In this episode we delve into the role of AI in creative fields. The goal is to find an answer to the emerging question "Are AI models creative?"

The episode goes through the official definition of creativity and puts AI to the test going through how AI creates new things. We also tackle the biggest legal and ethical dilemma in AI generative content with ownership.

A lot of people have been wondering whether AI systems have gotten to the point where they have their own thoughts and feelings. In this episode, we explore how AI systems detect and respond to human emotions through data, patterns, and learned behavior. Then, we turn the lens around and look at when machines appear to show empathy, anger, or sadness, is anything really going on inside? We break down the difference between emotion recognition, expression, and true emotional experience, and discuss what researchers and philosophers say about whether machines could ever feel anything at all.

In this episode we tackle AI training data.

More specifically the thought that more data will always improve your models. From overfitting and noisy datasets to issues of scale, labeling, and ethics you'll understand that with AI models Quality is almost always better than quantity.We tackle problems where lack of good data leads to real issues and the ethicality of using widely available data on the internet without consent.

Ai is often visualized as an objective neutral tool. One that makes decisions based on facts and no subjectivity. But is that true?

In this episode we explain what bias is in a technical sense, and how it shows up in AI systems.

The episode tackles some real world examples and the role of human influence over AI learning.

In this episode, we talk about AI learning. With AI constantly evolving, it's easy to believe these systems are teaching themselves to grow smarter every day. But how much truth is in this? We explore what it means like for machines to learn. And how much they really can do without any human interaction. We also take a closer look at some popular stories to tackles the concept of model sentience as well as models learning to keep themselves running.

In this opening episode, we go back to where it all began. Alan Turing’s iconic question: “Can machines think?” We unpack what he meant, how that question led to the famous "Imitation Game," and what it means today in the age of large language models. Can these systems really think or are they just really good at sounding like they do? Let’s explore the line between imitation and intelligence.

Today, we look at Retrieval Augmented Generation, in short, "RAG". What is it, and what benefits do we get from using RAG in our GenAI Applications?

In this episode we take a look at how AI is used in manufacturing context. What can we predict inside a factory? How does AI in robotics work?