Abstract: Emerging applications, e.g., machine learning, large language models (LLMs), and graphic processing, are rapidly developing and are both compute-intensive and memory-intensive. Computing in ...
When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a ...
Researchers create a photochromic fluorescent system that performs optical neural computing and visual output in one step, cutting power use and complexity. (Nanowerk News) The rapid growth of ...
The increasing computational demands of deep learning have brought power consumption to the forefront as a critical challenge, with matrix multiplications identified as a major performance bottleneck.
Even though traditional databases now support vector types, vector-native databases have the edge for AI development. Here’s how to choose. AI is turning the idea of a database on its head.
Git isn’t hard to learn. Moreover, with a Git GUI such as Atlassian’s Sourcetree, and a SaaS code repository such as Bitbucket, mastery of the industry’s most powerful version control tools is within ...
Design your own custom Google Maps in seconds! This high-quality vector map tutorial shows you how to create clean, editable maps for architecture, urban planning, and presentations. #CustomGoogleMap ...
As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...
A new technical paper titled “Leveraging ASIC AI Chips for Homomorphic Encryption” was published by researchers at Georgia Tech, MIT, Google and Cornell University. “Cloud-based services are making ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results