What is AI

AI: Software that uses statistical patterns, learned from data, to perform tasks that traditionally required human judgment.

Every word in that definition matters:

• Statistical patterns, not logical rules. Traditional code says "if X, then Y" because a developer told it to. AI says "when I see X, it's probably Y" because that's what the patterns it learned suggest. This is the shift from deterministic to probabilistic: it's why a fraud detection model is 94% accurate instead of 100% correct.
• Learned from data, not programmed. Nobody writes the rules by hand. You show the system thousands or millions of examples and it finds the patterns itself, similar to how you learned to spot spam email after seeing enough of it, minus any real understanding of what it's looking at.
• Tasks that traditionally required human judgment. Recognizing faces, understanding language, detecting fraud, these aren't calculations with a single right answer. They require pattern recognition and tolerance for ambiguity, which is exactly what modern AI is good at.

Deterministic vs. probabilistic

This is the core mental shift for developers:

Traditional code:

• Same input -> Always the same output
• Example: sort function returns the same order every time
• Nature: Deterministic

AI:

• Same input -> May give slightly different output
• Example: A sentiment model might be 89% confident one run, 91% the next
• Nature: Probabilistic

That variability isn't a bug, it's the fundamental nature of these systems.

It's all "narrow AI"

Everything in this space today is narrow AI: systems that excel at one specific task. A fraud detection model can't recognize faces; a language model can't play chess. Despite the headlines, this is not artificial general intelligence (AGI), that's still science fiction, even if the goalposts keep moving as the field advances. What counted as AGI five years ago is unremarkable today, which is part of why the terminology feels so slippery.

AI is an umbrella term

Machine learning, deep learning, neural networks, transformers, these aren't competitors to AI, they're techniques within it:

• Machine learning: e.g. decision trees, linear regression
• Deep learning: neural networks with many layers
• Transformers: the architecture behind models like GPT

Whatever the technique, they all do the same fundamental thing: learn patterns from data.

"The algorithm" isn't usually AI

Culturally, people blame "the algorithm" for everything, a video recommendation, a loan rejection. Most of the time, that's not AI at all:

• Traditional algorithm: explicit, developer-written rules. "If income > X and credit history > Y, approve."
• AI model: trained on millions of past applications, finding subtle patterns no one would have thought to program.

The distinction isn't academic, it changes how these systems get built, tested, deployed, and monitored.

Why AI is suddenly everywhere

The underlying ideas (neural networks) date back to the 1950s. What changed recently is that three things finally converged at once:

1. Massive amounts of diverse data
2. Compute cheap enough to process it at scale
3. Algorithms sophisticated enough to extract something useful

All three had to arrive together, remove any one, and progress stalls back into another "AI winter."

Why it's hard to keep up

New models ship monthly, and capabilities that felt impossible a year ago are live demos today. ChatGPT's first release (late 2022) changed everything, and today's reasoning models have already surpassed it by a wide margin.

But the fundamentals don't change: it's still pattern-matching software learning from data. The patterns get more sophisticated, the datasets get bigger, the compute gets faster, but that core principle is the one thing worth holding onto.