[ML News] Blind Chess AI Competition | Graph NNs for traffic | AI gift suggestions
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper mlnews rbc reconaissence blind chess blind chess neurips chess neurips chess neurips competition ai chess ai blind chess nimblephysics cerebras cerebras cluster cerebras wafer engine cerebras large scale ai gifts ai gift ai gift ideas val kilmer voice val kilmer ai voice ai voice generated
#mlnews #chess #neurips

OUTLINE:
0:00 - Intro
0:30 - Reconnaissance Blind Chess NeurIPS 2021 Competition
3:40 - Colab Pro no longer top priority for GPUs
4:45 - DeepMind uses Graph NNs to do traffic prediction
6:00 - Helpful Libraries: Isaac Gym, Differentiable Human, LVIS, BEHAVIOR
10:25 - Cerebras Wafer Scale Engine Cluster
12:15 - AI Voice Synthesis for Val Kilmer
14:20 - Can AI give thoughtful gifts?

References:
Reconnaissance Blind Chess NeurIPS 2021 Competition
https://rbc.jhuapl.edu/
https://rbc.jhuapl.edu/gameRules

Colab Pro no longer top priority
https://www.reddit.com/r/MachineLearning/comments/pdwxxz/d_colab_pro_no_longer_gives_you_a_v100_not_even_a/

Google Maps ETA prediction using Graph Neural Networks
https://arxiv.org/pdf/2108.11482.pdf

Isaac Gym: RL simulator on GPU
https://arxiv.org/abs/2108.10470
https://sites.google.com/view/isaacgym-nvidia
https://developer.nvidia.com/isaac-gym

Cerebras Cluster for massive AI models
https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/?utm_source=pocket_mylist

Helpful Libraries / Datasets
https://nimblephysics.org/docs/human-body.html?utm_source=pocket_mylist
https://www.lvisdataset.org/
https://arxiv.org/pdf/2108.03332.pdf

AI Voice Reconstruction
https://www.washingtonpost.com/technology/2021/08/18/val-kilmer-ai-voice-cloning/

Can AI make thoughtful gifts?
https://www.forbes.com/sites/anniebrown/2021/08/29/can-artificial-intelligence-give-thoughtful-gifts-an-exploration-of-the-possibilities-and-limits-of-ais-humanity/

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 We play some blind chess, graph neural networks are used in Google Maps to predict traffic, and AI makes for thoughtful gifts. Welcome to ML News. It's Monday. Hello and welcome friends of the Monday, welcome to ML News. Now to be honest with you, not a lot of stuff happened this week. I guess that's what they call a slow news day or something like this. So I thought we'd just take a look at more lightweight things that I came across. So the first one is reconnaissance blind chess, which is a chess variant that is now also a NURBS 2021 competition. The rules are the same as in regular chess, except you can't see what your opponent does. So every move that you have is actually split in two, you can first use sort of a oracle to sense the board or a piece of the board. And then after that, you can make your move. So now you have to be strategic about where you use this sensing. And when you make your moves, you have to be strategic because you can count on making your regular chess moves. But you can also make moves that you think your opponent won't scout, which makes for some nice surprise attacks, the notion of check is removed, and the game ends when a king is captured. So on the website, you can actually play ranked matchmaking or play a bot. So here on the white pieces, and it's my turn, first of all, to sense now at the beginning, it doesn't make much sense. But you can see you can sense a three by three square anywhere you want. So let's sense here. Wow, what a surprise. They're still in the initial configuration, and then make a move and now the opponent senses you won't see where they sense and you won't see their move. Now I'm not particularly good at chess, but I'm just gonna scout about here. And you can see that it reveals their move that they made. Now had I scouted somewhere else, I would not have seen that move. So now I can react with a bit of an attack. And not only do you have to pay attention to what your opponent does, but you sort of have to model what your opponent might know about you. And maybe even from the moves that your opponent makes, you can sort of parse out what they might or might not know about you and your pieces. So here my opponent goes for a bit of an attack. And I just like horses, horses are nice. Alright, so move has been made. Now you do get informed when a piece of yours is captured or when you capture a piece. So none of that happened yet. So let's sense around here. And that did not reveal anything. Oh, yes, you can pass as well in this game, which makes it even more complicated. So I'm going to guess the opponent guarded this pawn back there. I'm going to try some attack here. So now it's my turn to sense I'm going to sense about here to see if they countered any of my things. So now is an interesting situation, right? I have no indication that anything is in the way between me and the king. Now if my opponent had sense that I move my bishop there, they would have probably moved the king out of the way by now. So the king might be here in front. Yet if they hadn't scouted it, they have no motivation to move the king at all. Therefore, I could now just capture the king. I won. I won. Great greatest chess pro Magnus Carlsen, bring it on. Bring it on. All right, this is reconnaissance blind chess. If you're interested, I'll link it in the description. Let's see if you can win too. I played against an opponent level of trout here just for reference. There are various settings and they instruct you how to build a bot give it a try. Next news, there's some discussion on Reddit about collab pro. Now we've reported previously that collab now has a new tier called collab pro plus, which gives you even more priority access than collab pro to GPUs. So now people are starting to notice that collab pro subscriptions don't always give them very good GPUs anymore. Now the thread is filled with various comments and and the general opinions of the different people are that yes, probably now that people have even more priority access, if you are just a pro user, you might get less access be collab is still one of the most cost efficient ways of running on a GPU on the planet and see a lot of people still do get good GPUs with collab pro. So it could just have been a problem of some kind of usage spike. So make of that as you will for what it's worth Google never promised to give you good GPUs, they simply promise to give you priority access. And that's about that. It's just important to be aware if you're considering collab pro, if you really rely on getting good GPUs all the time, then the collab pro plus might be for you. In a big collaboration between deep mind Waymo Google, Amazon, Facebook, AI and CAI lab researchers have used graph neural networks to do better traffic prediction. Specifically, they talk about ETA prediction estimated time of arrival, and that in real time. So the way they do it is they segment roads or paths in general into these segments. And then they use graph neural networks to integrate all live information to give you an accurate estimate of when you'll arrive. The interesting thing is they don't do that much crazy stuff with these graph neural networks, they have some tricks up their sleeves, like the use of meta gradients in order to control hyper parameters. But in general, it just sounds like a really solid engineering effort. And this is deployed in Google Maps, the statistics here show you by how much the ETA prediction accuracies have improved. And sometimes this is really staggering. So you see great improvements across the board, sometimes up to 50%. I'm not exactly sure what the metric here is, but 50% is a big number. Can we all agree? Yeah, good job. Okay, let's look at some helpful libraries and data sets. The first is Isaac gym, a high performance GPU based physics simulation for something similar with a library called Brax, these physics simulations, they now run directly on accelerators, such that you can do end to end research on the accelerators, you don't have to switch between devices all the time, which massively speeds up research in control and reinforcement learning. So this one's called Isaac gym, you can get it from Nvidia, which is a bit worrisome, but it looks very cool in these demonstrations, they have an evaluation and they also do train some policies on it. Now that is disturbing. But in general, it seems like if you are on GPUs, and you're trying to do reinforcement learning and control settings, this might be a good option for you. Also in the domain of physics, Nimble physics releases the differentiable human body model. So this apparently is a gold standard human body model that was used for simulation. And now this library made it end to end differentiable human body model isn't just one body model, but it is a configurable body model where you can sort of control the size of all the different parts and still get accurate simulations out of it. And now with it being differentiable, there's a whole new range of applications in research that become possible with this. If you're into biomechanics or differentiable simulations, I think you should check this out. LV is is data set for large vocabulary instance segmentation. And the goal here is to do instance segmentations on categories that are vast. So there are a lot of categories in these instance segmentation problems. And a lot of them don't appear very often, which is what they're referring to here as long tail. So some of these things you might have never seen before, we've seen a couple of these data sets, this one is especially challenging, because not only do you have to recognize what it is, you have to segment the instances. So here you can see examples of donut, pineapple, teacup, wine glass, wrath. I don't even know what a wrath is. Wrath. An arrangement of flowers, leaves or stems fastened in a ring and used for decoration, or for laying on a grave. Wonderful. And bird feeder. So there are even competitions and leaderboards to go along with that. If you're into this kind of stuff, check it out. Next is behavior by Stanford University. Behavior stands for benchmark for everyday household activities and virtual interactive and ecological environments. I had to bend a lot of stuff to come up with this acronym, but now it's called behavior. This is a data set for doing robotics in what are supposed to be relatively real life scenarios in virtual environments. What's interesting is the creation of this data set, the data sets are modeled after real scenes. So people analyze what they call everyday situations, and they try to recreate them with objects from wordnet, you can let AIs run in this simulated environment, but you can even do it yourself by VR. And the data set includes VR demonstrations of these things by humans. On top of that, it's not a fixed set of environments, but the environments are sort of described by a little bit of a grammar. So therefore, potentially infinite variations of these environments can be generated. Here we see a bunch of examples of this grammar. So for example, fish can be burnt or cooked or frozen, the microwave can be open or closed, the apples can be on top of the plate, and so on. The AIs are supposed to fulfill tasks in these situations. And I guess the goal here is to come ever closer to real life robots that actually help you in everyday life. The problem I have a little bit with these things is that even though the simulations are modeled after real life, they're still very, very far from it being limited to wordnet, I guess limits the amount of stuff you can put into a scene, the scenes are probably still kind of regular real life happens to be much more messy. So it's a bit of a question how useful this is for the end goal. But still, it looks like an interesting problem. And it's definitely a step into the direction of robots that interact with real life in a more realistic and competent manner. Next news, wired writes a new chip cluster will make massive AI models possible. Cerebros says that they've built a cluster that can run a neural network with 120 trillion connections. For reference, that's about 100 times more than what's achievable today. So if you want to build a large scale neural network today, your options are you can use TPUs, which are somewhat large if you use a cluster of them, or you can just stack GPUs together and connect them with some sort of infini band, both are not really optimal, as the accelerators themselves are relatively small, and they have to communicate a lot. Therefore, cerebrosis strategy is to build giant chips. Here you can see one in comparison to the largest GPU currently available. So these things are actually huge. Now the article details the various engineering problems that you have when you want to create such a large chip. Notably, the chip itself has to be much more error tolerant as you can't simply switch out one piece whenever it breaks like you could switch out a GPU. Now GPUs by no means are cheap, but compared to this thing, a GPU is certainly a bargain. Now they didn't stop at building single chips, they built an entire cluster of those chips. Now, at least as the article states it, they're just waiting for someone to come around and actually train a model on it. Their CEO says, so we know we can but we haven't trained a model because we're infrastructure builders and well, there is no model yet. If you have an idea of how to use 120 trillion connections, maybe give Andrew Feldman a call. The bigger question is a little bit of whether scaling individual chips is the correct approach, or if it's just better to stick with the smaller accelerators but improve our abilities to communicate and shard models, I guess only time will tell. Washington Post writes AI gave Val Kilmer his voice back, but critics worry the technology could be misused. Of course, critics always worry the technology could be misused. So the article details about this startup called sonatic that used recordings of Val Kilmer's voice in order to make an AI that can synthesize any text in his voice. Val Kilmer lost his original voice due to surgery after throat cancer. And this model essentially gives him back the ability to communicate in audio in the way that people remember him speaking. Now, this isn't a prosthetic, I think he still has to type the things he actually wants to say. But with some good brain interface, this could be an actual technology for people who lost their voice to be able to speak again in the future. The article also goes into a little bit of the possible economy that could result from this, namely that as a voice actor, I don't actually have to voice act for every project I do, I could simply sell my voice for other people to use as a sort of a licensing deal. The article also voices skepticism with respect to that and quotes Jay Britton, who is a voice actor that says, when I'm an actor, I get to decide whether I support the content, it would be a devastating thing to drop on a voice actor that your voice is out there saying things that you might not necessarily support. So the criticism is that someone could buy your voice for a license fee, and then have it say something that you disagree with. And rather than sounding the alarm bells about this, I think we should simply adjust to the fact that yes, this is a new possibility we have, but it's not a new thing by any means. I mean, stock photographs have existed for about as long as the internet has existed. And if you're a stock photograph model, then it's absolutely expected that your picture can be used for something you disagree with. That's just part of the deal. And no one faults these models if they appear on such a picture. So I think what needs to shift is not people not using this for various things, but simply our attitude towards what can be done with voice technology nowadays. So the last article for today, Forbes writes, can artificial intelligence give thoughtful gifts and exploration of the possibilities and limits of AI's humanity? This is a bit of a fluff piece for a company that uses AI to sort of recommender system gifts for people, which is interesting, because usually the media is rather critical of these recommender systems. However, in this case, it's sort of framed as the AI really understands you and knows what the good gift is in a moment and what a thoughtful gift is, and so on. And you know, in my opinion, they're probably not wrong. Like most gift suggestions could be made by an AI much better than you just kind of sitting there and coming up with something. So the startup is called Gosby for people who are interested, I just want to show you how these things might look about. So this is one of these little plugins that you can have as a YouTuber that does a little bit of analysis for you. It's not super useful, but I always enjoyed this feature right here where it gives you ideas for your next videos. And I'm not going to say that the quality is anywhere near or close to what Gosby is doing. I have not tested them. I just want to show a little bit that you get the feeling of what this might be like. So here are videos I could do. I've not looked at these yet. I get three per day because I'm cheap and I'm on the free version of this product. So we're going to look at them together. Devlog tech demo interactive game. Well, I don't think that's exactly for my channel. How to enable CNBC news alerts. I think it just estimates my channel as sort of like a tech channel or something like this. Maybe this is because I made how to bypass neural hash. Dismiss a revolutionary product for Apple users. This is definitely because I made the videos on neural hash now. And that was it. Now, usually, usually, I have to say they're a little bit better, they're a little bit into the direction of what my channel is actually doing. I guess I've just confused it with the recent videos about neural hash. But safe to say, if you're searching for gifts for people that you kind of know, a system like this might actually be a good place to go. It will probably suggest you a bit of generic gifts, maybe personalized a little bit to what you input about the person you want to give to. And that's all we need. Okay, this was already it for ml news. As you can see, really nothing happened this week. If you're an ML researcher, if you're an industry, or even if you're just interested, please make something happen for next week. Please, I need content is very important. Yeah, all right, I'll see you next week. Bye bye.
 We play some blind chess, graph neural networks are used in Google Maps to predict traffic,
 and AI makes for thoughtful gifts. Welcome to ML News. It's Monday.
 Hello and welcome friends of the Monday, welcome to ML News. Now to be honest with you,
 not a lot of stuff happened this week. I guess that's what they call a slow news day or something
 like this. So I thought we'd just take a look at more lightweight things that I came across. So
 the first one is reconnaissance blind chess, which is a chess variant that is now also a NURBS 2021
 competition. The rules are the same as in regular chess, except you can't see what your opponent
 does. So every move that you have is actually split in two, you can first use sort of a oracle
 to sense the board or a piece of the board. And then after that, you can make your move. So now
 you have to be strategic about where you use this sensing. And when you make your moves, you have to
 be strategic because you can count on making your regular chess moves. But you can also make moves
 that you think your opponent won't scout, which makes for some nice surprise attacks, the notion
 of check is removed, and the game ends when a king is captured. So on the website, you can actually
 play ranked matchmaking or play a bot. So here on the white pieces, and it's my turn, first of all,
 to sense now at the beginning, it doesn't make much sense. But you can see you can sense a three by
 three square anywhere you want. So let's sense here. Wow, what a surprise. They're still in
 the initial configuration, and then make a move and now the opponent senses you won't see where
 they sense and you won't see their move. Now I'm not particularly good at chess, but I'm just gonna
 scout about here. And you can see that it reveals their move that they made. Now had I scouted
 somewhere else, I would not have seen that move. So now I can react with a bit of an attack. And
 not only do you have to pay attention to what your opponent does, but you sort of have to model what
 your opponent might know about you. And maybe even from the moves that your opponent makes,
 you can sort of parse out what they might or might not know about you and your pieces. So here my
 opponent goes for a bit of an attack. And I just like horses, horses are nice. Alright, so move
 has been made. Now you do get informed when a piece of yours is captured or when you capture a
 piece. So none of that happened yet. So let's sense around here. And that did not reveal anything. Oh,
 yes, you can pass as well in this game, which makes it even more complicated. So I'm going to guess
 the opponent guarded this pawn back there. I'm going to try some attack here. So now it's my
 turn to sense I'm going to sense about here to see if they countered any of my things. So now
 is an interesting situation, right? I have no indication that anything is in the way between
 me and the king. Now if my opponent had sense that I move my bishop there, they would have probably
 moved the king out of the way by now. So the king might be here in front. Yet if they hadn't scouted
 it, they have no motivation to move the king at all. Therefore, I could now just capture the king.
 I won. I won. Great greatest chess pro Magnus Carlsen, bring it on. Bring it on. All right,
 this is reconnaissance blind chess. If you're interested, I'll link it in the description.
 Let's see if you can win too. I played against an opponent level of trout here just for reference.
 There are various settings and they instruct you how to build a bot give it a try.
 Next news, there's some discussion on Reddit about collab pro. Now we've reported previously
 that collab now has a new tier called collab pro plus, which gives you even more priority access
 than collab pro to GPUs. So now people are starting to notice that collab pro subscriptions don't
 always give them very good GPUs anymore. Now the thread is filled with various comments and and the
 general opinions of the different people are that yes, probably now that people have even more
 priority access, if you are just a pro user, you might get less access be collab is still one of
 the most cost efficient ways of running on a GPU on the planet and see a lot of people still do get
 good GPUs with collab pro. So it could just have been a problem of some kind of usage spike. So make
 of that as you will for what it's worth Google never promised to give you good GPUs, they simply
 promise to give you priority access. And that's about that. It's just important to be aware if
 you're considering collab pro, if you really rely on getting good GPUs all the time, then the collab
 pro plus might be for you. In a big collaboration between deep mind Waymo Google, Amazon, Facebook,
 AI and CAI lab researchers have used graph neural networks to do better traffic prediction.
 Specifically, they talk about ETA prediction estimated time of arrival, and that in real time.
 So the way they do it is they segment roads or paths in general into these segments. And then
 they use graph neural networks to integrate all live information to give you an accurate estimate
 of when you'll arrive. The interesting thing is they don't do that much crazy stuff with these
 graph neural networks, they have some tricks up their sleeves, like the use of meta gradients in
 order to control hyper parameters. But in general, it just sounds like a really solid engineering
 effort. And this is deployed in Google Maps, the statistics here show you by how much the ETA
 prediction accuracies have improved. And sometimes this is really staggering. So you see great
 improvements across the board, sometimes up to 50%. I'm not exactly sure what the metric here is,
 but 50% is a big number. Can we all agree? Yeah, good job. Okay, let's look at some helpful
 libraries and data sets. The first is Isaac gym, a high performance GPU based physics simulation for
 something similar with a library called Brax, these physics simulations, they now run directly
 on accelerators, such that you can do end to end research on the accelerators, you don't have to
 switch between devices all the time, which massively speeds up research in control and
 reinforcement learning. So this one's called Isaac gym, you can get it from Nvidia, which is
 a bit worrisome, but it looks very cool in these demonstrations, they have an evaluation and they
 also do train some policies on it. Now that is disturbing. But in general, it seems like if you
 are on GPUs, and you're trying to do reinforcement learning and control settings, this might be a
 good option for you. Also in the domain of physics, Nimble physics releases the differentiable human
 body model. So this apparently is a gold standard human body model that was used for simulation.
 And now this library made it end to end differentiable human body model isn't just one
 body model, but it is a configurable body model where you can sort of control the size of all
 the different parts and still get accurate simulations out of it. And now with it being
 differentiable, there's a whole new range of applications in research that become possible
 with this. If you're into biomechanics or differentiable simulations, I think you should
 check this out. LV is is data set for large vocabulary instance segmentation. And the goal
 here is to do instance segmentations on categories that are vast. So there are a lot of categories
 in these instance segmentation problems. And a lot of them don't appear very often, which is what
 they're referring to here as long tail. So some of these things you might have never seen before,
 we've seen a couple of these data sets, this one is especially challenging, because not only do you
 have to recognize what it is, you have to segment the instances. So here you can see examples of
 donut, pineapple, teacup, wine glass, wrath. I don't even know what a wrath is.
 Wrath. An arrangement of flowers, leaves or stems fastened in a ring and used for decoration,
 or for laying on a grave. Wonderful. And bird feeder. So there are even competitions and
 leaderboards to go along with that. If you're into this kind of stuff, check it out. Next is
 behavior by Stanford University. Behavior stands for benchmark for everyday household activities
 and virtual interactive and ecological environments. I had to bend a lot of stuff to come up with this
 acronym, but now it's called behavior. This is a data set for doing robotics in what are supposed
 to be relatively real life scenarios in virtual environments. What's interesting is the creation
 of this data set, the data sets are modeled after real scenes. So people analyze what they call
 everyday situations, and they try to recreate them with objects from wordnet, you can let AIs
 run in this simulated environment, but you can even do it yourself by VR. And the data set includes
 VR demonstrations of these things by humans. On top of that, it's not a fixed set of environments,
 but the environments are sort of described by a little bit of a grammar. So therefore, potentially
 infinite variations of these environments can be generated. Here we see a bunch of examples of this
 grammar. So for example, fish can be burnt or cooked or frozen, the microwave can be open or
 closed, the apples can be on top of the plate, and so on. The AIs are supposed to fulfill tasks in
 these situations. And I guess the goal here is to come ever closer to real life robots that actually
 help you in everyday life. The problem I have a little bit with these things is that even though
 the simulations are modeled after real life, they're still very, very far from it being limited to
 wordnet, I guess limits the amount of stuff you can put into a scene, the scenes are probably still
 kind of regular real life happens to be much more messy. So it's a bit of a question how useful this
 is for the end goal. But still, it looks like an interesting problem. And it's definitely a step
 into the direction of robots that interact with real life in a more realistic and competent manner.
 Next news, wired writes a new chip cluster will make massive AI models possible. Cerebros says that
 they've built a cluster that can run a neural network with 120 trillion connections. For reference,
 that's about 100 times more than what's achievable today. So if you want to build a large scale
 neural network today, your options are you can use TPUs, which are somewhat large if you use a
 cluster of them, or you can just stack GPUs together and connect them with some sort of
 infini band, both are not really optimal, as the accelerators themselves are relatively small,
 and they have to communicate a lot. Therefore, cerebrosis strategy is to build giant chips.
 Here you can see one in comparison to the largest GPU currently available. So these things are
 actually huge. Now the article details the various engineering problems that you have when you want
 to create such a large chip. Notably, the chip itself has to be much more error tolerant as you
 can't simply switch out one piece whenever it breaks like you could switch out a GPU. Now GPUs
 by no means are cheap, but compared to this thing, a GPU is certainly a bargain. Now they didn't stop
 at building single chips, they built an entire cluster of those chips. Now, at least as the
 article states it, they're just waiting for someone to come around and actually train a model on it.
 Their CEO says, so we know we can but we haven't trained a model because we're infrastructure
 builders and well, there is no model yet. If you have an idea of how to use 120 trillion connections,
 maybe give Andrew Feldman a call. The bigger question is a little bit of whether scaling
 individual chips is the correct approach, or if it's just better to stick with the smaller
 accelerators but improve our abilities to communicate and shard models, I guess only time will tell.
 Washington Post writes AI gave Val Kilmer his voice back, but critics worry the technology
 could be misused. Of course, critics always worry the technology could be misused. So the article
 details about this startup called sonatic that used recordings of Val Kilmer's voice in order
 to make an AI that can synthesize any text in his voice. Val Kilmer lost his original voice due to
 surgery after throat cancer. And this model essentially gives him back the ability to
 communicate in audio in the way that people remember him speaking. Now, this isn't a prosthetic,
 I think he still has to type the things he actually wants to say. But with some good
 brain interface, this could be an actual technology for people who lost their voice to be able to speak
 again in the future. The article also goes into a little bit of the possible economy that could
 result from this, namely that as a voice actor, I don't actually have to voice act for every project
 I do, I could simply sell my voice for other people to use as a sort of a licensing deal.
 The article also voices skepticism with respect to that and quotes Jay Britton, who is a voice
 actor that says, when I'm an actor, I get to decide whether I support the content, it would
 be a devastating thing to drop on a voice actor that your voice is out there saying things that
 you might not necessarily support. So the criticism is that someone could buy your voice for a license
 fee, and then have it say something that you disagree with. And rather than sounding the alarm
 bells about this, I think we should simply adjust to the fact that yes, this is a new possibility we
 have, but it's not a new thing by any means. I mean, stock photographs have existed for about as
 long as the internet has existed. And if you're a stock photograph model, then it's absolutely
 expected that your picture can be used for something you disagree with. That's just part
 of the deal. And no one faults these models if they appear on such a picture. So I think what
 needs to shift is not people not using this for various things, but simply our attitude towards
 what can be done with voice technology nowadays. So the last article for today, Forbes writes,
 can artificial intelligence give thoughtful gifts and exploration of the possibilities and limits of
 AI's humanity? This is a bit of a fluff piece for a company that uses AI to sort of recommender system
 gifts for people, which is interesting, because usually the media is rather critical of these
 recommender systems. However, in this case, it's sort of framed as the AI really understands you
 and knows what the good gift is in a moment and what a thoughtful gift is, and so on. And you know,
 in my opinion, they're probably not wrong. Like most gift suggestions could be made by an AI much
 better than you just kind of sitting there and coming up with something. So the startup is called
 Gosby for people who are interested, I just want to show you how these things might look about. So
 this is one of these little plugins that you can have as a YouTuber that does a little bit of
 analysis for you. It's not super useful, but I always enjoyed this feature right here where it
 gives you ideas for your next videos. And I'm not going to say that the quality is anywhere near or
 close to what Gosby is doing. I have not tested them. I just want to show a little bit that you
 get the feeling of what this might be like. So here are videos I could do. I've not looked at
 these yet. I get three per day because I'm cheap and I'm on the free version of this product.
 So we're going to look at them together. Devlog tech demo interactive game. Well,
 I don't think that's exactly for my channel. How to enable CNBC news alerts. I think it just estimates
 my channel as sort of like a tech channel or something like this. Maybe this is because I
 made how to bypass neural hash. Dismiss a revolutionary product for Apple users. This is
 definitely because I made the videos on neural hash now. And that was it. Now, usually, usually, I have
 to say they're a little bit better, they're a little bit into the direction of what my channel
 is actually doing. I guess I've just confused it with the recent videos about neural hash. But
 safe to say, if you're searching for gifts for people that you kind of know, a system like this
 might actually be a good place to go. It will probably suggest you a bit of generic gifts,
 maybe personalized a little bit to what you input about the person you want to give to. And that's
 all we need. Okay, this was already it for ml news. As you can see, really nothing happened this week.
 If you're an ML researcher, if you're an industry, or even if you're just interested, please make
 something happen for next week. Please, I need content is very important. Yeah, all right,
 I'll see you next week. Bye bye.