2.3 The Evolution of AI

The journey of AI is a tale of constant transformation and innovation. In its early days, AI was characterized by symbolic AI, where computers relied on explicitly programmed rules and logic. Think of these early systems as exceptionally diligent clerks, following detailed instructions to the letter but unable to adapt or learn on their own.

As we moved into the 1980s and 1990s, a new paradigm emerged: machine learning. Instead of being meticulously programmed for every task, computers began to learn from data. This shift was like teaching those diligent clerks to recognize patterns and make decisions based on experience. Machine learning opened up new possibilities, but it was just the beginning.

The dawn of the 21st century brought the era of deep learning, a monumental leap forward. Inspired by the structure of the human brain, neural networks enabled computers to process vast amounts of data and learn in ways that were previously unimaginable. These advancements allowed AI to excel in complex tasks such as image and speech recognition, natural language processing, and even autonomous decision-making.

Amidst these advancements, a new breed of AI emerged: large language models (LLMs) like ChatGPT. These models, powered by deep learning, are trained on enormous datasets, allowing them to understand and generate human-like text. ChatGPT, for instance, can engage in coherent and contextually relevant conversations, write essays, and even assist with coding. It represents the culmination of decades of AI research, bringing us closer to the dream of machines that can genuinely understand and interact with us in meaningful ways.

These leaps in AI capability have been closely tied to advancements in hardware. Early AI systems were constrained by the limited processing power of the time, but as hardware technology progressed, so did AI. The development of powerful GPUs (Graphics Processing Units) and specialized AI chips has significantly accelerated the training of complex neural networks. These hardware advancements have enabled researchers to process larger datasets and execute more sophisticated algorithms, making modern AI systems like ChatGPT possible. Without these innovations in hardware, the remarkable progress in AI that we witness today would not have been achievable.