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.

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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 continuous evolution of LLMs, with newer models often surpassing the capabilities of their predecessors.

Finally, this episode discusses examples of different LLM types and their uses.

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 Transformers and its variations.

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 strict rules

It's like having a creative computer friend that can help make things

Listen to understand more!!

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 technologies in diverse fields.

The importance of responsible and ethical AI is highlighted to ensure listeners grasp the significant impact AI has on our daily lives.

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.