Articles about AI

Implementing cloud services with AI technologies, such as Microsoft Copilot, is fundamental for IT providers seeking to offer advanced solutions. However, with greater dependence on AI-generated tools to foster innovation and productivity in organizations, the necessity of enabling cloud environments to host these sophisticated capabilities has become paramount.

Their successful integration, however, comes at the expense of having additional investments in computing power, data analytics, and intelligent security solutions that shield sensitive information from unauthorized access. Many companies first need to accomplish a cloud migration to improve the security posture of the infrastructure before implementing AI.

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Google Assistant is being consigned to history. Almost a decade after it first launched, Google’s voice-activated digital assistant will be replaced by Gemini, Google’s updated AI-powered assistant.

If you’re an existing user of Google Assistant, you will no doubt have noticed the offer of switching to Gemini. While this has been, and remains for now, an optional switch, soon there will be no choice -- it will be Gemini all the way.

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It’s a fact that over 80 percent of AI projects fail. Not because of AI’s potential, but because businesses prioritize minor use cases over real transformation. Automated insights and meeting summaries may be impressive, but AI only drives impact when seamlessly integrated into workflows, turning insights into action.

Deploying AI successfully isn’t simple, and organizations are complex. Effective AI deployment requires a clear framework for human oversight. AI should usually enhance human decision-making, providing targeted, explainable, and interactive insights. But in some cases -- especially when decisions are time-sensitive or involve vast amounts of data -- humans cannot oversee every output in real time. This raises a key question: when should humans be ‘in the loop,’ actively making decisions, and when should they be ‘over the loop,’ overseeing AI without direct intervention? Getting this balance right is crucial for both AI’s effectiveness and its responsible use.

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This year started with a shockwave for the tech world, and the AI community in particular. Launched by a relatively obscure Chinese startup, DeepSeek not only challenged the rules of the AI game by sending Nvidia's stock plummeting 17 percent in one day and becoming the most-downloaded app on the App Store and Play Store, but also showed the persisting security problems by accidentally exposing its database and leaking sensitive data including chat histories, API keys and backend operational details.

Success and failures aside, DeepSeek made the world realize how quickly and deeply a single AI model release can impact global events, and this raises three questions. First, how legitimate (and sustainable) are the massive AI investments in the West? Second, what risks and opportunities does open-source development pose? Finally, is it possible to balance growth and innovation with data privacy and security amidst a global AI race?

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The subway in NYC has received a huge upgrade, but it’s probably not at all what you’d expect. No, it is not coming in the form of new trains or renovations to stations (even though that would be great). Actually, the MTA is testing a system that effectively transforms Google Pixel Android smartphones into powerful diagnostic tools for tracking rail defects!

The project, called “TrackInspect,” attaches Google Pixel phones to subway cars. Then, by using the Android devices’ built-in microphones and motion sensors, it detects vibrations and sound patterns. These sounds can indicate areas of track that may need maintenance. How cool is that?

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According to Gartner, over the next few years hybrid cloud will become the de facto approach for unlocking value from data. The projections are stark. Nine-in-ten organizations will adopt a hybrid cloud model by 2027, and end user spending globally on public cloud will grow by 21.4 percent this year alone, reaching more than $723 billion. Application services, system infrastructure services and Platform-as-a-Service (PaaS) will all see a boost in spending as well. By anyone’s money, these are huge numbers.

This second cloud revolution is being driven by data. When combined with analytics, data is a uniquely valuable asset for any business. If harnessed correctly, it can grow revenue, reduce costs, and entirely transform a business by opening up fresh market opportunities through the use of new technologies like GenAI.

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If you thought solid state drive manufacturers were going to be slowing down their progress, you should think again. Today, Samsung unveils its 9100 PRO series, and it’s insanely fast. Using PCIe 5.0, it achieves up to 14,800 MB/s read and 13,400 MB/s write speeds! That’s twice as fast as its predecessor. So, yeah, this is a pretty big deal.

There are two variants of the SSD -- the standard 9100 PRO and a version with a heatsink for better cooling. Samsung has equipped these drives with a power-efficient 5nm controller, reducing energy use by nearly 50 percent. Impressively, random read/write speeds can hit 2,200K/2,600K IOPS.

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According to estimates from Goldman Sachs, generative AI (GenAI) will constitute 10-15 percent of cloud spending by 2030, or a forecasted $200-300 billion (USD). The public cloud serves as the perfect vessel for delivering AI-enabled applications quickly, cost-effectively, and at scale. For organizations looking to profit from AI’s potential, the path effectively travels through the cloud.

For cloud security teams on the ground, however, the impact of AI can seem complicated. Understanding the challenges it presents, and the key capabilities it enables, can help them work smarter and more effectively. This article explores the three ways cloud security teams should think about AI to enhance protections, improve efficiency, and address resource constraints.

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Mark your calendars, fellow tech fans! Google I/O 2025 is happening on May 20-21 at Shoreline Amphitheatre in Mountain View, California. Can’t make it in person? No worries, you can join in the fun online at io.google -- registration is open now.

This is Google’s biggest bash for developers, where the search giant will show off all the cool new stuff in tech, software, and AI. The event will surely offer some big announcements -- think new gadgets and software updates. After that, there’s a whole bunch of workshops, sessions, and meet-ups for everyone to get hands-on and network.

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Elon Musk has a reputation for rewriting the rules, and his latest venture at the helm of the Department of Government Efficiency (DOGE) is no exception. He’s brought on a 19-year-old known as "Big Balls" to help overhaul how the government operates, causing quite the stir, not only because of the nickname, but because of his young age.

"Big Balls" isn't just a provocative moniker regarding large testicles, however. Actually, this name more likely embodies courage, bold decision-making, and a defiance against conventional challenges. These are the traits that fuel breakthroughs, and let's face it -- big tech could benefit from infusing some of that spirit into their operations.

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Industry 4.0 is revolutionizing how enterprises need to operate. Integrating cloud technology, robotics, mobile devices and IoT devices is essential to staying ahead in an increasingly competitive global market. To maximize the benefits of Industry 4.0, however, these technologies must always stay connected to a network.

This has become even more true as IT environments grow to massive scales and unprecedented digital dependencies drive business outcomes. In 2025, network downtime could carry a price tag with consequences we’ve never seen before. Therefore, as businesses look to cellular and/or hybrid approaches to enhance their wireless wide area networks (WWAN), they must depend on four things to create resilient connectivity -- artificial intelligence (AI), satellite, network slicing, and a renewed respect for zero-trust principles.  

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Artificial intelligence’s rise in business, while exciting for some, is unsettling for financial professionals. They worry AI will replace them, but understanding its true power in finance turns fear into opportunity. AI isn’t here to take over -- it’s here to level up the game. The real power lies in collaboration, not competition. Finance pros who embrace AI don’t get replaced; they get supercharged.

Traditional tedious and time-consuming tasks that keep accountants bogged down -- data entry, invoice processing, reconciliation, reporting, and more -- can now be managed by AI-powered automations. Companies gain two key benefits when they embrace the shift. First, human finance professionals are delivered from the day-to-day routine and freed up to focus on fine-tuning financial strategies in a way that can drive better business outcomes. The demands of manual accounting typically keep finance professionals from having any time to zoom out and deal with big-picture planning.

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Just how AI models should be trained has been a subject of debate for some time now, with there being a lot of focus in whether publicly posted social media content is ripe for the picking or not. Now a new lawsuit suggests that Meta has been using pirated ebooks as a data source.

Emails that are serving as evidence in a copyright case against Meta appear to show that the Facebook owner has torrented scores of terabytes of data from a number of online resources. Among the places mentioned in newly released unredacted emails are Anna’s Archive, Z-Library and LibGen.

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Modern enterprises are under fire from all angles. Attackers have become increasingly sophisticated and persistent in how they target enterprise data and systems. But as the threat landscape has evolved and become more complex, one tried and true method for malicious attackers stands out as a weak point for nearly every enterprise attack surface: outdated software. As much as patch management has advanced in recent years, the fact remains that most organizations struggle to deploy patches consistently and effectively, and that leaves systems exposed to cyber attacks.  

Cybercriminals have become quite adept at exploiting unpatched software, using it as an easy entry point into enterprise networks. Malicious actors have developed an incredibly sophisticated understanding of where enterprise weak points are. In fact, most criminal operators have a deeper understanding of enterprise attack surfaces than the security teams tasked with defending them. Enterprise networks often consist of hundreds of thousands of IT assets, and every single unpatched instance represents an opportunity for attackers to compromise data and operations.

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Until the late 1990s, manufacturing reigned as the lifeblood of the global economy -- leading in productivity, employment, growth, and investments across all points of the world. However, once we neared the close of the 20th century, manufacturing found its Achilles heel in the compounded complexity accrued from outdated processes, an over-reliance on human labor that simply couldn’t meet its extreme needs, supply chain disruptions, and rising costs.

I fear that today, the information technology industry finds itself at all-too-familiar cross-roads. Why is this?

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