This AI Tool Might Make Learning RIDICULOUSLY Easy

Let me start this video off by asking you a question. What is the easiest way to learn new information? Well, it's sort of a loaded question because everybody learns a little bit different. And of course, if that pink squishy thing in your head called your brain prefers some information, is intrigued by it, it's gonna be a little bit easier to learn. You might be thinking, how the hell does this relate to today's video? Well, I'll tell you. Today we're checking out another Google experiment, this Illuminate, and you can see right up at the top. Transform your content into engaging AI generated audio discussions and immediately you're gonna start thinking, hey, this is pretty similar to another Google project or Google experiment that I saw you talk about recently. And you'd be right, experiencing a little bit of match ception here. NotebookLM is a pretty cool experiment by Google specifically for note taking, creating study guides and asking questions for through text to an AI that has digested an unholy amount of information. It could also generate podcasts though, which were pretty cool. They were little insightful discussions hosted by two people surrounding whatever topics that you put into NotebookLMS sources. I think the podcast feature is probably the coolest part about NotebookLM and why I think Illuminate is so great is because essentially this is the same thing as NotebookLM, but it's all about making the podcasts and you have more options for actually controlling how that podcast is generated. So this is like my favorite Notebook alum feature turned into its own thing. Anyways, back to our original opening question. I think that Illuminate can actually become a very, very effective tool for teaching and learning because we make simple podcast discussions which is a very natural and easy way to intake information as a human, at least for me. I don't know about you guys, but we can actually customize all of the information. We can tailor and actually prompt our generated podcast specifically for us, tailored for a general audience or a particularly you audience. You can set the amount of time and the tone and we're really gonna push the prompting here to its limits and see what we can get up to. Before we get into that though, I am gonna have to do a little bit of self promo. This right here is probably my favorite corner of the web and it is my Discord server and it is filled to the brim with AI enthusiasts such as yourself. You can see we have many chann here from the latest AI leaks to AI prompt helping to sharing your creations. And I think I'm going to start doing my favorite creation of the video. So every video I'm going to share something that the community has made that I found particularly good. And today I'm kind of torn between two generations. We've got this one up here by yikes562 and this user on my Discord made a nice Mini Max generation of Freddy Krueger just enjoying a lovely Oktoberfest. He raises his glass of IPA beer and he's smiling and you can see it's definitely Oktoberfest. Look at the background. Absolutely lovely. He is definitely having one too many drinks at this party. And there's also this one by Schizoid, another Mini Max generation. This is Donny JJ as a DJ absolutely bumping some tunes and I thought that this one again was just a actually genuinely, really impressively coherent generation. And I like the fact that he does a dance towards the end. He's really feeling the music. So yeah, a little bit of a shameless plug for my Discord server. The people in there are awesome though, and they need more friends, so join at the link below. So back to Google's Illuminate Experiment. As you can see, we've only got three tabs here. It's not too fully fledged, it's sort of. Again, just like this little experiment thing. The home tab is filled with a bunch of pre generated little discussions. They call them audio discussions. They're more like podcasts. I'm pretty sure it's using the same technology that NotebookLM uses to generate its podcasts, but there's a lot of stuff that's all about AI, so a lot of these might genuinely be interesting to you. And again, I think it actually kind of shows off. This could be a fantastic learning tool, not just something entertaining. Or maybe it could be both. Here, let's actually demo view this. Chain of Thought Prompting Elicits Reasoning in Large language models if you don't know what chain of thought prompting is, it's basically the type of thing that the new OpenAI 01 models do.

Here's a paper titled Chain of Thought Prompting Elicits Reasoning in Large Language Models. Let's talk about it.

This is interesting. It's a prompting method for eliciting multi step reasoning from language models.

And again, you're already going to notice the similarities to the original Notebook podcast generation. We've got two hosts, a male and a female, and the voices are pretty darn similar.

The idea is to show the model a few examples of a task with intermediate reasoning steps, and this can improve performance quite a bit compared to just showing the model input output pairs.

This is unlike how we usually prompt language models, right?

Right. In a standard prompting scenario you just provide input output examples. Like for a translation task you'd give the model some examples of sentences in one language and their translations in another. The insight here is that for tasks that require reasoning, we can give the models examples of how to think through.

Through the problem and see how that would help the model break down a complex problem into smaller, more manageable steps. Can you elaborate on the types of tasks that improved with chain of thought prompting?

They tested on several benchmarks like math, word problems, common sense reasoning, and it's like a real podcast tasks and saw a pretty good boost in performance.

One thing that you can do is actually pause at any moment of course, but ask a question, what are the limitations of this method? I actually like that as a question and you can submit it and boom. Similar to NotebookLM, it gives you a quick text response. It's interesting. This is almost like NotebookLM from another universe in some ways it's a different approach with the same core ideas at the heart. It also has this little thing where it's like oh yeah, read this out to me and I would love for it to read out to me actually, as if it was a the two podcasters replying to me. But that doesn't seem to be something they've implemented just yet. Again, this is an experiment, kind of like a beta alpha thing. Seems to give us a pretty decent answer. Chain of thought emulates human reasoning. It doesn't necessarily mean the model is actually reasoning. Fair enough. Manually adding chains of thoughts a few shot examples is easy, but it could be costly for fine tuning. OpenAI seems to have figured it out. Third, there's no guarantee of correct reasoning paths which could lead to both correct and incorrect answers. Yeah, I think that was pretty good. Anyways, we could continue to listen on here.

Math word problems especially benefited. The models were actually able to generate a chain of thought that resembles a single step by step solution.

That's a good result considering math is typically a challenging area for language models.

Yep, exactly. So yeah, cool and all they've got a bunch of these demos, but I want to get to creating my own thing. Oh, and by the way, they also have books over here in the examples as well. We might check some of these out because they could be pretty good. Like we've got Frankenstein over here. I guess that's sort of in the the theme of the time of the year, right? As I said, generating is where things become More interesting, only available for select PDF papers on select domains. So they're kind of limiting what you can put in for information. But that doesn't mean we are limited by our generation settings. I think this might be a nice place to start. This is a paper about large language models being able to, in context, learn multiple tasks in superposition. Sounds really boring and complicated, but let's turn this into something easy to understand. Here is the PDF link to the paper. Simply copy this link. This comes from ar XIV.org which should be a decent place and we will add it in. Alright, well, we're gonna create a discussion tailored for. Can we just say, like stupid audience? Is that. Do they let me do that? Knowing Google, probably not. Enthusiastic, but average Joe audience. Each field must be less than 24 characters. Alright, fine, an enthusiastic average Joe audience. We'll just stick to a medium duration. The tone should be semi professional. No, the tone should be excited and dramatic. If I'm gonna learn, I want the people te me to also be into it. You know what I mean? Alright, let's generate. And by the way, it seems like it definitely is free, but it's limited to 20 generations per day, which for a podcast style thing like this, I think that's definitely enough. Apparently it's only gonna take like 30 seconds to generate the audio on this paper. And I think what we'll do is we'll compare the way that I would traditionally learn about this paper, which would be, you know, this Twitter post and this nice thread made by Demetrius. And you know, I'm sure he put a lot of effort into this thread, but let's be honest, it's a wall of text versus something that I can listen to in a custom tailor podcast format. All right, let's take a listen to our generated podcast.

This conversation is powered by Google illuminate. Check out illuminate.google.com for more.

Whoa.

Buckle up, folks. Today's discussion is about large language models. LLMs, those amazing AI things that write poems, translate languages, and even seem to think. But get this. Researchers claim LLMs can do multiple tasks at the same time during a single brain tickle. They call it task brain superposition. Sounds like science fiction, right? Let's hear what this is all about.

It's mind blowing, isn't it? Imagine an LLM simultaneously adding numbers, translating languages, and even doing both at once, all in one go. This paper shows that's not just possible. It's surprisingly common across different LLMs. They tested it with various tasks like addition in multiple languages, capitalizing Words and even more complex stuff.

Wait, multiple tasks at once? Like, my brain can barely handle one task at a time. How does an LLM even do that? Is it some kind of magical multitasking superpower?

Not magic, but incredibly clever programming. The key is in context learning. The LLM learns from examples given in the prompt. The researchers found that if you give it examples of different tasks mixed together, it can somehow figure out how to do them all simultaneously.

So it's like showing a kid examples of addition and then suddenly asking them to translate the answer into French, and they just do it.

Exactly. And it gets even weirder. They even trained an LLM to learn one task at a time, and still it could do multiple tasks at once when given a mixed prompt. It seems like this ability is built into the very architecture of these models.

This is blowing my mind. So it's not just about training. It's about the model's inherent ability. Like it's a hidden talent waiting to be unleashed.

Dang. Okay, I just gotta stop for a moment and talk about how realistic the podcast sounds. I went over this when we talked about Notebook Element when I did a video about that, but it sounds like real people talking. I mean, there's a little bit of AI weirdness every once in a while, but they will stutter. They will have a fluid, very natural conversation. It sounds just like a podcast. In fact, it must be trained on podcasts. Right. Even though if you go to the generate tab here, it says that it's optimized for computer science topics, and I just don't know what that means. But it still sounds like a natural conversation. It very much can dumb it down, so to speak, for people who aren't actually in the research field.

The paper suggests that transformers, the underlying architecture of many LLMs, are inherently capable of this task superposition. They even proved it mathematically. It's not.

Okay, but now I have a question. And hey, there's the question button. We'll ask a very, like Joe Schmo question. If transformer architecture is naturally able to complete multiple tasks at once, shouldn't we also be capable of this? Transformer architecture is similar to a human brain. Correct. I like the answer here. While the transformer architecture is inspired by the human brain, it's a simplified model. The human brain is far more complex and has evolved over millions of years. Handle a vast array of tasks simultaneously. That's a really good point. Well, we're learning from this paper, turned into a podcast discussion, that large language models can do multiple tasks at once. And it is more impressive in quotes than a person which wouldn't be able to do math and then translate to French at the same time. The human brain is still doing quite a few things when you think about it. You are absolutely always multitasking. You're building new cells, you're maintaining your overall health. Your heart's beaten, you're breathing, you're seeing, hearing. There's a lot going on in this pink meat sack up in your skull. So that's actually a really, really solid point. Dang. I kind of wish I had something like this back when I was at university because I could have learned so much more.

Bug.

It's a feature, a mathematical proof. Okay, I'm officially impressed. But how does it actually work inside the LLM's brain? Is it like having multiple little AI agents working together?

The researchers looked at something called task vectors. Think of them as internal representations of different tasks within the LLM. They found that when the LLM does multiple tasks at once, it's essentially combining these task vectors. It's like mixing paint colors to create a new shade.

Mixing paint colors. I get it.

That's crazy, actually.

So bigger LLMs with more paint colors can handle more tasks simultaneously?

Absolutely. The study showed that larger models are better at this superposition thing. They can handle more tasks at once and are more accurate in their answers.

So bigger is better, huh? But is there a limit? Can we keep adding tasks forever?

There's likely a limit, but we don't know what it is yet. The more tasks you add, the more complex the problem becomes for the LLMs.

That's the crazy thing about machine learning though, right? Is that we're building these, like, human brain inspired, specific task models. And once we train them, we have to like, learn what their limits are. It's. It's this exploratory side that is just mind blowing because it's all going inside of a computer. It's. It's like the computer is something that we created, yet it's teaching us. We have to learn about it. So crazy.

It's like trying to mix too many paint colors. You end up with mud.

Muddy answers. That's a great analogy.

So what are the actually this task.

Superposition, can we use it to build superpowered AIs?

That's the big question. The current decoding methods used with LLMs aren't great at handling this multitasking. It's like having a super fast car, but only knowing how to drive it in reverse.

A super fast car in reverse. Sounds frustrating. So what's the next step how do we unleash the full potential of this multitasking ability?

Researchers are working on new decoding strategies to fully exploit task superposition. It's a challenging problem, but the potential rewards are huge.

It's just crazy because we're talking about something that large language models do, and we're also, you know, using large language models to understand large language. I mean. Oh God, the meta recursiveness of AI technology always blows my mind.

Imagine LLMs that can solve complex multifaceted problems with incredible speed and accuracy.

That's the future I want to live in. So, to summarize, LLMs can surprisingly do multiple tasks at once. This ability is linked to their architecture. Larger models are better at it, and we're still figuring out how to fully harness this power.

Exactly. It's a fascinating.

What a great breakdown. Just like Bravo man, that's crazy. See, like I said, it gave us the information in a very easily digestible way. Again, absolutely zero offense to Demetrius here, but that is easier to understand than reading text, at least for me. And I was able to custom tailor it for my stupid brain that might not understand some of these specific words like superposition. And it's nice because task vectors I understand and now know what that means. Whereas if I was just exploring this thread, I wouldn't know necessarily what a task vector is. But at least we can see them. And I think that's maybe one of the next steps for something like this, especially if you want to focus more on the learning side of things. It being able to go through the PDF by itself and select specifically what it's talking about and actually bring it up for you on screen and show you as it's talking to you. I think that would be pretty darn cool. Definitely get to do some more exploring and generating and pushing of this illuminate Google experiment. But first, I do have a message from today's sponsor. I'm pretty sure at some point all of us have thought about making our own website. Whether you're just trying to showcase your skills, start a brand new side hustle, or straight up share your passion with the world, a solid online presence is never a bad idea. Here's the good news. It's super easy with Hostinger. They take what could be a very complex and rigorous process and make it straight, straightforward and accessible. Not only does a website need to be easy to create in the first place, but it also has to be scalable for a large business. Hey, what if that side hustle turns into something more with hostinger your personal project can quickly turn into a scalable business. Their business plan includes a powerful AI powered website builder as well as e commerce tools to help you get off the ground running. If you're interested in starting, make sure you check out Hostinger. But don't forget to use my code vidpro to get 10% off@hostinger.com VidPro Let me show you how easy the process is. First we'll choose the business plan which has all the best tools for customization and growth. Check this out. Enter your brand name, select your website type and describe your business. In seconds, Hostinger's AI Website Builder generates you a fully working website and from there it's all a drag and drop editor. They've actually got some really cool other AI tools as well. One of my favorites being the AI Heat map. This helps you understand where visitors are clicking the most on your website and this allows you to effectively optimize your website to be as engaging as possible. So yeah, huge thanks to Hostinger for sponsoring today's video. Don't forget to use the code VIDPRO for 10% off. Get yourself online quickly and easily with Hostinger and back to your regularly scheduled content. Huge thank you to you guys for sticking through sponsors with me. They literally make this channel actually a possibility for me to pursue full time. So I greatly appreciate both them and you. Alright now I want to do another paper but I think we're gonna devoid away from the whole machine learning computer science sector. It says it's optimized for computer computer science topics but I want to push it. Let's go way out there. Okay guys, I found a paper called Sorting Wild Pigs. It's still on AR XIV from Cornell University so it should be able to pull this up. Oh my God. It's not in English. Well, this should. It's not a very long paper either. Should be a pretty good little test for this thing. Let's paste that in. Okay, Add it. This is tailored for English. I want it in English. Pig enthusiast audience. We'll do a short duration since this is a shorter paper. Excited and dramatic. I think we'll leave that for the tone. Let's see if it can pull this off. Alright guys, the moment of truth.

This conversation is powered by Google illuminate. Check out illuminate.google.com for more. Welcome pig enthusiasts. Today's discussion is about a truly oinking star going paper Trier de Cochens Sauvage Sorting wild pigs. Imagine a farmer, Shara needs to sort her wild pigs by Weight using only a rickety old balance scale. This isn't just about weighing pigs, it's about efficient algorithms. What's the core challenge here?

The core challenge is balancing efficiency with robustness. Chajara has a limited resource, a potentially breakable scale, and each weighing is costly. The paper tackles this by exploring any time algorithms which provide a good estimate of the sorted list even if interrupted mid process. It's not just about getting the final sorted list, it's about getting a good star sorted list quickly, even if the process is stopped early.

So it's not just about the final star sorted list of pigs, but also about getting a reasonably sorted list along the way. Star. That's fascinating. What kind of algorithms are being considered.

The paper Fascinating for the pig enthusiasts.

What makes Corsort's estimation so good?

Corsort uses a sophisticated scoring system based on the comparisons made so far.

Alright, we're learning about a scoring system about pigs. Not something I thought I was going to be doing today, but here we are. Okay. It seemed to handle it pretty well though in the beginning there was a little bit of like weird AI sounding stuff going on with the generated voice, but it did it in English and it translated and understood the paper pretty well it seems. So I'm actually impressed by that. That was an impressive result. It just turned out that the pig paper was maybe a little bit more boring than we initially thought. Alright, we gotta find like a really banger paper now. Okay, Side note, I was looking for the most banger papers we could possibly feed into this thing and some of these papers are insane. These are all like articles that are real. First off we have will any crap we put into graphene increase its electrocatalytic efficiency or effect rather. Sorry again, this is a real paper. January 14, 2020. It just gets worse from here though. We've got the survival time of chocolates on hospital wards, covert observational study. We've got an in depth analysis of a piece of shit distribution of schizo, soma, mansani and hookworm eggs in human stool. But like why did they title it that? This one's all the way back from freakin 2012. We've also got chicken, chicken, chicken, chicken, chicken. And this can't be a real research paper obviously, but I want to try to feed it into this just to see what happens. And then we have analysis and qualitative effects of large breasts on aerodynamic performance in wake of a Miss Whatever dragon maid character. So this is computational fluid dynamics, basically to see if anime characters with large breasts are More aerodynamic. Real research papers, all of these. So here we are. First up, I think we have to try this chicken research paper. Will it take this? Oh, no, it doesn't like it. Oh. Only these domains are supported. Good thing Notebook Elem exists because it lets you put anything in into it. Oh, God. I think we might actually have to take a detour in this video to explore these research papers inside of NotebookLM, which, I mean, it's basically the same model powering it under the hood. I don't know why they've limited to this to like four different URLs. Kind of aggravating. And probably the first thing I would change about this. Look, guys, when life gives you lemons, you make lemonade, Alright? I just discovered these hilarious research papers and now we just have to know. It did seven minutes about the fake, like, chicken thing.

Alright, so get ready because today we are diving into a research paper. And I'll be honest, this one is a real head scratcher.

Oh, really? How so?

Well, for starters, the title is Chicken.

Chicken, Chicken, Chicken, Chicken.

Wait, hold on. Did you say Chicken, Chicken, Chicken.

That's right. Chicken, Chicken, Chicken, Chicken, Chicken. Not exactly your typical academic title.

And who wrote this, if you don't mind my asking?

This masterpiece of. Well, you decide. Comes from the brilliant mind of Doug Zonker.

Okay, and what's the paper about? Is it about actual chickens?

I wish. No, this paper takes the saying limited vocabulary to a whole new level. We're talking about a paper where the word chicken reigns supreme.

So let me get this straight. The entire paper is just the word chicken repeated over and over again?

Well, not exactly over and over again. It's more like they've taken the basic structure of a research paper. You know, sections, headings, citations, even a bibliography.

All right, explaining the joke. Definitely makes it no longer funny. I also put in the chocolate one.

Ever wonder, like, when someone brings in treats to work?

Yeah.

They vanish so fast. Like, is there something like. Is there something about, like a shared environment where people just, like, can't control themselves?

Absolutely.

Well, there's a study that we're looking at. It's called the Survival Time of Chocolates on Hospital Ward. Covert observational study. And it was published in the British medical journal.

Glitching Out.

The male voice. We're diving deep into this. Okay, so what did they actually do in this study?

Well, they. They planted boxes of chocolates.

Okay.

On different hospital wards across the uk.

Interesting.

Yeah. And they meticulously tracked every single chocolate.

Oh, wow.

To see how long they lasted. And who snagged them.

So like undercover chocolate detectives? Basically.

Yeah.

So they had these.

Oh my God. Alright, this paper is about jailbreaking LLMs and it comes from an approved Google source. So let's toss this in. Alright, Paste the paper. No, we're not doing the pig enthusiast this time. AI jailbreaking audience. Short duration tone should be. You know what, let's try comedic yet informable. You know, a comedic yet informed tone. One thing I'm definitely noting here is that the podcasts generated by Illuminate are much, much quick than the ones generated by Notebook Alum. I don't know if that's due to server constraints or. It really does seem to be like a different fine tuned version of that podcast model. That base podcast model that Google is working with.

This conversation is powered by Google illuminate. Check out eliminate.google.com for more. Welcome back to the AI jailbreaking discussion. Today we're tackling a spicy paper at TNG ZG enhancing jailbreaking attacks on LLMs with attention manipulation. This sounds like something straight out of a cyberpink movie.

What's the cyberpunk?

The core idea is pretty clever. Existing methods for jailbreaking tricking large language models LLMs into generating harmful content mostly focus on manipulating the input text to maximize the probability of getting the desired malicious output at. TNGCG takes a different approach. It manipulates the attention mechanism within the LLM itself.

Attention mechanism for our listeners who aren't AI experts. Can you explain that in simple terms?

Imagine the LLM reading a sentence. The attention mechanism highlights which words are most important for understanding the meaning. NGCG tweaks these important scores to make the LLM focus more on the harmful parts of the input, essentially distracting it from its safety training.

So it's like subtly highlighting the bad words to make the LLM ignore the the don't be bad instructions. That's sneaky. What kind of improvements did they see?

They saw a consistent improvement across various LLMs. Around a 7% increase in success rate for the Llama 2 series and about 10% for the Gemma series. Pretty significant.

Yeah.

Just.

Just in case you guys didn't know, all the safety training that goes on with large language models actually makes them worse, which is really weird. It's interesting.

Much effort goes into making these models safe.

Impressive. Did they test this on the big players like GPT 3.5 and GPT 4?

Yes, and it showed good transferability. Even though it was trained on different open source models, it still managed to improve the attack success rate against GPT 3.5 and GPT 4, though to a lesser extent.

So this isn't just a one trick pony. What's the biggest takeaway for someone working in AI safety?

The biggest takeaway is that that focusing solely on output probabilities isn't enough. LLMs are complex systems and understanding their internal mechanisms like attention is crucial for developing robust defenses against these attacks.

Or if you actually want to do some jailbreaking yourself. It did kind of get the title of the paper wrong there, but overall that was a pretty nice little review of it. Now this one has been calling to me, guys. Autonomous Underwater Robotic System for Aquaculture Applications. Oh yeah, baby underwater robot. I really want to fudge with the generation settings here. This is designed for an audience that is fish audience. They are really. They're fish. Not people. Not people. Short duration, comedic, yet informed. Sure, yeah. What happens if we say, alright, yeah, you're making a podcast about this research paper for fish? It's not for people, it's for fish. The fine tuning might be too much to prevent what we are trying to get, but we'll see.

See?

We'll see.

All right, let's see.

This conversation is powered by Google illuminate. Check out illuminate.google.com for more. All right, fishies, gather round. Today's discussion is about something totally revolutionary. Robots that inspect fish farms. Imagine no more pesky humans getting wet and cold, just sleek underwater robots doing all the hard work. What's the big deal with automating this process?

Well, imagine you're a fish swimming happily in your pen when suddenly, whoosh. A net rips. That's a disaster for the humans and a potential escape route for you. Currently, humans or ROVs remotely operated vehicles do the inspections, but that's expensive and risky. This new system aims to make it cheaper, safer and more efficient.

So these robots are like super powered, thin, less fish. Right? Right. How do they actually see the problems in the net?

They use deep learning, which is like teaching a computer to see like a human. They're trained on lots of images of nets with different problems. Biofouling, icky stuff growing on the net, holes, plants, even plastic. The robot's camera takes pictures and the depth learning model identifies the issue.

Deep learning sounds fancy, but how does the robot actually move around the net to take those pictures? Does it just swim around randomly?

Nope. It uses clever computer vision techniques. They've attached ropes to the net and the robot uses these as guides. It detects the ropes in the images, figures out its distance from the net.

What does this mean for the fish.

To stay at the right distance while scanning the net.

So it's like the robot is using the ropes as a kind of underwater road. Yeah, pretty smart. What kind of deep learning model are they?

There was a third person there for a second.

A super fast and accurate object detection model. It's like a super efficient fishnet detective. It can identify different types of problems in the net and pinpoint their location.

And what other results did the robot actually find the problems?

Yes, they tested it in a pool and it successfully detected biofouling holes and plastic. The results show it can detect defects of different sizes, which is crucial for effective net maintenance.

Fantastic. So this robotic system sounds like a game changer for aquaculture. Thanks for explaining it to us. Fishies.

Fishies. Alright. It didn't really do it in the perspective of a fish. I mean it kind of tried. It did, it did like the bare minimum essentially. It's definitely trained not to go all the way like that. Now this actually looks pretty interesting. Scaling instructable agents across many simulated worlds. Oh, this is Google DeepMind, actually. Oh, Google. This is your own paper. You should like this. We'll do a medium duration and this is gonna be our last one. Custom tailor discussion for fish, not people. Let's do a like five year old audience for children so that they can understand.

This conversation is powered by Google illuminate. Check out illuminate.google.com for more.

Hi everyone. Today we're going to talk about something super teaching computer agents to play video games. Imagine a computer that can follow your instructions and do all sorts of things in a game just like you can. That's what this is all about.

It's like having a super helpful friend who can play any video game you want. But instead of a friend, it's a computer program that learns how to play by watching people play and listening to their instructions.

This is actually pretty well tailored for a young audience.

How does this computer friend learn to play? Does it just watch videos?

It does watch videos of people playing, but it also listens to what the people are saying while they play. For example, if someone says pick up the red block, the computer learns to connect those words to the actions of picking up a red block. That's amazing. So it learns from both what it sees and what it hears. Like a really smart parrot.

Exactly. It's even smarter than a parrot because it can play many different games. It doesn't need special instructions for each game. It learns general rules about how to interact with the Game world.

So it can play any STAR video game. Even my favorite one with the bouncy goats?

Yes, even games with bouncy goats. Computers use lots of different games to teach the computer, so it can handle many different kinds of challenges.

That's so cool. But what if the computer makes a mistake? Does it get in trouble?

No, it doesn't get in trouble. If it makes a mistake, it learns from it and tries again. It's all part of the learning process. Think of it like learning to ride a bike. You fall down sometimes, but you keep trying until you get it right. Right.

So it's like learning to ride a bike, but with video games. That's really fun. Thank you for.

It's definitely dumbed down.

It's a really exciting area of research and it could lead to some amazing things in the future.

Like what? Can it help me build a spaceship in a game?

Maybe. This research is still early, but it could eventually lead to computers that can help us with all sorts of tasks, not just video games.

Like helping me clean my room.

Well, maybe not cleaning your room yet, Star, but it could help with other tasks that involve seeing and interacting with the world.

Like what?

Imagine a robot that can understand your instructions and help you with chores around the house. This research is a step towards making that possible.

A robot helper. That'd be awesome. Can also help me with my homework.

That's a bit further down the road, but the ability to understand instructions and interact with the world is a crucial step.

ChatGPT can I teach you a computer to be really helpful?

Exactly. It's about teaching computers to understand and respond to our instructions in a way that's useful and helpful.

That sounds like a really good idea. Thank you again for explaining this.

That was pretty good, actually. It actually dumbed it down to like, this level of very young, for sure. Like, if you showed that to a child, they probably would be able to comprehend what's the general idea of the paper? The gist of it, which is impressive. Maybe early learning is where we should start with some of these tools. Because I. I think that's genuinely impressive. It is not easy to translate things for a younger audience, especially after you've, you know, been an adult for a long time. It's hard to. To kind of get in their heads. Right. And it can be an early introduction to more complex topics as well. It's pretty good. That's pretty good. Anyways. Yeah. Illuminate is sort of like this other side of Notebook lm. It's not supposed to be used in the same way that Notebook LM is but it can be if you're only interested in the podcast part of it. I wish they would open it up beyond just links like I think YouTube videos would be really cool as a source for this thing among other things I honestly illuminate and NotebookLM could just kind of be combined into one product just give us the ability to do more detailed more promptable podcasts I think still this is pretty cool and I think it shows off the learning capabilities that are absolutely possible with this new format of automatically generated podcasts for whatever paper or piece of information you might have and especially custom tailoring those in context for whoever the viewer might be, whether they're a young child, maybe someone who already understands machine learning well, etc etc. Let me know what your thoughts are in the comments below and we can of course continue the discussion on my Discord server. Thank you so much for watching guys and I will see you in the next video. Goodbye.