Articles about AI

Cybersecurity has become one most crucial aspects of many organizations due to the speed at which cyber threats evolve. The "speed of cybersecurity" makes it vital to have timely and agile defense measures to detect, analyze, and mitigate cyber risks -- as it is the only way to stay ahead of attackers and protect assets in an increasingly dynamic and interconnected world. 

New technologies like cloud computing and automation have led to transformative changes in cybersecurity, though these changes weren’t immediate. The use of the cloud within other IT teams advanced much faster than it did in cybersecurity departments, as security teams were hesitant to cede control to technologies in the hands of others.  

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Generative AI is the big trending topic right now, and understandably is featuring prominently in the news. The popularity of platforms such as Open AI’s ChatGPT, which set a record for the fastest-growing user base by reaching 100 million monthly active users just two months after launching is unquestionably on the minds of businesses globally.

These tools can increase productivity and efficiency by automating repetitive tasks and letting employees focus on higher-value work. They can foster enhanced creativity and innovation by assisting in brainstorming and ideation processes and generating novel solutions to complex problems. Today, AI’s applications have already been well-documented in fields such as eCommerce, security, education, healthcare, agriculture, gaming, transport, and astronomy. The business, productivity, and efficiency gains that it provides these industries are enabling them to flourish and open up new revenue streams.

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A new study of 1,000 office workers across the US and UK shows half of us already use AI tools at work, one-third weekly and 12 percent daily.

But the report from Cybsafe finds 38 percent of users of generative AI in the US admit to sharing data they wouldn't casually reveal in a bar to a friend.

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The global financial landscape is witnessing a transformation as institutions increasingly embrace the power of artificial intelligence (AI) to combat money laundering and fraud. AI's technical capabilities present an array of benefits for Anti-Money Laundering (AML) processes, particularly in its proficiencies for data analysis and pattern recognition.

Though, as AI integration gains momentum, it also brings significant challenges that demand thoughtful consideration. Issues such as limited training data and over-reliance without human supervision can have serious implications for AML efforts.

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Artificial intelligence is finding its way into many areas of business. But its value depends on the quality of the training data and user prompts it receives.

Dynatrace is looking to address this with an update to its Davis AI engine that creates what it calls a 'hypermodal artificial intelligence', combining fact-based, predictive- and causal-AI insights with new generative-AI capabilities.

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Cloud security company Sysdig is launching a new generative AI assistant specifically designed to help with cloud security.

Whereas standard AI chatbots are designed to answer a specific question using a single large language model (LLM) and stateless analysis, Sysdig Sage uses a unique human-to-AI controller that mediates user interactions with LLMs to provide more advanced, tailored recommendations.

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With recent AI development, we have seen the priority of big tech companies be the creation of ever-larger language models. While sheer scale does have its benefits, the escalating costs associated with training and running these massive models has become a significant obstacle, particularly for small and medium-sized enterprises. With this in mind, and as new developers enter the space, we are seeing this trend slowly shift, the focus moving away from models of scale, and now onto how AI technologies can be made accessible and affordable for everyone. 

With limited funds posing a significant challenge for smaller organizations looking to invest in AI, we could see prohibitively expensive models stifling innovation and diversity within the market. Customizing models like GPT-4 for specific business use cases currently comes with a hefty price tag, often reaching tens of thousands of dollars. Moreover, as models become more complex, long-term operational expenses soar. For instance, maintaining servers for ChatGPT can incur a staggering daily cost comfortably in the six figures. Smaller enterprises require intelligent and optimized model architectures that can compete with the capabilities of larger models at a price point consummate with their business size.

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Experts reckon that over 90 percent of internet content could be AI generated by the end of the decade. But we all know that AI isn't perfect; it can introduce biases and errors.

Checking material to ensure it's suitable for the target audience is therefore essential. User experience research platform WEVO is launching a new research tool, WEVO 3.0, to ensure that AI-generated products and experiences are well received by their target human audience.

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Artificial intelligence (AI) models have been generating a lot of buzz as valuable tools for everything from cutting costs and improving revenues to how they can play an essential role in unified observability.

But for as much value as AI brings to the table, it’s important to remember that AI is the intern on your team. A brilliant intern, for sure -- smart, hard-working and quick as lightning -- but also a little too confident in its opinions, even when it’s completely wrong.

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The popularity of OpenAI's ChatGPT led to a seemingly endless stream of fake mobile apps popping up in Google Play. Now, a couple of months after the official app was released for iOS, ChatGPT for Android is due to land in the coming days.

OpenAI has announced that the Android version of the ChatGPT app is launching in the last week of July, but the company has not revealed a precise date. If you want to be sure to get hold of the app as soon a possible, you can pre-register, and it will be installed the moment it is released.

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Artificial intelligence (AI) chatbots like ChatGPT have become a tool for cybercriminals to enhance their phishing email attacks. These chatbots use large datasets of natural language and reinforcement learning to create typo-free and grammatically correct emails, giving the appearance of legitimacy to unsuspecting targets. This has raised concerns among cybersecurity leaders, with 72 percent admitting to being worried about AI being used to craft better phishing emails and campaigns.

Chatbots can help cybercriminals scale the production of advanced social engineering attacks, such as CEO fraud or business email compromise (BEC) attacks. Additionally, cybercriminals may use AI-powered chatbots to scrape personal or financial data from social media, create brand impersonation emails and websites, or even generate code for malware such as ransomware. In particular, without AI, creating malware is a specialized task that requires skilled cybercriminals. However, the use of chatbots could make it easier for non-specialists to do this, and we can also expect AI-generated outputs to improve over time.

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We're continuing to feel the effects of the COVID-19 pandemic in many areas, not least in the impact that it’s had on consumer behavior.

In order to cope with the shift in the way that people interact with businesses many have turned to increased automation and use of AI. We spoke to Sateesh Seetharamiah, CEO of enterprise automation specialist Edgeverve, to find out more about these changes and what they mean for the longer term.

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IoT devices are created to perform specific functions, so their technical specs are naturally quite limited. They are unlike smartphones or tablet computers that come with powerful processors and large data storage. Putting traditional security mechanisms like encryption and intrusion detection systems on these devices is impractical. Installing full-fledged security solutions in them is out of the question, let alone AI-powered systems.

However, this does not mean that AI cannot be used to secure IoT devices or entire IoT ecosystems. Here’s a rundown of how AI solutions for IoT are harnessing the benefits of AI to protect IoT and other low-resource devices, including actuators, sensors, wearables, and microcontrollers.

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New research shows 52 percent of the top 100 AI open source projects on GitHub reference known vulnerable open source software packages.

The report from Endor Labs explores emerging trends that software organizations need to consider as part of their security strategy, and risks associated with the use of existing open source software (OSS) in application development.

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Saildrone, a Bay Area startup, is redefining oceanic exploration and data gathering with its innovative use of autonomous uncrewed surface vehicles (USVs). With an impressive track record that includes tracking North Atlantic hurricanes, discovering underwater mountains, and mapping the world’s ocean floor, Saildrone is revolutionizing the way we interact with our oceans.

This game-changing technology utilizes a broad array of sensors in its USVs, with the data they gather processed by NVIDIA Jetson modules for efficient AI at the edge. The company is currently optimizing their prototypes with NVIDIA DeepStream software, a leap forward in intelligent video analytics.

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