The Evolution of AI
Rule-based AI (Expert Systems, 1950s-60s)
Meaning: AI built from hand-coded rules like "if this happens, then do that."
Detailed Example:
In the 1970s, a system called MYCIN helped doctors decide which antibiotics to prescribe.
In the 1970s, a system called MYCIN helped doctors decide which antibiotics to prescribe.
- Rule: If a patient has a fever AND a cough → suggest a chest X-ray.
- Rule: If infection type = bacterial → recommend penicillin.
Why it failed: Every new disease or situation required writing new rules → not scalable.
Statistical AI (1980s)
Meaning: Instead of only rules, AI started using statistics to find patterns in data.
Detailed Example - Spam filters:
- Looks at all words in an email.
- If words like "FREE $$" or "WINNER" appear too often → mark as spam.
- It doesn't "understand" spam but calculates probabilities based on word frequency.
Why it mattered: For the first time, machines could learn from examples, not just rules.
Data + Compute Era (2000s)
Meaning: Internet boom = tons of data + faster computers → better AI models.
Detailed Example - Google Search:
- Old search (1990s): matched exact keywords only.
- New ML-based search (2000s): learns that if you type "best pizza near me", it should show restaurants, not websites with the words "best" + "pizza."
Impact: AI became useful at internet scale (search, ads, recommendations).
The Beginning of an AI Journey
What you've learned here is the foundation of AI - how we moved from rigid rules to statistical learning.
This set the stage for the machine learning revolution that would follow.