Articles about Machine Learning

AI for Marketing and Product Innovation offers creatives and marketing professionals a non-tech guide to artificial intelligence (AI) and machine learning (ML) -- twin technologies that stand poised to revolutionize the way we sell. The future is here, and we are in the thick of it; AI and ML are already in our lives every day, whether we know it or not. The technology continues to evolve and grow, but the capabilities that make these tools world-changing for marketers are already here -- whether we use them or not.

This book helps you lean into the curve and take advantage of AI’s unparalleled and rapidly expanding power.

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Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data.

This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space. It begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python -- by using open source datasets or instructing you to create your own.

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2024 is fast approaching, and it seems likely that the new year heralds the same torrent of sophisticated malware, phishing, and ransomware attacks as 2023. Not only are these long-standing threats showing few signs of slowing down, but they're increasing by as much as 40 percent, with federal agencies and public sector services being the main targets.

Meanwhile, weak points like IoT and cloud vulnerabilities are making it tougher for cybersecurity pros to secure the wide attack surface that these edge devices create.

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Organizations have more data than ever, but unlocking the information it contains in order to make decisions can be a challenge.

The marriage of of real-time analytics and AI with vector search is a potential game changer for any business that has large amounts of data to crunch. We spoke to Rockset CEO and co-founder Venkat Venkataramani to find out more.

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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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In a powerful collaboration with Accenture, Intel has rolled out an impressive collection of 34 open-source AI reference kits. These kits are potential game-changers, aimed at simplifying and speeding up the process of deploying AI for data scientists and developers.

Think about it, dear readers -- every kit is a treasure chest of AI tools. Inside, you'll find model code, training data, instructions for setting up the machine learning pipeline, libraries, and oneAPI components. All of these are designed to optimize AI and make it more accessible, regardless of whether you're working in an on-premises, cloud, or edge environment.

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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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Artificial intelligence is generally only as good as the data that it's trained on. However, when data is collected and used in the training of machine learning models, the models inherit the bias of the people building them, producing unexpected and potentially harmful outcomes.

We spoke to Matthieu Jonglez, VP, technology at Progress, to discuss the company's recent research around this topic and what organizations can do to reduce bias.

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Written and edited by a team of experts in the field, this book reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

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There have been a number of instances lately where the line between artificial intelligence and artificial stupidity has been a pretty thin one. Microsoft's Bing insisting that we're still in 2022, or Google's Bard serving up factual errors, for example.

These problems show that human intervention is still key to getting the best from AI/ML models. We spoke to chief product officer at Tamr, Anthony Deighton, to discuss how organizations can leverage AI/ML while also keeping the key human component of the process.

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Due to the many advances and new developments in technology, the way in which businesses are communicating is changing. Technology holds a central role in reshaping how employees work, interact and engage with others. This is helping to create digital workplaces, where emerging technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) are powering organizations to utilize data and increase access to information.  

While technology such as AI and automation have been in existence for some time, the speed and rate of adoption across the business landscape is gathering pace as a means to digitize workplace settings. Currently, 15 percent of all UK businesses have adopted at least one AI technology, with the IT and telecommunications sector leading the way with the highest rate of adoption at 29.5 percent. But what features and technologies matter most to end users and how is this evolving across the digital workplace?

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Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricey much easier, faster, and cheaper. Machine Learning Security Principles will explain how you can use Machine Learning to keep data, networks, users and applications safe from prying eyes.

The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.

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A new survey of 100 chief data officers (CDOs) and chief data analytics officers (CDAOs) at companies with $1B+ in revenue shows that 95 percent say their company leadership expects investments in AI and ML applications will result in a revenue increase.

The study for Domino Data Lab, carried out by Wakefield Research, shows 67 percent are adopting a more offensive data policy seeking to drive new business value with analytics, ML and AI applications.

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A new survey of 200 US-based machine learning decision makers looks at the trends, opportunities and challenges in machine learning and MLOps (machine learning operations).

The study from ClearML finds that for 41 percent, the biggest challenge of their MLOps platform, tools, or stack, is friction in using tools with other technology. While 22 percent cite vendor lock -- difficulty switching to a different provider without significant costs, time, or disruptions -- as the biggest challenge.

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Artificial Intelligence (AI) has been a hot topic for a long time, but its impact on our society and in the enterprise are just beginning to be realized. AI and other forms of machine learning and deep learning will revolutionize business, automating repetitive tasks and accelerating outcomes -- all based on huge sets of data.

Developing deep learning applications generally follows a three-step process of:

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