DEEP LEARNING MEME REVIEW - Episode 1
deep learning memes meme review artificial intelligence review discussion reaction ai machine learning ml dnn gpu deep neural network ml memes deep learning memes machine learning memes funny gpus classifier hinton turing award bert xlnet optimization error rate culture community research
The wait is finally over! Antonio and I discuss the best, funniest and dankest memes of the machine learning world. Join us for a laugh!
 What? You haven't done memes before? No. Don't you have this show on YouTube when you review memes and stuff? No. You haven't? What is that? I think that's an entirely new concept. We're just gonna steal this concept from PewDiePie. Okay. But first actual meme review deep learning theme. Welcome. I'm joined by Antonio who is a bit of a memester himself. And today we're just gonna kind of look at deep learning memes. Nice. Let's jump in. So. Oh no, that's a paper. That's the meme. That is code. Okay. Being a DL researcher is not stress at all. 26. That is incredible how he says like, but now, oh, I already, I always knew that it worked. Of course. Yeah, yeah. There was no other way. There was no AI winter or anything. This was, this was always, Tep Hinton is so cool. Yeah. All right. Nice. Next meme. Next meme. I guess my brain is just really big. Oh, what else is really big? I thought you never asked. I agree. Gradient update on the edge of a really steep cliff. Big gradients are always good. I mean, look at that. Why wouldn't you want to land over there? Yeah, yeah, it's perfect. It seems much more interesting than down there. So perfect. I guess it's an, oh, minus seven over four. Wow. That's a small epsilon. Very small epsilon. Yes. Almost optimal. Crazy. Take the scientist when he sees a new problem. Classifier fit. This is, this is the old days. The old days, yes. Of scikit-learn. It still works pretty well. No, we must use deep learning for everything. Oh, sorry. No, no, sorry. Let's just look at the next meme, please. I don't know this template. This is a cool template. Yeah, it's a good template. NLP researchers BERT and then XLNet. What is XLNet? So XLNet is BERT just trained differently. Okay. And it costs like 10 times more to train it. Okay. And it's a bit better. How much does it cost electricity? Why? So people have calculated this to train one XLNet costs about 250K. It's insane. But does it work 1% better? It's like, that is like five PhD students. That's almost as good a language model as XLNet. And how much is better than BERT? A bit. A bit? Oh, a bit. A bit. That's all that counts. Wow. State of the art. Search archive for preprint. Search GitHub for code. Ask random idiots on Facebook. Me. Go. Let's go, Burbus. Go. In some ways, actually, it is simpler to publish something on archive and not being completely like people just saying, oh, you're an idiot and stuff like that. Because we've probably got unnoticed. Probably gets unnoticed, right? Yeah. On Facebook, it doesn't get unnoticed. Yeah, that's a real peer review. Exactly. If you are in a very good meme page on Facebook about deep learning, you're going to get wrecked. Yes, exactly. That's not going to happen. This software engineer designed a chat board to chat with his girlfriend while he's busy at work. However, the girl eventually got suspicious over the speed she was receiving messages from her boyfriend. Modern problems require modern solutions. But also like pretty good chat board. Got suspicious with the timing. Yeah. And now for the actual content. Well, what fashion companies try to sell us. What we really want. Fashion MNIST. Fashion MNIST is the new cool thing. So cool. Does anyone use it? I use it. Cool. By the way, I found a huge saddle point. Nice. MNIST. Wow. Huge saddle. It is not very MNIST. Where is it? Places. How much accuracy do you get on fashion MNIST? Like as MNIST. Because it's so easy. Like it's basically as MNIST. I don't know. I'm not a fashion person. So I don't know what to call this. What? What? This? This is a pants sweat. Me and the boys after using dropouts. Me and the boys. Also, I don't know where they come from. Where do they come from? I don't know. Some comic. They are so, so beautiful. Are you still watching machine learning tutorials on YouTube? Did you check my internet history? Why can't you watch porn like a normal child? I'm addicted. Andrew NG? I'm addicted. What is this Andrew NG? I must use more Keras code. Yes. Please. What is wrong with you? Because Andrew NG, boy, I don't know. But I understand that it makes you comfortable. And respected and loved. He does. He says it's okay if I don't understand everything. Whereas in porn it's completely different. It's not okay. I'm really with my notes trying to follow the plot. Wait, what was the plot? Why? When your binary classifier predicts 51% accuracy. It ain't much, but it's honest work. That's what you want to get. Better than random. Exactly. Just change your random seed until you get 51%. Your method works. Yes, exactly. And also like, you know about in finance, but it's actually state of the art, right? In what? In finance. Prediction of the last, if you have a profit time series, if you predict the next time point as the last time point, that's probably the best thing you can do. I'm going to switch my PhD topic. Yeah, and also like some people with their fancy methods do worse. Because they say, yeah, because of this and that and then it's just to be, and then... Because it's just like, you just predict whatever was there and you're good. Okay, next meme. Next meme. Deep learning research rather than video. Cheap view, cheap view, cheap view. Oh, damn. Too bad I don't use cheap views. I will start though. You know this Math Lab Deep Learning toolbox? Yeah. Recently they introduced neuronal stuff with the networks and the graphs, which is basically as the brain. Yeah. And so basically you can learn stuff with Math Lab. With Math Lab? Exactly. Wow. Exactly. Can you learn to uninstall it? I look like all you need. No, you don't look like an Envy that hide and not. Because that's what we really want. Exactly. Me, I sure hope my model's error rate isn't super high. Error rate. Sorry. So sorry. Optimization is hard. Yeah, it's hard. Just hard. You do as fancy methods and then there's SGD. Yeah. That beats you every time. Yeah. Bastard. Me and the boys about to receive the Turing Award. Me and the boys. So fancy. Yeah. Look at them. It's probably thinking about capsules. Yeah. Oh, oh. But wasn't it like two years ago? Yeah. Yeah. What is the state of that? It's still the same. He's still thinking about it. Okay. I didn't get what capsules are. To be honest. Well, they sort of are different. Oh, they're different? Yeah. Okay. Yeah. They're not like the same. Ah, I see, I see. So that means that they work in another way. Yes, but only kind of. So to do other things. Sort of. Sort of. I see. But then they do it on the same tasks. Ah, I see. No, they're like trying to abstract concepts into these capsules and then the capsules can route the information to other capsules dynamically. Yeah. Does it work? No, I don't think so. Right? Kind of. It kind of works. Yeah. Ah, why are people... Okay. Like you can make it do something. Okay. Capsules. Capsules. And like meme. My desires are unconventional. So show me. RTX 2060, 2070 and 2080. Ah, yeah. No, don't let me look at them. I want them so badly. I just can't. Use a transformer instead of an LSTM. I have failed you. You again. You again. No. RNNs must come back. Yes, exactly. They're too touring complete. Not. Assistant, remember this location. Okay, I remember that. What did I ask you to remember? I remember what you told me. This location. What does this location mean? Visitor top results. Assistant, machines are about to take over the world. Definitely. This is this intelligence. Yeah, exactly. Yeah, we must be very, very careful. Also with jobs and stuff. What? What? You finished the memes? Not yet. There's one more. So I have to preface this. So basically this is a... So the robot is supposed to get the ball to the target. And in one setting it has a reference motion of a human doing the same thing. So it learns to learn from that. And then for comparison, there is no reference motion. And it just learns from scratch. So first is with and three times and then without. With reference motion. Nice. Nice, yeah. Wow. And now without. Get the ball there. Get it there. Get it there. It's so cute. Yes, yes. We are AI Doom. Yes, done already. The damage is done. Yeah, I mean I can see an army of robots. Their arms. Their guns. They just take the bullet and go like... All right, this was it for episode one of Deep Learning Meme Review. Thanks so much for being here with us. And have a good time.
 What? You haven't done memes before?
 No.
 Don't you have this show on YouTube when you review memes and stuff?
 No.
 You haven't?
 What is that? I think that's an entirely new concept.
 We're just gonna steal this concept from PewDiePie.
 Okay.
 But first actual meme review deep learning theme.
 Welcome.
 I'm joined by Antonio who is a bit of a memester himself.
 And today we're just gonna kind of look at deep learning memes.
 Nice.
 Let's jump in.
 So.
 Oh no, that's a paper.
 That's the meme.
 That is code.
 Okay.
 Being a DL researcher is not stress at all.
 26.
 That is incredible how he says like, but now, oh, I already, I always knew that it worked.
 Of course.
 Yeah, yeah.
 There was no other way.
 There was no AI winter or anything.
 This was, this was always, Tep Hinton is so cool.
 Yeah.
 All right.
 Nice.
 Next meme.
 Next meme.
 I guess my brain is just really big.
 Oh, what else is really big?
 I thought you never asked.
 I agree.
 Gradient update on the edge of a really steep cliff.
 Big gradients are always good.
 I mean, look at that.
 Why wouldn't you want to land over there?
 Yeah, yeah, it's perfect.
 It seems much more interesting than down there.
 So perfect.
 I guess it's an, oh, minus seven over four.
 Wow.
 That's a small epsilon.
 Very small epsilon.
 Yes.
 Almost optimal.
 Crazy.
 Take the scientist when he sees a new problem.
 Classifier fit.
 This is, this is the old days.
 The old days, yes.
 Of scikit-learn.
 It still works pretty well.
 No, we must use deep learning for everything.
 Oh, sorry.
 No, no, sorry.
 Let's just look at the next meme, please.
 I don't know this template.
 This is a cool template.
 Yeah, it's a good template.
 NLP researchers BERT and then XLNet.
 What is XLNet?
 So XLNet is BERT just trained differently.
 Okay.
 And it costs like 10 times more to train it.
 Okay.
 And it's a bit better.
 How much does it cost electricity?
 Why?
 So people have calculated this to train one XLNet costs about 250K.
 It's insane.
 But does it work 1% better?
 It's like, that is like five PhD students.
 That's almost as good a language model as XLNet.
 And how much is better than BERT?
 A bit.
 A bit?
 Oh, a bit.
 A bit.
 That's all that counts.
 Wow.
 State of the art.
 Search archive for preprint.
 Search GitHub for code.
 Ask random idiots on Facebook.
 Me.
 Go.
 Let's go, Burbus.
 Go.
 In some ways, actually, it is simpler to publish something on archive and not being completely
 like people just saying, oh, you're an idiot and stuff like that.
 Because we've probably got unnoticed.
 Probably gets unnoticed, right?
 Yeah.
 On Facebook, it doesn't get unnoticed.
 Yeah, that's a real peer review.
 Exactly.
 If you are in a very good meme page on Facebook about deep learning, you're going to get wrecked.
 Yes, exactly.
 That's not going to happen.
 This software engineer designed a chat board to chat with his girlfriend while he's busy
 at work.
 However, the girl eventually got suspicious over the speed she was receiving messages
 from her boyfriend.
 Modern problems require modern solutions.
 But also like pretty good chat board.
 Got suspicious with the timing.
 Yeah.
 And now for the actual content.
 Well, what fashion companies try to sell us.
 What we really want.
 Fashion MNIST.
 Fashion MNIST is the new cool thing.
 So cool.
 Does anyone use it?
 I use it.
 Cool.
 By the way, I found a huge saddle point.
 Nice.
 MNIST.
 Wow.
 Huge saddle.
 It is not very MNIST.
 Where is it?
 Places.
 How much accuracy do you get on fashion MNIST?
 Like as MNIST.
 Because it's so easy.
 Like it's basically as MNIST.
 I don't know.
 I'm not a fashion person.
 So I don't know what to call this.
 What?
 What?
 This?
 This is a pants sweat.
 Me and the boys after using dropouts.
 Me and the boys.
 Also, I don't know where they come from.
 Where do they come from?
 I don't know.
 Some comic.
 They are so, so beautiful.
 Are you still watching machine learning tutorials on YouTube?
 Did you check my internet history?
 Why can't you watch porn like a normal child?
 I'm addicted.
 Andrew NG?
 I'm addicted.
 What is this Andrew NG?
 I must use more Keras code.
 Yes.
 Please.
 What is wrong with you?
 Because Andrew NG, boy, I don't know.
 But I understand that it makes you comfortable.
 And respected and loved.
 He does.
 He says it's okay if I don't understand everything.
 Whereas in porn it's completely different.
 It's not okay.
 I'm really with my notes trying to follow the plot.
 Wait, what was the plot?
 Why?
 When your binary classifier predicts 51% accuracy.
 It ain't much, but it's honest work.
 That's what you want to get.
 Better than random.
 Exactly.
 Just change your random seed until you get 51%.
 Your method works.
 Yes, exactly.
 And also like, you know about in finance, but it's actually state of the art, right?
 In what?
 In finance.
 Prediction of the last, if you have a profit time series, if you predict the next time
 point as the last time point, that's probably the best thing you can do.
 I'm going to switch my PhD topic.
 Yeah, and also like some people with their fancy methods do worse.
 Because they say, yeah, because of this and that and then it's just to be, and then...
 Because it's just like, you just predict whatever was there and you're good.
 Okay, next meme.
 Next meme.
 Deep learning research rather than video.
 Cheap view, cheap view, cheap view.
 Oh, damn.
 Too bad I don't use cheap views.
 I will start though.
 You know this Math Lab Deep Learning toolbox?
 Yeah.
 Recently they introduced neuronal stuff with the networks and the graphs, which is basically
 as the brain.
 Yeah.
 And so basically you can learn stuff with Math Lab.
 With Math Lab?
 Exactly.
 Wow.
 Exactly.
 Can you learn to uninstall it?
 I look like all you need.
 No, you don't look like an Envy that hide and not.
 Because that's what we really want.
 Exactly.
 Me, I sure hope my model's error rate isn't super high.
 Error rate.
 Sorry.
 So sorry.
 Optimization is hard.
 Yeah, it's hard.
 Just hard.
 You do as fancy methods and then there's SGD.
 Yeah.
 That beats you every time.
 Yeah.
 Bastard.
 Me and the boys about to receive the Turing Award.
 Me and the boys.
 So fancy.
 Yeah.
 Look at them.
 It's probably thinking about capsules.
 Yeah.
 Oh, oh.
 But wasn't it like two years ago?
 Yeah.
 Yeah.
 What is the state of that?
 It's still the same.
 He's still thinking about it.
 Okay.
 I didn't get what capsules are.
 To be honest.
 Well, they sort of are different.
 Oh, they're different?
 Yeah.
 Okay.
 Yeah.
 They're not like the same.
 Ah, I see, I see.
 So that means that they work in another way.
 Yes, but only kind of.
 So to do other things.
 Sort of.
 Sort of.
 I see.
 But then they do it on the same tasks.
 Ah, I see.
 No, they're like trying to abstract concepts into these capsules and then the capsules
 can route the information to other capsules dynamically.
 Yeah.
 Does it work?
 No, I don't think so.
 Right?
 Kind of.
 It kind of works.
 Yeah.
 Ah, why are people...
 Okay.
 Like you can make it do something.
 Okay.
 Capsules.
 Capsules.
 And like meme.
 My desires are unconventional.
 So show me.
 RTX 2060, 2070 and 2080.
 Ah, yeah.
 No, don't let me look at them.
 I want them so badly.
 I just can't.
 Use a transformer instead of an LSTM.
 I have failed you.
 You again.
 You again.
 No.
 RNNs must come back.
 Yes, exactly.
 They're too touring complete.
 Not.
 Assistant, remember this location.
 Okay, I remember that.
 What did I ask you to remember?
 I remember what you told me.
 This location.
 What does this location mean?
 Visitor top results.
 Assistant, machines are about to take over the world.
 Definitely.
 This is this intelligence.
 Yeah, exactly.
 Yeah, we must be very, very careful.
 Also with jobs and stuff.
 What?
 What?
 You finished the memes?
 Not yet.
 There's one more.
 So I have to preface this.
 So basically this is a...
 So the robot is supposed to get the ball to the target.
 And in one setting it has a reference motion of a human doing the same thing.
 So it learns to learn from that.
 And then for comparison, there is no reference motion.
 And it just learns from scratch.
 So first is with and three times and then without.
 With reference motion.
 Nice.
 Nice, yeah.
 Wow.
 And now without.
 Get the ball there.
 Get it there.
 Get it there.
 It's so cute.
 Yes, yes.
 We are AI Doom.
 Yes, done already.
 The damage is done.
 Yeah, I mean I can see an army of robots.
 Their arms.
 Their guns.
 They just take the bullet and go like...
 All right, this was it for episode one of Deep Learning Meme Review.
 Thanks so much for being here with us.
 And have a good time.