Artificial intelligence can predict stock market swings, generate photorealistic deepfakes, and even compose symphonies. But ask it to do basic arithmetic, and it flounders like a malfunctioning calculator. Why? Because we’ve been feeding it mountains of raw data instead of teaching it actual concepts.
Modern AI thrives on pattern recognition. Show it enough cat pictures, and it gets pretty good at spotting whiskers and fur. But numbers? Numbers don’t have whiskers. Arithmetic isn’t just a pattern—it’s a set of rules, and rules require reasoning, not just repetition.
The difference becomes painfully obvious when comparing AI to humans. A child learning addition doesn’t memorize every possible sum like a chatbot memorizes text snippets. They learn a system: carry the one, line up the digits, and boom—math happens. AI, on the other hand, brute-forces its way through data, hoping for the best.
Enter knowledge engineering—the lost art of actually encoding concepts into AI. Instead of making machines guzzle endless datasets, knowledge engineering gives them structured rules. Classic AI systems used this approach before the deep-learning revolution buried it under mountains of raw data. And guess what? Some of those ancient systems could still out-reason today’s neural networks in certain areas.
Take arithmetic. Humans don’t need millions of examples to understand that 2 + 2 = 4. But large language models? They rely on statistical guesswork. That’s why ChatGPT can write a Shakespearean sonnet but struggles with straightforward math unless it piggybacks off a calculator. It’s not actually thinking—it’s just playing the odds.
The limits of pattern-based learning become even clearer in edge cases. Ask an AI to add 999,999 + 1, and sometimes it stumbles. Why? Because it wasn’t explicitly taught the fundamental rule of place-value addition. It just saw a lot of numbers and made a probabilistic guess, like a student who crammed for a test by memorizing answers instead of understanding the material.
The solution? Hybrid models that combine data-driven learning with explicit rule-based reasoning. Imagine an AI that doesn’t just regurgitate what it’s seen but actually understands the logic behind it. Instead of predicting the most likely answer based on past examples, it could derive the correct answer based on fundamental principles.
Knowledge engineering may sound old-school, but it’s what separates true intelligence from a glorified autocomplete tool. Until AI learns concepts instead of just patterns, it will remain an overhyped parrot—capable of mimicking brilliance but fundamentally lacking understanding.
Five Fast Facts
- Early AI in the 1950s relied on logic-based rules, not neural networks, to solve math problems.
- ChatGPT can summarize complex papers but often miscalculates 7 x 8 when forced to rely on its training data alone.
- The term “knowledge engineering” was coined in the 1980s to describe expert systems that encoded human reasoning.
- Some AI models trained on vast data sets still struggle with basic reasoning puzzles that a five-year-old can solve.
- Deep Blue, the AI that beat chess champion Garry Kasparov in 1997, didn’t use deep learning—it relied on brute-force search and rule-based evaluation.