The World of Artificial Intelligence - What Are Large Language Models?

In our very first episode two years ago, which was entitled, Cryptocurrency,, The Seventh Layer, we suggested that cryptocurrency might very well be a seventh, major technological event going back over the past 40 to 45 years, starting with centralized computing architectures. In that first podcast, we hinted that an eighth layer was in hiding, unknown at that time to most of us. We are now convinced that this 8th layer is now quite clearly in our midst, arriving with a bang in the past 6 months. Both economic and technological segments of our society are under cyclonic pressures, due to the rapid progress of artificial intelligence (AI) components.

This is a brief overview of the gradual developments in AI over the past decade that have led to significant advancements in the field.

Deep learning: The past decade has seen a resurgence of neural networks, specifically deep learning, which has been a primary driver of AI advancements. The development of new techniques, such as convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) and transformers for natural language processing, has greatly improved AI's performance in various tasks.(Module 2)
Large-scale datasets: The availability of large-scale datasets for training AI models has played a crucial role in improving their performance. These datasets have facilitated the training of more complex models, enabling them to learn from a wide range of examples and generalize to new situations.
Computational power: Increased computational power, especially with the advent of Graphics Processing Units (GPUs) and specialized hardware like Tensor Processing Units (TPUs), has allowed researchers to train larger and deeper neural networks, which has contributed to the rapid advancements in AI.
Transfer learning: Techniques like transfer learning, which involve pre-training models on large datasets and fine-tuning them on specific tasks, have enabled AI models to achieve state-of-the-art performance with relatively smaller amounts of task-specific data.
Large Language Models (LLMs): Over the past few years, there has been a significant shift towards training larger and more powerful language models, such as GPT-3 by OpenAI. These models have demonstrated remarkable capabilities in various NLP tasks, including text generation, translation, summarization, and question answering.