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The 3 Reasons Enterprises Need an AI Operating System for Intelligent process Automation

This new whitepaper, “The 3 Reasons Enterprises Need an AI Operating System for Intelligent process Automation,” from Veritone highlights how evolving technology meets enterprise demand for agile, intelligence-based solutions in the shape of AI-based operating systems (OS) across three areas: (i) AI OS for automation of human work; (ii) AI OS for process automation across all data sources; and (iii) AI OS for democratization of AI across the

Solidifying Absolute and Relative Data Quality with Master Data Management

In this contributed article, editorial consultant Jelani Harper highlights that contrary to popular belief, data are not the oil, fuel, energy, or life force coursing through the enterprise to inform decision-making, engender insights, and propel timely business action rooted in concrete facts. Data quality is.

A Laundry List for Cleaning Messy Data and Making It Business Ready

In this special guest feature, Mark Palmer, TIBCO SVP & GM of Analytics, Data Science & Data Virtualization, believes that companies who learn to leverage their data will beat out their competition. Making data-driven decisions for business strategy is essential in today’s tech-centric environment, and anyone who is not taking advantage of the information they’ve gathered will fall behind.

How the Shift to Remote Work is Accelerating Speech Recognition

In this contributed article, Ryan Scolnik, VP of Data Science at FortressIQ, discusses the technology’s applications and what the future of speech recognition may hold. The speech recognition market was projected to reach just over $29 billion by 2026, but that figure will likely end up much higher due to the move to remote work driven by the pandemic.

AI Under the Hood: Object Detection Model Capable of Identifying Floating Plastic Beneath the Surface of the Ocean

A group of researchers, Gautam Tata, Sarah-Jeanne Royer, Olivier Poirion, and Jay Lowe, have written a new paper “DeepPlastic: A Novel Approach to Detecting Epipelagic Bound Plastic Using Deep Visual Models.” The workflow described in the paper includes creating and preprocessing a domain-specific data set, building an object detection model utilizing a deep neural network, and evaluating the model’s performance.

Don’t Import It, Embed It! 5 Reasons to Embed Business Intelligence into Your Enterprise Applications

In this special guest feature, Daniel Jabaraj, Vice President of Syncfusion, Inc., discusses the 5 important reasons analyst firm Gartner lists embedded BI as important emerging tech, and reasons why it has the power to significantly affect how business is conducted in the future.

Petabytes to Zettabytes: Operational Challenges of Cluster Infrastructure

In this contributed article, Mark Yin, CEO of Platina Systems, points out that infrastructure and how it will be managed is undergoing a metamorphosis due to several key factors. The solutions outlined can help organizations stay on top of complex data issues with solutions that automate infrastructure provisioning and cloud resource life cycle management.

Mastercard’s Five Pillars of AI

Businesses are rushing to adopt AI — but they need to consider ethics right off the bat if they want to build trust and future-proof their business. This special report from Brighterion highlights how more than 60% of consumers consider brands more trustworthy if they think their use of AI is ethical — meaning today’s businesses must be able to demonstrate responsible AI as the technology becomes critical to the future of work.

How AI and Behavioral Science are Addressing Vaccine Hesitancy

In this special guest feature, Ram Prasad, Co-Founder, The Final Mile, a Fractal Analytics Company, discusses how AI can help identify problem areas and how behavioral science could be pivotal in understanding why individuals have delayed immunization.

2021 MLOps Platforms Vendor Analysis Report

The Neuromation team has just published a new report on the state of Machine Learning Operations Platforms in 2021. MLOps was defined as a separate discipline only recently when the ML practitioners moved from university labs to corporate boardrooms. AI and ML leaders today already have a better understanding of the MLOps lifecycle and the procedures and technology required for deploying new models into production and subsequently scaling them.