Word Embedding (Python) is a technique to convert words into a vector representation. Computers cannot directly understand words/text as they only deal with numbers. So we need to convert words into ...

Cloud Architect at Amazon Web Services (AWS) specializing in cloud-native development, AI/ML, serverless compute, and event-driven architecture. Cloud Architect at Amazon Web Services (AWS) ...

Have you ever wondered what separates a good design from a truly exceptional one? Often, it’s not just the creativity of the designer but their mastery of the tools at their disposal. Enter Affinity 3 ...

I am using the preview feature of vertexai vector store that support embedding_metadata. When using the curl command I can retrieve the metadata associated with a datapoint. However, this doesn't work ...

Unity Software (U) was in the spotlight on Friday after investment firm Macquarie upgraded the video game software company after it posted stronger-than-expected earnings earlier this week. Shares ...

The latest embedded world North America conference is in Anaheim this year (Nov. 4-6). It's not quite as big as the one in Nuremberg, Germany, but it's growing. We will add new content to this page as ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Abstract: Contrastive quantization (applying vector quantization to contrastive learning) has achieved great success in large-scale image retrieval because of its advantage of high computational ...

Oracle Corp. today announced the general availability of Oracle AI Database 26ai and Oracle Autonomous AI Lakehouse, both aimed at supporting artificial intelligence training and inference across ...

On Wednesday, Wikimedia Deutschland announced a new database that will make Wikipedia’s wealth of knowledge more accessible to AI models. Called the Wikidata Embedding Project, the system applies a ...

Fixed-Dimensional Encoding (FDE) solves a fundamental problem in modern search systems: how to efficiently search through billions of documents when each document is represented by hundreds of vectors ...