This post presents an architectural approach to extract data from different cloud environments, such as Google Cloud Platform (GCP) BigQuery, without the need for data movement. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. We highlight the process of using Amazon Athena Federated Query to extract data from GCP BigQuery, using Amazon SageMaker Data Wrangler to perform data preparation, and then using the prepared data to build ML models within Amazon SageMaker Canvas, a no-code ML interface.

Apple Intelligence rolls out today, marking a new era of AI-powered personalisation across iOS, iPadOS, and macOS.

As the tech giant readies a report of what’s forecast as its slowest quarterly growth in a year, shareholders want to see genuine demand for the AI applications it’s spent billions to develop.

Whisper, OpenAI’s transcription service, is apparently making things up. While all AI is susceptible to hallucination, Whisper is being used for important work, like transcribing medical conversations.

xAI and NVIDIA brought Colossus online in just 122 days when it would ordinarily take “many months to years” to operationalize such a system.

There are, without a doubt, two broad technological fields that have been developing at an increasingly fast pace over the past decade: artificial intelligence (AI) and the Internet of Things (IoT).

These new countries/territories will likely grab Google’s recent AI Overview updates.

OpenAI’s Whisper tool may add fake text to medical transcripts, investigation finds.

If you don’t have a brand-new Apple device, you might still be able to take advantage of Apple Intelligence features.

The update also brings a handful of fixes and features to all iPhones.