The Australian federal police says it had "no choice" but to lean into using artificial intelligence and is increasingly using the technology to search seized phones and other devices, given the vast amount of data examined in investigations. The AFP's manager for technology strategy and data, Benjamin Lamont, said investigations conducted by the agency involve an average of 40 terabytes' worth of data. This includes material from the 58,000 referrals a year it receives at its child exploitation centre, while a cyber incident is being reported every six minutes. "So we have no choice but to lean into AI," he told a Microsoft AI conference in Sydney on Wednesday. "It's beyond human scale, so we need to start to lean in heavily on AI, and we're using it across a number of areas."

We hear about artificial intelligence all the time nowadays--but what is it doing for astronomy? New research papers are published almost every week using AI for some new investigation in astronomy: classifying galaxies, identifying solar flares, exploring exoplanet atmospheres, and more. AI's biggest strength is that it can sort through mountains of data much faster than a human--a skill that's particularly timely as new telescopes are generating more and more data for astronomers to handle. "We can use [AI] to tackle problems we couldn't tackle before because they're too computationally expensive," said Daniela Huppenkothen, astronomer and data scientist at the Netherlands Institute for Space Research, in MIT Technology Review. One telescope in particular has many astronomers abuzz about AI: the Vera C. Rubin Observatory, scheduled to be completed in January 2025, just a few short months away.

NASA's Marshall Space Flight Center and computing giant IBM plan to use artificial intelligence (AI) tech to improve climate change research, according to an announcement IBM posted on Feb. 1. Under the new partnership, NASA and IBM will create AI foundation models to analyze petabytes of text and remote-sensing data to make it easier to build AI applications tailored to specific climate change questions and tasks. "We hope these models will make information and knowledge more accessible to everyone and encourage people to build applications that make it easier to use our datasets to make discoveries and decisions based on the latest science," said Rahul Ramachandran, a senior research scientist at NASA's Marshall Space Flight Center. Foundational AI models can ingest massive amounts of raw data and find their underlying structure without explicit instruction. NASA is currently sitting on 70 petabytes of earth science data – a number expected to quadruple this year and into 2024 with future mission launches.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Digital transformation has fundamentally changed how businesses interact with their partners, supply chains, and customers. It has also exponentially increased the amount of data generated and stored by organizations. Modern enterprises generally have hundreds of terabytes, if not petabytes, of data, much of which is unstructured. This type of data can make up 80 to 90% of an enterprise's entire data footprint, and because it is unstructured, it is largely ignored.

Imaging Storage: Cloud Healthcare API, part of the Medical Imaging Suite, allows easy and secure data exchange using the international DICOMweb standard for imaging. Cloud Healthcare API provides a fully managed, highly scalable, enterprise-grade development environment and includes automated DICOM de-identification. Imaging technology partners include NetApp for seamless on-prem to cloud data management, and Change Healthcare, a cloud-native enterprise imaging PACS in clinical use by radiologists. Imaging Lab: AI-assisted annotation tools from NVIDIA and MONAI help automate the highly manual and repetitive task of labeling medical images, and Google Cloud also offers native integration with any DICOMweb viewer. Imaging Datasets & Dashboards: Organizations can use BigQuery and Looker to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.

Google Cloud on Tuesday announced Medical Imaging Suite, new technology it says can help with accessibility and interoperability of radiology and other imaging data. WHY IT MATTERS The new suite includes components focused on storage, lab, datasets, dashboards and AI pipelines for imaging, according to Google Cloud. It's designed to offer flexible options for cloud, on-prem or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements, officials say, while providing centralized management and policy enforcement with Google Distributed Cloud, enabled by Anthos. Google's Cloud Healthcare API enables secure data exchange using the international DICOMweb standard for imaging and offers a scalable, enterprise-grade development environment and includes automated DICOM de-identification. Other technology partners include NetApp for seamless on-prem to cloud data management, and Change Healthcare's cloud-native enterprise imaging PACS.

As providers look for more ways to add efficiencies, produce better patient outcomes and reduce costs, many are turning to partnerships with large technology companies. On July 20, University of Pittsburgh Medical Center announced that the health system would enter a five-year partnership with Microsoft to better utilize data the provider collects throughout its 40 hospitals. UPMC's clinical teams will have access to Microsoft's cloud computing, artificial intelligence and machine-learning tools to improve patient care. The two companies will work together to mine more than 13 petabytes of clinical data and 18 petabytes of imaging data with the goal of creating actionable insights for care teams. Previously, UPMC has leveraged data to identify higher-risk patients pulling information from more than 1 million surgical procedures.

As part of my annual tradition to tell you about how AWS makes Prime Day possible, I am happy to be able to share some chart-topping metrics (check out my 2016, 2017, 2019, 2020, and 2021 posts for a look back). My purchases this year included a first aid kit, some wood brown filament for my 3D printer, and a non-stick frying pan! According to our official news release, Prime members worldwide purchased more than 100,000 items per minute during Prime Day, with best-selling categories including Amazon Devices, Consumer Electronics, and Home. Powered by AWS As always, AWS played a critical role in making Prime Day a success. A multitude of two-pizza teams worked together to make sure that every part of our infrastructure was scaled, tested, and ready to serve our customers.

When most people picture an astronomer, they think of a lone person sitting on top of a mountain, peering into a massive telescope. Of course, that image is out of date: Digital cameras have long since done away with the need to actually look though a telescope. But now the face of astronomy is changing again. With the advent of more powerful computers and sky surveys that generate unimaginable quantities of data, artificial intelligence is the go-to tool for the keen researcher of space. But where is all of this data coming from?

Developing the next generation of advanced AI will require powerful new computers capable of quintillions of operations per second. Today, Meta is announcing that we've designed and built the AI Research SuperCluster (RSC) -- which we believe is among the fastest AI supercomputers running today and will be the fastest AI supercomputer in the world when it's fully built out in mid-2022. Our researchers have already started using RSC to train large models in natural language processing (NLP) and computer vision for research, with the aim of one day training models with trillions of parameters. RSC will help Meta's AI researchers build new and better AI models that can learn from trillions of examples; work across hundreds of different languages; seamlessly analyze text, images, and video together; develop new augmented reality tools; and much more. Our researchers will be able to train the largest models needed to develop advanced AI for computer vision, NLP, speech recognition, and more.