Articles about Artificial Intelligence

AI is exploding, particularly as large language models (LLMs) have infiltrated everyday life. Almost every new mainstream product seems to promote some usage of AI, and industry after industry is being transformed by its capabilities. But despite AI’s potential, some sectors have been slow to adopt it. Risk management is one of them. Fortunately, that is starting to change.

According to a 2023 Deloitte study, only 1.33 percent of insurance companies had invested in AI. Data from this year indicates a shift is underway. In Conning’s 2024 survey, 77 percent of respondents indicated that they are in some stage of adopting AI somewhere within their value chain. This may sound a bit nebulous -- some stage, somewhere -- but it represents a sizable jump from the 61 percent of respondents the prior year. Additionally, 67 percent of insurance companies disclosed they are currently piloting LLMs.

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We live in an age that is witnessing the rise of Artificial Intelligence. Many companies have begun incorporating AI features in their operating systems and apps, whether we like it or not.

AI assistants are not new per se, the likes of Apple's Siri, Google Assistant, Amazon's Alexa have existed for over a decade. But, the emergence of Open AI's ChatGPT changed how people view digital assistants. Chatbots offer a more interactive experience, you can text them like would a friend, access a history of your chats, and get relevant results. This was something that the old-gen couldn't provide, contextual interaction.

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Machine Learning Algorithms and Applications is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems.

It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms.

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Artificial intelligence is underpinned by the data used to train it, and even in this early stage of the game this has already proved controversial. In addition to complaints about the use of copyrighted content to train AIs, concern has also been voiced about the use of personal data.

Elon Musk, unsurprisingly, wants a slice of the AI pie, and is looking to train up the Grok AI model. The social platform formerly known as Twitter, X, is being used as a source of training data, meaning that your tweets (sorry, posts) are, by default, being sucked up for this very purpose.

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Artificial intelligence (AI) is fundamentally transforming the way businesses operate and the value they can deliver to customers. Industry body techUK cites estimates that the UK’s GDP could be up to 10 percent higher by 2030 thanks to AI adoption. But first there are major cost, efficiency and data governance challenges to solve. This is where edge computing comes into its own -- offering a fast, resilient and cost-effective way to run transformative AI applications. Even better, it can help organizations to meet requirements around sustainability and data sovereignty.

The key will be finding a database platform that can seamlessly support both traditional cloud and edge computing environments in this context.

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ChatGPT for Cybersecurity Cookbook takes you on a journey toward enhancing your cybersecurity skills, whether you’re a novice or a seasoned professional. By leveraging cutting-edge generative AI and large language models such as ChatGPT, you'll gain a competitive advantage in the ever-evolving cybersecurity landscape.

This book shows you how to automate and optimize various cybersecurity tasks, including penetration testing, vulnerability assessments, risk assessment, and threat detection.

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AI growth and adoption in the UK are surging, with the market valued at more than £16.8 billion and expected to reach £801.6 billion in the next decade. Approximately 15 percent of UK businesses are already using AI technologies such as data management and analysis, natural language processing, machine learning, and computer vision. And across the pond in the US, AI is expected to contribute a significant 21 percent net increase to US GDP by 2030, showcasing its substantial impact on the economy.

Growth in any new technology is never without its challenges. For AI, these include ensuring data privacy, addressing ethical concerns, and navigating the complexity of integrating with existing IT infrastructure. Data quality is central to resolving these challenges. To be useful, the data used for AI must be high-quality, well-structured, and from trusted sources. These properties are the foundation for all AI models and determine their effectiveness and reliability.

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In an age where cyber threats are becoming increasingly sophisticated, the management and verification of digital identities are at a critical juncture. As various sectors rapidly evolve, decentralized identity (DCI) systems emerge as a revolutionary approach to managing and verifying user identities. These autonomous systems promise to change how we access and use online services. However, many organizations need help with adopting this promising technology.

A recent survey by Ping Identity, which included responses from 700 IT decision-makers worldwide, highlights these challenges. In the UK, 82 percent of IT decision-makers see value in decentralized identities for their customers and employees, yet only about a third (34.5 percent) currently offer this option. A significant reason for this gap is the need for more clarity about the benefits, with 31 percent of respondents unsure what advantages decentralized IDs would bring.

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AI for Absolute Beginners begins with an engaging introduction to the world of Artificial Intelligence, making it approachable for those with little to no knowledge on the subject.

The book unravels the mysteries of AI's evolution, from its historical roots to the cutting-edge technologies shaping our future. By explaining complex concepts in simple terms, this guide aims to illuminate the path for those curious about how AI impacts our world.

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Modern cybersecurity leaders are expected to balance an almost comical number of responsibilities. Threat intelligence, vulnerability management, asset tracking, identity management, budgeting, third-party risk -- and that’s just what the company is willing to put in the job description.

To be a cybersecurity expert is to spend your entire career deepening your well of knowledge in one or a few domains. To be a cybersecurity leader, on the other hand, is to spend your career attempting to drink an ocean through a straw. What makes this moment in cybersecurity so interesting is that generative artificial intelligence (AI) brought a fundamental change to both the ocean and the straw.

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Since the arrival of generative AI, its potential to increase challenges associated with privacy and cyber security has become a major concern. As a result, government bodies and industry experts are hotly debating how to regulate the AI industry.

So, where are we heading and how is the crossover between AI and cyber security likely to play out? Looking at the lessons learnt from previous efforts to regulate the cyber security market over the past few decades, achieving anything similar for AI is a daunting prospect. However, change is essential if we are to create a regulatory framework that guards against AI's negative potential without also blocking the positive uses that AI is already delivering.

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Supply-chain vulnerabilities loom large on the cybersecurity landscape, with threats and attacks such as SolarWinds, 3CX, Log4Shell and now XZ Utils underscoring the potentially devastating impact of these security breaches. The latter examples of Open Source Software (OSS) attacks are a growing attack vector. In fact, nearly three-quarters (74 percent) of UK software supply chains have faced cyber attacks within the last twelve months.

Expect attacks on the open source software supply chain to accelerate, with attackers automating attacks in common open source software projects and package managers. Many CISOs and DevSecOps teams are unprepared to implement controls in their existing build systems to mitigate these threats. In 2024, DevSecOps teams will migrate away from shift-left security models in favor of “shifting down” by using AI to automate security out of the developers’ workflows.

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In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history.

The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples.

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With AI-generated content now proliferating the internet, companies are scrabbling to put polices in place to handle such material on their platforms. Among them is YouTube, and the Google-owned video site has updated its privacy guidelines to better take into account artificial intelligence.

It is now possible for anyone to issue a request to YouTube to remove content if it simulates their face or voice. This is separate to the way in which deepfakes are handled, and it is interesting to see such AI-generated content being seen as a potential violation of an individual's privacy.

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Artificial Intelligence (AI) and Machine Learning (ML) are more than just trending topics, they’ve been influencing our daily interactions for many years now. AI is already deeply embedded in our digital lives and these technologies are not about creating a futuristic world but enhancing our current one. When wielded correctly AI makes businesses more efficient, drives better decision making and creates more personalized customer experiences.

At the core of any AI system is data. This data trains AI, helping to make more informed decisions. However, as the saying goes, "garbage in, garbage out", which is a good reminder of the implications of biased data in general, and why it is important to recognize this from an AI and ML perspective.

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