Gerry Villoslado

Introduction

Artificial Intelligence (AI) has come a long way since its inception in the 1950s. Back then, scientist Alan Turing introduced the concept by testing computer intelligence, exploring whether machines could make decisions and solve problems like humans. However, progress was initially slow due to limited computing power and high costs.

Fast forward to the 21st century, and we've seen remarkable advancements, thanks in part to Moore's law. This principle suggests that speed and computing capabilities double every two years, making processing power faster and cheaper. With today's explosion of data collection, some worry that Moore's law might be slowing down, fearing we lack the computational capacity to train large language models (LLMs). But many believe that, as always, technology will adapt to meet these challenges.

At its core, AI is about machines simulating human intelligence. It involves computer programs that use vast amounts of data and computational power to perform tasks like decision-making and problem-solving with minimal human input. AI algorithms are typically rule-based and improve through iterative processing, recognizing patterns and making predictions. Recent advancements in cloud computing, hardware, and Big Data have made AI faster, cheaper, and more accessible.

Key Factors Behind AI's Surge

1. Graphical Processing Units (GPUs): These specialized processors optimize the training of large language models, enabling complex computations to run quickly and efficiently.
2. Transformer Technology: Introduced in 2017, this breakthrough changed how neural networks process sequential data. It allows AI models to compute relationships between input and output data without strict sequencing, significantly reducing training time and reliance on structured datasets.
3. Big Data: The proliferation of data has been crucial in training AI to be smarter and more efficient. The sheer volume of data generated daily has provided AI with an unprecedented resource for learning and improvement.

AI encompasses various subfields, including:

• Machine Learning (ML): Enables machines to learn from data. It includes supervised learning (using labeled datasets), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error with feedback). Machine Learning algorithms learn from historical data to make predictions and identify patterns. They can be trained through supervised or unsupervised learning techniques.
• Neural Networks: These function similarly to neurons in a human brain, processing information through multiple interconnected nodes. Neural Networks process data through multiple layers, each serving as a knowledge hub that filters and classifies information. They can use feed-forward processes or back-propagation algorithms to refine their outputs.
• Deep Learning: A subset of machine learning that uses layered neural networks to process data in increasingly complex ways. Deep Learning takes machine learning further by using artificial neural networks to learn independently from unstructured data, reducing the need for human intervention in feature selection.
• Natural Language Processing (NLP): Allows machines to understand, interpret, and generate human language. Natural Language Processing involves techniques like tokenization, lemmatization, and part-of-speech tagging to help computers understand and process human language.

As AI continues to evolve, it promises to bring transformative changes across various industries. Understanding these fundamental concepts is crucial as we navigate a future increasingly shaped by intelligent systems.