Personal website of Alex L. Zhang, a PhD student at MIT CSAIL focusing on the efficiency and utilization of language models. His research spans ML systems, language model benchmarks, and specialized model development.
Key areas of work include:
- Recursive Language Models (RLMs) and Project Popcorn
- GPU programming competitions via KernelBot and GPU MODE
- Benchmarking capabilities through VideoGameBench and KernelBench
- Development of models like Neo-1 and KernelLLM-8B
Key areas of work include:
- Recursive Language Models (RLMs) and Project Popcorn
- GPU programming competitions via KernelBot and GPU MODE
- Benchmarking capabilities through VideoGameBench and KernelBench
- Development of models like Neo-1 and KernelLLM-8B
This is an open, unconventional textbook covering mathematics, computing, and artificial intelligence from foundational principles. It's designed for practitioners seeking a deep understanding, moving beyond exam preparation and focusing on real-world application. The author, drawing from years of experience in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
This article features Noyuri Mima, professor at Hakodate Future University, and explores her journey in the field of science and technology. Miwa recounts a pivotal moment in her youth when she visited the Japan IBM headquarters in 1977 and witnessed a computer instantly print a calendar based on her birthdate. This experience ignited her passion for computers. The article delves into her background, including her education at Toyo English Women's College and her early interest in mathematics and science, highlighting her as a pioneer for women in STEM.
This page provides an overview of the 'Missing Semester' course, focusing on the importance of the shell as a powerful tool for computer scientists. It covers motivation, class structure, the basics of navigating and using the shell, and exercises to reinforce learning. The course aims to equip students with practical skills beyond rote memorization of commands, enabling them to automate tasks and solve complex problems efficiently.
A new proposal suggests that complexity increases over time, not just in living organisms but in the nonliving world, potentially rewriting notions of time and evolution. Researchers propose a law where entities are selected for richness in information enabling function, challenging traditional views and sparking debate about its testability and implications for understanding the universe.
Welcome to AP Computer Science with Python! This is a level-2 intelligent textbook currently under construction.
The quest to find the longest-running simple computer program has identified a new champion. It’s physically impossible to write out the numbers involved using standard mathematical notation.
A paraphrasing of Gerald Jay Sussman's explanation for MIT's switch from Scheme to Python in its undergraduate computer science program, focusing on the changing nature of programming and the need to adapt to modern systems and libraries.
A connection between descriptive set theory and computer science has been discovered, allowing problems in one field to be rewritten and solved in the other by Anton Bernshteyn.
Problems in descriptive set theory (measuring infinite graph colorings) are mathematically equivalent to problems in distributed algorithms (efficient network coloring).
Problems in descriptive set theory (measuring infinite graph colorings) are mathematically equivalent to problems in distributed algorithms (efficient network coloring).
A new paper by SFI Professor David Wolpert introduces a mathematically precise framework for the simulation hypothesis, challenging several long-standing claims and opening up new questions about simulated universes.
