DeepMind's New AI: Generating Games From Scratch
Learn about DeepMind's groundbreaking new artificial intelligence technology that can generate fully playable computer games from scratch. Dive into the details of this innovative research and how it compares to previous advancements in the field.
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Video Summary & Chapters
1. Introduction 🌟
Overview of DeepMind's groundbreaking new AI technology for generating games from scratch.
2. Game Generation Process 🎮
Exploring the unique approach of creating games by observing gameplay instead of traditional programming methods.
3. Nvidia's Game-Gun Comparison 💻
Contrasting Nvidia's previous work with DeepMind's innovative game generation techniques.
4. DeepMind's Jaw-Dropping Paper 🤯
Impressive collaboration and advancements in AI game creation from text inputs.
5. Text to Video Game Conversion 🎥
Unveiling the capability of converting text inputs into fully playable video games.
6. Real-World Photo Integration 📸
Discussing the incorporation of real-world photos into the game generation process.
7. Unsupervised Learning Approach 🧠
Highlighting the unsupervised nature of DeepMind's AI in understanding gameplay dynamics.
8. Resolution and Progress 🖥️
Addressing the current pixelation and frame rate limitations while envisioning future advancements.
9. Future Evolution Speculation 🚀
Envisioning the potential growth and capabilities of AI game generation technology in upcoming versions.
10. Impact Beyond Games 🤖
Exploring the broader implications of DeepMind's AI advancements for applications like robotics.
11. Revolutionizing Robotics with AI
AI's impact on solving data problems in robotics.
12. Creating Games for Training Robots
Utilizing AI to develop games for training future robots.
13. Lambda's GPU Cloud Service
Introduction to Lambda's cost-effective GPU cloud compute service.
14. On-Demand H100 Instances
Availability of on-demand H100 instances on Lambda's GPU Cloud.
15. Joining Leading Research Organizations
Collaborating with top research institutions using Lambda Cloud instances.
Video Transcript
Goodness, DeepMind's new work might be one of the best papers of the year.
So, what is going on here?
Well, today we can use AI techniques to generate images from text, videos from text, but wait,
are you thinking what I am thinking?
It is great to look at all this, but this is a game.
I don't just want to look, I want to play.
And DeepMind's amazing new paper is about exactly that.
Dear fellow scholars, this is two-minute papers with Dr. Karojona Ifehir.
Now, believe it or not, we already looked at an earlier Nvidia paper that did something like this.
So, is this a solved problem?
What did it do exactly?
And what does DeepMind's new work do that's perhaps even better?
Well, this is Nvidia's game-gun.
Normally, if we wish to write a computer game, we first envision the game in our mind,
then we sit down and do the programming.
But this paper did this completely differently.
It first looked at someone playing the game, and then it was able to code up the game so
that it not only looks like it, but it also behaves the same way to our key presses.
You see it at work here.
Yes, this means that we can even play with it and it learns the internal rules of the game
and the graphics just by looking at some gameplay.
We don't need access to the source code or the internal workings of the game as long
as we can just look at it, it can learn the rules.
And scientists at DeepMind just put out a paper that made my jaw drop.
I mean, look at the list of authors.
This is essentially a supergroup.
Wow, I am very excited.
So what does this do?
Well, it doesn't even need to look at an already existing game because it makes a game
from scratch.
Oh my, this sounds not like text to image, not even text to video, but text to video
game.
So, here's the promise. In goes a piece of text, the text goes into a text to image AI that produces an image and now hold onto your papers as we can now start playing with that image.
Wow, just look at that. A fully AI-assisted workflow. It recognizes who should be the playable character and which of this is the environment creates the controls for this character like moving around
and jumping. It also learned the parallax effect, so it knows which the foreground and background is,
how far away they are and how quickly they should move compared to each other. Bravo! This is
already incredible, but it gets better. So far the input was eventually an image and that image
can also be a photo from the real world. You add the photo and out comes a playable game.
Yes, we will talk about the fact that this is quite pixelated in a moment.
But you see, we don't even necessarily need a photo from the real world. We can also use a sketch.
Just draw something and you get a game out of it. My goodness, isn't that the dream? So good.
And it does all this watching videos on the internet and let's have a look at how it
relates to previous techniques.
Those required additional information, for instance they needed to know the buttons
that were pressed.
But this one...
Look, this one is completely unsupervised.
That means that we don't even need to label the videos and show which is the playable character
and what buttons were pressed.
Just nothing like that.
