Articles about AI

Screenshotting stuff can seem like a great way to remember something for later, but captures soon get lost and forgotten. Google is looking to change this by bringing new Gemini-powered features to Google Maps.

The company thinks that it can help you to make sense of the “travel blogs, news articles or social media posts” you’ve screengrabbed while researching a trip. Turning to AI to make sense of a heap of images is Google’s solution. Here’s how it can help you.

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By now we’ve all been beaten around the head with the message that AI is the new rock star. It writes poems, paints like Picasso, and can probably tell you what you had for breakfast three Tuesdays ago. But really, if all we’re doing with AI is making it admire itself in a digital mirror, we're doing it wrong. AI is capable of all sorts of technological wizardry, but the most important thing it should be doing is keeping us from having a full-blown existential crisis trying to keep up!

These days, change doesn't politely knock -- it barges in like a caffeinated squirrel. A recent Nexthink report found that 94 percent of companies think their survival depends on AI in the next three years. But here's the kicker: less than half (47 percent) of employees have the skills to actually get to grips with AI. For some, it’s going to be like trying to teach a goldfish quantum physics. The divide between where they are now and where they need to be? It's a gap, a chasm, a digital Grand Canyon! But for the brave few who are willing to build a bridge for them, it’s a massive opportunity.

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Artificial intelligence is now all but unavoidable, and Microsoft is taking every opportunity to crowbar more AI features into Windows 11. Copilot+ PCs are a big part of the company’s AI vision and now powerful new AI agents have been unveiled.

Described as being part of a “new generation of Windows experiences”, Microsoft has revealed new agents that use on-device AI to interpret natural language input to help a user find and change system settings. What could possibly go wrong?

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You’ve possibly seen people online try to use ChatGPT to generate valid product keys for paid software, including different versions of Microsoft Windows.

YouTuber Gameboy Hub set out to test how successful ChatGPT is at generating, or sourcing, product keys to activate various versions of Microsoft Windows -- starting from Windows 95 and working up to Windows 7 (from where, in theory, it could be possible to upgrade to Windows 10 and Windows 11).

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Powerful new tools are only useful if people know what they can do and how to get them to do those things. For what feels like an eternity, Microsoft has been pushing Copilot as the greatest addition to Windows since... well, since Cortana it probably thinks.

For tech heads who have kept up with the latest news and developments, it’s easy to know what Copilot is, what it is about, and what it can do. But Microsoft realizes that not every Windows user is a tech head and is adding a guided tour to the app.

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Despite making claims to the contrary, Microsoft is not really thought of as being a company that takes much notice of user opinion. All too often it seems that incredibly unpopular options, features and changes are introduced with little regard for what users actually want.

But, to buck the trend, Microsoft has apparently been listening to feedback from people who are unhappy about the encroachment of Copilot into... well, pretty much everything, really. The decision to include a physical Copilot key on keyboards has been one such unpopular move, and the reaction from Windows 11 users has forced Microsoft’s hand.

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If we are lucky, Artificial intelligence might one day help scientists understand the human brain the same way language models predict the next word in a sentence. And now, that future is closer to becoming reality thanks to a new project from Google Research, Harvard University, and HHMI Janelia. You see, these teams have introduced the Zebrafish Activity Prediction Benchmark, better known as “ZAPBench,” which could help researchers create more accurate models for predicting brain activity.

ZAPBench isn’t just another dataset, folks. Actually, this new tool is based on two hours of brain recordings from larval zebrafish, capturing how roughly 70,000 neurons fired in response to different virtual reality scenarios. These tiny fish were shown various environmental changes, including shifting light patterns and moving water currents, while researchers recorded brain activity at an impressively detailed scale.

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The latest buzzy concept in the world of AI is now transcending its initial hype. Advances in Agentic AI are accelerating the development of autonomous business systems, building upon progress in machine learning. Acting as an independent ‘agent’, the technology can make informed decisions based on the multimodal data and algorithms they’re built on and then ‘learn’ from its experiences.

But even more excitingly, Agentic AI aims to execute tasks independently. It’s this ability to adapt, plan, and execute complex tasks without direct human intervention is what differentiates Agentic AI from its predecessors.

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AI agents are having a moment. From automating customer service to optimizing supply chains, AI agents are constantly promising to transform how organizations operate -- faster, smarter and more efficiently. In fact, recent research from Salesforce shows that 93 percent of IT leaders plan to implement AI agents in the next two years. But what exactly is an AI agent?

An AI agent is a software system that can autonomously perform tasks like answering customer inquiries and translating documents in multiple languages, improving overall efficiency and customer experience. Unlike traditional automation tools that follow static rules, AI agents continuously learn from data and adapt to changing conditions to make decisions on their own, in real time. That’s what makes AI agents powerful and risky.

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AI and machine learning tools have had an important role in software development for many years, helping to drive efficiency and automation. The new generation of AI tools has the potential to supercharge this transformation, bringing even greater improvements to efficiency, cost-effectiveness, and innovation cycles.

However, these tools also come with new risks, including security vulnerabilities, governance challenges, and regulatory uncertainty. As with any new technological approach, organizations bringing new AI tools and specifically AI-generated code into their development lifecycles must balance benefits with the potential risks.

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Artificial intelligence (AI) is transitioning from an emerging technology to a business mainstay. While many businesses are already reaping the benefits of strategic AI implementation, others are adopting AI solutions without first considering how to integrate the tools strategically. While some AI tools offer tangible gains in automation and efficiency, others overpromise and underdeliver, leading to costly investments with little return.

Distinguishing marketing buzz from real-world impact is critical for businesses looking to make AI a true driver of operational success. Despite AI’s potential, many businesses fall into common pitfalls that prevent them from realizing the full value of innovative technology. From unclear objectives to poor integration and security risks, these challenges can turn AI from a competitive advantage into an expensive mistake.

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Google is hitting back at the Department of Justice over a proposed plan that could force massive changes to how the company operates. The DOJ, which sued Google back in 2020 over search distribution practices, is now pushing for remedies that Google says go way too far -- and could end up hurting American consumers, innovation, and even national security!

In a new blog post, the search giant says the proposed changes are not only unnecessary but also dangerous. At the heart of the issue is how Google partners with phone makers and browsers to make its search engine the default. According to Google, people use its search because they want to -- not because they’re forced to. The company says blocking these deals would just make it harder for people to get to the services they prefer, with alternatives like Microsoft’s Bing being pushed on users instead.

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Donald Trump may be trying to slow down Chinese imports, but his tariffs are no match for free open source software! You see, Chinese Linux distribution deepin just dropped version 23.1, and no policy can stop it from reaching American citizens. With a ton of awesome updates and refinements, this new version delivers both improved stability and better ease of use. So what that it comes from our friends in China?

The heart of this update lies in major kernel upgrades to versions 6.6.84 and 6.12.20, delivering enhanced compatibility with the latest hardware. Got NVIDIA graphics or rocking an Intel or AMD CPU? You’re in luck, as deepin has integrated updated graphics drivers and CPU microcode that promise better performance across the board. Plus, Vietnamese users will appreciate the improved language input with the latest fcitx5-unikey update.

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For many years, the IT community has consistently emphasized the inherent value and significance of data. Data is one of the greatest resources within a business, even referred to as an organization’s crown jewels, and as a result, has become a vital part of business’ security strategies.

However, as the global interconnectivity of technology continues to grow, securing data and its integrity has become one of the most complex parts of cybersecurity. The driving factor behind this increasing complexity is the broadening use of generative AI (GenAI) and large language models (LLMs), for which training data has largely become the world’s publicly available data.

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Imagine you had to finish every sentence in every book ever written -- with just your best guess of the next word. That’s how large language models (LLMs) like GPT-4 start learning. 

LLMs use self-supervised learning, meaning they don’t need someone to label or explain the data to them. Instead, they learn by reading vast amounts of text from books, code, academic papers, Wikipedia (and its 57 million+ articles), Reddit forums, and news articles, in addition to billions of others, and then predicting what word comes next in a sentence -- over and over again.

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