We taught an AI in the White Room 😲

How AI Learns in the White Room: Inspired by ‘Classroom of the Elite’

Hey guys, if you’re into tech and anime, brace yourself for an awesome journey! Today, we’re mixing intense anime training with advanced AI development. Get ready to see how AI can be perfected with the same passion as Ayanokoji. Let’s jump in and get pumped!

Enter the White Room

So in “Classroom of the Elite,” the White Room is this super tough place where students face crazy challenges to bring out their genius. It’s all mysterious and super intense. Now, imagine if we could put AI through that kind of hardcore training. Sounds interesting, don’t you think?

Training AI In The White Room From Classroom Of The Elite- Techiral

Secrets of the White Room

In the White Room, students go through a combination of classes, workouts, and mental preparation under intense pressure. This tough environment pushes them to their maximum potential. And can AI go through the same kind of training? Totally.

[The allure of the unknown always pulls us in deeper… there’s more to uncover in another exciting format…]

NEAT — The AI Revolution

In the White Room, students go through a combination of classes, workouts, and mental preparation under intense pressure. This tough environment pushes them to their maximum potential. And can AI go through the same kind of training? Totally.

How NEAT Works

Here’s a simple breakdown:

1. Start with simple neural networks.
2. Use genetic algorithms to evolve these networks over generations.
3. The best-performing networks survive and reproduce.

NEAT Evolution Algorithm

Neural Networks Unveiled

Before we get into the nitty-gritty of coding, let’s cover the basics. Neural networks are like the brain of our AI, with layers of interconnected neurons processing information. This is how our AI will learn and improve.

Animation of Neural Networks with Evolving Layers

Neural networks consist of:

• Input Layer
• Hidden Layers
• Output Layer

These networks adjust their weights (connections) through a process called backpropagation to minimize errors and improve over time.

The Power of Connections

In NEAT, these neural networks’ connections evolve and adapt, mirroring the intense mental training in the White Room. The networks change and grow, making AI more complex and efficient.

Traditional neural networks are fixed, but with NEAT, new neurons and connections can be added over time, increasing complexity.

Evolutionary Algorithms

NEAT uses genetic algorithms to mimic natural selection. It’s like starting with simple networks and evolving them over generations, just as Ayanokoji grows and adapts in the White Room.

Key Steps:

1. Selection: Choose the best-performing networks.
2. Crossover: Combine traits from two networks.
3. Mutation: Introduce random changes for diversity.

Seeing these evolutionary algorithms in action would be quite the experience, wouldn’t it?

Flappy Bird AI Challenge

Let’s put NEAT into practice with a classic game: Flappy Bird. Our AI will learn to play Flappy Bird by itself, mirroring Ayanokoji mastering challenges in the White Room.

Flappy Bird AI By Python NEAT!

How It Works:

• AI starts with zero knowledge.
• It plays the game, learning from its mistakes.
• Each generation gets better at navigating obstacles.

Imagine the progression… intriguing, isn’t it?

The Learning Process

Our AI’s neural network evolves, improving with each generation. It’s like watching Ayanokoji get better with every challenge he faces.

Stages:

1. Random movements and frequent failures.
2. Selection and combination of best-performing agents.
3. Development of better strategies.

Simulation of AI-based genomes eat the food and evolve with the help of NEAT.

Mastering Classic Games

Our AI isn’t just good at Flappy Bird. We also trained it on the Chrome Dino Game and Snake Game. Each game presents unique challenges, and it’s mesmerizing to see how the AI adapts and improves.

Chrome Dino Game:

• Involves jumping over obstacles at increasing speeds.
• Requires quick reflexes and timing.

Snake Game:

• Navigate a snake to eat food while avoiding collisions.
• Requires strategic planning and spatial awareness.

Creating the Ultimate AI

Imagine combining the best-performing AIs from each game into one ultimate model. It’s like creating a super genius, just like Ayanokoji using his diverse skills.

How We Do It:

1. Select the best strategies from each game.
2. Integrate them into a single model.
3. Test it against increasingly complex challenges.

The ultimate AI… now that’s something worth exploring, isn’t it?

The Ultimate Challenge

With our ultimate AI model, we’re pushing the boundaries. This AI isn’t just limited to games; it has the potential to tackle real-world problems and scenarios.

Next Steps:

• Present complex challenges beyond simple games.
• Observe how the AI adapts and learns.

The ultimate challenge… the final frontier. Captivating, right?

What’s Next?

Exciting times lie ahead! If you found this journey fascinating, there might be something even more thrilling that awaits you. Imagine seeing all these concepts come to life, step by step. Ever wondered how this would look in action?

Kid Ayanokoji Playing Chess In The White Room from Tenor.com

Thanks for joining me on this incredible journey of AI training inspired by ‘Classroom of the Elite.’ For those who are curious to explore deeper and see these concepts in action, there might just be a place where it’s all unfolding in real time. Stay curious, and perhaps you’ll find your way there.

Bye-Bye From Ayanokoji- Tenor.com