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AI isn't magic β it's mathematics, statistics, and data. When you ask Claude a question, it doesn't "think" in the human sense. Instead, it runs complex computations that convert your words into numbers, then picks the most appropriate next word based on billions of training examples.
Artificial Intelligence (AI) is the field of computer science that aims to build systems capable of performing tasks that normally require human intelligence β like recognizing images, understanding language, and making decisions.
1. Narrow AI This is everything that exists today. Every AI system is designed for one specific task:
None of them can operate outside their defined task.
2. Artificial General Intelligence (AGI) Theoretical for now β an AI system capable of performing any mental task a human can do. Not yet achieved.
3. Artificial Superintelligence (ASI) A future concept surpassing human intelligence across all domains simultaneously. Science fiction currently.
Three factors converged at the same time:
| Factor | Detail | |--------|--------| | Data | Trillions of words and images on the internet | | Computing | GPUs 1000Γ faster than in 2010 | | Algorithms | The Transformer architecture (2017) changed everything |
Traditional programming: You write the rules manually.
Machine learning: The model infers rules from data automatically.
This fundamental difference opened the door to everything we see today.
# Traditional programming (rule-based)
def diagnose(temperature):
if temperature > 38:
return "Fever"
elif temperature > 37:
return "Borderline"
else:
return "Normal"
# Machine learning approach
# Instead of rules, we learn from 10,000 patient records
# The model finds the patterns automatically
model.fit(patient_data, diagnoses)
prediction = model.predict(new_patient)