Researchers at MIT CSAIL have developed the Y-zipper, a three-sided fastener that enables objects to transition between flexible and rigid states. Inspired by a decades-old patent from Professor Bill Freeman, this new mechanism uses an automated software tool and 3D printing technology to create custom shape-shifting structures. The device can be used to quickly assemble camping gear, adjust medical wearables like wrist casts, or enable robots to change their limb dimensions for varied terrain.
* Three-sided triangular design for tunable stiffness
* Automated customization via software and 3D printing
* Rapid transition between soft and rigid states
* Versatile applications in robotics, medical gear, and outdoor equipment
* Three-sided triangular design for tunable stiffness
* Automated customization via software and 3D printing
* Rapid transition between soft and rigid states
* Versatile applications in robotics, medical gear, and outdoor equipment
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
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
MIT CSAIL Professor Hal Abelson emphasizes the importance of free software for securing our agency in our digital worlds. His project, App Inventor, enables anyone to create apps without coding. He also discusses free software freedoms, the book 'Blown to Bits', and the importance of privacy and data provenance in today's digital age.
This paper introduces Cross-Layer Attention (CLA), an extension of Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) for reducing the size of the key-value cache in transformer-based autoregressive large language models (LLMs). The authors demonstrate that CLA can reduce the cache size by another 2x while maintaining nearly the same accuracy as unmodified MQA, enabling inference with longer sequence lengths and larger batch sizes.
MIT CSAIL researchers have developed three neurosymbolic frameworks - LILO, Ada, and LGA - that use natural language to help large language models (LLMs) build better abstractions for coding, AI planning, and robotics tasks.
