This post outlines a 4-step process for optimizing ML systems for faster training and inference. The steps are: benchmark, simplify, optimize, and repeat. The process involves profiling the system, identifying bottlenecks, simplifying the code, and optimizing compute, communication, and memory. The goal is to improve system performance and efficiency.

This tutorial demonstrates the process of using transfer learning and an LLM (Language Model) to create a text classification model.

IBM Research has developed a new computer chip called NorthPole that significantly improves the speed of AI-based image recognition applications. The chip, inspired by the human brain, offers a 22-fold increase in processing speed compared to current market offerings. It enables faster data processing and response times by bringing data physically closer to AI applications.…

Latent Consistency Models (LCMs) are a new generation of generative AI models proposed by researchers from Tsinghua University. LCMs efficiently generate high-resolution images by predicting augmented probability flow ODE solutions in latent space. This approach reduces computational complexity and generation time compared to existing models. LCMs excel in text-to-image generation, delivering state-of-the-art performance with minimal…

This week’s news roundup highlights various AI-related topics. The FCC is exploring solutions to tackle the issue of robocalls powered by AI. The mayor of New York City used deepfake technology to deliver automated calls in multiple languages. The UK government released a schedule for the AI Safety Summit and a report on potential risks.…

This text provides a comprehensive guide on how to handle different CUDA versions in a development environment. It discusses the potential issues and consequences of installing multiple CUDA versions and provides step-by-step instructions on downloading and extracting the desired version, installing the CUDA toolkit, and setting up the project to use the required CUDA version.…

Facebook AI Research (FAIR) is focused on advancing socially intelligent robotics. Their goal is to develop robots that can assist with everyday tasks and adapt to human preferences. They have introduced three significant advancements: Habitat 3.0, a simulator for human-robot collaboration; the Habitat Synthetic Scenes Dataset (HSSD-200), a 3D dataset for training navigation agents; and…

Probabilistic diffusion models are cutting-edge generative models that have gained importance in computer vision. These models use a Markov chain to map the latent space and have impressive generative capabilities. A joint study explores the denoising process of diffusion models using a Fourier domain approach. The study reveals the impact of the U-Net architecture on…

A smart and stretchable soft sensor has been developed for robotics and prosthetics. It provides touch sensitivity and dexterity to prosthetic arms and robotic limbs, enabling tasks like picking up soft fruit. The sensor skin is also soft like human skin, making human interactions safer and more realistic.

AI relies on data, but its legal and ethical origins are often unclear. Large language models like LLM require substantial amounts of text data, which can be found on platforms like Kaggle, GitHub, and Hugging Face. However, many datasets lack clear licensing information, posing copyright and fair use concerns. The Data Provenance Initiative has audited…