Everybody forced tier ripping 2122 contenders optic basketball hobby 10 box half case number 5 pick your team style Anthony C Mavericks Anthony L Raptors Anthony P nuggets Bernard H Rockets bohia B Clippers Bradley s 76ers Carson C Nick's Chen Kai W Nets Chihuahuan C Pistons Darren B Lakers Eric W magic Gwan s with the heat Hilton thunder Hung Y sons Jason H Hawks Jass Vinder with the Blazers Jeremy s jazz. Thanks for grabbing that last team Jeremy. Good luck, man Jihao H Timberwolves Jonathan Q Lost my spot Celtics Joseph K Hornets Justin M bucks Kira corn with the Kings Laszlo Pelicans Sean M Warriors Sugar Nora you wizards T Huang Grizzlies Tim V Cavaliers Victor F Pacers William s spurs and young s bulls Good luck everybody. Just Think it's down to four in the next one now Yeah, Hornets just sold so there's four to go those four on the next one are the Pacers Lakers jazz and wizards some teams on sale There here we go everybody Come on gold vinyl. Come on now. It's number five and it says on the screen my stack of To a campaign the cold All right D book for the Sun's red season ticket Seth Curry Nets a Blue Jalen Brunson season ticket Mavericks number 12 out of 99 a good year for the The Knicks currently wizard sophomore contenders Otto Denny of Dia 59 of 149 Clippers hoop dreams Brandon Boston, Jr So it's only rookies on the hoop dream. It's just pretty cool. Haven't seen any vets and Red ice numbers game not numbered jazz Rudy go bear. You know decent insert there. It is actually miscut I'll send a box wrapper to the jazz spot You see that blue ice at the bottom there and the cut the cut at the top is really close to the The numbers so I'm gonna call that miss cut.  Come on. I didn't witty base rookie tickets anniversary Chris Duarte Pacers and Kauai Leonard season ticket blue 30 of 99 clippers Knicks RJ Barrett veteran ticket auto red. It's a nice one numbered one of 35 on the RJ Barrett all-star aspirations Mavericks Luca Donchich and Red ice hoop dreams Pistons Cade Cunningham Great-looking card for the Pistons there nice Cade You got today Jake Morocco or France Morocco to bias Harris season ticket Sixers Red season ticket D'Angelo Russell Timberwolves Timberwolves season tickets orange Carl-Anthony Towns 21 of 49 Oh Spree legendary contenders auto for the Warriors let trail spree. Well 49 of 75 Nick's legend Timberwolves legend didn't know he was in this there you go dubs Legendary Tandems sons Amari Sotomayor Steve Nash And a red ice superstar Lamello ball hornets No, this is 21 22. So it's the 2021 draft the first product that would have a Palo Bancaro Rookie in his NBA uniform is probably gonna be hoops in January. There's some collegiate stuff out, but Looking for a lino magic uniform January box for God poor zingas the Wizards Paul George 75th anniversary Clippers season ticket gold for Denver Aaron Gordon double a OG 10 out of 10 10 out of 10 76ers variation rookie ticket out of silver Jaden Springer superstar Nikola Jokic nuggets And a red ice legendary Tandems Clyde the Glide Drexler and a Kim Elijah one Keem the dream Clyde the Glide a keen the dream nicknames are easy back then like what rhymes What rhymes what's cool? J. What was your nickname when you when you played sports in high school? Miles Bridges season ticket for the Hornets silver LeBron James Lakers Marvin Bagley the third green Pulsar two out of 25 pistons and Pistons rookie ticket autos silver Luca Garza We got a heat up here. We got a heat up here.  That's a cool. That's I mean, that's they just named you somebody else I think it was other ones Did you just call you Jay from Stay Farm? Yeah, like a bunch of jerks That mean either the monosubbonus silver season ticket deer and Fox Kings Season ticket rad Miles Bridges Hornets not numbered Pacers rookie ticket auto Chris Duarte silver Superstar die-cut timberwolves Anthony Edwards and DeMar DeRose and red ice aspirations bulls all star aspirations Where to go here? Lakers and jazz on the next one All right, Devin with self spurs red season ticket, you know something to combo bucks a Silver season ticket Fred van Vleet raptors Blazers rookie ticket auto silver Keon Johnson Time Lords Celtics playing the numbers game Robert Williams the third and In Kings whoop dreams blue ice Davion Mitchell Number 48 of 75, but I played like fresh and basketball. That's about it. I just like no transportation and I just stopped. I look like 15 miles from high schools Kevin Durant 75th anniversary Joshua Primo Spurs Scotty Barnes rookie ticket blue Toronto 88 of 99 rookie ticket auto silver grizzly Zair Williams Chris Paul uniformity Sons and Red Ice superstar Jason Tatum Celtics Tatum man Tatum probably MVP for on it right now He's too good He's too good last two boxes y'all I thought you played soccer Never I've never played soccer. I'm into it, but yeah, aren't we got a Marcus smart Jalen Green on the red Red rookie ticket nice color match not numbered for the Rockets Kuzma blue numbered Five of 99 Yeah, five of 99 And he is live Rookie ticket auto silver pistons Kade Cunningham. There he is. He is live Still no redemptions love that Congrats pistons. This auto looks a little different too. Yeah, I guess it is a good. It's a good time for that Jason Stefan clay legendary tandem silver for the Warriors Red ice lottery tickets Pacers Chris Duarte. Oh, yeah, it just came out today. It's a 21 22 product So for the 21 22 season the first product for 22 23 will be in January Usually products take a few months to put together Kawhi Leonard Clippers Silver season ticket McCollum for the pelicans and silver season ticket cabs Darius Garland Isaiah livers rookie ticket out of red for the pistons 49 of 99 Legendary Tandems white chocolate Jason Williams and Chris Weber for the Kings And legendary Tandems red ice Celtics Kevin McHale Larry Bird Not number. Let's do a quick recap not the best break some decent color there was the Cade but Tough hitting tough hitting that one guys Gold to 10 Aaron Gordon nuggets get autos Clippers or not Clippers Blazers Keon Johnson Grizzly Zaire Williams Pacers Chris Duarte Pistons Luca Garza 76ers Jaden Springer Sophomore contenders FD to 149 Wizards Isaiah livers Pistons to 99 But trellis free. Well legendary contenders Warriors auto 275 Nick's RJ Barrett auto number to 35 And Cade Conningham silver rookie ticket auto pistons That'll do it everybody number six coming up


So let's take for example creating a traditional normal binary search tree with the insertions as listed. So, since I say insert four first, it gets inserted first. It's the root, three becomes the left child, two becomes its left child, one becomes its left child. Now, you might notice nothing's going on over here on the right side and that's actually a major issue with a traditional binary search tree. What happens if I had started from a hundred and worked my way down? Well, this would just continue trailing outward. So, one of the worst case scenarios in a traditional binary search tree is that it might have a height of n. However, many nodes, n nodes that I have will be the height of my tree. So, what can we do? Well, we can take this idea of a binary search tree, two children, and work off of some different implementations. One of those is known as an AVL tree. And the entire premise is that it is self-balancing. So, how does it do this? It does it through something we like to call a balance factor. What we do is we look at the difference in height between the right and left nodes of any given node, the right and left children of any given node, and if we find them, and we hope that they are within a plus or minus of one. If they're not, we would classify that as being imbalanced. So, say for example, I made another style, we come in here and I have that same four at two. So, in this tree, we would classify the heights as being, well, my leaf node. My leaf node can go no further, so it's a zero. My four node has no children, so I can't go any further, it's also a zero. And if I look at my root node, my three, it can go one node to the left or it can go one node to the right. So, it has a height of one. Now, so far, this has not done anything outlandish. We can call this a proper binary search tree and we can also call it balanced. The reason why is, again, if we look at that balance, we look at any given nodes, right child, height, so right, I guess, dot height, and we subtract that by that node's left child's height, height. In our case up here, we see zero and zero, zero minus zero, and we get ourselves a, what we would call a balanced, so equaling zero, we would call this a balanced node. But what happens if I were to insert in a single node? Let's say, for example, I came in and added that insert one. Well, every node that I had to go through to get to the one, again, if we're thinking about this in a binary search tree, I had to touch the three, touch the two to get to the one. I need to make their changes. Now, the height of one, since it's a leaf node, is a zero. The height of two just went up, so it's no longer zero. It's now one, and the height of three just went up because we look for the maximum depth it can go. I can either go one node here, or I can go one, two nodes over here. So, if we looked at that same kind of premise on our three node for a second, we could say, well, what's the right height? Well, we just changed it, it's now one, and what's my left height? Sorry, my right height is still zero. My left height is now one. Well, if we calculated this out, we get a negative one. This is not balanced, but it's still okay, because obviously, we need to have a slight, you know, plus or minus one for the balance factor. And so, what we would call this is left heavy, heavy. So, what happens if I were to come in and I added, say, two nodes to the four slot? So, I came in, I gave a five, and then I gave a six. Well, both times, as I were updating those, six has a zero height, five has a one height, four can go one, two times, so it now has a height of two, and three, since it can either go one, two, or one, two, three, gets a height of three. So, the same kind of factors are coming into play. If I looked at the balance factor of my three node, I take the height of my right child. The height of my right child now is two, subtracted by the height of my left child, which is still one, and in this case, we get a positive one. Now, same kind of thing that we saw. It's not balanced, it's not left heavy, it's right heavy.


Tetzel Moscow Moscow's vibe fluctuates between formal capital and cultural powerhouse. The Kremlin is at the core of political life, a building with an intimidating architectural style. Go inside, its cathedrals and museums whisper a thousand stories about tsars and churinas, patriarchs and princesses. The Kremlin is Moscow's Kremlin de la Krem. The 12th century construction began with wood and blossomed into the world's largest medieval fortress. Waltz threw red square past Lenin's mausoleum, with his century-old body lying in state to find palaces, museums and a startling art collection inside the armory, including some of the famed fagrige eggs. Wander through the world's largest gallery of Russian art. In the contemporary wing, analyze Malovich's provocative black square. Inside the main gallery, learn to separate your Kandinsky's from your Keprensky's, and then let your eyes fall on masterpieces, including Ilya Repin's famous The Volga Boatman. Tank herring for a set of Gorbachev matryoshka dolls. Got a fur trim hat fixation? Getting hard rock cafe cravings? Then take a stroll along Arbit Street, where you will find a never-ending procession of street artists, kitschy souvenir shops and the art accordion-wielding musician. Wander past the colonnades at the Vaktangoth Theatre for a whiff of Old World Moscow, or peek down side streets filled with local curiosities.


It is December 16th again, and this is the Microsoft DevSync. So let's get started here. We've had some technical problems, so we'll just jump right into it. Ken, let's do you first, I know you've got a time constraint. Yeah, I'm still working on the integration with the URLs for the backend and for the continuous integration training. I haven't seen any new data. Chris, is there an ETA? When we're going to have some more data in there? Hopefully today, I did find some code changes I need to make after I talked with Michael yesterday, so I made those and testing them and yeah. So hopefully today I'll have some more data out there. Okay. Good. Yeah. So that's what I'm working on. Yesterday, well, yesterday, today and tomorrow. Okay. All right. We'll look forward to getting that online. I'm going to jump to Kevin since he kindly offered to join us here or accepted my offer to join us. So, Kevin, what's going on with the hardware? I'm curious. So I've been working on the hardware, putting all the last pieces together. I think everything is ready to go to ship to our PCBAs. I know Michael is just looking over the last bits of it. I'm bringing out some extra GPIO. Josh, I added your rotary encoder bits. I have, we're really using a lot of almost every GPIO on the Raspberry Pi anyway. So there's a couple GPIO on the XMOS chip that I'm pulling out just as solder-on pins, but they should work pretty well. What else? I think it's ready. Whoops. So great. I'll take one more final look over that with you after this. Who are we going to go with for the PCBA? Well, that's the next step, I think. We still need to send out our codes. So we're at RFQ. OK, great. Thanks, Kevin. Let's go on to Chris Vair then and see how that's going. Excuse me. Hey, so yesterday, aren't you day? Hello. Was that me?  I don't know how to respond to that. OK, sorry, Siri decided to pipe up on me. That was kind of strange. Siri?  Siri's on my laptop. It comes in all the Macs, man.  Yeah, I don't know why. And now my dogs decided to park at the front door. So let's start over. Today, I made a change to the code that selects wakeboard samples based on a discussion I had with Michael yesterday. What I'm trying to do, and I didn't support, was the ability to select a higher priority tag when a lower priority tag already existed, which is something that we're going to need when we have all of these files in the precise database that are already tagged as wakeboards or not, but not tagged as human speech yet. So I made that change. I'm testing it. And then as soon as that's tested, I also spent some time working on the load of all the precise data into the database. And that caused a couple problems. That code was not up to date. It's a Python notebook. It wasn't up to date with some of the changes I had made to some of my code since the last time I wrote that. So I had to make some changes there, too. So by the end of today, I should have mine changes tested and some data for Ken to play with tomorrow. So tomorrow will be then preparing for. Sorry, folks. It looks like my internet connection has frozen. Hopefully it'll come back here in just a minute. Up on a database as well. So I can use that as validation for the selects and for the queries and stuff. OK. And then if you find anything that sorts it could be a problem with my conversion routine. So but I just have to. Yeah, but once you're ready, once you're all ready, then I'll be able to validate it against the existing data set. So that's good. OK. Let me know when you have that ready. All right. So I'm sorry, Chris. I missed the last thing that you said before Ken started talking because my internet dropped out again. I'm not sure what's going on. I'm not on Wi-Fi network. Well, that's to solve my Wi-Fi problems, which are all over the place. But I'm on a weird connection here. So I don't know what's going on. I have your tracking numbers in your email. It says December 17th. Thanks. So yeah, so tomorrow I'm going to work. I think I don't know what you missed, but I think probably just tomorrow I'm working on preparing for the production and eventually by the afternoon and installing everything I have to production so we can start doing some tagging. And then I'll do patch releases if I have to if Ken finds anything in his testing. Gotcha. All right. Thank you. Appreciate it. All right, let's go to Gets. Yeah, so you get some testing of the tag yesterday and tagging so we can get that. Everything we just talked about. Also, building new packages from the Microsoft GUI repos and Microsoft GUI Mod 2 so that the screen should default to the current correct orientation, but then we might flip it again is what I hear. But anyway, it was also just making sure I had that build process down pat. And I did a PR to add a bunch of that feedback configuration directly into the CMake list file so that we hopefully be consistent with the packaging rather than each person that chooses their own configuration variables. And that moves us towards CI CD process for those packages. So that's good. Had some good discussions with the lingua franca. What will be the official maintainers of the lingua franca from the community side being Chance, Jarvis, and OK at the moment? So that is able to have right permission to do stuff. Obviously, it's a lot of other contributors, but just trying to put a tiny bit of structure there so that the permissions are explicit and we can update the team list and stuff. And that people understand our merge policies and all that sort of thing. Well, yeah, they're the main teams, I think. OK, great. So this is a little bit of an aside. A little bit. Nope, my. I think Michael might have dropped out again because he's looking very still. Yeah. If you move to Australia, your internet seems like mine. I don't know what's going on. Maybe there's some kind of thing happening in LA. Yeah. I do have my choice of one cable provider, so. Hi. You need to look at your neighborhood. Well, fortunately, we have monopoly laws to. Right. From that sort of thing. So it's really hard to build a competitor in that market, it turns out. Yeah, yeah, I was supporting a local broadband company for a year or so. They had a tower on the top of my house to retransmit their wireless network. But yeah, they had to fold after about a year. So anyway. And our tax dollars paid for the wires that they're running their cable on. Well, yeah. Anyway, you guys do a free market broadband, too, right? Yeah, yeah, totally, totally free market when we were taxed to help them lay the infrastructure. Once the infrastructure was laid, they became very territorial. Yeah, yeah. So, guys, on the lingua franca stuff, I know we're going to talk that up in an upcoming newsletter. I just wanted to ask you to add a couple sentences at the beginning of that just to explain what it is in general, because there's a lot of details like, hey, this and this and this is happening. But just like a couple sentences saying, hey, this is our system for translating between languages. Yeah, yeah, yeah. And that sort of thing. OK, that would be great. Were you anything from our friends at PanaCora? I think they're recent to the hardware today. What are you? I missed the last bit, but I haven't looked at the messages from them yet. But they got better like the last night. They got the road four. And they were interested in the fact that we were going to have to slash every single Raspberry Fire we need firmware. I have to go. I have to go. Don't the new pie is to have the new firmware?  Well, sorry, folks. I normally have a very good internet connection, but today I don't. Bugs and I can update it. Yeah, yeah, that's true. All right, sorry, guys. My network dropped out again. I'm sure you noticed. So what was this issue about the updating the pie firmware? I guess I'm not really back. All right, well, we're going to call it here for today. Thanks, everybody. We'll figure this out and be back tomorrow.


So, I've been giving lectures, public lectures about robotics for probably 15 years now. And early in that period I realized that people kept asking me the question how intelligent exactly are intelligent robots? And of course the subtext was, is always, and should we be worried? And it's a good question. So really this talk is trying to address that question, or at least the first part of the talk. That's the rough outline. And the, so the first part of the talk really is about what, what we mean by robot intelligence, and perhaps what we don't mean by robot intelligence. And then the second part is going to be showing you some experimental work that I've been doing in the last five years or so, which taken together is, if you like, experiments in artificial theory of mind. And I think, you know, you might be interested in that to Elliot. So can a machine think, well, what do we mean exactly by thinking? And of course most people, when they talk about thinking or think about thinking, they typically think about Rodan's thinker. So thinking we tend to regard as the kind of reflective, introspective activity, the very, the thing that you might in fact be doing right now. You know, wondering what on earth is this guy talking about? But actually the picture on the left is just as much thinking as the picture on the right. So when you make a cup of tea, you're having to do a lot of thinking. Most of it of course will be subconscious. You won't really have to think very hard. It's a matter of kind of routine activity, but you know, one of the interesting things is that in 60 odd years of artificial intelligence, we've made plenty of AIs that can do the kind of thinking on the right. So in other words, AIs that can play chess, can play in fact really quite difficult games like Go. Actually better than any human. So really super human AIs doing one thing. But I tell you what, we cannot make a robot that could go into your kitchen and make you a cup of tea. That robot simply doesn't exist. In fact, I'm calling it the windfield tea test. As a kind of my, if you like, alternative to the Turing test. So thinking is a lot more than the kind of reflective thought of Rodan's thinker.  Well, we have a kind of folk sense that intelligence somehow goes from not very much to a lot. So we have a kind of general sense that a cat is smarter than a crocodile, and a crocodile in turn is smarter than a cockroach. Well, that might be true, but I'm not sure it's thinking about things the right way. And of course we believe ourselves to be the smartest thing on the planet. I'm not entirely sure about that given the state that we're making of the planet and leaving it for our children. Now where do robots fit? Well, you know, here is my estimate of where your robot vacuum cleaner fits. So it's nowhere near as smart as a cockroach. In fact, I would judge it to be a little smarter perhaps than an E. coli. OK, so really not very smart at all. What about this robot? It doesn't matter about the sound on the movie. So the point is, if you were to see that robot across the other side of the crowded room, this room for instance, you would probably imagine that that was a person. Only if you actually interacted with the robot would you realize that it's not. It's a robot. So how smart is that robot? Well, I would estimate probably not much smarter than your washing machine. And in a sense that really reflects an ethical problem, which is that we can build robots that look very much like humans, but we cannot build the AI to match human intelligence. We're far, far from that. So in a sense we can build the bodies but not the brains. And I think that, so it's what I call the brain-body mismatch problem. And for that reason I don't believe we should be building android robots, robots that look like humans, at least not until we can make them with an equivalent, human equivalent AI. No, OK, so that linear scale from not very intelligent to very intelligent I think is wrong. I believe that there are four kinds or categories of intelligence. I'll very briefly introduce you to these four categories. The first category is what I call morphological intelligence. So this is the kind of intelligence that you get from having the kind of body that you have, the kind of morphology. Morphology is just a fancy word for shape. And it's clear, isn't it, that you and I would have difficulty swinging through the trees, brachiating, like that monkey. We certainly couldn't fly, at least without a machine to fly in. We just don't have the physical body to do that. And really in morphological intelligence there's a kind of trade-off, if you like, between the computation that you would have to do if you didn't have the right kind of body and the body.  In fact it can walk because it has the right physical machinery, has springs and is designed in such a way that there isn't a computer actually controlling that walking gate. The Mars rover, in fact, has been designed deliberately with the rocker bogey arrangement of wheels so that in fact the Mars rover can drive over rocks without any computation at all, zero computation. Simply the machinery that designed the morphology of the robot allows it to navigate obstacles in a way that otherwise would need a lot of computing. If you built that with legs, for instance. So I think that's an interesting kind of intelligence. It's an important and often overlooked kind of intelligence. Another one that's often overlooked is swarm intelligence. And swarm intelligence is the kind of intelligence that we see dramatically in social insects. Termites, for instance, are remarkable. And this picture in the middle is a murmuration of starlings. A murmuration, this thing. And what we understand about swarm intelligence is that there is no hierarchy. So for instance, in a termite's mound, there's no brain termite that directs the actions of all the rest of the termites. Each termite acts, in fact, autonomously, completely, has no idea what it's doing. But the net effect, the emergent property, if you like, self-organizing property, of all of those hundreds of thousands of termites is the nest.  These robots are moving towards a beacon, which is over here, an infrared beacon, so you can't see it. And no single robot can get to the beacon on its own. So without a number of robots, they wouldn't reach the beacon. And of course, people say to me, well, how does the robot know when it's finished? Well, the answer is it doesn't, because it doesn't know what it's doing in the first place. And that's not only true for insects, social insects, swarm robots. It's also true for every cell in your body. So our cells, in a sense, I mean, the word that's often used is a colony. So I mean, it's a joined up colony, or at least we hope. But the point is that we are also aggregates of we're multicellular organisms. And there's a lot of emergence, a lot of self-organization going on inside our bodies, too. So that's the second, my second kind of intelligence. My third is perhaps more obvious and less controversial. And here's the thing. So the little girl on the left is playing with blocks. And she's learning all kinds of stuff automatically. It's a process of self-discovery.  That robot on the right is doing something very similar. The big difference is that the robot on the right required a team of probably about a dozen computer scientists over four years or so of a significant EU-funded research project. And at the end of that project, yes, the robot is pretty impressive in what it does. But it only does that one thing. Of course, the point is that little girl, while she's learning how to stack blocks, she's been learning how to speak, how to play with other children, how to dress herself. Lots of other things have been going on automatically without us having to program in the way that we do with the robot. So we're very, very far behind. I mean, that little robot on the right is the state of the art in individual learning in robots. We really are very, very far behind even the kind of learning capability that a young child has. The final and fourth kind of intelligence I want to suggest is what I call social intelligence. And it's by far the most powerful. This is the kind of intelligence that allows us to learn from each other socially. So the most fundamental kind of social learning is imitation. And human beings are extraordinarily good imitators. And I think imitation is so powerful that without it I doubt if we would have culture and civilization. I think imitation is right at the root of all of this, if you like. It's very hard to do imitation, in fact, with robots. But I will turn to an experiment that we've been doing or we did a couple of years ago, a bit later in the talk. So we have these four kinds of intelligence. Now let's put them all together. So if we draw these on a graph like this with four axes, and I'm deliberately drawing the morphological and swarm intelligence axis vertically, because I think there's some equivalence, certainly the individual and social intelligence go together nicely on the horizontal axis. And if you think of the point in the middle as zero of all of those. So in other words, if you have more of a particular kind of intelligence, then you'll go up that particular axis. So if we then plot, for instance, animals, in fact, even the plant, because plants are actually intelligent as well, then I think, for instance, a crocodile has a reasonable amount of morphological intelligence. Quite a lot of individual intelligence. There's learning going on. And possibly a little bit of social intelligence. I think the crocodiles certainly do, as it were, look after their young and at least show their young something. I may be wrong about that, but I don't think there's any swarm intelligence. I don't think crocodiles herd or flock or shoal or any of those things. If we look at an ant, then, of course, you have lots and lots of swarm intelligence, an enormous amount of swarm intelligence, a lot of morphological intelligence. I would suggest more than a crocodile. Because an ant, actually, even though it's only that big, has an extraordinarily complicated body where a lot of the capabilities of the body don't need a brain. And it's well known that you can cut the heads off some insects, and they'll still run across the floor, which demonstrates morphological intelligence in a rather gruesome fashion. And ants, in fact, do have some individual intelligence. So there is evidence that ants will learn individually. But we don't think ants don't teach each other anything. So in the words, they score zero on that particular scale. The plant, this is the mustard plant, has some morphological intelligence, certainly, because it can move, for instance, towards sunlight and open and closed petals, stuff like that. And interestingly, there's some evidence of swarm intelligence in the sense that plants, it's only recently been discovered, can give off chemical markers that communicate to other plants that there's a predator or something like that. I mean, this is all very hand-wavy, and I'm not going to apologise for that because it's just an idea. And of course, we would like to think that humans, I mean, we undoubtedly are kind of off the scale as far as individual and social intelligence are concerned. We're only average in terms of morphological intelligence. I don't think we have as much as an ant, for instance. And we certainly don't have anything like as much swarm intelligence as social insects, certainly not as groups. I mean, we have some because we do crowd behaviour, for instance, but not as much as we don't build buildings in the same way that termites build termite mounts. Now, if we then ask ourselves where robots fit on this graph, this is very revealing indeed. And I won't go all the way around the graph, but the thing that I want you to notice is that none of those robots have more than two kinds of intelligence, none of them. I mean, they all have some degree of morphological intelligence. Actually, that's not true. The epochs in the bottom right have really zero morphological intelligence. Simple robots there. And the other thing to notice is or that I should point out is that if you were to superimpose that graph on the previous one, then all of those measures would be right in the middle of the graph. In other words, the quantitative levels of kinds of intelligence are tiny, even for these pretty advanced robots, than any or most animals. After all, the robots haven't had to survive in many animals' cases, hundreds of millions of years of evolution. So, I've been talking quite a lot about individual kinds of intelligence, and I made the point about narrow intelligence. What about general intelligence? Well, the term artificial general intelligence is often given to, if you like, human intelligence. So, humans, possibly uniquely among the animals on the planet, are able to, actually not uniquely, that's not true. But rarely we're able to, I mean, what I'm saying is that it's rare to be able to generalise. So, the point is that what we're capable of doing is learning something in one domain and then transferring that understanding into a completely different domain. Of course, we do it all the time in language. Language is a superstructure of metaphors, and metaphor is, in a sense, a word that somehow describes the ability to change frame of reference from one set of ideas to another. Actually, another word for that is creativity. Creativity often comes about when you move frame of reference, move frames of reference. Arthur Custer in his wonderful book, The Act of Creation, said the difference between humour, a joke, and creativity is the difference between ha-ha and ah-ha. Actually, the same mechanism, I recommend that book. So, where are we? Well, we don't have artificial general intelligence. I think we are many, many decades, if not hundreds of years away, from being able to build artificial general intelligence. In other words, an AI that is as capable as you and I, or even our young children, if you have young children, or grandchildren. Now, there are three ways, essentially, of getting from where we are now, which is narrow artificial intelligence to general AI, and I think data from Star Trek is a really great kind of thought experiment of what a general, generally intelligent robot might be like. And there are three approaches. The bottom, I'll start at the bottom, the bottom one is what's sometimes called whole brain emulation. In other words, reverse engineer human or animal brains, and then build, as it were, an artificial version of that wiring, that connectome, it's sometimes called. I mean, the big problem with whole brain emulation is that if you ask yourself, well, what's the most complicated animal on the planet that we've done that for so far? And the answer is C. elegans, the nematode worm, the nematode worm. In fact, the nematode worm has 302 neurons, exactly. And about 3,000 connections between neurons, and in fact, it's the only animal for which we have the complete wiring diagram. So we are able to build whole brain emulation for the nematode worm. It doesn't get us very far because the nematode worm is not very smart. You can't be very smart with 302 neurons. So I think this is hopeless, whole brain emulation, I think it's crazy. Another approach is to evolve your intelligence. So to use a process akin to actually artificial selection, so Darwinian artificial selection to evolve, artificially evolve, a more intelligent thing. I think it's an interesting approach, but I wrote a paper a couple of years ago, I think 2014, on the energy costs of evolution. And I think the amount of energy that would be required to artificially evolve something as smart as a human, even if it were technically possible, is so colossal that it's out of the question. You'd have to give up, as it were, the entire energy output of the whole of humanity for a number of years, just to power the machine that's doing that evolution. So that's a problem. So really, we're only left with one approach, which is to design it, to design this artificial general intelligence. The problem is we don't know how to design it. And often people say, well, we have lots of computing power. Well, that's true, but just having a lot of computing power isn't enough. It's like having lots of beautiful Italian marble. Imagine you had 150,000 tonnes of Italian marble. Would you be able to build a cathedral? No, because just having the raw material isn't enough. You need to have the design and the know-how of how to transform that large amount of raw material into a cathedral. The same is true for artificial intelligence. We just don't have the architecture. And I think the gap between where we are now, narrow AI, and where we'd like to be, artificial general intelligence, if indeed we would like to be, and that's another question, is about the same as the gap between present-day space, if you like spacecraft engine technology and the warp drive. I think it's about that kind of gap. And I choose that analogy deliberately because warp drives, I mean, some physicists think that warp drives faster than light travel is just about theoretically possible. So I think that's, I'm careful that I'm choosing an analogue, an analogy of something that's just about theoretically possible, maybe, because I think artificial general intelligence is like that. Okay, that, I'm going to skip that slide because I'm a little bit behind time, I guess. But, and my agrabah, which was that, that was mine, thank you. So what I want to do now is to show you some experimental work that I've been doing the last four or five years in artificial theory of mind. Now theory of mind is really important to humans. Theory of mind is the, it's the facility that we each have to be able to infer the beliefs and intentions of others, of others like us. And it's really, it's really a critical function without, without a working theory of mind. Well, I mean, you have problems and there's reasonable evidence that autism, for instance, is an impairment of theory of mind. So I have a paper which was out a few months ago called Experiments in Artificial Theory of Mind. Now this is my working definition of theory of mind. I mean, the problem is there are hundreds of definitions so I had to choose one. The one I choose is to explain and predict the actions both of yourself and other intelligent agents. And the, if you like, the working hypothesis behind the work of the paper is that simulation-based internal models, and I'm going to tell you what that is, simulation-based internal model, allow us to build artificial theory of mind. So in a sense it is theory-based. In fact, this may surprise you. There are, there isn't one, there are several theories of theory of mind. And curiously, they are called, firstly, the theory theory of mind. And the theory theory of mind basically says that the reason that you can read other people's minds is because you know the rules. So in other words, you kind of have an innate sense of how other people might be thinking, and you know the rules. You don't actually have to do very much inference. The simulation theory posits that in fact, because you have a model of yourself inside yourself, then you can use that model to put yourself in other people's shoes. So in other words, you can use your internal cognitive apparatus for modelling yourself to then model others. If you like others like you, conspecifics. And this is particularly attractive to me because for a number of years I've been putting simulations of robots inside those robots. And that's what I call a simulation-based internal model. And this is in fact for the, as it were, the geeks among you, this is the block diagram, the architecture of this approach. So the thing on the right, I'll use the mouse to show you, the thing on the right from here, the sensory input down to motor commands, this is what the inside of a robot looks like. So it senses the world, then it chooses some actions here. It's called action selection. It's what all of us do all the time. We have to select the next thing that we do, like I'm selecting the next word I speak. And then we enact that decision. Now, what we've done here is that we've put in parallel with that the thing we call a consequence engine. And it allows the robot to predict the future. What it does is that it has a simulation here. So this little bit here is a simulation of the robot itself. So it's a simulation of itself. This is a model of the world right now. And yeah, so it's a simulation of both itself, its body, that's the robot model, and its brain, that's the robot controller. So it's simulating both its own body and its own controller, its own mind, its own brain, if you like, and the world. And for that simulation, it loops through all of its next possible actions, all of the things that it could do next. And it looks into the future and tries to predict what would happen for each of those next possible actions. It's a bit like you, if you're playing drafts, for instance, you will try and predict what would happen for each of the moves that you could possibly make. And then you'll choose the move that you think gives the best outcome in terms of playing the game. So this is our consequence engine. It's a piece of cognitive machinery for predicting the consequences of next possible actions. And it was only really after I'd been working on this simulation-based internal model for a couple of years that I realized that this could be a basis for an implementation of the simulation theory of mind, because essentially this is what we have. We have a robot that has a model of itself inside itself, and that means that it can also model other robots like itself, not other humans, but other robots like itself. So we've done a number of experiments. This is one of the experiments. We call it the corridor experiment. So here we have a corridor and the blue robot is trying to get to the end of the corridor, but the corridor is full of red robots that are milling around all over the place. Now the blue robot has a simulation of itself and all of the other robots inside itself, and it's using that to keep away. It's like having a very large kind of personal space. The blue robot doesn't want to get anywhere near any of the red robots, so it'll even back away, as you can see it. So those little filaments, the yellow filaments and the light blue filament is essentially the blue robot's prediction of where the other robots will go next and its decision about where it's going to go next. That's the light blue. So you can actually, in a sense, see inside the brain of the robot. So the red robots don't have this internal model. They're just bouncing around like billiard balls. And just to show you the same thing with the real robots, I'm hoping this... Yeah, this is the corridor with the intelligent robot and the non-intelligent robots. This one has got to get to this end of the corridor, so... And why can't we see? I'm really having problems tonight with my... Never mind. Doesn't matter. Oh, maybe that's it, yes. OK, maybe it isn't it. I'll just let that... Yeah. No, it isn't. All right. Oh, it is. Hooray. OK. So keep your eye on the guy at the left in the middle, now heading towards the middle. Actually, he's not really had to do very much avoiding until now, but the way is pretty much blocked, as you can see. But eventually, eventually, he makes it. I shouldn't be calling it a he, of course. It's an it. Robot should not be gendered. So yes, there is the experiment, the corridor experiment with real robots. Now, here's the thing. I said to my students, wouldn't it be interesting if we had two robots, each with a simulation of themselves and another robot, walking towards each other, like pedestrians, on a pavement?  And I wondered if we would get the same result with our robots. And the answer is yes, you do, which is amazing. I think this is the first time with robots that we've actually demonstrated that little dance, pedestrian dance, with robots. And what we find is that about one in 10, between one in 10 and one in five runs, we get the thing on the right. This shows the paths of the two robots where there's a little dance, whereas the rest of the time, most of the time, four out of five or nine out of 10 times, something between those, we get the picture on the left where the robots just pass each other nicely in the way that you and I mostly pass each other nicely on the pavement. And I think I'm hoping that we'll even have a little picture here of the kind of experiment that results in them passing each other nicely. And you can see each of these little, so the dotted lines are paths that it's testing, they're paths that they're testing and predicting. And the little spiky lines that you can see are going out, all of the next possible directions it could go in. So, again, you can really see what's going on. And if I show you the case where they do a dance, and in fact, we've got them right in the middle of doing the dance, look. And the interesting thing is that they each have to turn about five times in order to resolve the conflict, because it were. And I think I've even got a video of that if it works, that is. So, oh yes, this is normal passing. So, again, our little epoch robots. So, very nice, yeah. All good, all good, great. And then the dance. So, of course, they both go in the same direction. And no, okay. Actually, that particular run was relatively quickly resolved. So, it wasn't a great big long dance. It was a fairly short dance. But nevertheless, you get the idea. Really interesting. So, theory of mind. You know, those robots are kind of using theory of mind for each other, and sometimes get it wrong, which is exactly what humans use, or humans do. Now, I mentioned imitation, and I don't have any movies here, I'm afraid, which is good because there's less to go wrong, of course. But we've used exactly the same architecture, the simulation-based internal model, to show how robots can imitate each other, but importantly, imitate not the actions, but the goals. So, in other words, robots can infer the goals of each other, which is a really powerful form of imitation. So, something that is, again, it's very hard to say if there are no animals that imitate the goals, but certainly humans are extraordinarily good. Typically, as infants, we imitate the actions of somebody until we reach about 18 months old. And at about 18 months, we start to figure out, actually, you're not really intending to do that. This is what you really intend to do. In other words, they'll work out that, well, so here's a famous experiment. So, I think it was a guy called Meltsoff, child psychologist, and he had a big button on a table, and if you hit the button, then it lights up, okay. And there were two experiments. There was one in which the human being, the mum, if you like, would hit the button, that they'd have their hands on the table, they would hit the button with their head, okay. And the children typically would also hit the button with, no, that's right. The young children, pre-18 months, would do the same, but post-18 months, they would actually hit the button with their hands. Except if mummy keeps her hands behind her back, then the post-18 month children would also hit the button with their head. And the theory goes that the difference is that if you have your hands on top of the table, then you're signaling that your hands are available. And of course, the child thinks, well, if your hands are available, you could have used your hands to light the button, to press the button, so I'm gonna do that. If the hands are hidden, then the child is thinking, well, actually, I don't, I mean, she didn't have her hands available, so maybe I won't either, so I will have to hit the button with my head. Very famous experiment. And I won't go through the detail, but what we've done here is to demonstrate exactly that. We've demonstrated not hitting buttons on a table, of course. This is robots moving towards a goal position and having to do so in the middle column, the blue robot is moving towards a goal position, but it's going the long way around, okay? And the robot which is imitating, which is the red robot, in fact, realises that it's important that even though the blue robot could have gone directly to the goal position, so the red robot infers that that's important and therefore actually itself goes via that intermediate position on the little graph called F, whereas if, in condition three, the blue robot, in fact, cannot go directly to the goal position because there's something in the way, then the red robot infers that it doesn't have to go via that intermediate position because the blue robot had no choice, but to go round, to go around. So I think it's a really powerful example, or I should say that the red robot has this simulation-based internal model, so it's able to effectively model what it thinks the red robot's doing. I mean, I'm using the word think in a very loose and unscientific way. The robot isn't thinking at all, but anyway. And that really brings me toward the final experiment that I want to show you this evening, which is rather relevant, perhaps, because, as Geraint said, I'm Professor of Robot Ethics, and one of the things I'm doing in my work in Robot Ethics is trying to figure out if you could make ethical robots. I have to say that most of my work in Robot Ethics is about how human roboticists, that is, should be ethical, but nevertheless it's interesting to ask if a robot itself could be ethical. The answer is yes, in fact. So this work started with a simple thought experiment. So imagine that you are walking along the pavement and you see somebody else heading towards a hole in the ground. Maybe that's someone else's peering at their smartphone, not looking where they're going. Of course that never happens in real life, does it? Now, you, I guess, I suspect, would intervene to stop that person falling into the hole. Now, why is that, or should I say how is that? It's not just because you're a good person.  It's because you also have the ability to predict the future in a trivial kind of way.  If they don't stop looking at their smartphone. And you can do even better than that. You can also predict whether or not you can intervene. So if you're close enough, you might be able to rush over. If you're too far, then you can shout or wave your arms or something like that. So you can not only predict the consequences of their action from what they're doing right now to them, the consequences for them. You can also predict how and the best way for you to intervene. Okay, so let's imagine now that this isn't you, but a robot. So it's a robot who's observing this human being. This time carrying a briefcase. I couldn't find an image with a... Actually, this was an image that appeared in a newspaper when the work was being picked up by the press. So the robot here, and I'm simplifying things, has four next possible actions. So there are four things it could do next. A is standstill. That's always a possible action, do nothing. B is turn to the left, in which case of course the robot is perfectly safe, but the human, of course, is not. C is go straight ahead, in which case of course both the human and the robot plunge to their doom in the hole in the pavement. Whereas if the robot chooses action D to move towards its right, then it might, with luck, intercept the human, and a gentle collision with the human is of course much better than the human falling into this deep hole in the pavement. Now, if we express that as an ethical rule, this is what it looks like. So if, for all of the robot actions, the human is equally safe, then just do the safe thing for the robot. You don't need to worry about the human, okay? Otherwise, output the robot action for the least unsafe human outcome. Now, when I was thinking about this, I was rather embarrassed to realise that in fact, oh, I've gone on too fast. I was rather embarrassed to realise that that ethical rule is in fact Asimov's first law of robotics. So Asimov's first law of robotics states that a robot should not harm a human or through inaction cause the human to come to harm. The important thing is that all through inaction. So Asimov's first law of robotics essentially says, do no harm, but also intervene in order to prevent harm from occurring, which is essentially what I've described in my ethical rule. So rather embarrassingly, because of course in robotics, Asimov's laws of robotics are not taken particularly seriously, they should be, I think, because Asimov for sure established the principle that robots should be governed by principles. Of course, they were a fictional device and the wonderful stories that Asimov wrote are mostly about the conflicts, the dilemmas, if you like, between different rules, you know, obeying or keeping a human safe, for instance. So we did a couple of experiments, series of experiments. The first experiments were with these epoch robots that you've seen already and you can see the setup up there. So we have this arena, which ignore the fact that it looks like a football pitch. It's nothing to do with robot football. But we have a hole in the ground, which is that square yellow patch. It's not a real hole in the ground. It's a pretend hole. We don't want to dig holes in the lab floor. And the H robot, H for human, is heading towards the hole. So that's the haplos human not looking where it's going. The A robot, A after Asimov, is the robot that has this simulation-based internal model plus the ethical rule. So it's the same simulation-based internal model. We've just added that ethical rule to choose essentially that determines how the robot chooses the particular action it's going to take. And in fact, it's more than four. We actually have around 30 next possible actions. And again, you'd be interested to know that we're essentially evaluating the consequences, potential consequences of all of those 30 next possible actions every half a second. And each evaluation is looking 10 seconds into the future. So we're actually looking in a second, we're looking 600, that's a second of real time, we're looking 600 seconds into the future. So we're actually doing this in real time, which is, you know, this is tricky stuff. This is where I'm extremely grateful to my students because I couldn't code this. It's hideously complicated. I mean, think about it.  So the second set of experiments by Dieter with these now robots, these humanoid robots. So let me show you the experiments. So here's the trial of the epochs where we have one H-robot and one A-robot. And you can see H moves A, notices at that point that H is heading toward danger, gentle collision, and all's well, and the A-robot then proceeds to its destination. Also itself, of course, having to avoid the hole in the ground. And it works. And you can see that this is speeded up. It works every single time without fail. In fact, it worked. Oh yeah, I'll show you the same experiment with the now robots first. So here, this time, the blue robot is the ethical robot. The red one is the human. Danger is top right. The destination for the blue robot is at the bottom in the middle. And you can see that the blue robot has correctly prevented the red robot from heading toward danger. And this particular paper published last year, an architecture ethical robot inspired by the simulation theory of cognition. So there's not only a simulation theory of mine, but there's a more general simulation theory of cognition, which I think is very powerful. Okay, so after we did the previous experiment, the one with the epochs, and realized that it works every time, I thought, well, it's a bit boring, isn't it? And how can we publish a paper that just says, well, we built this and it works? Let's make it more interesting and add a second human. So let's have two humans heading toward the hole at the same time. And this is the trial that shows that. So I believe that this is the first time ever that a real robot has faced an ethical dilemma. So here we are. So let's try and save this one, ah, no, too bad. Both H-robots plunge to their doom in the hole. So when we were running these experiments, we thought, well, hang on, this is something that's not right here. Now that one works. Did you see that the A-robot saved the H-robot at the top, but then turned back to try and save the other one but couldn't? It wasn't time. So the A-robot definitely is doing its best, but is clearly not working very well. And in fact, in around 30, I think 35 runs, the A-robot saved both H-robots twice, saved one of them about 14 times and about 15 or 16 times saved neither of them. So in fact, it performed extremely badly. So the question is, if we know that the A-robot can reliably save one of them, why doesn't it just do that? Well, the answer actually is very simple when we kind of sat down and thought about it. The reason that the A-robot is performing so badly is because it's basically recalculating its decision every half a second. So in other words, we've made not only an ethical robot, but also a pathologically indecisive robot, which is clearly a very bad idea. If you think about it, imagine yourself in that situation and you go, I'll save that one. No, no, I'll save that one, I'll save that one. You're clearly not going to save anybody. So clearly a real robot with this kind of decision-making process needs to have some kind of sticky decision, a decision that in a sense has a half-life, but could still be changed because we want the ability to change our minds. Imagine you're in a situation where you decide to save that one, but then she realizes she's not in danger at all and she's in danger and stops walking towards the hole, so that you then change your mind. So it's important that the decision should be a little bit sticky, but still changeable. Now, of course, I think there are other good reasons that we shouldn't be building robots that solve ethical dilemmas at all. So we have another paper called The Dark Side of Ethical Robots, where we argue that actually ethical robots may not be a good idea after all. Maybe we can talk about that in the Q&A. And I will just finally show you that essentially the same problem happens. So Blue Ethical Robot, shall I save this guy? No. I'll save the other guy. So, yeah, that phenomenon of being indecisive, dithering essentially happens with the now robots as well. So, Goraint, I think that brings me to the end. So, here are some general conclusions. So I think until we have a clear understanding of what intelligence is in plants and animals, I think the quest to create AI. And I don't just mean general AI. I mean all AI is ad hoc. I think the problem that we have, fundamental problem, and of course the AI people don't like it when I say this, is that we have no general theory of intelligence. So, in a sense, trying to build artificial general intelligence I think is a bit like trying to understand the nature of matter with a particle accelerator without a standard model, which would be absurd. So, in other words, we have no theory underpinning all of the work that's going on in AI. We have little bits fragments of theory, general theory, certainly not a general mathematical theory equivalent to the theories that we have for quantum physics. And my second bullet, I think that simulation-based internal modelling is a really interesting and powerful approach to solving all sorts of problems. I've shown you safety. I've shown you imitation and ethical robots, and I don't think ethical robots are a good idea. So, at that point, Geraint Tal, say thank you very much indeed.


An election monitoring group, Yaga Africa, says the Independence National Electoral Commission's official announcement on the vote cast during the Oshun Governor's election was consistent with the groups watching the vote, WTV estimated range, addressing a news conference in Oshubu. The group's board chairman, Dr. Hussein Abdu, said, had the official result been changed at the world level or local government or state coalition centers, the results would not have fallen within the Yaga Africa WTV estimated ranges. Abdu said that the group employed the parallel vote tabulation methodology and applied 500 observers in pairs to a representative random sample of 250 polling units, 32 mobile observers in all 30 local government areas and 30 resort coalition observers.


What's going on everyone? Sam here with late in sports guards ripping 22 Panini flawless collegiate football hobby one box break number 29 random left side serial numbers so look again at your random results as always you can find these on the website under the results tab as well as the link in chat it's good luck here everybody let me get these sorted we're on mancam beautiful see something crazy come out of this man see some one-o-ones do another box where you get four one-o-ones in one box that'd be cool man on that a good start a good start right there we've got numbered eight out of 20 that's gonna be Kenny Pickett Malik Willis Mackarral nice one there eight spot Gregory M congrats buddy okay nice start there for the eight spot we've got going to the 18 spot 18 out of 25 it's gonna be Damian Pierce 18 spot is John M there you go John congrats buddy here we go John next up this is going to the three spot that's Andrew M we've got Arnold Abakite team logo signatures and Andrew M on the three spot yeah no recap on this one guys everybody has a hit here so uh going to the 21 spot that is Nathan Q we've got rookie Otto from last year that is gonna be Dax Milne for BYU yeah personals are full SG personals are full till Monday they're full till full till Monday I'll be doing personals uh Monday night though uh going back to the eight spot Gregory M we've got RPA it's gonna be Kennedy Brooks nice one there he has an eagle right now then we've got going to the seven spot that's Jeremy H we've got seven out of 20 is Chris Godwin Claire Warren on the Godwin nice one there two colored Hatch Auto C God and then going to the four spot Joseph H we've got four of ten on the gold Jameson Williams nice one there congrats on the Jameson there you go Joseph start here it's kind of cool go into the 15 spot we've got Tony Dorsett 15 spot Paul P Tony Dorsett on the blue blue diamond encased congrats on that Paul next up we've got numbered five of five that's going to Todd C in the five spot uh Roger Stalback on the emerald congrats on that there Todd nice emerald Roger Stalback and we've got nice hit here that is going to be numbered three of ten going to Andrew M Kenny Pickett three colored RPA there you go congrats Andrew beautiful Kenny Pickett there you go that will do it for the break everybody thanks as always for the fill uh next up we have another box of this


What a time are we living in. MSI is getting deeper and deeper in the thin notebook market, which focuses on productivity and portability. While that may be a far cry for the Taiwanese company's usual market of monstrous gaming hardware, it can be good for a manufacturer to enter a relatively new space, as it introduces you to new difficulties which sometimes actually open your eyes to engineering ideas that you might have never thought of before. Now, to go back to the device that is in the spotlight today, here is the MSI Modern 14B11. It has more than enough performance, due to Tiger Lake, which has a caveat, in the way that it needs a proper cooling setup and RAM configuration, in order to truly succeed. Today we are presenting you our top 5 picks about the MSI Modern 14B11. MSI introduced its new logo at CES 2021, don't worry, it's just the wordmark that they replaced, the dragon is safe for now, and we feel like it's quite tasteful. It resembles the old one, while still feeling fresh, which is mandatory for a good rebrand. The design of the laptop itself is quite good, with the new logo able to be seen on the center of the lid as well as the bezel below the display. Build materials are on point, with the whole laptop being made entirely out of aluminum, resulting in a weight of 1.30 kilograms and a height of just 16.9 millimeters. The body is sturdy, with little to no flex to be seen from both the lid and the base. The lid easily opens with a single hand, which reveals another feature of the laptop. The lid lifts up the base, which gives a better angle for typing on the backlit keyboard, while at the same time allowing for more air to enter the device through the bottom vents. The keyboard and touchpad combo is 50-50 as while the keycaps are well-sized, the feedback is a bit mushy and the key travel isn't as long as we would like. The touchpad is the same, as it is accurate and smooth, while the inputs sometimes fail to register. Only 2 per 100 people watching this video are subscribers. If you decide to just start following us, we'll be able to reinvest more in our laboratory thus making even more helpful videos for you. Thank you, you're awesome! As with many MSI laptops, due to the factory sticker, which is placed on top of one of the screws and will void your warranty in some regions, we couldn't give you a photo and video of the internal components, but we do know for a fact that the MSI Modern 14 B11 has one Sodom RAM slot for future expansion, which supports up to 32GB of DDR4 memory, as well as 1M.2 PCIe slot for SSD upgrades down the line. One more positive about the Modern 14 is the relatively wide IO selection, which includes the power plug, an HDMI connector, one Thunderbolt 4 port, and a micro SD card slot, which are all placed on the left side. On the right side, you have two USB type A3.2 Gen.1 ports and the mandatory 3.5mm audio jack. Considering the 52Wh battery that is inside, and the power efficient Core i3 processor we think that the battery life should have been better than this. However, it is still enough to last you through a whole day of work, and in general, the battery life is quite balanced, as there is not that big of a difference between web browsing and video playback scores. The laptop lasted for 8 hours and 40 minutes of web browsing and 8 hours and 4 minutes of video playback. Due to an excellent combination of processor, RAM configuration, and cooling setup, this laptop achieves a great amount of performance even though our unit only had a Core i3-1115G4. If that is not enough for you, there are other Tiger Lake CPUs such as the Core i5-1135G7 and the Core i7-1165G7, which when paired with the potent cooling solution, will be an unrivaled combination at this price point. Imagine a world where this laptop gets a better display than the one that it has. It would honestly make it a contender for one of the best-selling notebooks on the market. However, MSI has decided to go the cheaper route and put a 14-inch full HD IPS panel which generally has decent qualities such as comfortable viewing angles, decent maximum brightness, and a good contrast ratio of 1200 to 1. Furthermore, it doesn't use PWM across any brightness levels. Sadly the color coverage is just a measly 49% of the sRGB color gamut. The color accuracy isn't anything to write home about, as even our design and gaming profile couldn't bring it down to a professional standard. Here you can see the results of our test with both the factory settings, left, and with our design and gaming profile, right.


Hey there guys, have you ever considered on making brand deals on your channel? Let me tell you guys if you have an opportunity to make brand deals with a certain company, take it because it's gonna have wonders on your channel. Today, let's talk about the impacts on what brand deal does to your channel, but before we do guys, you know what to do, like, subscribe, and gong that bell. Hey freedom, my name is Nolden, welcome to Freedom Central, the home of all freedom content. Like I said a while ago, we will be talking about the impact on brand deals on your channel. And let me tell you guys, brand deals will open doors for your channel. So guys, if you have an opportunity to make brand deals with a certain company, take it. On today's episode, let's talk more about that, so without further ado guys, freedom. So we all know that brands can do all sorts of magic for your channel, can potentially help you get more out of your channel and maybe even beyond it. You can get that celebrity feel when you do. Brands that have the same virtues and interests as you will see your channel as something they can partner with because you tackle the same crowd. Your relationship with your audience is tantamount to them because they know they can trust you. Trust can lead to purchases. Imagine if you have a ton of subscribers, there will be someone bound to make a purchase. You see it now, the more you build trust on your audiences whether it be small or big, the higher the chances for you guys to make a business out of it for brand deals. Okay, now guys, the perspective is set. Let's talk about the mutual benefits you get as a creator and for the brand. Number one benefit is you develop an additional source of revenue and gain access to product. Number two is products can spark new video ideas. Three is your audience can benefit from learning about new products and services. Sometimes you'll get early access and coupon codes to offer your fans. That means guys you can also giveaways or like those coupon codes you see on videos that they're kind of selling a product. Number four brand deals can help establish your channel as an influencer and open doors to career opportunities beyond YouTube. This is where I'm saying guys that you can be a celebrity online. Number five brands benefit because they are able to reach a specific audience with their products and expand their target market. Meaning guys you not just help yourself but also you help these people or the company widen their spectrum. So before we start creating branded content, let's get the term underway first. For videos that have brands, it's not just a video anymore. It is a branded kind of content. Branded content is usually paid for by a brand and features their products or services in some way. The next terminology is a brand deal. Brand deals is a partnership between creators and brands and sometimes referred to as a brand sponsorship. Another terminology is brand integration. Brand integration are a type of branded content where a creator finds a way to seamlessly feature products or services in their videos. The idea is to do something with a product in the video that makes sense and aligns with your own channel's brand. Alright guys I know that some of you guys fear branded content and that's okay. I'll have that fear because we may think we are not the right person for this and of course our audience will not like it. But it's not the case.  Partner with a brand you believe in and think your audience will like. Brands want to work with creators who believe in them and are a good fit. Be transparent with your audience. Consider asking them what they think about a potential brand partnership. Are their products or types of videos they've been asking you about? Remember what you think your audience will benefit the most from may not always be what they want. If you're still not sure or afraid of branded content, look at other content creators and ask for insights about it. They will be more than happy to help you step out of your fear of branded content. And that's the episode guys. Hope you guys liked it. And if you have other insights or tips for us on how to make a branded content, let us know down below. Score share this to a friend who wants to take the next level and take branded content on their channel. Maybe we can help them out by sharing this video. Also guys before you leave, leave a like, subscribe and gong that bell. We will be making another episode about branded content. So guys stay tuned. My name is Noel guys bringing you tips on how to grow your channel. Always remember freedom. Quick tips. I'm going to be seeing you guys on the next episode. Goodbye.


Hello wrestling fans and welcome to Historic Fenway Park for Wrestling All Stars in New England brought to you by Beltime Club and Dante Luna. My name is Drew Kazoo, I'm joined at this time by Joey Business and the one and only The benevolent, intelligent, extravagant and eloquent Donny Wonderful aka Tall On Door. It's main event time folks. This one's gonna be a treat. We've got Toto Loco taking on one of the most feared men in the Professor of Relief. A David and Goliath battle. A true David and Goliath battle as Toto Loco takes on Warbeard Hansen. This is gonna be a treat. What's up? Mr. Business, how are you today? I'm fantastic. The last, I'm gonna tell you right now, the last person that I'd wanna bump into in a dark alley might be Warbeard Hansen. Would you just look at this man? This guy is a mammoth. Of course. Huge. World renowned tag team, War Machine as we see Toto Loco now making his way ringside. Ladies and gentlemen, one of the most electrifying, one of the most impressive, one of the biggest fan favorites here. All that too, Mr. Business, I'm glad you're paying attention, Toto Loco. A good throwback to the luchador style. This is gonna be a great main event. I just got one question. He says he's hailing from Tokyo, Mexico. Does anybody have an explanation for me? I don't know if I've found that on the map yet. Well, you know what? I'm gonna stop looking here because we've got a match underway. We're waiting for the bell here, Toto Loco, Warbeard Hansen. This one's gonna be a doozy. Classic big guy versus little guy. You always root for the little guy because they're the underdog, but I can tell you right now, just looking at these two guys lined up, it's gonna look tough for them. Well, I gotta tell you, this is what I love most about professional wrestling, is matchups, the chemistry. I like to see the two different styles clashing in the middle of the ring and see what comes out. We couldn't get any bigger contrast of styles than we do right now. We've got the unorthodox, high-flying martial arts, hard-hitting style of Toto Loco against the pure, brutal aggression and size here of Warbeard Hansen. I must say, I hate to discriminate, but the Vegas are to definitely against Toto Loco. Well, you know what? Some may say so just because of the pure size and strength, but we've seen Toto Loco come out victorious to come through some grueling battles. Yes, we have. Man can take a lot of punishment, he can dish it out too as we see both men fighting for support to the crowd here, playing mind games with each other, trying to get the crowd on their side, letting them know who's boss. But you never want to turn your back against your opponent. That was a great opportunity for Toto Loco to attack, sneak him up by surprise that he let go of. Well, this is a contest, it's a big fight feel here. Both of these men signed the contract, they both want to win, they both got something to prove. I don't think they're out for any cheap shots. I think they want to walk away with a clean W. Absolutely. There's a method of winning in this industry called by any means necessary. You take that opportunity, you use it, you walk out a winner. Mr. Business, I got to ask you, what's the key to victory here for Toto Loco being at the size disadvantage? Well, since he's at the size disadvantage, the only thing he can hope to do, in my view, is survive and capitalize on some type of humongous mistake because just everything's against him. You're absolutely right. You're not going to overpower the much larger guy, but you are quicker and you can use that to your advantage. You can slip into spaces that he can't fill and you can use that, potentially beat him and outsmart him. As his made evident right there, we saw that manifest right before our eyes.  Oh my goodness, it is Wally. Wally, oh my God, Toto Loco risking life in limb, going straight over the top with that somersault senton, almost into the front row here at Fenway Park, almost out at the center field. My goodness, much to the pleasure of the Fenway faithful here. Toto Loco now. Sizing him up for a beautiful kick. On the offensive here. That may be the most effective way to hit him is when you do striking attacks like that, even though they may be light against a big man like that, they do damage over time.   That was, that's pretty, actually folks, I would referee should go over, take a look now at the forehead of Toto Loco and see if we got any blood because he hit awfully hard on that steel post outside. Unbelievable. As we see now, Hanson showing no quarter now. Vicious forearm smash. Toto Loco now, definitely, definitely. I'll tell you, Jack Hill definitely has his hands full to try to referee this one and make sure that both men can at least go home to dinner tonight. And I'll tell you what, Warbeard Hanson, oh my goodness. Warbeard Hanson looks like he could eat Toto Loco for dinner himself. And he may do just that before this contest is over now as he makes his way back into the ring.  Very athletic, very athletic for a big man here. As we see now, Warbeard Hanson now. Oh, are we going to see a beard wash? Oh, this is, oh. That is amazing. Now, Drew, I know you're from around here. Is Tokyo, Mexico next to Harvard, Cambridge? You're going to need to give me a minute to Google Earth that if I have it. I'm going to have to find, I'm not sure what Tokyo, Mexico is, but if you give me, if you hit his wall again. He's really one of the Fenway faithful and loves taking in this wrestling action. You can tell by the conviction of Wally's face as he watches this suplex with authority. Goodness gracious, that should be enough. Took the win right out of him. Two counts. Only two. Only two. Folks, definitely a feel it, guys. Feeling a history in the air, man. I got to tell you, I love being here at Fenway Park. I love the atmosphere. I love taking in the whole, just the feel here. It's a bit, usually it's for a baseball game, but we're not here for baseball, guys. We're here for pro wrestling. Absolutely, there's no do-overs, no strikeouts. One hit, you're gone. It's over. That's right. And as we see on the outside of the ring, they have a sign telling us where fans with bags need to go and Toto Loco letting us know where that fist needs to go, straight to the solar plexus of Warbeard Hansen. Huge boot to the jet. Look at Toto Loco now. Oh, and just like that. It's like chopping down a tree. Toto Loco's doing his best with all his striking. Go for the pit now. That's taking time. Oh, just rubbing that forearm right in his face. You got to wonder, when is this? We're looking for that mistake that Mr. Business was alluding to. Oh, and look at this, touring away at the mask. Where is Joe Business going? Joey Business, they're not selling beers anymore. I think they should, Joey Business, come back here. He's on his way to get to Fenway, Frank. Joey Business, leave to Kila Rose alone. All right, well, we're going to carry on here, folks. As Warbeard Hansen now. The show must go on. Unbelievable happenings here at Fenway Park, folks. As we see Warbeard Hansen now, getting ready for maybe another suplex. Small package, one, two. I would have to say that's a package, that's a big package. Where's Joey Business when you need him? And there's the set up for one, two, holy. As Warbeard Hansen now, huge boot. That could be it, folks, but he's going up the rope. Old school style splash. One, two, only a count of two. Good discretion with the resilience to kick out of that. He's given up at least 150 pounds. We told you, Toto Loco has been through wars here in the world of New England wrestling. He's been through some of the worst battles.  He's been broken through tables. He's been smashed, he's hit with chairs. He's been flown from heights, the highest heights, and come through victorious in the end. So we know that this man can definitely, definitely take punishment. And he is definitely taking punishment now at the hands of Warbeard Hansen. Oh, nobody on there. The way with all to move, here we go. He has to capitalize on this. Toto Loco now, look at this. Going for that spinning.  If you can cover him, he can end this. Doesn't quite have it in him now to go for the cover. He's got to gather himself. He needs the help of Finlay Faithful. They need to lift his spirit so he can regain his control and win this match. Both men now digging deep, trying to make their way to their feet so they can leave historic Fenway Park now with the victory. Toto Loco, a lot of steam behind that elbow smash. And there we go. Oh, scissors. Hurricane Rana there. Well, back from his bathroom break. Here comes Joe Business. Sitting heel kick.  I had the only kind of explosive diarrhea. You could have at the place like Fenway Park. Let me tell you it is hard, but I hope the people in the ring hit harder. Well, I hope that you feel better because I'll tell you right now, somebody who's not feeling great is Warby at Hanson. Actually, maybe I spoke too soon. Big boot to the face. No, what's not feeling great was I couldn't let you two clowns close the show without me. Oh, huge. Springboard in Zagiri now. Joe Business, did it soak into your socks? What is that smell? Brutal. That smell is excellence, gentlemen.  Oh, top rope Frankensteiner. Folks, if he goes to the pin for the pin, that could be it. Total loco now. One, two, oh. And two count. Only a two. Which only means the votes you have is two and they don't count. Here we go now. Total loco getting the Fenway faithful into this. He's feeling it. He's pumped up. My goodness, that top rope. Moonsault was incredible. It sure was, because no one's talking about it. So let's get back to it. Okay, here he goes. Warbared, throwing them over the shoulder. Reversal by Total loco. Oh my goodness, look at the big man. What a cartwheel and a laria. Somebody. That should do it. Two. Two only. No, no, no. Somebody go pick up Total loco's head because that thing just got shot all the way over to the kid's zone. Two. But a count of two only. Warbared Hanson now. Definitely front, the frustration is setting in. It smells like the city of Lenovi. Hey now, listen. Total loco now, definitely, definitely. Maybe wishing that he was in the locker or maybe he was ocean on the outside of the ring to recuperate because he is on the receiving end of some clubbing, clubbing blows there by Warbared Hanson. Lot of mustard behind those shots. Absolutely. A lot of mustard, but I want to Fenway Frank so come on, folks. Lord knows, Joe business does not deserve to eat anything else. We don't want it coming out of any other holes. Look at this, Warbared Hanson out to the second rope. Oh! This is just like your humor.  Go for the cover now, Total loco. Not going, was that a mistake? Not going for the cover, Mr. Business. There's totally a mistake coming back to this booth. They had what's better in the other room. Oh! Springboard moonsault, nobody home. Warbared Hanson now setting him up. He's going back up top. What's this? It's Warbared Hanson now. We know the big man's not afraid to fly. Are we going to see a moonsault? Look at the crowd here. They can't believe what they're witnessing. Comes a meteor shower. Oh my goodness! But to no avail, Total loco moving out of the way. The crowd here cannot believe what they've just seen. Total loco now making his way to his feet. He's on the top rope. What are we going to see here, folks? Oh, huge splash! One, two, three! And that's it! Total loco victorious with the big top rope splash. David Slugoliath. What a contest here, folks. My goodness, Total loco showing guts and determination as he comes out victorious here with a victory over Warbared Hanson. My goodness.  Let me tell you, there's thousands in attendance and try to see. Folks, I gotta tell you, that was probably, if not one of the toughest contests thus far for Total loco as he comes out with the victory. A perfect underdog story. The smaller guy wins. As you mentioned before, Joey Business, David Slays Goliath. And you gotta wonder what's going through the mind of Warbared Hanson as he comes into historic Fenway Park and leaves on the losing end of a contest with the New England favorite, Total loco. Oh, look at this. He's in his face. What's this? We're gonna see. Is this a sign of bats? What's gonna happen? Oh, look at this, folks. Extends the hand of respect to Total loco. Telling him, excellent match, young man. He's earned it. That is about the highest sign of respect that you could imagine there, folks. Thanks for watching. Wrestling All-Stars in New England. I'm Drew Kazoo for Joey Business and Donny Wonderful. Thanks for watching, folks. We'll see you next time.


Our study found that the COVID-19 pandemic has had a significant impact on the amount of time antimicrobial stewardship pharmacists spend on non-clinical tasks such as pre-authorizations and administrative meetings. This shift away from clinical activities could lead to decreases in antimicrobial use, which could have implications for patient safety. This article was authored by Megan R. Wimmer, Lucas T. Schultz, Ashley G. Hamel and others.