Emerging Technologies

Learning Objectives Analyze the core principles of Quantum Computing, including superposition and entanglement. Evaluate the ethical implications and societal impact of advanced AI and Generative Models. Design a high-level system architecture incorporating a decentralized technology like Blockchain. Compare and contrast the computational models of classical, quantum, and neuromorphic computing. Explain the role of Edge Computing in reducing latency and processing data for IoT systems. Deconstruct the training and inference pipeline of a large language model (LLM). Ever wonder how a computer could solve problems in seconds that would take today's supercomputers millennia? ⚛️ Let's explore the technologies that are shaping the future of computing. This lesson explo...

TermDefinitionExample Quantum SuperpositionThe ability of a quantum system, like a qubit, to exist in a combination of multiple states simultaneously. Unlike a classical bit which is either 0 or 1, a qubit can be both 0 and 1 at the same time until it is measured.A single qubit can represent both 0 and 1. Two qubits can represent four states (00, 01, 10, 11) simultaneously, allowing quantum computers to explore a vast number of possibilities in parallel. BlockchainA distributed, immutable digital ledger that records transactions in 'blocks'. Each block is cryptographically linked to the previous one, forming a secure and transparent chain that is managed by a peer-to-peer network.Bitcoin's public ledger, where every transaction is recorded and verified by a network of compu...

CAP Theorem (for Distributed Systems) A distributed system can provide at most two of the following three guarantees: Consistency, Availability, and Partition Tolerance. Essential for designing systems using blockchain or edge computing. When designing a decentralized application, you must trade off between ensuring all nodes have the same data (Consistency) and ensuring the system is always responsive (Availability), assuming network failures will happen (Partition Tolerance). Amdahl's Law (for Parallel Computing) Speedup = 1 / ((1 - P) + (P / S)) Used to predict the theoretical maximum speedup of a task when using multiple processors, where P is the proportion of the program that can be parallelized and S is the speedup of that part. It highlights that the sequential...