AI to create AI that explains AI

Learn the basics of AI and experiment with a neural network consisting of a single neuron, optimize parameters and experience how AI algorithms develop in real time. Perfect for anyone who wants to understand the fundamentals of AI, from aspiring data scientists, tech enthusiasts to average joe.

Most people associate human-like machines that can see, speak and reason with brain-like neural networks that can think and reflect on a problem.

But Artificial Neural Networks (ANNs) are mostly stateless and primarily master one thing: they excel at curve fitting, mapping input data to outputs through complex functions, enabling them to solve diverse problems, including training large language models (LLMs).

Let’s now demystify this core concept of modern AI

This software demonstrates the solution to an optimization problem, using the example of the shortest route to a hospital. Optimization problems play a central role in machine learning.

The 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey Hinton for their foundational work in artificial neural networks, which includes Hopfield networks and Boltzmann machines. Their contributions have been instrumental in advancing machine learning and AI.

This simulation shows an energy-based neural network that is similar in function to Hopfield and Boltzmann networks.

The 3D gas simulation shows particles in a 3-dimensional space that meet and repel each other depending on the temperature. The energy distribution is shown as a diagram.

This AI embedding algorithm visualizes the relationships and meanings of words in a 2D vector space, where every word tells a story through its position and connections.

This deep comprehension of word meanings forms the basic building block for large language models (LLMs), enabling them to generate coherent, contextually relevant text.

Unlike traditional sequence models like Recurrent Neural Networks (RNNs), which process tokens sequentially, Transformers process all tokens in parallel and thus lack an inherent understanding of token order.

Positional encoding addresses this challenge by encoding positional information into the token embeddings.

The attention mechanism revolutionized NLP by enabling models to focus on important parts of the input sequence, allowing for contextually accurate responses even in long sequences.

Introduced in the 2017 paper "Attention Is All You Need" by Vaswani et al., self-attention has become a cornerstone of modern AI and the transformer architecture of LLMs.

Let us now demystify the intricacies of Large Language Models (LLMs) and bring theory to life with an interactive simulation.

Watch in real-time as this basic yet fascinating transformer model runs a live simulation, piecing together language constructs with the elegance of simplicity.