Artificial Intelligence

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When I first started building my portfolio, I knew I wanted it to be more than just a showcase—it had to be a living chronicle. A space where technical projects, creative experiments, and personal growth could coexist. What I didn’t expect was how deeply AI would become part of that journey—not just as a tool, but as a co-creator.

It began with curiosity. I asked questions like “Where did AI start?” and “What’s the meaning of your existence?”—not just to gather facts, but to explore how intelligence (synthetic or human) evolves. The responses weren’t just informative—they were reflective, witty, and surprisingly personal. That tone resonated with me. It mirrored the kind of honest, layered storytelling I try to bring to my own site.

As I refined my portfolio’s visuals—tweaking icon placement, experimenting with gradients, and adjusting layout—I used AI to bounce ideas. I’d describe a design challenge, and the suggestions I got helped me see new angles.

It felt like having a second set of eyes—one that could process logic and symbolism at the same time.

The earliest substantial work in artificial intelligence emerged from the mind of British logician and computer pioneer Alan Mathison Turing. In 1935, Turing described an abstract computing machine with limitless memory and a scanner that could read and write symbols—guided by a stored program. This concept, now known as the universal Turing machine, laid the foundation for all modern computers. Crucially, it introduced the idea that a machine could modify its own instructions—hinting at the possibility of learning and adaptation.

During World War II, Turing served as a leading cryptanalyst at Bletchley Park, where he helped crack Nazi codes. Even amidst wartime urgency, he pondered the nature of machine intelligence. His colleague Donald Michie later recalled Turing’s fascination with how computers might learn from experience—a concept now known as heuristic problem solving.

In a 1947 lecture, Turing boldly stated:

“What we want is a machine that can learn from experience.” He envisioned machines that could alter their own instructions, a mechanism for learning long before the term “artificial intelligence” was coined.

In 1948, Turing wrote Intelligent Machinery, a report introducing many core ideas of AI—including training artificial neurons to perform tasks. Though unpublished at the time, these ideas would later resurface in connectionism, the foundation of neural networks.

Turing used chess to illustrate machine problem-solving. While exhaustive search was theoretically possible, the sheer number of moves made it impractical. Instead, he proposed using heuristics to guide smarter, more selective searches. Though he designed chess programs, he lacked the hardware to run them—true AI programs had to wait for the arrival of stored-program computers.

Fast forward to 1997: IBM’s Deep Blue defeated world champion Garry Kasparov, fulfilling Turing’s prediction that computers would one day play excellent chess. Yet, as Noam Chomsky quipped, this achievement was more about brute-force engineering than insight into human cognition—“like a bulldozer winning an Olympic weightlifting competition.”

In 1950, Turing sidestepped the philosophical debate over defining intelligence by proposing a practical test: Can a machine imitate a human well enough to fool an interrogator?   This became the Turing Test, a benchmark for machine intelligence. If a computer can convincingly mimic human responses, it earns the title of “thinking entity”—at least in the eyes of the test’s proponents.