David J. Berg*, David Casler^, Romain Cledat*, Qian Huang*, Rui Lin*, Nissan Pow*, Nurcan Sonmez*, Shashank Srikanth*, Chaoying Wang*, Regina Wang*, Darin Yu**: Model Development Team, Machine Learning Platform^: Content Demand Modeling Team A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing …
Qualcomm did not violate a license with Arm when it acquired Nuvia for $1.4 billion, in a ruling by a jury today.Read More
From layoffs to the return of Gamergate, video games—and the people who make and play them—had a rough year.
Artificial intelligence that is as intelligent as humans may become possible thanks to psychological learning models, combined with certain types of AI.
Artificial intelligence (AI) has already become an invisible but indispensable collaborator in our lives. It helps filter spam from your inbox, improves your Netflix recommendations, and, as an automotive copilot, suggests optimal routes, monitors blind spots, and assists with parking.
Generative AI offers many benefits for both you, as a software provider, and your end-users. AI assistants can help users generate insights, get help, and find information that may be hard to surface using traditional means. In addition, they can help your employees reduce repetitive tasks and focus on high-value work. However, adding generative AI …
Read more “Add a generative AI experience to your website or web application with Amazon Q embedded”
Today, generative AI is giving organizations new ways to process and analyze data, discover hidden insights, increase productivity and build new applications. However, data sovereignty, regulatory compliance, and low-latency requirements can be a challenge. The need to keep sensitive data in certain locations, adhere to strict regulations, and respond swiftly can make it difficult to …
ChatGPT can now support more coding platforms and applications on its desktop apps, opening the door for more agentic use in the future.Read More
An international team of scientists has used machine learning to help them develop perovskite solar cells with near-record efficiency. In their paper published in the journal Science, the group describes how they used the machine-learning algorithm to help them find new hole-transporting materials to improve the efficiency of perovskite solar cells.
Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used …
Read more “Accelerating LLM Inference on NVIDIA GPUs with ReDrafter”