Articles about AI

Software-driven digital innovation is essential for competing in today's market, and the foundation of this innovation is code. However, there are widespread cracks in this foundation -- lines of bad, insecure, and poorly written code -- that manifest into tech debt, security incidents, and availability issues.

The cost of bad code is enormous, estimated at over a trillion dollars. Just as building a housing market on bad loans would be disastrous, businesses need to consider the impact of bad code on their success. The C-suite must take action to ensure that its software and its maintenance are constantly front of mind in order to run a world-class organization. Software is becoming a CEO and board-level agenda item because it has to be.

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Large-language models (LLMs) and other forms of generative AI are revolutionizing the way we do business. The impact could be huge: McKinsey estimates that current gen AI technologies could eventually automate about 60-70 percent of employees’ time, facilitating productivity and revenue gains of up to $4.4 trillion. These figures are astonishing given how young gen AI is. (ChatGPT debuted just under two years ago -- and just look at how ubiquitous it is already.)

Nonetheless, we are already approaching the next evolution in intelligent AI: agentic AI. This advanced version of AI builds upon the progress of LLMs and gen AI and will soon enable AI agents to solve even more complex, multi-step problems.

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The world of AI voice assistants has been moving at a breakneck pace, and Google's latest addition, Gemini, is shaking things up even more. As tech giants scramble to outdo each other, creating voice assistants that feel more like personal companions than simple tools,

Gemini seems to be taking the lead in this race. The competition is fierce, but with Gemini Live, we're getting a taste of what the future of conversational AI might look like.

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If you’ve bought a computer or a keyboard recently, you may have spotted an extra key near the spacebar. The Copilot key is Microsoft’s attempt to push, prompt and encourage use of its AI-powered digital assistant, but not everyone is convinced.

Even if you are someone who sees the value in Copilot as a tool, you may well not feel the need to have a dedicated physical key to access it. Thankfully, Microsoft understands that the latest addition to keyboards is not something that everyone needs. As such, the company is testing the ability to remap the Copilot key.

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Today, 37 percent of respondents said their companies were fully prepared to implement AI, but looking out on the horizon, a large majority (86 percent) of respondents said that their AI initiatives would be ready by 2027.

In a recent Riverbed survey of 1,200 business leaders across the globe, 6 in 10 organizations (59 percent) feel positive about their AI initiatives, while only 4 percent are worried. But all is not rosy. Senior business leaders believe there is a generational gap in the comfort level of using AI. When asked who they thought was MOST comfortable using AI, they said Gen Z (52 percent), followed by Millennials (39 percent), Gen X (8 percent) and Baby Boomers (1 percent). 

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For those who are keenly awaiting the release of the Windows 11 24H2 update, a recent Microsoft blog post caused a good deal of excitement. It appeared to reveal that this significant feature update is due to roll out this very month; but all was not as it seems

Microsoft has now updated the blog post to clarify that the information it includes has been misinterpreted -- or perhaps that it was not sufficiently clearly written in the first place. Where does this leave us?

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If you have been wondering just when you’ll be able to get your hands on the final version of the Windows 11 24H2 update, wonder no more.

Although Microsoft has not made a big announcement about the release date for this eagerly anticipated feature update for Windows 11, the company has -- seemingly inadvertently -- revealed the release schedule in a blog post. This spills the news that the release of Windows 11 24H2 is just days away.

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When the European Commission proposed the first EU regulatory framework for AI in April 2021, few would have imagined the speed at which such systems would evolve over the next three years. Indeed, according to the 2024 Stanford AI Index, in the past 12 months alone, chatbots have gone from scoring around 30-40 percent on the Graduate-Level Google-Proof Q&A Benchmark (GPQA) test, to 60 percent. That means chatbots have gone from scoring only marginally better than would be expected by randomly guessing answers, to being nearly as good as the average PhD scholar.

The benefits of such technology are almost limitless, but so are the ethical, practical, and security concerns. The landmark EU AI Act (EUAIA) legislation was adopted in March this year in an effort to overcome these concerns, by ensuring that any systems used in the European Union are safe, transparent, and non-discriminatory. It provides a framework for establishing:

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The question of how to train and improve AI tools is one that triggers fierce debate, and this is something that has come into sharp focus as It becomes clear just how Meta is teaching its own artificial intelligence.

The social media giant is -- perhaps unsurprisingly to many -- using data scrapped from Facebook and Instagram posts, but only in Australia. Why Australia? Unlike Europe where General Data Protection Regulation (GDPR) necessitated Meta to give users a way to opt out of having their data used in this way, Australia has not been afforded this same opportunity. What does this mean?

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AI is revolutionizing the landscape of software development, but it isn’t about replacing human developers. Instead, we are entering an era of “AI-augmented development,” where AI tools are becoming invaluable allies, enhancing human abilities across the software lifecycle. AI will help close the gap between the high demand for custom software and the limited engineering capacity worldwide.

In this new paradigm, AI is stepping in to assist with repetitive and time-consuming tasks, allowing developers to focus on more complex problems. The evolution of software teams will include a new breed of AI-native developers specializing in integrating AI into applications and leveraging AI tools. With AI, the potential productivity boost for developers is extraordinary, allowing them to work faster and smarter. However, while AI can amplify a developer's capabilities, it cannot replace the human creativity, problem-solving, and decision-making that are essential to successful software development. The future belongs to teams that can skillfully blend AI with human expertise.

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Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs.

In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society. Professionals, technophiles, and anyone with an interest in the future will need to understand how GenAI is set to redefine jobs, revolutionize business, and question the foundations everything we do.

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Any leading large language model will do. To succeed with retrieval-augmented generation, focus on optimizing the retrieval model and ensuring high-quality data. 

The decades-long pursuit to capture, organize and apply the collective knowledge within an enterprise has failed time and again because available software tools were incapable of understanding the noisy unstructured data that comprises the vast majority of the enterprise knowledge base. Until now. Large language models (LLMs) that power generative AI tools excel at processing and understanding unstructured data, making them ideal for powering enterprise knowledge management systems.

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Alludo has announced the release of Parallels Desktop 20.0 for Mac. Available in Standard, Pro, Business and -- new to version 20 -- Enterprise editions, the virtualization software makes it easy for Mac users to run Windows, macOS and Linux virtual machines on their desktop.

Version 20’s headline new feature is the Parallels AI Package add-on for Business, Enterprise and -- until the end of the year -- Pro users running Macs with Silicon hardware. This provides access to AI-ready virtual machines to aid in development.

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The internet was a massive, revolutionary invention. A once-in-a-lifetime breakthrough. And yet, it was not an overnight sensation in terms of consumer adoption. This may surprise some people today. From the early web browsers in 1992 to the explosion of dot-coms in 1998, it took roughly six years for the general public to truly embrace the world wide web. Fast forward to today, and the landscape has dramatically shifted.

Consider the recent phenomenon of ChatGPT, the large language model chatbot launched by OpenAI in late 2022. Within a year, consumer adoption of this AI technology reached a fever pitch. For a while, it was all anyone in tech and business circles could talk about. In fact, they still are. This highlights a critical difference in our current technological era, which is that innovation is happening and being adopted at an unprecedented pace.

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Despite the importance and timely arrival of the EU AI Act, there remain some major compliance concerns and the impact it will have on AI adoption and governance strategies. In fact, a recent survey found that having the proper AI governance in place is a top priority for 41 percent of business decision-makers. However, around one-quarter of UK firms have yet to make preparations for AI, and this is partly due to lingering confusion over their obligations. 

Yet, the requirements set out by the Act are specific, particularly for “businesses or public authorities that develop or use AI applications that constitute a high risk for the safety or fundamental rights of citizens.” This high-risk category can include anything from law enforcement and employment systems to those used by life sciences and critical infrastructure organizations. 

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