All articles tagged with #computer science
Originally Published 8 days ago — by WIRED
Descriptive set theorists have established a surprising connection between the abstract mathematics of infinity and practical computer science, showing that problems involving infinite sets can be translated into algorithms and network communication issues, thereby bridging a gap between theoretical and applied mathematics.
Originally Published 18 days ago — by The New Stack
Donald Knuth's 2025 Christmas Lecture at Stanford focused on the Knight's Tour problem, exploring its mathematical beauty, solutions, and recent computational breakthroughs, highlighting the intersection of math, art, and computer science.
Originally Published 3 months ago — by Business Insider
UC Berkeley professor Hany Farid highlights that the computer science industry is experiencing significant upheaval, with students struggling to find multiple internship offers and high salaries, partly due to AI and industry changes. He advises students to diversify their skills and embrace AI to stay competitive in the evolving job market.
Originally Published 4 months ago — by Morning Brew
The value of a computer science degree as a direct path to employment is declining due to reduced entry-level hiring in tech companies and increased automation of basic coding tasks by AI, leading to higher unemployment rates among recent grads and a shift in hiring practices that favor upskilling and AI fluency.
Originally Published 4 months ago — by Business Insider
Lovable CEO Anton Osika argues that a computer science degree is no longer the sole entry ticket to a career in tech, emphasizing skills like curiosity, adaptability, and rapid product development, especially in the age of AI, while acknowledging the ongoing value of deep technical knowledge for specialized roles.
Originally Published 5 months ago — by Futurism
Recent computer science graduates are facing unprecedented challenges in the job market due to widespread layoffs and the rise of AI coding tools, leading many to accept jobs outside their field, such as fast food positions, amid high unemployment rates and bleak prospects for tech careers.
Originally Published 5 months ago — by Slashdot
The article discusses the decline of lucrative tech jobs for new graduates, highlighting the story of Manasi Mishra, who, despite a computer science degree and early coding experience, struggled to find tech employment and was only contacted by Chipotle. It also explores how AI tools are both helping and hindering job seekers in the current competitive market.
Originally Published 5 months ago — by TechCrunch
The promise of prosperity in computer science has collapsed, with fresh graduates facing high unemployment rates and limited job prospects due to AI automation and layoffs at major tech companies, leading to a challenging job market and stories of frustration among new graduates.
Originally Published 7 months ago — by futurism.com
Recent computer science graduates are facing higher unemployment rates than the overall average, with the 'learn to code' movement backfiring due to market saturation, automation, and a shift in industry demand, leading to job insecurity and layoffs in the tech sector.
Originally Published 1 year ago — by WIRED
Researchers have proven that a version of Dijkstra's algorithm, a classic path-finding method developed in 1956, is the best approach for solving the single-source shortest-paths problem across any street grid, assuming worst-case traffic patterns. This algorithm, which is a staple in computer science education, has been shown to be universally optimal, meaning it performs best on every possible network layout. The breakthrough was achieved by focusing on the data structure used in the algorithm, leading to a simpler and more efficient design.
Originally Published 1 year ago — by Daily Nous
A study led by James Zou from Stanford reveals that 7-17% of sentences in peer reviews for computer science articles in 2023-2024 were generated by large language models (LLMs). These AI-generated reviews are characterized by a formal tone, verbosity, and a lack of specificity, often appearing close to submission deadlines. Zou suggests that fostering more human interactions in the review process, such as through platforms like OpenReview, could mitigate the dominance of AI in peer reviews.
Originally Published 1 year ago — by NJ.com
Avi Wigderson, a professor at the Institute for Advanced Study in Princeton, has been awarded the 2023 A.M. Turing Award for his influential work in incorporating randomness into computer algorithms, earning him the title of the "Nobel Prize for computer science." Wigderson's contributions to theoretical computer science and his leadership in the intersection of math and science were recognized in this prestigious honor.
Originally Published 1 year ago — by ScienceAlert
Researchers at the University of California, Riverside have developed simultaneous and heterogeneous multithreading (SHMT), a new process that could potentially double the speed of existing computers by taking advantage of multiple processors in modern devices. The innovative approach aims to increase efficiency and reduce energy use, with promising test results showing a 1.95 times faster execution of sample code and a 51 percent reduction in energy use. While still in the early stages, the research presents a new direction for improving processor efficiency and performance in smartphones, computers, and other gadgets.
Originally Published 1 year ago — by Quanta Magazine
After 70 years, computer scientists have mathematically proven the optimal trade-off between time and space efficiency for hash tables, a fundamental data structure in computer science. Two recent papers presented a groundbreaking hash table design that achieves the best combination of time and space efficiency yet conceived. This invention sets a new upper bound for the most efficient hash tables, and a subsequent team proved that it was as efficient as any data structure could possibly be. Despite its unprecedented efficiency, the complexity of constructing this hash table means it's unlikely to be built anytime soon, but it has opened up new possibilities for related problems in theoretical computer science.
Originally Published 1 year ago — by Quanta Magazine
Researchers have made a significant breakthrough in the computational efficiency of integer linear programming (ILP) by tightening the upper bound on the covering radius, achieving a dramatic speedup of the overall ILP algorithm. This advancement brings the runtime to (log n)O(n), where n is the number of variables, marking a triumph at the intersection of math, computer science, and geometry. While the new algorithm has not yet been applied to solve logistical problems due to the effort required to update existing programs, it represents a major theoretical advancement with fundamental applications. Further improvements in computational efficiency would necessitate fundamentally new ideas.