5:49

Ep. 07 Nov 28, 2024

From Models to Deployment: the Road to Production

This podcast explores the essential components for building and deploying AI applications, including models (mathematical functions for data processing), prompts (instructions for the model), knowledge (data sources for training), integrations (connecting AI to real-world systems), testing (ensuring quality and reliability), and deployment (running applications in production). It emphasizes the significance of open-source models, highlighting their role in maintaining data privacy and regulatory compliance while enabling companies to innovate effectively.

12:33

Ep. 06 Nov 24, 2024

From Perceptrons to GANs: A Journey Through Neural Networks

This podcast dives into the five key types of neural networks driving advancements in artificial intelligence. Starting with basic models like perceptrons, feedforward networks, and radial basis networks, it explores their role in linear information processing. It then moves to recurrent neural networks, which handle temporal data dependencies, and auto-encoders, designed for data compression and representation. Convolutional neural networks, known for their image classification prowess, and generative adversarial networks, celebrated for creating unique content, round out the discussion. A comprehensive guide to understanding the diverse capabilities of neural networks shaping AI today!

7:37

Ep. 05 Nov 19, 2024

Decoding AI: The Layers of Machine and Deep Learning

This podcast explores the fascinating world of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). It breaks down the differences and relationships between these interconnected fields, from AI’s broad scope of mimicking human intelligence to ML’s focus on improving task performance through experience, and DL’s use of multilayered neural networks to uncover complex patterns. The discussion also highlights the three main approaches within ML: supervised learning, where algorithms learn from labeled data; unsupervised learning, which finds hidden structures in unlabeled data; and reinforcement learning, which teaches algorithms through trial-and-error interactions with their environment. A clear and insightful guide to understanding the building blocks of modern AI technologies!

24:15

Ep. 04 Nov 18, 2024

Open-Source AI: OSI’s New Standards for Transparency and Accessibility

The Open Source Initiative (OSI) has introduced a new definition for open-source AI to clarify its principles and guide policymakers. This definition emphasizes transparency, accessibility, and user rights, including the ability to modify and share AI models. While some criticize it as overly strict, others view it as a balanced compromise between ideals and practicality. The OSI plans to enforce these guidelines and release a list of models that comply with the criteria, aiming to standardize open-source AI practices.

6:30

Ep. 03 Nov 17, 2024

Understanding Tokenization in Language Models: How AI Processes Text

Language models, like those used in AI, process and generate text using tokens, which are units of text smaller than words but larger than characters. The way text is divided into tokens is determined by the model’s training on large datasets. Tokenization is a key step in processing text for language models, as it allows them to more efficiently encode, process, and generate coherent text. By analysing the probability of different tokens following a given sequence, the model can predict the most likely next token and generate text that mimics natural language patterns.

25:11

Ep. 02 Nov 17, 2024

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of large language models (LLMs) with external knowledge sources. LLMs, while powerful, are limited by their training data, which can be outdated or incomplete. RAG addresses this by allowing LLMs to access external information, such as databases, knowledge bases, or websites. This enables LLMs to provide more accurate, up-to-date, and relevant responses, increasing user trust and confidence. RAG operates by converting user queries into numeric representations called embeddings, which are then compared to embeddings stored in a vector database. Relevant information is retrieved and presented to the LLM, which then generates a response. This process enhances LLMs with additional information and context, making them more versatile and useful across a wide range of applications.

20:13

Ep. 01 Nov 17, 2024

Attention Is All You Need: The Transformer Revolution

Dive deep into the groundbreaking world of AI and natural language processing with Attention Is All You Need: The Transformer Revolution. This podcast unpacks the story behind the transformer model, the game-changing architecture that powers modern AI applications like ChatGPT, Google Translate, and more. From its origins to its impact on language, vision, and beyond, we explore how attention mechanisms have redefined what’s possible in technology and our daily lives. Perfect for enthusiasts, students, and professionals eager to understand the science shaping the future.