolerate this inscrutability? The answer is yeah. So just to build on that a little bit, I think, you know, one of the ways to think about this that we use is we do a lot of investing in what we think of as applied robotics companies. So taking, you know, some hardware, software combination with some machine intelligence on top to solve some interesting problem.  And where sort of activities and enterprises and sort of value creation points fall in that matrix sort of drives a lot of the use cases around machine learning we feel. That if you have a sort of super low cognition job which is highly repeatable, like think about accounting. You know, accounting has essentially been automated by these smart sort of software backends. You get to something very complex like a hotel made, you know, folding up a hotel towel in a bathroom. That's a really challenging thing for a machine to do right now. So I think there's gonna be a continuum of opportunities as entrepreneurs look at these fields where you can find good places and bad places to put machine intelligence to work. That reminds me of maybe, it would be worth talking about what is, what are the difference between the things that machine learning and systems that use artificial intelligence can do today versus the ones that we keep seeing in movies, we keep getting excited about and dreaming about, but really we're not gonna see industries for another 10 or 20 years. Like what's your take on that? I think there's, I mean, the nuance here that I think has been brought out, I think very importantly is whether or not something can be done or whether it should be done. So whether machine learning should be used for this particular application and not necessarily can it be used. I think, you know, there's lines of research fairly recently into trying to develop methods for actually understanding the thought process, if you will, behind the machine learning algorithm. So try to interrogate that black box and so in some kind of quasi forensic way understand why it made a decision that it did. I think if researchers, you know, people down the street here can make progress in that domain, I think it unlocks potential in these safety critical environments that I was mentioning earlier. Right. Yeah, I think one distinction we draw is between, you know, I think the perfect example of an application or a use case that I would love to see, but I don't think I will in my lifetime, is anybody who's a fan of the Jetsons, you know, the robot mate who did everything in the house from picking up the laundry to making the dinner to hoovering the floor. That's a super challenging problem because it is such an unstructured, you know, so many different things in terms of the interaction with the physical world. I think, you know, the machine learning algorithms can probably handle sort of atoms to bits, but going from bits back to atoms again is a really challenging thing to do. So that's, I think, one area where we'll see, from an artificial intelligence sort of research domain perspective, it's a concept of a specific intelligence versus a general intelligence. I think, you know, specific problems will be solved, but general purpose, you know, helpful machines will be a lot more challenging. I think another challenge I think that we'll see that machine learning will have to overcome is the environment of irrationality. Right, think of ourselves trying to hail a cab in New York City at 5.30 in the evening and you're in downtown Tribeca and trying to get up to, say, upper west side, right? I have two choices. I have an autonomous car and I have, you know, one of the yellow cabs. Obviously the cab is really dirty, but guess which one I choose? In this hybrid environment where humans exist along with, you know, autonomous driving techniques, I would choose that yellow cab any day, especially if I'm gonna miss a flight or an important appointment, why? Because he drives it rationally. You know, think about the playing chicken thing, right? Pedestrian crossing the road, you're looking at him and saying, let's see who plays chicken here, you know? My autonomous car is always gonna take the safe route. So until the point of time, so it may take me two hours with the autonomous car, it'll take me 30 minutes, but yeah, I might have a heart attack on the way with the yellow cab, but I'll still get to my appointment. I'm just trying to make the point here, drawing out the absurdities of it. And the thing that we're trying to see here is the environmental conditions that surround the problem that you're trying to solve, as long as we live in a hybrid environment, the complexity involved with our machine learning techniques have to be even higher than one where everything is connected and everything is very rational. Those are much easier to design, but unfortunately we live in a world where we're beginning with a much higher problem to solve and then scaling down. Right. And Carl, you sort of touched on something that we've discussed too that's very interesting, is like should we build these systems even if we can? And so that's when ethics kind of comes in and Sama was talking about that earlier. So I mean, clearly there is a technology piece that either it will be ready or not, then there is the human piece, like can we tolerate it? And then there is the ethics, right? So maybe you wanna say what's your take on and what should we be thinking about or what should companies be thinking about when taking these on? One of my fears is I think the technology is sort of advancing at a pretty fast pace. And I think the policy issues related to ethics and other things are sort of not keeping pace with the technology advances, right? You know, we talk about the classic trolley problem, right? You know, in an autonomous driving scenario, somebody is gonna, the system is gonna make the decision and somebody is gonna feed data to train the system to act in a particular way. What is the right decision here, right? And if I sort of, you know, use a system that is built by new autonomy, will it work the same way as a system built by somebody else? Should it be same? Should it not be the same? Where do you wanna have some level of policy control to decide what action should be taken? Where do you let the different technology providers and the different sort of models that are being trained to make the so-called right decision for what they think is right, right? I think there's a tremendous amount of work that needs to happen and I don't think we are spending enough time thinking about those aspects of what it means to live in a world where there's gonna be more machine learning or artificial intelligence-driven systems that are gonna play a critical part in our fundamental day-to-day activities. Yeah, I think that's a really valuable point by Soma. I think as humans, we struggle with exponential change, right, and it's very hard to internalize that technology is accelerating every year and as sort of capabilities are put into play by, you know, infrastructure vendors like Qualcomm and Intel and NVIDIA and others, it just enables a whole host of applications to become solvable that maybe weren't solvable 12 months ago, 24 months ago. It's on this constant, you know, 18 to 24-month cycle. It's just unstoppable. And I think one way to solve that might be or sort of get around those thorny issues might be the concept of augmenting humans, right?  It's helping humans make better decisions with the ability to this, you know, massive super intelligence which can spot patterns, make inferences from incomplete data, helping them to make those decisions. Yeah, we certainly adhere to that. We call it augmented intelligence as opposed to artificial intelligence for that reason because our point of view is that it shouldn't really be artificial and independent from the human, but it really should be supporting and augmenting the whatever we're trying to do. And I think that's an ethical point of view, right, that we're taking right there. Yeah. Did you, so you're building autonomous cards, right? So what is- Well, that's cool. I mean, it's a really interesting topic for discussion. At the moment, it's kind of a parlor discussion because we don't really have good techniques or reasoning about complex ethical scenarios from a machine intelligence point of view. It's not clear that machine learning per se would be the right tool to use in those cases. I mean, machine learning is good at observing, imitating, and adapting. Ethics is something of a different topic. I think when we think about that from a naive approach, we might think about codifying a set of rules that a machine would follow in a complex scenario. The difficulty today is that it often implies that we've got sufficiently rich information to make a complex judgment. In the case of a car and this trolley problem example, for those who aren't familiar, it's this scenario where if you had to get in an accident, should you turn right and hit the elderly grandmother or should you turn left and hit the school bus full of children and how do you weigh these two options? The fact is we typically lack the inputs required to make such a decision. In other words, when we look out at the world, we can't tell if that person is an adult, male, female, elderly, a good person, a bad person, an atheist, a Christian, any of these inputs we might want to have somehow. And then even if we could, it really places a very high bar on the machine. I mean, to be totally frank, the whole conversation makes me a little nervous from now from a technical perspective. I mean, as an engineer, because I think boy, this is a really high bar to place on the technology. We as humans, in a scenario like that, I would say virtually none of us would be able to make a reason principled, ethically correct in some sense, choice in the fraction of a second you would have to make that judgment. By, as a society, kind of this conversation implicitly putting that burden on the machine intelligence, I think we're setting ourselves up as a community for disappointment. I do think it's a valuable conversation to have. I just think we have to be realistic about how this will be deployed in products over the coming years. And it comes back to the point about sense of control or loss of control there, right? So, I saw one of the startup pitches of drones for humanity, a very interesting idea, right? And the thought going in my head was, if it's gonna do visual recognition, a vision-based recognition of how many people are in distress in one place versus another, if you have one place which has got five people which are on top of a roof needing help and it needs to make a payload drop, versus another place where there are 500 people in far more challenging conditions, which one would it choose? Would it just drop it at the first one or not? So that's where you've given up control to the machine to make that drop, or there has to be control somewhere in the back end, so somebody to make that judgment call, this is what I mean by the sense of control on that one axis of where should I be dropping that payload? So, I'll give you a simple example. So we come from union environments. So, some of our unions are the UAW, the United Steel Workers, and that's kind of Detroit, that's us, Muscle City, you know, Motor City, whatever you wanna call it. But the big question when we were designing our product was from an ethical perspective, we're saying Saika, that's great that you are able to warn people when somebody gets into an area which they're not supposed to be in restricted access area or somebody has high gas levels in their environments or somebody's just had a fall, but I don't want my supervisor to have the ability to know how long has Ben been smoking, or how long of a break has he been taking in the restroom? And now, where do you have the ethical lines to design the system? Because you're capturing that data anyways. How do you firstly handle that data? How do you suppress it? How do you bring it out? Those are questions that, from an engineer's perspective, is hellish to determine. Now imagine adding a machine layer and a machine learning complexity on top. That's gonna become a whole different level of, you know, a whole different level of hairball that you gotta deal with when you take it to that level of understanding. That's a good, I think a great point. And to kind of follow on and then circle back to another point that was discussed earlier, I think we think about, the worst case scenario is when something happens where the machine didn't behave as it would, and injury results, whatever the case may be. There will be a need. It's a human instinct to wanna understand why it happened. Why did, in the case of a person, you ask them, well, why did you do that? Why did you do the thing you did? In the case of a machine, you wanna do the same thing. You wanna understand why it did the thing it did. We live in a world where all of us fly airplanes, in our case every other day, or frequently, and planes crash, you know, they fall out of the sky. But we do it anyway because the risk is below some threshold. And I think also because when accidents happen, there's an international effort and interest in understanding, well, why did that happen? And I think as people, we can take comfort in the fact that if we can understand why it happened, we can correct it so it doesn't happen again. In the case of machine learning, in the absence of that ability to really interrogate those systems, I think there'll be a lot more resistance to putting these systems in positions where they have to reason, judge, make safety critical decisions. Right, exactly. I mean, I think that's sort of what I was trying to get to when I asked what are the types of use cases or what are the intelligence that we're gonna be really delegating to machines, right? Because we don't wanna delegate the decision. We made that very clear. But so can they do the understanding of huge amounts of tax for us? Can they reason on that tax and then provide that evidence, right? So that we, as humans, make the decision. So I think that we kind of sort of all agree that that would be a pretty good set of use cases right there. So maybe back to the technology now, since we've been talking about the human side and the ethics. So what are the existing limitations of the machine learning methods that we currently have at hand? So I think I will take a stab at this and it could be interesting for you, Carl, as well. Like one question we think about a lot is sort of the system architecture between stuff that happens in the cloud with all the processing that's there and stuff that happens on the edge of the network. So if you think about a self-driving car, which with the best will in the world, connectivity to cars, it's okay, but it's not great a lot of the time. If I'm relying on the centralized command and control to control my driving and make decisions for me, if my radio craps out, that's kind of a bad situation to be in. So the ability to build sort of lightweight, smart, possibly resource constrained processing models that sit in very distributed computing architectures, I think that's something we think about a lot. Interesting, Qualcomm makes the chips. Yeah, yeah, it's self-interest right there. The other thing to think about is in that context, just like cars, think about a mine or a steel mill or an aluminum refinery for that matter, or a petroleum refinery. Think of the amount of metal in these environments and a lot of the traditional IoT big data models fail in these environments, because first of all, you're not allowed to carry your cell phone in these environments because these are intrinsically unsafe, which what means is it can cause a spark by the fact that it's an electronic device and hence cause a bigger explosion. If you have the traditional channel missing in these big environments of mining steel and so on and so forth, then your device is automatically a hybrid model where the design itself has to be a lot of the ML components of it or lightweight ML components of it have to reside on the edge device, on the wearable or on the whatever is the device at the front end. So while there's a lot of talk off late of taking everything back to the cloud, I think I'm of the firm believe that there are certain industries where latency requirements, network requirements demand that the ML light versions gets pushed out back to the client. It's like the old IBM versus, so my excuse me, the Microsoft Intel debate of IBM and I was an ex IBM or push everything back to the mainframe versus and then, forget about the powerful wind tell platform and now I'm the other end saying, no push a little bit more out of the smarts in the front end because the other thing about ML that's interesting is at some point of time, we're gonna run out of pipe capacity, right? Just to give you a sense, our hard hats generate 17 megabytes of data of uncompressed data without audio and video every eight hour shift. Now, assuming that in a sample refinery you have 3,000 people working in a shift with another 4,500 contractors. I think of the amount of data that's hitting you. Now, ML can actually be under the radar where it's not visible to the human interaction where you say, let me figure out how I optimize what goes back and through so I can optimize pipe capacity for lack of a better word. So those are other areas to think about where the limitations could be and what you need to design around. But I love what Carl said about if you cannot at the end of the day do that post-mortem as to why things went wrong and if it remains a black box, it remains a black box. One-on-one decision modeling in business school where if you can't explain your model, I'm sorry mate, doesn't make any sense. Right. Yeah, I think this black box nature, I would add scalability. We want to apply machine learning to increasingly complex problems. The more complex the problem, the higher the dimensionality, the longer it takes to train models for these problems. We've got a lot of compute power in the world, so these days we can tackle quite complex problems and reasonable amount of time. But there's another kind of dimension to that is kind of the scope of the problem. So for example, the limitation inherent in machine learning by the very nature of the fact that it's data driven. So by that I mean machine learning can be very good at learning models based on the data that it's seen. It's not very good at generalizing to scenarios that it hasn't encountered before or data that it hasn't seen. Right. And that can be tricky. What you'd love to do is be able to give the technology a few examples and have it learn general enough models that it can then apply those examples to scenarios that had never been trained on. More commonly in the case is, if you wanted to recognize dogs, you give it examples of every type of dog you could find. And you're confident that when it sees those types of dogs again, it'll recognize as a dog. But I would say today there's limitations there. It's not the case that you can expect the system to generalize robustly. Right, perfect. So I think I'm gonna make sure that you guys have time to ask questions since I've been asking a lot of questions. Anybody? There's someone in the back. Okay. So I'll repeat the question for the video. He's asking about bias and what are the implications on systems that use machine learning? Right. So I would say that it's a classic problem of, hey, the person who's feeding in data, what bias they have or they don't have and how does that translate into it? Because it's all about, as you feed in massive amounts of data to train the models, if there is any bias that is sort of, whether advertently or inadvertently introduced into that, and that bias is gonna show through and it's gonna get replicated and the decisions that get made are gonna be a reflection of that, right? There have been sort of classic reports about like, hey, when Google surfaces ads, right, somebody has done a study to figure out that 80% of the ads that get surfaced for high-paying jobs are surfaced to men. Why did that happen? Did somebody consciously make a decision that that's what they're gonna do or was it some inherent bias that got injected into the system without people realizing that? I think those are all, I sort of think about them in the same context of ethics that we talked about in the past and I think, right? How do you make sure that the data that is used to train the model is as unbiased as realistically possible, knowing there is a lot of human beings involved and sort of that data getting fed into the machine learning models? Exactly, yeah. Yeah. Anybody, there's a question here. Hi, I have more of a macro question based on the upsides and downside rigs that were discussed today. How do you see this trend towards machine learning and AI playing out in emerging markets where the infrastructure, of course, is less developed as it is here in the US and the regulations will adapt even more slowly that they could be adopted here? That's a great question. Yeah, you stumped me on that one, mate, but when you think about it, the emerging markets are always the place to leapfrog, right? Think about connectivity issues in the mid-90s and how now mobile medicine is so prevalent in these sub-Saharan, India, Pakistan, South Asian markets per se, with mobile connectivity certainly becoming the bridge. So I think you would see these markets in itself evolve on their own organically. Just to give you a simple example, I think it would be very difficult and very presumptuous of us to say, okay, this is the model that fits these emerging markets. That goes back to the example of the Whirlpool washing machine. I don't know if you ever heard this example. Back in India, when Whirlpool was launching its washing machines, they thought, this market's gonna be on fire, people are gonna, it conserves less water, and suddenly they realized people don't use it to wash clothes anymore. Up in the northern part of India, they use it as a continuous yogurt stirrer for lassi, you know what lassi is. So it was just sugar being fed in, yogurt, and the next thing's coming out of the pipe is lassi. So that's exactly what you've got to be aware of. Like any machine learning technique that you think you can come up here and outthink people from there, they'll outthink you better than that, because they know the ground conditions there better than anybody else can presume it. And you have to just let that organically come up on its own. So you may see a lot of heavy machine learning on the client side because of the lack of good connectivity, right? So this could be certain presumptions you can make, but believe me, you'll always be surprised when the end results come out. We have an eager question over there. So like you said, machine learning can encompass quite a few different use cases. So I had a question on the competitive landscape for machine learning right now, because you have really large enterprise companies like Google, IBM kind of making their play in this space, and you have a bunch of small startups. So who do you think will ultimately lead in this space? Is it more of an enterprise play or tiny startups? So I can give you, I think two years ago, three years ago, you asked me, I would have said the data aggregators, the big guys, the Googles, the Facebooks, they had an inherent advantage just because of the amount of data they could gather. I think my view on that has changed in the last two years. I believe startup companies can be very successful because techniques have evolved to where you don't need to have Google's 100 zillion hours of training data. You can actually build very effective models with much more sparse datasets. So I see a big, I mean, we funded a number of machine learning-based, computer vision image recognition companies who are competing directly with the Facebooks and Googles, and we think they'll be just fine. Yeah, I agree. I mean, there's techniques for synthesizing data, for example, techniques for using sparse datasets. I mean, the currency is really, the smarts behind the algorithm, the team. I think what we're seeing in a cross space is often is that the team is valued as much or more as the technology. Whether that persists is a little bit hard to say. If the technology all starts to plateau, maybe not. But at the moment, I think for the foreseeable future, I wouldn't expect that to change. Sorry, just to round up one last thing on that, I do agree with both these guys. I'd also say that there are gonna be probably a smaller number of machine learning platform folks, and then a lot of people who are gonna use machine learning platforms and models and algorithms, and feed their own data for specific use cases or industry-specific things. I think there is a tremendous amount of opportunity for all kinds of companies, including startups, to be able to have a meaningful impact in this space. Exactly, I was gonna say, the platforms are lowering the bar, and so for entrance, it's much easier for anyone to get to these machine learning models and even data, right? So there is a couple, like... Thank you very much, but we... Is that it? I'm not allowed to take any more questions. Okay, I guess that's it. You asked you to finish up, please. All right, thank you. Thank you. Thank you. My pleasure. My pleasure. Thank you.


Somebody was killing people on the streets of this Oklahoma City metro area.  He shot her in the face. So I was a patrolman for probably less than a year when they knew that I was an artist, that I painted and was doing things.  And I said, yeah, I think I could do that. So I did the drawing and they had seen a car at the crime scene and then they saw it in a neighborhood. And so they were taking that witness description drawing through the neighborhood and knocking on doors and talking to people. And they knocked on a door of a young girl and she answered the door and they showed her the painting, the drawing, and she said, it's my husband. He's the one that you're looking for. I've always been involved in the culture for as long as I can remember. Even when I was born, before I was Harvey Pratt, I was Vihunschkis, which is Cheyenne, which means he wants to be a chief. So my first painting in high school, St. Patrick's Indian Mission, so my very first painting, and I sold it for $90. $90 and 61 or 62 was almost a half a month salary. The money factor kind of counted a little bit. So he kind of made it possible that I could earn a little bit. So I did stuff in the Marine Corps. When I joined the Marine Corps, I did the guide on for our platoon and I made PFC out of boot camp. And they sent me overseas as a member of the 3rd Marine Division military police. I've always been very proud of the Marine Corps and what I did in the Marine Corps and also what I did in law enforcement. You know, I've done over 5,000 witness description drawings.  That nobody else was doing, you know. Next thing I know, people were calling me from all over the United States. What is the best case? And I say they're all best. They are all the best, you know. If I do something that catches somebody that abuses or rapes children and I catch him, that's the best. Or if I work on a case and I help catch a serial murder, that's the best. To me they're all, they all have value and not only job security but they give me a lot of job satisfaction.


The Everton News Daily. Jamie Vardy is reportedly a target for Everton this summer. The 36-year-old striker is available, lest there apparently want a small fee for Vardy. He scored three times in 37 games last season and got four assists following on from Ashley Young. Not quite the age bracket. I want Everton to be operating and I won't lie. Vardy is the latest to be linked with a move to Gwladyswn Park. Everton have also been linked again to Musa Demboli today. He's someone that has been on Everton's list since January. They couldn't get it over the line in January, but he hasn't gone anywhere so far. There's some lots of clubs interested in them, apparently. A particular interest in the likes of Galatasaray in Turkey, but apparently the 26-year-old still on Everton's list. The Blues have obviously just missed out on Rodrigo, who joined Al Reyhan today in Qatar. Everton were interested in taking in, but Qatar offered him over £200 a week, so Everton were out at that price. So they are looking around for strikers. The third centre forward linked today is Wetherbremans striker, Nicholas Fulkrig. He is a target for Everton, 30 years of age. He got 16 goals last season. There is other centre forwards in the world, Everton, who are younger than 30, and younger than 26 as well. Demboli, I'm okay with that, but we do have to be looking in other leagues as well, where there's better value, but Everton linked to three strikers. At least that's the area we need to be bringing players in, and three centre forwards. I certainly think Everton needs to, this window, to take the pressure off them and to call the loom. So we'll see what happens, but I imagine if Everton were to bring two in, one would be able to play wide as well, rather than just three number nines, if you like. Westline United are reportedly interested in signing Alex Owowe from Everton. The Nigerian has less than a year left on his contract with Goddison Park. He had been offered a new deal, looked as though he was about to sign it around February, March time, before talk stopped, and now reportedly Westham Ffollum Crystal Palace have all shown an interest, as well as a couple of clubs in Italy in Alex Owowe will be. So it will be interesting to see whether or not Everton do cashing on him with less than a year to go on his deal. He did score twice and have eight assists for Everton last season in the Premier League. Sheffield United are showing an interest in Everton's strike in Neil Mopai, but it is only on loan, that is according to reports today. I can't imagine Everton will sanction this, unless there's an obligation to buy Mopai at the end of the season. I think Everton wanting a sack and options. Mopai only scored one goal last season for the Blues. Everton are happy to let him go, but Thornton wants a fee for the striker who they bore for 12 million from Brighton last summer. Everton have brought in goalkeeper coach Billy Mercer. He is going to come in on a short-term deal. It's for cover for goalkeeping coach Alan Kelly, who has undergone a knee operation and will be out for a while. So Billy Mercer, who's already in Switzerland with Everton taking the goalkeepers, putting them through the paces. He is joining the club on that short-term deal there. That is it for the news data. Give the video a thumbs up and subscribe if you haven't. If you want to become a Trophy TV premium member, the link is in the description and the QR code will be on the screen now. See you later.


news update. Good afternoon folks. This is Steve Rhodes coming to you live from the shores of Delray Beach, Florida. This is your 2 p.m. update and currently we've got a mixed bag out here. The Dow down 55 while the S&P is up 11. The Russell's down 17 while the Nasdaq 100 is up 115. The trannies are off 111 while the semis are up 41 points out there. You kind of get it. A very mixed bag out here. The spot follow techniques is basically flat trade in 1715, but it's below the 50 day exponents moving average. That says the wind is at the sales of the back for the S&P 500. Gold's off 9 bucks. Silver's down 27 cents. Slight's recruit up a buck 13. As we do during our 2 p.m. update, we go take a look at the indice charts out here. So, oh, those are the charts that are up on the screen. How about that? I didn't even have to switch over. So let's begin by taking a look at the Dow Jones. Now the Dow Jones, and I did this with you yesterday. I want to do this again today. The Dow trade out at 35874. The Dow equity future contract 35819. You can do the math difference there. What you're watching for it, for those of you that don't have the Dow equity future contract, if there's a close below 35759, that's the bottom of the Dow's equity future contract bottom of its profile, that would suggest lower price. That would then signal to you and I that the Dow should target 35545. That is its breakout area. But those are the requirements that are needed in order to generate that signal to us. The S&P 500 is still bearish because it still has a TD9 count. Price would need to close above bar number eight on this pattern. That high out there is, if I can get it, appears to be 471850. So unless price closes above that level, it still has a top that's in place. The NDX100 right now is trying to take out its TD9 count top. It needs a close above 1645448. Let me make sure of that. Did I write that down properly? I did. So 1645448. If price closes above that today, folks, as you come into the close, you have no reason at all to be short. The NDX100. The Russell 2000, now the Russell 2000 equity future contract is trading below the bottom of its daily profile. But for us right now, that doesn't matter because although I don't show it on this daily chart here, what I want you to know is price is testing, its consolidation breakout. And as long as the price holds, let's say the level is about $23.42. As long as price remains above $23.42, it still has a, well, it's, look, it's got a top in place, but then key level of support will upheld. Folks, we're running out of time here. Stay tuned for David White. He's up next with the Power Training Hour. Tom O'Brien, he'll take us on home. I'll be back with you tomorrow eight o'clock, well, really about 8.06 sharp. So we'll see you early in the morning. Have a terrific Thursday, folks. The reality.


What is a multi-brand cloud kitchen? It is basically having multiple different brands under one roof. We're talking about the same roof, but with different designs. However, all prepped in that one kitchen. From the outside, these brands, they all look like individual companies. And on the third party delivery apps, they all have their own listings. And this is exactly how some of the biggest franchise cloud kitchen brands operate, like Rebel Foods. Initially, they first started with one concept, later on to expand into multiple different concepts that have high demand. Beauty with doing this is that all these brands are housed under one roof, under one operation.


If you want to increase the FPS count in any game, then in this video we will show you how you can boost higher FPS in any game using the new Nvidia image scaling feature. First is download latest Nvidia drivers. First of all, in your browser window, head to Nvidia's download web page. Once you are there, provide the details of the graphics card that you have and then download and install the latest driver available on your system. If the latest graphics card is already installed on your computer, then you can update them using a dedicated tool like Advanced Driver Updater. To use this tool, download and install Advanced Driver Updater. You can also use the link in the description. Launch Advanced Driver Updater. Click on the Start Scan Now button. Wait for the scanning process to complete. Now it will show you a list of all the updated drivers. Click the Update Driver next to the driver you wish to update. If you are a registered user, you can update all outdated drivers at once by clicking the Update All button. Reboot the system after updating the driver. Second is enable Nvidia image scaling. Right-click on the desktop. Select Nvidia Control Panel from the drop-down menu. In the next window, click on the Adjust Image Settings with Preview option from the left panel. Now select Use the Advanced 3D Image Settings option from the right panel window and then click on the Apply button. After that, go to Manage 3D Settings on the left-hand side. Once you are there, you can choose to enable NIS globally or for a specific game. In case of a specific game, switch to the Program Settings tab and choose the game that you wish to enable NIS for from the drop-down menu provided. After that, click on the Global Settings tab. Under Settings, you will find the Image Scaling option. Simply select On GPU Scaling and Sharpening. Now adjust the slider for sharpening effect according to your preference or leave it default. However, you can set the slider of sharpening from 20-30% which will be good enough for standard gamers. Now make sure to tick the overlay indicator checkbox which will indicate when NIS is in action. Click on OK to save changes and then click on Apply. And that's it! Hope you found this video helpful. If yes, don't forget to like, share and subscribe to our channel.


My name is Jeffrey Pelz, and I'm a thoracic surgeon at Hackensack Hospital along with Hackensack Meridian Health and John Thurr Cancer Center. So as a thoracic surgeon, I treat diseases of the long esophagus and mediastinum. Being a surgeon really is a unique opportunity to take care of patients such that you can see a patient at the beginning of the disease process and then follow that patient through to the end. So if any patients are looking for a place to establish care, Hackensack Meridian Health and John Thurr Cancer Center is an excellent place. Because we have such a vast wealth of knowledge and experience at Hackensack and the John Thurr Cancer Center, we're able to deliver excellent quality care to patients and that really impacts the results. So more rare tumors are things that other places may not have seen as much. We do high volume and so that that helps to improve the care and the protocols and ultimately the outcomes for treating any disease. In addition to the physicians and surgeons working in the thoracic surgery division, there's also a big group of nurse practitioners, nurses and other administrative staff that all work very well together as a team to help guide patients through from the initial phone call to the office, through the visit, any follow-up testing that needs to be done and ultimately getting the patient to the operating room and then follow-up visits. So it's not just the surgeons that are working for the patients but there's also nursing care and there's a big team that all helps to contribute to the best outcomes for the patients.


Hello children! Hello, I am Julia from the future. I'm going to kind of be your master of ceremonies for this video because I fully finished filming and got most of the way through editing and then realized that I never filmed an intro or an outro and there's some other stuff that I wanted to say so you will be seeing me again. So I don't know if you've noticed that horror is kind of my entire personality but it is and so when I'm looking for a book that's gonna keep my attention I often look towards creepy or disturbing things. So when I'm online and I hear someone recommend a book that's very disturbing I look it up to see some reviews see if I'll maybe like it but it happens so often that the reviews for these disturbing books don't review the book.  All I know is that the book is disturbing which I knew already because why else would I be googling it. So I decided to start a series called Disturbing Book Reviews. I'm hoping to give these books an actual fair review which I feel like just by their nature of covering taboo topics they're they're just doomed to never get and I don't think that's fair. So if you can think of some horror books or books that you find disturbing comment them and I might review them in a future video. Anyway the premise of this video that I did not set up or introduce it all is that I'm going to be doing my makeup while reviewing two disturbing books. And the first book that I'll be reviewing is... Do you want to be in the video? Bebo.  I just want to talk about this somewhere. Recently an account on Instagram who I don't know I've never interacted with them sent me a DM and told me that I look like Kyle Rittenhouse when I'm not wearing makeup. That was humbling. I'm gonna be real that gave me a lot to think about. So things have gotten worse is set in the early days of the internet when everything was still like I ams and chat rooms and stuff.  Oh and be sure to comment about how pulling on my skin is gonna give me wrinkles and that's a bad thing because a woman is only worth anything when she's youthful. There's a lot of discourse surrounding this book for a lot of reasons. I'm only gonna touch on a couple things because it's not relevant to the actual quality of the book.  I personally disagree with that for a few reasons. One is because it's very clear that the author is critical of the relationship. He's not portraying it as a good thing. I'm speaking as someone in the LGBT community.  Like you know it's reality.  Also there's so many books about messed up straight relationships and it's like that's fine as long as it's like looked at with a critical eye I don't see anything wrong with that. Now that I'm done passionately defending this book I'm going to talk about why I did not like this book.  Let's start with the stuff that I liked about the book. If there's one thing that the author is really good at it's voice and characterization. Initially because of the format of the book I was worried I would have trouble following along because it's formatted like a chat room. Like it's not their names it's their user names and stuff I was worried I wouldn't be able to tell who is who but that was not a problem at all. Both the characters have such distinct voices that by like a little ways in I didn't even have to look at their user names to know who was who. Zoe speaks in a very like professional to the point way you can tell that she's older whereas Agnes speaks with a lot more energy and emotion which is also relevant to their characters it's who they are.  Another thing the author is very good at is he's great at giving descriptions. He's great at making you feel like you're there or seeing what people are talking about and that's a good thing because as we're gonna get into there's a lot of that in this book. Another positive I know this isn't about the writing but I think the cover is gorgeous. This is one of the few books where I think the cover is perfect for the story and I think it like enhances it. It's also just cool. It's beautiful artwork.  I truly think this book's biggest weakness is just its length. It's so short. I was able to read it in a couple hours and definitely kept my attention the whole time but it's because it's so short it means that all the events just kind of happen in really rapid succession without much buildup between them. This rapid fire pacing kind of results in the book losing some of the suspense because things just happen so quickly and you're just kind of holding on for dear life trying to keep up.  So for some more context Zoey and Agnes get into a relationship and Zoey reveals to Agnes that her fantasy her sexual romantic whatever fantasy is to have complete control over someone as in she would take care of them they would never pay rent they would never worry about anything she would buy them all that they need but in return they have to be completely obedient to her including giving her like access to their bank account and all of their information so that they are truly at her mercy.   Like Zoey's character goes from being a fairly normal person a little eccentric she has an unusual fetish to like a week later a full-on psychopath demanding that someone kills a small animal for her pleasure and then like 50 pages later she's the voice of reason she's like this relationship is unhealthy we shouldn't do this but no shit you fucking weirdo I'm not saying that the killing animals part shouldn't have been in it I just think that if you're going to have a character demand something like that it kind of explain why they want that I don't know it happens so fast and so out of nowhere that it's like another one of the bigger problems I have with this book is it does this thing kind of a lot where it like bring up an idea discuss it in detail and then move on and never fucking talk about it again when you're reading these things it feels significant because like why are they spending multiple pages describing this event this must have something to do it must be a metaphor for their relationship it must come back later in the story and it just never really does here are just a few examples of that the tie burial thing at one point uh Zoey talks in depth about a thing she saw on the travel channel about how you can go to Thailand and simulate your own funeral that never shows up again baby christ little christ or whatever agnes tells a really long story about a thing she saw on tv about a baby that got killed by his older brother I don't fucking know the zoo story at one point agnes tells a story about a back in college um she went to the zoo and saw a rabbit give birth and then eat its babies good good to know uh the cat and the priest the priest and the cat whatever agnes tells another story about how one time she saw a priest run over a cat in his car and she was sad about that awesome thanks because it happens so much it feels like this book is just built on red herrings like subverting your expectations like you thought you thought we were talking about something important but we're not and also like maybe i'm just too stupid to understand how these things are relevant to the story but i i've read this book multiple times now and i just i can't get it it just makes me feel like a large portion of this book's very limited uh length is wasted so the pace of this book it goes back and forth between just zooming through really cool story ideas and then just slamming to a halt to spend several pages describing something that isn't relevant or maybe again maybe it is relevant maybe i'm done all in all would i recommend this book um i don't know if you're curious about it check it out if you can handle these trigger warning things then check it out see what you think about it let me know did i miss something i want to hear what other people think i don't hate this book it's very interesting and intriguing i just wish that it had taken its time more one thing that i know for absolute certain is i want to see more from this author when they come out with more stuff i will absolutely read it because he has real talent real talent and i want to see more of it i just hope he learns and grows from this next let's talk about dead inside um there's no way that i can really describe the premise of this book without sounding like i'm trying to be shocking on purpose so i'm just gonna say exactly what it is dead inside is the story of a necrophiliac who works as a security guard in a hospital so he can have sex with the dead bodies in the morgue who forms an unlikely friendship with a maternity ward doctor who is addicted to eating dead babies are we all good are we all good to move on okay now from that description you might think this book is just a lazy gore shock fest at at times it is yeah but there's also a lot more there so to start off with things that i like this author is great at characterization specifically the narrator our protagonist his voice is just awesome he's an asshole he has no empathy he has no emotion he has no love for anyone he's completely unapologetic in his heartlessness and in his crimes and lifestyle choices and it's so entertaining like not only is he completely unapologetic about his love for having sex with dead bodies but he believes that it makes him superior to other people it's fucking hilarious he's kind of like the embodiment or the patron saint of those people who are like i'm different and that makes me better i'm desensitized i'm dark i'm oh i'm not like other people i never once got tired of his voice and i was never once confused by his actions he's a very consistent and well-written character and i really like that another awesomely written character in this book is helen the cannibalistic maternity ward doctor who is a complete perfect foil to the protagonist unlike the narrator she is apologetic for what she does she is remorseful she is ashamed she has worked so hard to try to fit into society she is a well-respected member of the community she has friends she lives in an affluent neighborhood she has a successful career but she just can't stop eating babies this results in a lot of actually really engaging and thought-provoking dialogue this book is at its best when it's just conversations between these two discussing their outlook on life discussing their opinions of themselves and of the society that surrounds them the narrator encourages helen to just give up on being normal of fuck society and be a freak recluse like him whereas helen as a friendship begins to form between these two urges him to accept that he's human accept that he can find joy in things other than death he can have friends he can be happy as their relationship progresses and the narrator realizes that he likes helen like he doesn't hate her he enjoys her company he like panics because this means that his entire identity this whole persona that he built for himself isn't real see i told you this wasn't just going to be a cheap gore fest there's that when you give a book a chance there's actually some really cool stuff in it i would recommend this book um with the huge caveat of if you can handle very descriptive scenes of necrophilia also i want to give a specific warning that there is a scene at one point where um you know that one scene in serbian film that got cut from most versions of it there's a scene of that except it's not cut it's very graphically described hello future julia i told you we'd meet again i just wanted to pop in here and address something that is both relevant to dead inside and is going to be relevant to other disturbing books that i might review in the future i was talking to my friend about the premise of this video and i got to the part where i brought up that i give dead inside a good review because spoilers i enjoyed the book and they were like oh okay just be careful i mean won't people find it kind of iffy that you're talking about how much you love a book about like necrophilia so let's talk about this let's talk about enjoying the media with taboo subject matter i want to establish that first of all i go into every video that i make operating under the assumption that my audience are intelligent adults who are capable of critical thinking and able to come to their own conclusions without me spoon feeding it to them i mean obviously you guys are intelligent you have to have a very high IQ to understand my videos second of all i hate the mindset that liking a piece of media means that you have to like the subject matter and i feel like i shouldn't have to spell this out but this is a mindset that a lot of people have you like american psycho so you like murdering people you like the godfather while are you aware that the mafia is bad i think that this mindset aside from just being annoying really throws away a lot of the work that authors put into their writing taking everything at such face value disregards the possibility that an author was using sarcasm or satire to criticize the characters through their actions or there might be an unreliable narrator in fact at least for me these fucked up characters make for some of the most engaging stories i've ever read it challenges the reader to see the world through the eyes of someone who thinks nothing like them i think that's fascinating if you disagree with my review of dead inside that's perfectly fine you don't have to agree with everything i say but if you use my review as evidence that i am a necrophiliac you i i don't know what to tell you go ahead think that about me i don't care anymore anyway a little rant over back to the review now contrary to how i sound right now i don't think that this book is perfect i do have some criticisms with it um my main one is that i don't like how it ended i feel that the ending was disappointing and unsatisfying and predictable kind of because like i said the best part of this book or at least my favorite part is uh the conversations that it has the way it looks at taboo and society and you see these characters getting their ideologies questioned and they're put into places where they actually have to like look and think and change things about themselves and then in the ending i feel like all of that all those really cool ideas just kind of fell away and they weren't really used the main character kind of just went back to being exactly who he was at the beginning of the book and maybe that's what the author intended you know maybe this is just supposed to be a very bleak story about how you are what you are and you're never gonna change like here there's no chance of becoming a different person if that was the intention then kudos i guess you succeeded made me frown also the twist or i don't know reveal whatever i'm using that term very loosely because i i think anyone could see it coming basically at one point a little over halfway through the book this one character very minor character gets introduced and as soon as he gets introduced i'm like okay i know exactly how the story is going to go from here and i was right it happened exactly the way that i predicted so yeah i don't know i didn't love that um i found that kind of cringe despite not liking the ending i honestly think that didn't really ruin the book for me because i went back and reread it for this video and i still enjoyed it so would i recommend this book honestly i think yeah if you can handle graphic descriptions of these things i think it's a very dark and entertaining way to have a very intriguing conversation that i feel like a lot of people don't have that's all i really had planned to talk about um i'm gonna review those two so uh for the rest of the video it's gonna be you and auntie julia having a little chit chat right i love you ben how's school mmm yeah make any new friends wizard have you guys seen squid game i know i'm like super fucking late to that conversation but squid game hi ghost of christmas future back again i never filmed an outro for this video i also never filmed the final makeup look that i was doing for the whole video so you're never going to see that and if you're asking julia how could you have made such a large oversight like that um the answer is that i'm a bad youtuber


This shouldn't be scary. This shouldn't be stressful.  Our streets don't have to look like this. We can rethink the system. We need to reimagine a system that helps us get to where we need to go. We need to change the way we move. We can dream smarter, cleaner, better. When we act together, we move together. Join Cow Bike.


It was like, cool. I worked very hard to do an interview when this song ends. I'm like, ah, it's cool. It's like, cool. Let me listen to the end of that. And it wasn't too bad. It was like, well, it was just 70, yeah. Well, it was just 70, yeah. Well, it was just 70, yeah. Well, it was just 70, yeah. And I'm rockin' from G to D, yeah. Well, it was just 70, yeah. Well, I'm stronger all the time. Well, it was just 70, yeah. Well, it was just 70, yeah. Well, it was just 70, yeah.


Hello everyone, I'm Lisa Martin coming to you from the SiliconANGLE News Desk powered by theCUBE. Our top story today, Zoom Communications, the popular video conferencing and chat platform has joined forces with Anthropic, a leading AI company to introduce a new AI assistant to its product suite. Let's dive into the details. Zoom intends to integrate Anthropic's AI assistant, Claude, with its product lineup, starting with the Zoom Contact Center. This combined unified communications and contact center service, hosted entirely in the cloud, will now benefit from the power of generative artificial intelligence. By leveraging the capabilities of generative AI, users will be able to utilize their own large language chatbot models. These models are capable of understanding human conversational speech and answering both employee and customer questions on the fly. The AI assistant can act as a valuable resource for employees, assisting them during calls, or as a first line of support for customers, providing answers to simple queries. If the AI can't resolve an issue, it can smoothly escalate the conversation to a human representative. Smita Hashim, the chief product officer at Zoom, expressed excitement about the partnership, stating, with Claude guiding agents toward trustworthy resolutions and powering self-service for end users, companies will be able to take customer relationships to another level. The collaboration between Zoom and Anthropik follows Zoom's previous partnership with OpenAILP, the developer of ChatGPT, in March. Zoom has been dedicated to providing its customers with a wide range of AI models through a federated approach. This approach allows users to choose from various AI models, including those from OpenAI, Anthropik, and select customers' models on the back end. As part of their partnership with OpenAI, Zoom integrated generative AI capabilities into their Zoom IQ smart assistant. This addition allows users to prompt the AI to automatically compose custom messages and chat and emails through Zoom IQ Chat and email compose features. Furthermore, Zoom IQ meeting summary automatically generates meeting summaries that can be easily shared via team chats. To provide the best customer service experience tailored to specific business needs, Zoom aims to incorporate various AI models into its platforms. This collaboration with Anthropik enables Zoom to bring robust and steerable AI to more people streamlining everyday processes. Although Zoom has not provided a specific timeline, they have announced that the Zoom Contact Center will be the first product to receive access to the AI assistant cloud. Following that, Zoom plans to expand its integration across its entire product portfolio, including team chat, meetings, phone, whiteboard, and Zoom IQ. In recent months, other leading companies have also recognized the value of generative AI. Microsoft added chat GPT to its teams premium in February, enhancing meeting notes, summaries, and intelligent recaps. Salesforce announced integration to natively incorporate generative AI in its Slack collaboration platform earlier this month, offering writing assistance and meeting summaries. As part of this partnership, Zoom Ventures, the venture capital arm of Zoom, has invested in undisclosed amount in Anthropik. This investment aims to strengthen the relationship between the two companies and further advance the capabilities of AI. That wraps up our coverage of Zoom's partnership with Anthropik and the introduction of the AI assistant cloud into Zoom's product suite. From the SiliconANGLE news desk powered by theCUBE, I'm Lisa Martin. That's all for now. Thank you for joining us.


According to an American research center, Pew, millennials have been described as people born between 1981 and 1996. People who fall in this category are very different from their older counterparts and are more likely to work in startups that value creativity and expertise over cultural notions of dressing and ethical behavior. They are young and free. They generally do not want to be bound by old corporate cultures. They, however, sometimes exhibit dangerous habits that could harm their health and their future. In this video, I'll be sharing with you 9 dangerous habits common among millennials. 1. Poor concentration. Millennials typically have poor concentration. This is partly because devices such as smartphones have a relatively quick response time. This makes the average millennial to expect such a fast response time in reality. This mindset affects their activities and makes it difficult to concentrate on something that is not easily understood. There are also a lot of distractions on smartphones that make it difficult to concentrate on task at work. Some millennials leave their phones on airplane mode at work to deal with social media chatter. 2. Straining the neck to look at devices. This is quite common among millennials. According to center Pew research, 93% of millennials own smartphones. This is a huge population indeed. Using smartphones can strain the neck and cause an ailment popularly referred to as text neck. According to Robert Bullash, a medical doctor in Cleveland, U.S. Typically, incidents of neck pain increases with age. But today, we are seeing and treating more patients, more younger patients. The incidents are also likely to increase with the continued use of smartphones all over the world. To be free from this habit, millennials need to regulate the use of their devices. 3. Playing loud music over headphones. This is one of the dangerous habits of millennials. They can walk across intersections and not look up because of their headphones. Headphones are causing a lot of trouble for young people. People who use them regularly can suffer hearing loss. According to research in the Journal of American Medical Association, the use of headphones and earbuds has led to a major increase in the prevalence of hearing loss in adolescents and young adults. Loud noise is generally harmful to the ear. Using headphones concentrate the sounds. Smartphone developers have put in safeguards that warn users that the music is too loud. But most users ignore this warning. This dangerous habit will not only place people in danger when crossing the road, but could also lead to hearing loss in the future. As a millennial, it is advisable to avoid headphones as much as possible. 4. Social media addiction. Social media is one of every millennial's favorite. Not being on Twitter, Facebook and Instagram are seen as being outdated and people compete with increasing content for these platforms. Events sometimes seem not to have validation unless it is posted on social media. It is therefore not surprising that a lot of millennials are addicted to social media. Social media addiction means spending a lot of time online and being hooked on other people's lives. It doesn't allow for self-reflection because self-image on social media is dictated by other people's opinions. A person is deemed unpopular if they have fewer followers and irrelevant if they have none. Social media has damaging consequences for the confidence levels of average millennials. This addiction is a problem. 5. Preference for fast foods. According to a survey by a renowned food brand, Sweet Earth in 2019, millennial customers try an average of 46 new foods per year. This bears witness to the millennial's attitude towards food. They are less likely to cook and prefer to eat out. While this means well for the fast food industry, it may lead to poor financial habits. As a millennial, you need to learn how to cook your own food. It will save you a lot of money and also prevent exposure to food poisoning or allergies. Cooking is a bonding activity in families. It is such a more bonding opportunity to make pancakes for your children than to order pizza, even if they would ordinarily prefer pizza. Eating out rarely guarantees healthy diets. The fast food restaurants are more interested in making a profit than your diet. Make out time to learn how to cook. 6. Impatience over things. Millennials are quite impatient because technology has made everything fast and easy. Learning new things can be especially hard. They lose interest just 5-10 minutes into an activity and want to head over to social media. Social media does not encourage patients either. Anyone who makes a disagreeable statement is attacked immediately without buttering to understand their points of view. Also, well-written articles are hardly read but simply scrolled. Social media contributes a lot to the behavior and conduct of millennials and this is a problem. Many superstars on Twitter use pseudonyms that allow them to troll innocent people without repercussions in reality while indirectly encouraging others to model their behavior. To become more intimate reality, millennials have to do away with virtual standards and pay more attention to people. 7. Too much screen time. This can have adverse health effects. According to a digital health care outfit, rally health, staring at a screen for extended periods of time can cause computer vision syndrome. The symptoms include dry eyes, blurred visions and headaches. Too much screen time can also mean less people time. Networking virtually, though effective, can hardly build the type of relationships physical connections can bring. Millennials should strive to reduce their screen time as much as they can to avoid these problems. 8. Poor sleep culture. This is also caused by too much screen time. Social media never sleeps and has many keeping vigils alongside it. To have a healthy and productive life, it is advisable to never go to bed with your phone beside you. If you do, you are unlikely to sleep. Always being on the phone can keep you awake. 9. Preference for virtual friends over real ones. Most millennials have lots of friends online but very few offline. Your online friends can chat and talk about the latest trends but are unlikely to give you a solution to your debt problem. Your virtual friends are also unlikely to call you every now and then to check up on you because they are in reality, strangers. Filling safe in social media and giving yourself the illusion that you have friends is dangerous. This is because everyone needs a physical display of affection. The habits of millennials reflect the trends of the age but just because something is trending does not make it right.


Abstract banking cryopreserved organs could revolutionize transplantation by making it possible to provide organs to patients anywhere in the world, regardless of their location or time constraints. Previously attempted organ cryopreservation has been unsuccessful due to ice formation, but a new technique called vitrification, or rapid cooling to a glass-like state, has shown promise. Unfortunately, re-warming these organs can also fail due to ice crystal formation if the process is too slow or uneven. To overcome this problem, researchers have developed a method called nanowarming, which uses alternating magnetic fields to heat up nanoparticles inside the organs' blood vessels, allowing for rapid and even re-warming. This method was tested on kidney tissue and found to be successful at restoring full renal function in raps with nephrectomy. If this technology can be scaled up, it could make organ banking a reality, providing a much needed solution to the global shortage of donor organs. This article was authored by Zohuhan, Joseph Sushil Ayo, Laxia Gangwa, and others. We are article.tv, links in the description below.


I am truly excited to share my experience and encourage you all to join me at this year's UX India 23 conference. I can confidently say that this experience is a transformative journey that has the power to shape your career and your perspective. The energy, insights and connections that I have gained from the past conferences were nothing short of remarkable. Each session is led by industry leaders with practical insights and innovative ideas. But it wasn't just about the content. It gave me a sense of community that left a lasting impression. Connecting with fellow attendees, engaging in meaningful conversations and building relationships that extended beyond the conference have left an invaluable dimension to this experience. Now as we approach UX India 2023, I am very excited. The intersection of AI and design is an area of immense potential and this conference promises to delve deep into this fascinating reality. It's an opportunity to gain insights into the future, explore how AI is reshaping design and be a part of a community that actively shapes the course of this industry. So if you're looking for an event that will not only enhance your skills but also ignite your passion for AI and design, look no further. Join me at UX India 2023 where you're not just attending a conference but are stepping into an environment of growth, inspiration and endless possibilities. Let's come together, learn, create and innovate at UX India 2023. See you all there.