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

Reggie And Royal Perspectives, on Economy, History, Cryptocurrency, Business Podcast

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

1. 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)
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.
3. 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.
4. 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.
5. 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.
   

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