Articles about AI

Last year was an AI milestone marked by enthusiasm, optimism, and caution. AI-powered productivity tools promise to boost productivity by automating repetitive coding and tedious tasks and generating code.  A year later, organizations are struggling to quantify the impact of their AI initiatives and are reevaluating metrics to ensure they reflect the desired business outcomes.

Measuring developer productivity has historically been a challenge, with or without the introduction of AI-powered developer tools. Last year, McKinsey & Company described developer productivity measurement as a “black box,” noting that in software development, “the link between inputs and outputs is considerably less clear” than other functions.

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It was surely only a matter of time before AI made its way to the Raspberry Pi and today sees the launch of the  Raspberry Pi AI Kit. Developed in close collaboration with Hailo, it provides a way of seamlessly integrating local, high-performance, power-efficient inferencing into a number of applications.

The Raspberry Pi AI Kit comprises the M.2 HAT+ preassembled with a Hailo-8L AI accelerator module. Installed on a Raspberry Pi 5, the AI Kit allows users to build AI vision applications, running in real time, with low latency and low power requirements.

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Enterprises plan to spend roughly $35.5 million on IT modernization in 2024, with over a third going to AI to boost productivity. But it’s not all sunshine and rainbows. At the same time, 64 percent of IT leaders worry about rushing to adopt generative AI without understanding what's needed to use it effectively and safely. And while 75 percent of organizations have experimented with generative AI, only 9 percent have adopted the technology widely. There’s so much more potential to tap into.

To get the best out of AI to supercharge operations it all comes down to intelligence. Afterall, AI is only as intelligent as those using it. There are the four types of intelligence that CTOs need to build, and it’s nothing to do with coding or super complicated technology. It’s about cultivating soft skills and human talent to control AI in a responsible way.

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Artificial Intelligence is dramatically transforming the business landscape. It streamlines operations, provides critical insights, and empowers businesses to make data-driven decisions efficiently. Through machine learning, predictive analytics, and automation, AI assists in identifying trends, forecasting sales, and streamlining supply chains, leading to increased productivity and improved business outcomes. It isn't, unfortunately, without problems.

We talked to Matt Hillary, Vice President of Security and CISO at Drata, about the issues surrounding AI when it comes to critical security and compliance.

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The C-Suite’s perception of cybersecurity has evolved dramatically over the past decade. It’s gone from being an afterthought for technology departments to worry about, to a cornerstone for business survival and operational strategy. The heightened awareness of cybersecurity stems from a deeper grasp of the legal, reputational and financial implications of data breaches. This, combined with regulatory pressures such as the original NIS directive, has forced leaders to enhance their organizations’ cybersecurity measures.

The result is that 75 percent of organizations now report that cybersecurity is a high priority for their senior management team. While on the surface this should be celebrated, when digging deeper, conversations between CISOs and the wider C-Suite often just revolve around high-profile or user-centric security risks. More technical and advanced threats such as those related to application security are overlooked. The race to embrace AI and increasingly complicated cloud infrastructures have also made communicating cybersecurity priorities even more difficult for CISOs.

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The path to widespread AI is a bumpy one. While its potential to enhance consumer experiences and streamline business operations through personalization, autonomy, and decentralized reasoning is evident, the technology comes with inherent risks.

AI can produce conclusions that aren’t true, spread misinformation and in some cases, perpetuate existing biases. This -- the darker side of AI’s impact -- can leave business leaders facing financial, legal, and reputational damage.

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AI is one of the most powerful innovations of the decade, if not the most powerful. Yet with that power also comes the risk of abuse.

Whenever any new, disruptive technology is introduced to society, if there is a way for it to be abused for the nefarious gain of others, wrongdoers will find it. Thus, the threat of AI is not inherent to the technology itself, but rather an unintended consequence of bad actors using it for purposes that wreak havoc and cause harm. If we do not do something about these cyber threats posed by the misuse of AI, the legitimate, beneficial uses of the technology will be undermined.

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When Google was founded in 1998, it ushered in a new era of access to information. The groundbreaking search engine wasn’t the first to debut (that was World Wide Web Wanderer in 1993), but it was the one that caught on. By 2004, Google was fielding over 200 million searches per day; by 2011, that number had exploded to about three billion daily searches. By that time, the word “Google” had morphed from just the name of the search engine to a verb that meant “to use an internet search engine.” Twenty years later, Google still dominates the market with an almost 84 percent share as of late 2023.

Though Google is still the most popular search engine, new technology has emerged that could threaten its supremacy -- LLMs. The use of this technology is growing at an astonishing rate. In fact, in February 2024, ChatGPT generated over 1.6 billion visits.

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The secret to generative AI’s success is data. Vast volumes of data that are used to train the large language models (LLMs) that underpin generative AI’s ability to answer complex questions and find and create new content. Good quality data leads to good outcomes. Bad, deliberately poisoned, or otherwise distorted data leads to bad outcomes.

As ever more organizations implement generative AI tools into their business systems, it’s important to reflect on what attackers can do to the data on which generative AI tools are trained.

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With the arrival of Large Language Models (LLMs) such as ChatGPT, BERT, Llama, and Granite, the operational dynamics within the enterprise sector have significantly changed. LLMs introduce unique efficiencies, paving the way for innovative business solutions. LLMs currently stand at the forefront of technological advancement, offering enterprises the tools to automate complex processes, enhance customer experiences, and obtain actionable insights from large datasets.

The integration of these models into business operations marks a new chapter in digital transformation and therefore requires a closer look at their development and deployment.

The lifecycle of LLMs and the role of APIs

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Hitting a new release cycle, Microsoft has unleashed PowerToys v0.81.0 complete with a brand-new module. With the arrival of the Advanced Paste utility, users gain access to a AI-powered clipboard that makes it possible to paste copied text in any format needed.

It is possible to use a range of keyboard shortcuts to paste text as plain text, markdown, or JSON, but there are plenty more features baked into Advanced Paste. Harnessing AI, the module can use natural language descriptions to explain what you want to do to the copied text. There are also plenty more changes and additions.

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Technology companies are experiencing tremendous challenges on several fronts. On the one hand, emerging technologies like Generative AI (Gen AI) are opening up new possibilities for revenue and growth. On the other, a tight labor market in general and an even tighter IT labor market means companies can’t just “buy” talent -- they’ve got to “build” it as well.

Encouraging creativity can reap significant rewards for businesses. People can think more deeply and freely about problem-solving and develop creative solutions by sparking curiosity. Research has shown that greater creativity can result in fewer decision-making errors, more innovation, reduced group conflict, more open communication and sharing of information, and better team performance.

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Traditionally the legal sector has lagged behind other industries when it comes to embracing new technologies. However, generative AI is proving to be the exception to the rule. Its potential to transform the profession, driving lawyers and firms to unlock new levels of productivity and efficiency, is too great to be ignored.

Growing numbers of law firms are putting their money where their mouths are, with global spending on legal AI software tools at already over $1 billion and projected to increase by almost 20 percent (CAGR) every year across the rest of the decade, reaching an estimated $37bn by the end of 2024. There’s a huge societal and industry shift underway; lawyers and law firms must act now, or risk being left behind.

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Microsoft Build kicks off today but -- as is usually the case -- there have been various pre-event announcements, not least of which is the unveiling of AI-powered Copilot+ PCs. The hardware side of things is both powerful and exciting, with huge implications for not only computing capabilities, but also privacy.

This new breed of computers features neural processing units (NPU) meaning AI-tasks can be performed on-device, without the need to transmit data via the internet. One of Microsoft’s first tools to take advantage of this is Recall (once known as AI Explorer) which is an astonishingly powerful workflow tool that records and maintains a timeline of your computing activities and gives you a way to instantly locate content you have been working on. Microsoft describes it as like having a photographic memory, but it is perhaps better thought of as the ultimate productivity assistant.

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In the current era of digitalization, cybersecurity has become a topmost priority for businesses, regardless of their size and nature. With the growing dependence on digital infrastructure and data, safeguarding against cyber threats has become crucial to ensure uninterrupted business operations. However, the evolving nature of cyberattacks poses significant challenges for traditional security measures.

This is where Artificial Intelligence (AI) emerges as a game-changer, offering substantial benefits and inherent risks in cybersecurity.

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