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AI-Driven Data Catalogs: How to Find the Right one for Your Business

The commoditization of data has opened a world of opportunities up for countless enterprises. But as big data explodes, metadata initiatives are failing, and data discovery and retrieval is getting more and more difficult. A new white paper from IO-Tahoe explores data catalogs as a potential answer to this challenge.

Machine Learning Based Traffic Sign Recognition ‘Most Influential’ Innovation of Past Decade

A research paper which revolutionized how cars read traffic signs has been recognized as the ‘most influential over the decade’ at a ceremony in Tokyo. The ideas the paper put forward have now found their way into everything from autonomous cars to controversial upcoming changes in EU law.

AI’s Role in Unleashing Intelligent Sensing

The rise of artificial intelligence (AI) is unlocking a wave of new sensor applications and driving market demand for intelligent sensing – the ability to extract insights from sensor data. To guide innovation and investment in this fast-evolving market, the team at Lux Research, a leading provider of tech-enabled research and advisory services for technology innovation, took a deep dive into how and where enhanced AI analytics are rapidly improving the capabilities of software-defined sensors.

SparkCognition’s Darwin Machine Learning Platform Designed to Accelerate Data Science at Scale

As machine learning technology becomes more widely available on an enterprise scale, differentiating and studying which platform can be best for your business can be difficult. A new white paper from SparkCognition explores one of the solutions on the market that works to accelerate data science at scale. Its Darwin machine learning platform is designed to automate the building and deployment of models.

Survey: 96% of Enterprises Encounter Training Data Quality and Labeling Challenges in Machine Learning Projects

IDC predicts worldwide spending on artificial intelligence (AI) systems will reach $35.8 billion in 2019, and 84% of enterprises believe investing in AI will lead to greater competitive advantages (Statista). However, nearly eight out of 10 enterprise organizations currently engaged in AI and machine learning (ML) report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, according to information released from Alegion.

AI Skills — 93% of Organizations Committed to AI but Skills Shortage Poses Considerable Challenge

Most organizations are fully invested in AI but more than half don’t have the required in-house skilled talent to execute their strategy, according to new research from SnapLogic. The study found that 93% of US and UK organizations consider AI to be a business priority and have projects planned or already in production. However, more than half of them (51%) acknowledge that they don’t have the right mix of skilled AI talent in-house to bring their strategies to life.

The Data Talent Market Continues Its Ascent

We’re at an important inflection point in history where a glaring shortage of data-centric skills, coupled with an increasing demand for data professionals, represents a unique opportunity for those willing to make a commitment to “tool up” or “retool” as the case may be, in preparation for a career in analytics. The good thing is, after all the time and effort, the newly acquired skills will keep on giving because the analytics field shall continue to be in favor for a very long time.

How Alternative Data is Paving the Way for the Future of Investment Management

Many hedge fund managers to mutual funds — and even private equity managers — are turning to alternative data to pave the way for the future of investment management. SparkCognition contends that alternative data has the power to improve valuation of securities and ramp up clarity of the investment process. Download the new report, “Alternative Data for Investment Management,” courtesy of SparkCognition, to learn more about how alt data and machine learning is changing the future of investment management.

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

What Makes GPUs, GPU Databases Ideal for BI?

What makes GPU databases ideal for BI? That’s what a new white paper from SQream DB wants to explain — incorporating real-world use cases to explain how you can turn your existing BI pipeline into “a more capable, next-generation big data analytics system.” Download the new report, courtesy of SQream DB, to learn more about how GPUs and GPU databases can help you organize and benefit from your next big data analytics system.