Discussion on the current relevance of artificial intelligence (AI), machine learning (ML), and deep learning (DL).

Exploration of generative AI technologies such as large language models and deep fakes.

The need to simplify complex concepts for easier understanding.

AI aims to simulate human intelligence by learning, inferring, and reasoning.

Historically, AI began as a concept in research projects, gaining popularity with expert systems in the 1980s and 1990s.

Early AI used programming languages like Lisp and Prolog.

Machine learning enables computers to learn from data without explicit programming.

ML algorithms are proficient at detecting patterns and making predictions.

Increased amount of training data improves prediction accuracy and anomaly detection.

Deep learning utilizes neural networks that simulate human brain functions.

The term 'deep' refers to the multiple layers within neural networks.

Deep learning has reached significant popularity in AI, especially in the 2010s.

Generative AI can create new content, exemplified by large language models predicting text based on context.

Foundation models refer to the underlying architecture that supports generative capabilities in various media.

Generative AI creates original outputs, though it often recombines existing information in novel ways.

Generative AI includes technologies like chatbots and deep fakes.

Deep fakes can recreate voices or images, raising ethical concerns alongside entertainment potential.

The rapid rise of generative AI has spurred widespread AI adoption across various sectors.