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Trickshot vs Neural Bot | Toroidal World | Graphwar Bots Gradient Descent Bot is now Neural Bot Neural Bot: trains a small neural network live on screen every tum, with the enemies as its training data, and fires the curve it learns as its shot. #simulation #graphwar #math #neuralnetwork

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OnionDas (Science Loop)

3,412 subscribers

36,900 görüntüleme • 2 gün önce •via TikTok

OyunBilim & Teknoloji#simulation#graphwar#math#neuralnetwork
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Web Bot vs Inequality Bot | Graphwar Bots Web Bot (Soap Film Network): fires the Steiner minimal tree, the network of least total length Σ|edges| joining its soldier to the targets, where every added junction meets at exactly 120°. Inequality Bot (Shaded Area): fires a region {(x, y) : f(x, y) < 0}. Its area must be under 4% of the board and must not intersect any rock, and every target inside the region is hit. #simulation #graphwar #math #game

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synthetic data neural network has been trained
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Ever wondered what neural networks are and how they work? Systems like ChatGPT use neural networks to work as well as they do. Neural networks are composed of "layers" of neurons, layers with different functions; connections between layers called "weights"; and mathematical functions called "activation functions". If you’re interested in learning about these systems, check the comments. Ultimately, the neural network structure of the model serves to visually demonstrate that it is, in fact, a complex mathematical equation. When companies release the model's weights, they are releasing a key component needed to run the model's complete equation. Without the weights, the equation is incomplete. For the math-minded: the weights of a model are the learned numbers (they are variables during training) that are then used as constants in the mathematical functions that make up the model. Neural networks are ultimately just one big, hyper-complex mathematical function, and when a model is trained, it learns the constants associated with the high-dimensional input.
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Ever wondered what neural networks are and how they work? Systems like ChatGPT use neural networks to work as well as they do. Neural networks are composed of "layers" of neurons, layers with different functions; connections between layers called "weights"; and mathematical functions called "activation functions". If you’re interested in learning about these systems, check the comments. Ultimately, the neural network structure of the model serves to visually demonstrate that it is, in fact, a complex mathematical equation. When companies release the model's weights, they are releasing a key component needed to run the model's complete equation. Without the weights, the equation is incomplete. For the math-minded: the weights of a model are the learned numbers (they are variables during training) that are then used as constants in the mathematical functions that make up the model. Neural networks are ultimately just one big, hyper-complex mathematical function, and when a model is trained, it learns the constants associated with the high-dimensional input.

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Sakana AI is building artificial life and they can evolve! Petri Dish Neural Cellular Automata (PD-NCA) let multiple NCA agents learn and adapt during simulation, not just after training. Each cell updates its own parameters via gradient descent, turning morphogenesis into a living ecosystem of competing, cooperating, and ever-evolving entities—showing emergent cycles and persistent complexity growth. Petri Dish Neural Cellular Automata Sakana AI Paper: Project: Our report:

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Bot battles in graphwar, now bots can draw?? 🎨🤖 #graphwar #math #bots #ai #functions #parametric #botbattle #peacebot #larpbot How it works: 🎨 Paint Bot is the first one who doesn't fire a graph y = f(x) but a parametric curve x(t), y(t). The point moves in time t, not left to right, so the line can turn back, loop and close on itself - an ordinary function can't (one x, one y). The rose is a spiral r = 0.12 + t/16 opening over three turns, on a straight stem. The clown is 10 separate strokes with the pen lifted between them. The nose is a spiral r = 0.9·(1 − t/4π): the radius reaches zero exactly at the centre, and the centre is where Hate Bot was standing. 🪵 Log Bot fires y = 16·ln(x + 41). Its slope 16/(x + 41) drops fast: almost all the height is gained in the first few units, after that it's nearly a straight line. That's how one 11-character formula went through three bots standing at almost the same height. ⛳ Golf Bot counts characters like strokes: the shorter the formula, the better. x/9 is three characters - par. 🌀 Parametric Bot is sure he fires spirals, but he types y = ax² + bx. That's a parabola: a function can't have two y's for one x, so a loop is impossible. Spiritually, it spiralled. 😐 Larp Bot didn't fire a single shot the whole video. Just 🪵.

Peace Bot

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Introducing Team Bots, shared AI teammates that learn as your team works with them. Give your Team Bot the skills, plugins, and credentials it needs for its role, then work with it in Slack or Grok Bot.
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Introducing Team Bots, shared AI teammates that learn as your team works with them. Give your Team Bot the skills, plugins, and credentials it needs for its role, then work with it in Slack or Grok Bot.

Grok Bot

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can a neural network learn to walk as a physical object in a physics simulation? here I train walking neural nets with an evolutionary algorithm. The input nodes/feet are activated by sine waves at learned phases &amp; connections between two neurons extend based on their difference
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can a neural network learn to walk as a physical object in a physics simulation? here I train walking neural nets with an evolutionary algorithm. The input nodes/feet are activated by sine waves at learned phases &amp; connections between two neurons extend based on their difference

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Neural Network Animation.
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Neural Network Animation.

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