How AI Learns to Talk - Recurrent Neural Networks & Transformers

Discover the fascinating world of AI language processing as
you take a journey documenting how AI learned to talk. This insightful episode is part of our ongoing series delving into the intricacies of neural networks in an easy-to-understand way, made for all career fields.
We start by contrasting previously discussed network types, like feed-forward and convolutional networks, with the unique ability of Recurrent Neural Networks (RNNs) to analyze sequential data.Witness the evolution of Natural Language Processing (NLP), beginning with Michael Jordan's pioneering work on simple RNNs in the mid-1980s. Discover how these networks, by learning sequences, navigate through state space, laying the groundwork for future advancements. Continue that journey through the 1990s with Elman's RNNs, which showed remarkable skill in word partitioning and text generation, albeit with limitations. Enter Yoshua Bengio's groundbreaking 2003 paper advocating for neural networks in NLP and introducing the concept of word embeddings. Then leap to the transformative works of Geoffry Hinton, Ilya Sutskever, Andrej Karpathy and others, exploring how RNNs evolved into powerful text generators, as demonstrated by OpenAI's relatively massive RNN that was trained on Amazon reviews and developed an emergent capability to detect sentiment. Our journey culminates in the game-changing paper "Attention is All You Need", introducing Transformers. Discover how OpenAI leveraged this to create models like GPT-1, GPT-2, GPT-3, InstructGPT, ChatGPT, and GPT-4; a journey marked by emergent properties and in-context learning capabilities.Finally, we tease our next video, promising a deeper dive into the capabilities of Large Language Models (LLMs) and other
Transformer-based models in the ongoing AI gold rush.Enjoy this video and stay tuned to explore how AI is redefining the way we understand language and communication!The link to a course playlist of DAU recommended AI courses is: https://dau.csod.com/ui/lms-learner-p...

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