Articles about Artificial Intelligence

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 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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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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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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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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AI’s promise of time and money saved has captivated employees and business leaders alike. But the real question is… is it too good to be true? As enticing as these rewards may be, the risks of this new technology must also be seriously considered.

Balancing the risks and rewards of AI is causing pause for many organizations as they grapple with the right way to adopt AI. Every deployment in every organization is going to look different -- meaning that the balance of risk and reward is also going to look different depending on the scenario. Here, we’ll talk through the promised rewards and the potential pitfalls of adopting generative AI technologies, as well as some guiding questions to help determine if it’s the right move for your business.

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Generative AI entered the global consciousness with a bang at the close of 2022 (cue: ChatGPT), but making it work in the enterprise has amounted to little more than a series of stumbles. Shadow AI use in the enterprise is sky high as employees are making day-to-day task companions out of AI chat tools. But for the knowledge-intensive workflows that are core to an organization’s mission, generative AI has yet to deliver on its lofty promise to transform the way we work.

Don’t bet on this trough of disillusionment to last very long, however. A process called retrieval augmented generation (RAG) is unlocking the kinds of enterprise generative AI use cases that previously were not viable. Companies such as Meta, Google, Amazon, Microsoft, OpenAI and a number of AI startups have been aggressively rolling out enterprise-focused RAG-based solutions.

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To be a world leader in AI, the UK must leverage its position as Europe’s number one in open source software. As the PM said on Friday, open source “creates start-ups” and “communities”. The UK’s open source community has flourished under the UK tech sector radar in recent years. OpenUK’s 2023 report showed 27 percent of UK Tech Sector Gross Value Add was attributable to the business of open source from the UK.

On the back of the AI Safety Summit last November, the UK has not taken the European Union’s route to a legislative solution. We will soon see the outcome of the EU’s gamble in being the first in the world to legislate. The very prescriptive legislation will likely be out of date before it is in use and this may engender regulatory capture in AI innovation in the EU. Few beyond big tech will be able to manage the compliance program necessary to meet the regulation. The risk is obvious and real.

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Most of us are familiar with chatbots on customer service portals, government departments, and through services like Google Bard and OpenAI. They are convenient, easy to use, and always available, leading to their growing use for a diverse range of applications across the web.

Unfortunately, most current chatbots are limited due to their reliance on static training data. Data outputted by these systems can be obsolete, limiting our ability to gain real-time information for our queries. They also struggle with contextual understanding, inaccuracies, handling complex queries, and limited adaptability to our evolving needs.

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No business is immune to the cyber threats that exist today, ranging from malicious software and ransomware to AI threats and more, which occur daily, weekly and often even more frequently than this. To counter them, companies must have strategies in place to minimize the potential damage of an attack by protecting data and putting plans in place to recover from a cyberattack as quickly and effectively as possible.

The increased adoption of AI by everyone from employees to cyber criminals is adding further risk and complexity to the security landscape. While cybercriminals are incorporating AI into their arsenal to enhance their attack strategies, employees are unwittingly helping these attackers gain their sought-after prize, data. Many employees today are experimenting with generative AI models to assist with their jobs, but many put vast amounts of data, ranging from personal details to company information, into these systems, often without the organization’s knowledge.

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Pioneering AI firm OpenAI has launched the latest edition of its LLM, GPT-4o. The flagship model is being made available to all ChatGPT users free of charge, although paying users will get faster access to it.

There is a lot to this update, but OpenAI highlights improvements to capabilities across text, voice and vision, and well as faster performance. Oh, and if you were curious, the "o" in GPT-4o stands for "omni".

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