Generative AI: Essentials for Leaders and Curious Minds

We explore the key concepts behind the most successful Generative AI architectures, from GANs to Autoregressive models, Diffusion models and beyond. We also review their main areas of application.

A journey through some of the most exciting applications of this technology, focusing on the creative industries, business and healthcare

Navigating the Ethical Landscape: Understanding the ethical Challenges of Generative AI

On the horizon: anticipating the next wave of Generative AI breakthroughs as this technology reshapes industries and redefines possibilities

We explore an introduction to prompt programming, how to use prompting patterns to program sophisticated behaviours in conversational AI agents, agents that are typically built with generative autoregressive architectures

In this fun and insightful section, we will be combining tangible physical elements like papers, lines, colors, etc with advanced digital representations in order to understand the very essence of how the neural networks that power Generative AI learn their internal mappings that connect their inputs with their objectives

We start at the base of the challenge, by exploring the dimensionality of the inputs and outputs that define the framework for the mapping the neural network is tackling

From simple lines to complex creations: unveiling the power and limits of linearity in neural networks. In this lecture we explore linear transformations, the powerhouse of neural networks"

Beyond the straight line: we explore how non linear activation functions allow neural networks to introduce more complexity into the input-output mappings they learn

The bias-variance tradeoff, finding the sweet spot between underfitting and overfitting, as the neural network learns the mapping that produces a great fit between its inputs and outputs

We increase the dimensionality of the input and visualize and reflect on how the non linear mappings behave in the latent spaces of the neural network

We explore how to increase the expressive power of neural networks by visualizing the impact of depth on the complexity of the mappings created at the latent spaces of these architectures

We arrive to very complex mappings, from high dimensional manifolds to other complex mathematical surfaces and objects, and to the next phase of AI, made of agents that update in real time their dynamic and ever changing latent spaces

Through advanced digital representations and simulations, we reflect on the way the complexity of the latent spaces of neural networks changes and evolves as we train these networks and as they get deployed in a near future within dynamic agents that will be constantly updating their world models in response to their environment.

Navigating Loss Landscapes: we explore how to create visualizations that connect the weights of the neural network with its performance, through the creation of 3D landscapes that relate weight combinations with the loss values at the end of the network

Exploring a visualization of the loss landscape of the generator of a Generative Adversarial Network that is being trained to learn to generate images of human faces. The loss value (performance) at the center of the representation corresponds to the current weight values of our network. The surrounding landscape (around the center) represents other combinations of weight values in the vicinity of our current ones.

Exploring a real time visualization of how the weights of a neural network change as its training process progresses.

A quick summary as we complete our exciting journey to the depths of the latent space of a neural network

A recap of some of the key areas we have explored in the course