Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month.

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“Above the Trend Line” – Your Industry Rumor Central for 9/1/2021

Above the trendline

Critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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“Above the Trend Line” – Your Industry Rumor Central for 8/17/2021

Above the trendline

In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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[Papers] Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2021

Filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month.

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“Above the Trend Line” – Your Industry Rumor Central for 7/29/2021

Above the Trend Line

News items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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MLOps: Bringing AI to the tactical edge – and making it work

Edge computing

Machine learning (ML)—the ability for machines to perceive, learn from, abstract, and act on data—has been a catalyst for innovation and advancement across sectors, with national security being no exception. In the last year alone, there have been several prime examples of the enormous opportunity ML offers regarding artificial intelligence (AI) for defense and the intelligence community. The U.S. Department of Defense (DoD) is continuing efforts to scale AI and celebrating new achievements, like using AI to help control a U-2 “Dragon Lady” reconnaissance aircraft – the first time AI has been put in command of a U.S. military system. 

The possibilities for advancement are endless: by helping with tasks related to data collection, processing, and analysis, ML can catch cyber breaches and hacks before humans can, speed up responses to electronic warfare attacks, and more closely target responses to kinetic fire through its continual updating and learning capabilities. Warfighters can also use ML to look across domains and resources, from ships to artillery, to match targets to resources.

As we settle into 2021, there’s one aspect of AI/ML that should not be overlooked: how to effectively get it into the hands of warfighters at the tactical edge, where fast decisions are at a premium and compute power and connectivity are often scarce. It is critical that these edge use cases characterize and shape planning for AI and ML-driven investment as digitization continues to accelerate the pace of war.

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TensorRT 8 provides leading Enterprises Fast AI Inference Performance

Inference is complex

NVIDIA today launched TensorRT™ 8, the eighth generation of the company’s AI software, which slashes inference time in half for language queries — enabling developers to build the world’s best-performing search engines, ad recommendations and chatbots and offer them from the cloud to the edge.

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Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals.

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The IMPACT 50 List for Q3 2021

The Impact list

Quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

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“Above the Trend Line” – Your Industry Rumor Central for 7/13/2021

Above the trendline

In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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Tableau extends Augmented Analytics, bringing the power of AI to everyone

Tableau

Tableau, a leading analytics platform (NYSE: CRM), is bringing data analytics and AI together in a suite of new and expanded augmented analytics features. Tableau’s latest release will empower more people with the right technology to make smarter and faster decisions regardless of their role and skill level.

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The amazing applications of Graph Neural Networks

The predictive prowess of machine learning is widely hailed as the summit of statistical Artificial Intelligence. Vaunted for its ability to enhance everything from customer service to operations, its numerous neural networks, multiple models, and deep learning deployments are considered an enterprise surety for profiting from data. But according to Franz CEO Jans Aasman, there’s just one tiny problem with this lofty esteem that’s otherwise accurate.

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“Above the Trend Line” – Your Industry Rumor Central for 6/23/2021

Above the Trendline graphic

In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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[White Paper] Reduce Compliance Risk in your Call and Contact Center with AI

This white paper, “Reduce Compliance Risk in Your Call and Contact Center With AI,” from Veritone, discusses how compliance represents a major risk for contact centers, with violations potentially costing organizations millions of dollars in fines and causing reputational damage to the brand. The current method of monitoring calls — using manual review of recorded conversations — does not come close to meeting the requirements for comprehensive monitoring of regulated interactions.

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Best of arXiv.org for AI, Machine Learning, and Deep Learning – May 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month.

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“Above the Trend Line” – Your Industry Rumor Central for 6/4/2021

Above the trendline

In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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Quantum Machine Learning – An Introduction to QGANs

Quantum GANs which use a quantum generator or discriminator or both is an algorithm of similar architecture developed to run on Quantum systems. The quantum advantage of various algorithms is impeded by the assumption that data can be loaded to quantum states. However this can be achieved for specific but not generic data.

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