Receiving an unsolicited image is an unpleasant experience at the best of times, and one that technology has made all too common. At WWDC, Apple announced iOS 17 will use an on-device machine learning model to scan both images and videos for nudity. When detected, you'll get a pop-up, telling you the system thinks the file may be inappropriate. I wonder how much of this is a response to the practice of AirDropping inappropriate images to an unsuspecting person's phone. One notable incident from 2022 saw a person removed from a flight after they had shared an image of themselves with other passengers.

The Active Archive Alliance announced that Seagate Technology has joined the organization. The Active Archive Alliance is a collaboration of industry-leading storage and IT vendors that collectively support the use of active archive solutions for data lifecycle management. "The goals of Seagate and the Alliance are now more closely aligned than ever, making this the ideal time to join forces." "Seagate is proud to join the Active Archive Alliance to support their important mission to maximize data access across all storage media through its entire lifecycle. We are dramatically broadening our technology investments and product portfolio to offer customers hardware, software and services that solve their most challenging mass-capacity storage, transfer and migration needs," said Ted Oade, product marketing director at Seagate.

Seagate Technology Holdings plc (NASDAQ: STX), a world leader in mass-data storage infrastructure solutions, announced the launch of Lyve Cloud Analytics platform, a complete cloud-based analytics solution that includes storage, compute, and analytics, to help Lyve Cloud customers lower the total cost of ownership (TCO) and accelerate time to value with their DataOps and MLOps (machine learning operations). Adding the analytics capability to the most frictionless cloud storage, Seagate enables enterprises to activate their stored data in an open data lake architecture for analytics at petabyte scale, further accelerating time to implementation and innovation, yielding up to 40% cost saving compared to other available offerings in the market. In today's datasphere, multicloud has become a mainstream cloud strategy for enterprises, but complex data access challenges and unpredictable costs hinder optimal data utilization. These challenges pose even larger roadblocks for data-intensive industries where enterprises rely heavily on the vast amount of collected data to train their data analytics, AI, and machine learning systems. According to the research firm Gartner, only 53% of AI projects achieve production from prototype due to the lack of right tools to scale.

Google has devised a machine learning (ML) model that predicts disk failures with 98 per cent accuracy. The idea is to reduce data recovery work when disks actually fail. According to a Google blog by technical program manager Nitin Agarwal and AI engineer Rostam Dinyari, Google has millions of hard disk drives (HDDs) under management, some of which fail. "Any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services." When a disk in Google's data centres encounters non-fatal problems, short of an actual crash, then data is (drained) read from the drive. The drive is then disconnected from production use, they apply diagnostics and it is fixed and returned to production.

Ensuring the quality of each one is a top priority, but not easy. The disk drive manufacturing process is incredibly complex. For example, it takes 1,400 steps just to manufacture the drive head. Even the smallest errors can lead to product flaws. "Mistakes are costly," said Raghavan Srinivasan, senior director of global marketing at Seagate, which makes more disk drives than any other manufacturer. "If there are any anomalies, we're going to end up with poor yield later on, and we wouldn't know that until the long process is completed."

No matter the domain, data-intensive apps share one requirement in common: a reliable file system that ensures data is available to them on demand. Pure Storage, NetApp, VAST Data, IBM Spectrum Scale, and Dell EMC provide this, as does San Jose, California-based company WekaIO. WekaIO's high-velocity Matrix platform takes advantage of flash storage, off-the-shelf components, and sophisticated software techniques to deliver enormous speedups at exabyte scale. In fact, the company claims Matrix is the fastest parallel file system on the market for AI and technical compute workloads, as measured by independent SPEC SFS 2014 benchmark tests. To lay the groundwork for future growth in AI and analytics, life sciences, manufacturing, media and entertainment, and financial services, WekaIO has closed a $31.7 million series C financing round led by Hewlett Packard Enterprise (HPE), with participation from a host of storage and computing industry giants including Mellanox, Nvidia, Seagate, Western Digital Capital, and Qualcomm.

When you think about it, the modern age can be boiled down to data -- images you upload to social media, cloud services, videos that are online, even your personal details. It all ends up as data. To make sense of how the future looks like, Seagate sponsored a IDC white paper called Data Age 2025, which aims to predict how the datasphere will be shaped by the year 2025. Predicting trends is quite tricky considering so many technologies are in their nascent stage right now. Virtual and augmented reality is starting to be take shape and is slowing becoming more accessible to the masses. The same is true for connected devices.

The growth and evolution of big data in the enterprise vertical is leading to a massive data explosion wherein, the major challenge remains to be storing enormous amounts of data in a cost-effective manner plus processing to get business benefits. We spoke to Mr. Tim Butcher, Senior Vice President, Seagate about the current big data management solutions and technology innovations. Every business is generating some kind of data and storing them either over the cloud or locally. Only storing the data is not enough, you need to use machine learning or artificial intelligence kind of technologies to get the max out of it. Accessing the data is absolutely critical, as there are different levels of accessibility.

The 2017 CES show featured the latest in consumer products and services. The CES show featured Virtual and Augments Reality, 3D printing, Artificial intelligence with voice recognition and automated vehicles, high resolution and high dynamic range displays, health products, robots, toys and just about every other consumer product that you can think of. However one thing that all of these electronic devices have in common is their need for digital storage and memory. Seagate and DJI (the leading Chinese drone manufacturer) announced a strategic partnership to work jointly to solve the data demands of UAV users. As drone cameras gain resolution and drone flight times grow longer, DJI and Seagate are focusing their efforts to securely and efficiently store, manage, download and share the hundreds of gigabytes of data that can be generated from a single drone mission.