Articles about ML

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms.

The book covers in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters.

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The data flood gates have opened wide for clinical trial research. In fact, the amount of data gathered may be more akin to a tsunami or a monsoon. For decades, researchers struggled with a lack of data available in clinical trials; however, they may have received more than they asked for. Research shows that the biopharmaceutical industry generates up to a trillion gigabytes of data annually and clinical trials, one of the principal contributors to these data points, generate an average of up to 3 million data points per trial. This influx of sources can make it challenging to discern relevant from superfluous information, complicating analysis and delaying critical decision-making.

An increase in decentralization paired with expanded collection methods in clinical trials have increased access to and accumulation of data. Information gathered from remote monitoring devices, electronic health records (EHRs), laboratory tests, surveys and questionnaires and third-party databases, all contribute to the data challenge in clinical trials. In reality, the number of touchpoints across clinical trials, from sponsors to clinical research organizations (CROs) to site staff, combined with the complexity and disparity of data sources leads to challenges in ensuring data quality.

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Artificial intelligence (AI) is set to be the next major technological advancement to dramatically impact modern society. From transforming the way we work, to increasing efficiency in outdated systems, the changes promised by AI have the potential to be utterly transformational. While this brings a huge range of opportunities, there are also some enormous challenges to overcome if humanity is to strike an effective balance between progress and risk.

History shows that society and the law do not always handle rapid innovation well. Take technologies such as the steam engine and automated loom, for instance, where progress was met with varying degrees of resistance and fear before the benefits were fully realized. In the case of AI, harnessing its potential while safeguarding against misuse means legislators must take a measured, risk-based approach to regulation that embraces change alongside effective safeguards.

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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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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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UK businesses are inadvertently wasting over £10.4 billion ($12.97 billion) each year as developers manually carry out routine operations tasks that could be automated.

Software delivery platform Harness calculates the figure based on the fact that developers spend only around 52 minutes a day on actual coding.

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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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Web scraping -- collecting data from websites -- has been around almost as long as the internet has existed. But recently it's gone from a little-known niche to a serious activity, using automation to collect large amounts of information.

We spoke to Julius Černiauskas, CEO of data acquisition company Oxylabs to find out more about web scraping and how it has evolved.

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The call to implement Artificial Intelligence (AI) is becoming difficult for businesses to ignore. Offering the promise of increased organizational productivity, speed and accuracy, some applications can be greatly beneficial to firms across a wide variety of industries and sectors.

That said, companies will naturally have some difficulty deciding on how best to implement AI, and where to achieve the best return on investment in innovative technology. Given the inherent difficulties involved in building an AI solution, finding a solution that is the perfect fit can be a mammoth task, involving great resource and even greater costs. For some, the drawbacks might even outweigh the benefits; perhaps this is why less than 15 percent of firms have implemented AI in their operations.

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It's an increasingly rare application these days that doesn’t claim to incorporate some form of artificial intelligence or machine learning capability.

But while this may be great from a marketing standpoint it does pose a challenge for developers. We spoke to Luis Ceze, CEO and co-founder of OctoML, to find out more.

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Artificial intelligence (AI) and machine learning (ML) seem to have piqued the interest of automated data collection providers. While web scraping has been around for some time, AI/ML implementations have appeared in the line of sight of providers only recently.

Aleksandras Šulženko, Product Owner at Oxylabs.io, who has been working with these solutions for several years, shares his insights on the importance of artificial intelligence, machine learning, and web scraping.

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Artificial intelligence (AI) and machine learning (ML) have been two of the most disruptive technological advancements of the past several years.

Gartner predicts that by 2024, 75 percent of enterprises have shifted from AI pilot stage implementation to operationalization. Their effects have been wide-ranging and promise to continue into the foreseeable future. Businesses will gain a substantial competitive advantage by capitalizing on the benefits of AI and ML.

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