Overview

AI Fundamentals Introduction

Understand what artificial intelligence is, its history, and the landscape of modern AI.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve.

The AI Hierarchy

text
         Generative AI
              |
        Large Language Models
              |
          Deep Learning
              |
       Machine Learning
              |
   Artificial Intelligence

Brief History

  • 1950s: Alan Turing proposes the Turing Test; first AI programs
  • 1960s-70s: Rule-based "expert systems"
  • 1980s: Neural networks gain interest; ML algorithms
  • 1990s: Machine learning becomes mainstream; chess AI beats world champion
  • 2012: Deep learning revolution begins (ImageNet, AlexNet)
  • 2017: Transformer architecture introduced ("Attention is All You Need")
  • 2020: GPT-3 demonstrates remarkable language abilities
  • 2022-2024: ChatGPT, GPT-4, Claude, Gemini — AI goes mainstream

Current AI Capabilities

  • Natural language understanding and generation
  • Image and video recognition
  • Code generation
  • Medical diagnosis assistance
  • Drug discovery
  • Scientific research acceleration
  • Business development

Example

python
# Demonstrating AI capabilities with simple examples

# 1. Rule-based AI (traditional)
def rule_based_diagnosis(symptoms):
    rules = {
        frozenset(['fever', 'cough', 'fatigue']): 'Possible flu',
        frozenset(['fever', 'rash']): 'Possible measles',
        frozenset(['chest_pain', 'shortness_of_breath']): 'Possible cardiac issue - seek medical help',
    }

    for symptom_set, diagnosis in rules.items():
        if symptom_set.issubset(symptoms):
            return diagnosis
    return 'Unable to diagnose - consult a doctor'

symptoms = {'fever', 'cough', 'fatigue', 'sore_throat'}
print(rule_based_diagnosis(symptoms))

# 2. Statistical AI (machine learning)
from sklearn.naive_bayes import GaussianNB
import numpy as np

# Training data: [temperature_above_normal, has_cough, has_rash] -> diagnosis
X_train = np.array([
    [1, 1, 0],  # flu
    [1, 1, 0],  # flu
    [1, 0, 1],  # measles
    [0, 0, 0],  # healthy
])
y_train = ['flu', 'flu', 'measles', 'healthy']

clf = GaussianNB()
clf.fit(X_train, y_train)

patient = np.array([[1, 1, 0]])  # fever, cough, no rash
prediction = clf.predict(patient)
probability = clf.predict_proba(patient)
print(f"Prediction: {prediction[0]}")
Try it yourself — PYTHON