12 posts tagged with "Computer Science"

In his 2019 essay The Bitter Lesson, Richard Sutton outlined a key observation in AI research: general-purpose methods that leverage massive computation consistently outperform human-crafted, specialized approaches. While this claim has strong empirical support, its theoretical justification is often overlooked. This post explores the fundamental learning theories, computational complexity principles, and optimization frameworks that underpin The Bitter Lesson.

Distributed systems operate across multiple independent nodes, making it difficult to establish a single global clock. Logical clocks enable efficient event ordering without synchronized physical clocks, ensuring consistency in distributed systems. However, physical clocks may still be preferred in scenarios requiring real-world timestamping, such as financial transactions or legal record-keeping, where absolute time consistency is crucial. This blog post explores the concepts of logical clocks, their types, and their use cases in distributed systems.

Synchronization is a fundamental challenge in distributed systems, where multiple independent nodes must coordinate their actions despite network delays, failures, and asynchrony.

A common example is cloud-based databases, where multiple servers must stay synchronized despite operating independently. Similarly, blockchain networks, such as Ethereum, must ensure all nodes agree on the latest state despite network delays and decentralization.

Unlike traditional single-machine systems, distributed environments lack shared memory and global clocks, making synchronization complex. Various solutions exist to address this. One example is Google Spanner's TrueTime, which uses globally synchronized clocks to mitigate uncertainty. This technique helps ensure timestamps reflect a bounded range rather than a single point, enabling safer transaction ordering and enforcing strict consistency.

Ordering, in the context of distributed systems, refers to the ability to maintain a well-defined sequence of events or operations across multiple independent nodes. It is a fundamental challenge impacting everything from database consistency to consensus protocols and event-driven architectures. Ensuring a well-defined sequence of operations in an unreliable network is inherently difficult. This blog post explores why ordering is hard in distributed systems, common strategies to address it, and trade-offs involved.

In 2019, Richard Sutton, one of the pioneers of reinforcement learning, wrote a short but impactful essay titled "The Bitter Lesson". The core argument is simple: history has repeatedly shown that leveraging more computation trumps human-designed domain knowledge in AI. Despite decades of attempts to encode expert rules and handcrafted heuristics into AI systems, raw computational power combined with scalable methods has consistently outperformed these approaches over time.

In today’s hyper-connected world, the demand for efficient, reliable, and scalable data transmission across networks is ever-increasing. Traditional approaches to data communication rely heavily on forwarding techniques, where intermediate nodes simply relay data packets. While effective in many scenarios, these methods struggle with packet loss, bandwidth constraints, and dynamic network topologies. Enter Random Linear Network Coding (RLNC)—a revolutionary paradigm that leverages coding theory to enhance network performance.

Richard W. Hamming’s The Art of Doing Science and Engineering: Learning to Learn is more than a guide to technical disciplines; it’s a philosophical treatise on how to think effectively and pursue meaningful work. Hamming, known for his groundbreaking contributions to mathematics and computer science, offers insights that blend practical tools with timeless wisdom for anyone striving to make a difference in their field. In this post, I’ve distilled some of the book’s most impactful lessons and tools.

Erasure coding is a critical method used in data storage systems to enhance data durability and availability, surpassing traditional techniques like mirroring in storage efficiency. This blog post explores what erasure coding is, how it operates, and its significant role in modern storage environments.

In today's digital landscape, the ability to collaborate in real-time on applications like Google Docs, collaborative coding platforms, and online gaming environments is not just a convenience but a necessity. One of the fundamental technologies enabling this seamless, concurrent user interaction without the typical latency or conflict issues is the Conflict-Free Replicated Data Type (CRDT).

In graph theory, finding the shortest paths from a single source to all other vertices in a graph is a crucial problem that extends beyond theoretical computer science to practical applications in network routing, urban planning, and optimization. Traditionally addressed by sequential algorithms like Dijkstra’s or Bellman-Ford, the rise of parallel computing has prompted the development of methods that use multiple processors to manage larger graphs efficiently. This blog post explores the key concepts and advancements in parallel single-source shortest path (SSSP) algorithms.