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The Modern CMO: Advancing Marketing from Reactive to Predictive

CMOs have always been expected to predict the future. But, now they actually can. Today, emerging technology with artificial intelligence (AI) enables CMOs to see patterns and anticipate client needs at a scale and speed never before possible. Our friends over at Intapp recently released a new whitepaper on the topic, “The Modern CMO: Advancing […]

Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for 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 month.

XAIN Puts AI Privacy First, at No Cost to Efficiency, with its Distributed AI Solution

XAIN, the AI startup that specializes in privacy-oriented Federated Machine Learning (FedML), is developing an infrastructure to train artificial intelligence applications through FedML technology, a mechanism that emphasizes data privacy. XAIN’s distributed approach to machine learning, which intends to comply with the European Commission’s General Data Protection Regulations (GDPR), also provides greater efficiency in the way data is trained, marking a major breakthrough in a field otherwise burdened by costly and onerous processes.

How AI Is Transforming Business Strategy as We Know It

In this contributed article, freelance human Avery Phillips believes that while AI already provides value to different industries and aspects of business, it’s set to disrupt the future of business and industry practices even more. In the coming years, cognitive computing and AI will surely transform the face of business as we know it.

Blockchain Supercharged with AI: The Next Revolution?

In this contributed article, InWara Business Analyst Gregory S. Mathew, discusses the convergence of blockchain and AI and how recent developments in big data has created a conducive environment for the amalgamation of these technologies.

Survey Reveals Significant Disparity in Consumer Sentiment Towards Artificial Intelligence

Blumberg Capital, a leading early-stage venture capital firm, released a new survey, “Artificial Intelligence in 2019: Getting past the adoption tipping point”, detailing consumer comfort level, knowledge of and sentiment towards artificial intelligence (AI). The findings demonstrate that we’re at a critical tipping point when it comes to consumer understanding and attitudes towards AI, as half of consumers feel optimistic while the other half feel fearful.

The Future of AI Surveillance Around the World

In this contributed article, tech journalist Paul Bischoff discusses the increasing use of AI-driven surveillance around the world, and the dangers it creates. Realistically, the best way to protect yourself against intelligent surveillance systems is to stop them before they become a problem.

The AI Opportunity

The tremendous growth in compute power and explosion of data is leading every industry to seek AI-based solutions. In this Tech.Decoded video, “The AI Opportunity – Episode 1: The Compute Power Difference,” Vice President of Intel Architecture and AI expert Wei Li shares his views on the opportunities and challenges in AI for software developers, how Intel is supporting their efforts, and where we’re heading next.

Infographic: The AI Economy

By 2030, 70% of companies worldwide will be using some form of AI tech. This infographic from our friends over at Noodle.ai outlines how AI will affect the global economy as it is integrated into more businesses. Sign up for the free insideBIGDATA newsletter.

Why You Need a Modern Infrastructure to Accelerate AI and ML Workloads

Recent years have seen a boom in the generation of data from a variety of sources: connected devices, IoT, analytics, healthcare, smartphones, and much more. This data management problem is particularly acute in the areas of Artificial Intelligence (AI) and Machine Learning (ML) workloads. This guest article from WekaIO highlights why focusing on optimizing infrastructure can spur machine learning workloads and AI success.