AI in Everything: First Post (reposted)
What is AI?
What is Artificial Intelligence?
Let’s begin with a simple yet profound question: What is AI?
As a species, we’ve named ourselves Homo sapiens, from Latin, where homo means "human" or "man," and sapiens means "wise." So quite literally, we’ve called ourselves “the wise man.” That’s a strong statement about how much we value intelligence and self-awareness.
Our mental capabilities, our ability to reason, reflect, imagine, and learn, are deeply central to how we understand ourselves. Intelligence is not just a trait; it’s something we associate with being human.
The field of artificial intelligence, or AI, is rooted in the attempt to understand what intelligence really is. In that sense, AI is not only about machines…it’s also a mirror through which we study ourselves. By trying to recreate intelligent behavior in computers, we’re indirectly learning more about the nature of human cognition and problem-solving.
Now, you might ask: How is AI different from fields like psychology or philosophy, which also deal with intelligence?
The key difference is this: psychology and philosophy aim to understand intelligence…how it arises, how it functions, what it means. AI aims to build it. It is both an engineering and a scientific endeavor: we try to construct systems that can act intelligently, and in doing so, we gain insights into the very nature of intelligence itself.
So, how do we define AI?
That’s a tricky question….there’s no single definition that everyone agrees on. In fact, there are hundreds of definitions of artificial intelligence, reflecting different goals, approaches, and philosophies within the field.
But broadly speaking, we can say:
Artificial Intelligence is the study and design of systems that can perceive, reason, learn, and act in ways that we would consider intelligent if performed by a human.
AI is also one of the newest scientific disciplines. While some foundational ideas had been forming earlier, AI was formally launched as a field in 1956, during a now-famous summer workshop at Dartmouth College. That’s when the term “artificial intelligence” was coined….and since then, the field has grown in scope, complexity, and impact in ways no one could have imagined.
Next, we’re going to take a brief but important journey through the history of artificial intelligence. AI is often thought of as a modern breakthrough—powered by recent advances in deep learning, massive data, and computational horsepower—but its intellectual roots go much further back than most people realise.
In fact, the idea of intelligent machines can be traced all the way back to ancient Greek mythology. Myths from that time imagined artificial beings—automatons, crafted by the gods—that could reason, think, or even feel. These stories show that the dream of creating intelligent systems is deeply embedded in human culture.
However, the modern field of artificial intelligence, as a scientific discipline, only began to take shape in the 20th century.
Let’s rewind to the 1700s. A name you might not immediately associate with AI is Thomas Bayes. In 1763, Bayes developed a mathematical framework for reasoning under uncertainty, a concept that is still critical to AI today.
Bayesian reasoning allows us to update the probability of a hypothesis as new evidence becomes available. It’s fundamental when an AI system has to make decisions based on incomplete or uncertain data. In fact, Bayes’ Theorem underpins many modern algorithms in machine learning, robotics, and natural language processing.
Fast forward to the 19th century, and we encounter Ada Lovelace, often regarded as the world’s first computer programmer. While working with Charles Babbage on his Analytical Engine, the first design for a general-purpose mechanical computer, Lovelace envisioned something remarkable.
She imagined that this machine could do more than just crunch numbers. She speculated that it could manipulate symbols, analyze patterns, and even compose music. In short, she saw that the potential of computers extended far beyond mere calculation. At the time, this was revolutionary.
Now let’s shift to the 20th century—the birth of ideas that would evolve into modern AI. In the 1940s and 50s, researchers began developing simplified models of neurons to explore how networks of artificial “neurons” might simulate learning and logic. This gave rise to the perceptron, an early type of neural network. These early models laid the conceptual groundwork for what we now call deep learning.
In 1950, Alan Turing, one of the giants of computer science, published a landmark paper titled “Computing Machinery and Intelligence”. In it, he posed the provocative question: “Can machines think?”
But Turing quickly acknowledged that terms like “machine” and “think” are difficult to define. So he proposed a more precise formulation, known today as the Turing Test. Rather than ask if machines can think, he asked whether a machine could imitate a human well enough in conversation that a person couldn’t tell the difference. This shift in framing was profound—and it remains a benchmark in discussions about AI capabilities.
Turing also developed one of the earliest game-playing AI programs—teaching a computer to play checkers. By the mid-1970s, his program could challenge respectable amateur players, demonstrating how machines could learn strategy and improve through play.
Up until this point, there had been progress, but the field still didn’t have a name. That changed in 1956.
In that year, a group of visionary scientists - John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon - submitted a proposal for a summer research conference at Dartmouth College. It was in this proposal that they coined the term “artificial intelligence.”
Their goal? To bring together the best minds of the time and solve the problem of machine intelligence. They were ambitious, hoping that significant progress could be made within a few months.
Of course, the problem turned out to be much harder than anyone anticipated. But these individuals made lasting contributions not just to AI, but to computing more broadly. McCarthy became a father of AI logic and languages; Minsky led the famous MIT AI Lab; Shannon laid the foundation of information theory; and Rochester helped develop early computing hardware and architectures.
And so, from this pivotal moment at Dartmouth, the field of artificial intelligence officially began.
Let’s fast forward now to today. AI is evolving faster than ever before. But it’s important to remember that this is not just a trend or a passing technology. AI has a long lineage, rooted in mathematical logic, cognitive science, and philosophy.
And because the pace of change is so rapid, it’s important not to anchor ourselves too firmly to any one tool, product, or vendor. What matters most is building the right mental models and conceptual frameworks—so that we can continuously learn, adapt, and evolve with this field.
AI isn’t just about automation. It’s about augmenting human decision-making, discovering new insights, and imagining new possibilities. And if history has taught us anything, it’s that the journey of AI is far from over.
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