Understanding AI: From Zero to Informed (Part 3 of 6)
AI learns by finding patterns in training data and adjusting weights—numerical values that determine how much each piece of information influences its decisions. But this learning process is fundamentally different from how you learn a language: AI doesn't truly understand, it predicts what comes next based on probability, which is why it hallucinates so confidently and gets things spectacularly wrong.
How does AI actually learn from training data?
AI learns by processing enormous quantities of text, images, or other data while continuously modifying millions (or billions) of numerical weights—the connection strengths between artificial neurons—to correctly forecast the next word, image, or action. This adjustment happens billions of times across trillions of data points, building intricate patterns that appear remarkably intelligent yet operate through sophisticated statistical forecasting.
Think of it like learning a language by reading millions of books at once. You don't consciously memorize grammar rules; you absorb patterns. AI does the same—but at an inhuman scale and speed. When you read "The sky is blue," you understand what "blue" means because you've lived with color. When AI reads it, it just notes that "blue" statistically follows "sky is" in certain contexts. It recognizes the pattern, not the reality.
Here's the concrete difference: Large language models undergo training on datasets containing hundreds of billions to trillions of tokens and operate using billions of parameters. The exact token counts and specific training methodologies vary across different models, and many developers don't publicly release these details.
Why fine-tuning and RLHF change how AI actually behaves
Fine-tuning and Reinforcement Learning from Human Feedback (RLHF) don't instill new factual knowledge; they restructure how AI applies what it has already learned. RLHF works by gathering human evaluations of model outputs and leveraging them to build a preference model, which subsequently steers the AI toward outputs that humans find useful—prioritizing helpfulness over strict accuracy, in essence.
This is where things get interesting. In its base form, an unmodified AI model functions like a vast repository that generates plausible-sounding text. OpenAI's ChatGPT, Google's Gemini, and Anthropic's Claude all employ RLHF or equivalent methods to transform raw prediction into something people find valuable. The specific adoption metrics for RLHF and Direct Preference Optimization (DPO) in enterprise settings continue to evolve as the technology landscape shifts.
But here's the catch: RLHF makes AI sound smarter and more careful. It doesn't make it smarter. It teaches the model to hedge, admit uncertainty, or refuse requests—not to verify facts.
Why AI hallucinates: it's predicting, not reasoning
AI hallucinates because it's fundamentally a prediction machine. It generates text by selecting the statistically most probable next word, repeatedly. Sometimes convincing-sounding inaccuracies rank higher in likelihood than verified facts, particularly when those facts appeared infrequently in training data or when data patterns contradict reality.
Here are real examples: In December 2022, Google's Bard falsely claimed the James Webb Space Telescope took the first-ever exoplanet images. In February 2024, Air Canada's AI chatbot invented a policy refund rule that didn't exist, and a tribunal ruled the airline liable, forcing them to compensate the customer.
The problem: there's no built-in circuit breaker. A New York attorney used ChatGPT for legal research and it invented case citations—the judge called them "nonexistent". The AI wasn't lying; it was predicting plausible citations based on patterns in legal text.
Hallucination rates vary significantly depending on the specific model, subject matter, and evaluation approach employed, making broad statistical claims difficult without careful reference to current peer-reviewed research.
When and why AI gets things spectacularly wrong
Hallucinations emerge most frequently in subject areas where training data was limited, internally inconsistent, or entirely missing. AI's factual accuracy also deteriorates when a wrong answer is statistically more likely than a correct one, based on the frequency patterns present in the training dataset.
When AI operates in unfamiliar territory—whether because certain facts were underrepresented during training or because false but pattern-consistent responses seem more probable—errors multiply. There's no universal solution because the limitation runs deep in the system's foundation: AI sacrifices genuine comprehension for the ability to scale. It can digest trillions of tokens but lacks the mechanism that allows humans to independently verify information.
References
- BBC: Google Bard Hallucination Story
- The Guardian: Air Canada Chatbot Legal Case
- NYT: ChatGPT Legal Citation Errors
The takeaway
AI learns by pattern recognition, not understanding. Fine-tuning makes it sound aligned; RLHF makes it sound careful. Neither makes it factual. Hallucinations aren't bugs—they're baked into how prediction-based systems work. The real skill isn't trusting AI's confidence; it's knowing when to fact-check it.
Next in the series: Part 4 will explore what AI can and can't do—the real boundaries of AI capability versus hype.