Classical Computing (Binary, Silicon-Based)
How it works: Uses transistors (0s & 1s)
Examples: Laptops, servers, GPUs.
Limitations: Slows down with complex problems
Cloud Computing (Remote Processing)
Key Players: AWS, Google Cloud, Azure.
Pros: Scalable, cost-efficient.
Cons: Latency, data privacy risks.
Edge Computing (Decentralized Processing)
Use Cases: Self-driving cars, smart factories.
Advantage: Real-time processing, lower latency.
Quantum Computing (Qubits & Superposition)
Potential: Drug discovery, cryptography.
Challenges: Extreme cooling, error rates.
Neuromorphic Computing (Brain-Inspired Chips)
Example: Intel’s Loihi, IBM TrueNorth.
Goal: Mimic human neurons for efficient AI.
Biocomputing (DNA & Molecular Computing)
Breakthrough: Storing data in synthetic DNA.
Future: Medical nanobots, bio-processors.