AI Explained: A Straightforward Breakdown of Machine Learning, LLMs, and Artificial Intelligence (Part One)
Discover the fundamentals of Artificial Intelligence in this comprehensive guide. From the basics of machine learning to the transformative power of Large Language Models (LLMs) like ChatGPT, this article unpacks the evolution, impact, and future trends of AI technology. Whether you're new to the world of AI or looking to deepen your understanding, this article provides an accessible overview of how AI is reshaping industries, enhancing communication, and setting the stage for the next wave of technological innovation.
What is Artificial Intelligence (AI)?
Artificial Intelligence, or AI, is a term that refers to machines or software that can perform tasks that usually require human intelligence. These tasks can range from recognizing patterns in data to understanding human language, solving complex problems, and even making decisions on their own [1].
Historical Definition
The idea of AI has been around for a long time. For centuries, people imagined machines that could think and act like humans. However, AI as a formal field of study started in the 1950s. In 1956, a group of scientists, including John McCarthy and Marvin Minsky, held a conference at Dartmouth College where they officially coined the term "Artificial Intelligence." This event is considered the birth of AI as an academic discipline, meaning it became a serious subject of study and research [2].
One of the most important figures in the early days of AI was a British mathematician named Alan Turing. He’s famous for proposing the idea of a "universal machine"—a machine that could solve any problem if given the right instructions.?
In 1950, Turing published a paper called "Computing Machinery and Intelligence," where he introduced the concept of the "Turing Test." This test was designed to see if a machine could behave in a way that was indistinguishable from a human, which would suggest that the machine was "intelligent" [3].
In the years following, AI researchers made some progress, but they also faced many challenges. Early AI programs like ELIZA, which could mimic a conversation with a human, were impressive for their time. However, these early systems were very limited in what they could do. They couldn’t handle complex or real-world situations very well. This led to periods in the 1970s and 1980s where interest in AI faded, a time known as the "AI winter." During these years, funding and enthusiasm for AI research significantly decreased [4].
Evolution of AI
Machine Learning
Despite these setbacks, AI continued to develop, especially with the rise of a new approach called machine learning. Machine learning is a type of AI where, instead of being programmed with specific rules, machines are designed to learn from data. Imagine showing a computer thousands of pictures of cats and dogs and letting it figure out on its own how to tell the difference. This approach, which became popular in the 1980s and 1990s, allowed AI to get much better at tasks like recognizing objects in photos or translating languages [5].
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Deep Learning and Neural Networks
In the 2000s and 2010s, AI saw huge advancements thanks to something called deep learning. Deep learning is a more advanced type of machine learning that uses structures known as neural networks. These networks are designed to function somewhat like the human brain, with layers of processing units that help the AI understand very complex patterns. This breakthrough led to major improvements in things like recognizing speech, understanding images, and, importantly, processing natural language—this is the technology behind today’s AI models like ChatGPT [6].
Transformers Lead to Modern AI
A major milestone in this evolution was the development of transformers in 2017. Transformers are a type of neural network architecture that changed the game for natural language processing. Unlike previous models that processed language in a linear, sequential manner, transformers can process multiple parts of a sentence simultaneously. This allows them to understand context much better and generate more accurate and coherent text. The introduction of transformers enabled the creation of Large Language Models (LLMs) like GPT-3 and GPT-4, which are capable of generating human-like text, translating languages, summarizing documents, and much more [7].
Artificial Intelligence Today
Today, AI isn’t just an idea in science fiction; it’s everywhere. From self-driving cars to virtual assistants like Siri, AI is being used in countless ways to make our lives easier and more efficient.
As AI has evolved, so has our understanding of intelligence. We now know that there are different "levels" or "types" of AI, each with its own strengths and weaknesses. This brings us to the part two of AI journey: the different levels of AI, from basic systems to advanced models that might one day surpass human intelligence. AI has come a long way, but there is much more to explore on this fascinating topic. Stay with us as we unpack more about AI in the next part of our series.
Author's Note: I use AI in my writing to help with formatting, readability, and fact-checking. I do my best to double check every source and fact, but just like how AI can make mistakes, so can humans. If I missed anything or if something is incorrect, please let me know by connecting/messaging with me on LinkedIn here.
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