Conceptually, Al

Significant majority of AI projects fail to yield a positive return on investment. The podcast advocates for a  strategic, phased implementation  instead of large-scale, "big-bang" initiatives. It suggests that companies should  break down ambitious AI goals into smaller, manageable steps , much like building a house one stage at a time. If you'd like the original post can be found h...

This episode  explains large language models (LLMs) and details their features , such as being  large, general-purpose, pre-trained, and fine-tunable . It then  discusses various prompting techniques  used to interact with LLMs, including zero-shot, few-shot, chain-of-thought, ReAct, and Tree of Thoughts prompting, highlighting their applications and advantages. The episode also  notes the continu...

This episode explains the attention mechanism in Transformer architecture, a crucial component of large language models (LLMs). It breaks down the process into key steps: creating and updating word embeddings to reflect contextual meaning, and attention scores. The explanation uses analogies and illustrations to clarify complex concepts. This episode also covers the encoder-decoder structure of Tr...

Let's start with the basics! What is Generative AI? "Imagine having a super-smart art student who learned by looking at millions of paintings. After learning, they can create new artwork based on what you ask for. That's kind of what generative AI does! It learns from examples and then creates new stuff." It's different from older computer programs because it learns patterns instead of following s...

This episode outlines the relationship between  artificial intelligence (AI) ,  machine learning (ML) ,  deep learning (DL) , and  generative AI (Gen AI) , highlighting their key differences and applications. It also provides examples of common use cases for supervised and unsupervised ML techniques, including classification, regression, and clustering, demonstrating the versatility of these techn...

This episode discusses the history of artificial intelligence (AI), highlighting key developments and breakthroughs. It begins with the origins of AI research in the 1950s and walks through how it has advanced over time boosting AI's capabilities to where we are today.

Introducing the ' AI Explained ' Series to the audience.