With the right technical infrastructure and data-literate work culture, the challenges with the adoption of data science and machine learning technologies can be easily addressed. Successful companies today need to be data driven. A survey by NewVantage Partners found that 92% of organizations are increasing their investments in data and artificial intelligence (AI) capabilities. On the flipside however, only 19% of companies feel that they are truly being data driven. This analytics gap continues to widen and conspires to impede organizational process.

In this special guest feature, Zach Thomas, VP of Product Marketing, Conga, discusses the ways emerging technologies like AI and ML can expedite business operations to accelerate revenue. Zachary brings more than 20 years of experience in the technology sector to his role as Vice President of Product Marketing at Conga. Prior to Conga, he served as Senior Director, Head of Cloud Applications at Ellucian, a leading provider of software and services designed for higher education. His previous roles include leadership positions at Saba Software, Sage, Oracle, and Ultimate Software. Thomas holds a Bachelor of Arts degree in English and Political Science from Hamilton College in Clinton, New York. From predicting COVID-19 mortality to content personalization, artificial intelligence (AI) and machine learning (ML) are expanding the possibilities for organizations across the globe.

A tsunami of medical imaging procedures – all with higher image counts and resolutions to analyze – are drowning the limited number of radiologists available to interpret them. This makes their chosen healthcare profession increasingly difficult. This abundance of medical imaging data can be put to work to train today's efficient Convolutional Deep Neural Network models[1] running on the latest NVIDIA GPU processors to assist clinicians in their diagnostic tasks. And none too soon, because all that medical image data must be read by increasingly overwhelmed diagnostic clinicians. For the last decade, most of the focus in artificial intelligence (AI) has been on GPU processing, and rightfully so, with all the advancements going on there.

In this latest Data Science Central webinar, taking advantage of the newest innovations across the business intelligence and analytics landscape is critical to stay ahead of the competition and drive real value from your data. AI, machine learning, and cloud are all raising the bar, and you can position yourself to take advantage of these new capabilities now and in the future. Join Wayne Eckerson from the Eckerson Group along with Chris Mabardy and Denise LaForgia from Qlik as they explore how the BI market is evolving, and the key technologies that will unlock the value of your data for everyone. Topics discussed will include: - The importance of augmented analytics that leverage AI and natural language processing - How automated machine learning can bring the power of data science to analytics teams - Why the rise of cloud analytics is critical to harnessing BI innovation Register today to get our insights on the evolving BI market and future proofing your BI investment.

The design and use of artificial intelligence is proving to be an ethical dilemma for companies throughout the United States considering its implementation. While currently only 6% of companies have embraced AI-powered solutions across their business, according to a survey by Juniper Networks, another 95% of respondents indicated they believe their organization would benefit from embedding AI into their daily operations, products and services. Which begs the question, if there is so much interest in the application of AI, then why is it taking companies so long to get on board? The lagging and inconsistent adoption of responsible AI is one of the challenges companies are grappling with when it comes to AI. Currently, there are three elements that contribute to ethical concern around AI: privacy and surveillance, bias and prejudice, and the role of differing human values in the implementation and execution of AI.

Adam Scraba: I think we're on a path where AI is actually going to become kind of like a utility. It's going to be accessible to pretty much everyone. James Kotecki: This is Machine Meets World, Infinia ML's ongoing conversation about artificial intelligence. I am James Kotecki and my guest today is Adam Scraba, Director of Product Marketing at NVIDIA. James Kotecki: So Adam, NVIDIA, which I should say is not Infinia, these are two totally separate companies that we work for here.

These and many other fascinating insights are from McKinsey's latest survey and report on AI, The State of AI in 2020. McKinsey's methodology relied on an online random sampling of 2,395 respondents representing a broad spectrum of regions, industries, company sizes, functional specialties and senior management tenures. One thousand one hundred fifty-one respondents say their enterprises had adopted AI in at least one function. For additional details on the methodology, please see page 13 of the report. What makes this report noteworthy are the insights into how enterprises who adopted AI early focused on creating revenue-based business cases are getting results today.

These and many other insights are from a Crunchbase Pro analysis completed today using Glassdoor data to rank the best machine learning startups to work for in 2021. According to LinkedIn's 2020 Emerging Jobs Report, demand for Artificial Intelligence Specialists has grown 74% in the last four years. Demand for Data Scientists has seen a 37% growth in the same period. Indeed's latest average reported base salary of a Machine Learning engineer in the U.S. is $146,085. The job site found the number of machine learning engineer openings grew by 344% between 2015 and 2018.

Faced with the challenge of reinventing themselves into a digital business, organizations are scrambling for AIOps and DevOps expertise to work on multiple platforms. Gartner's latest Hype Cycle for Artificial Intelligence, 2020 found that the faster the democratization of AI occurs, the greater the importance of developers and DevOps to create enterprise-grade applications. The 2020 BMC Mainframe Survey provides new insights into just how quickly enterprises rejuvenate mainframes as part of their broader AIOps and DevOps initiatives. Most noteworthy about the study's results is how enterprises pursuing next-generation business models find value in rejuvenating mainframes as part of their digital business strategies. They are now core to DevOps and AIOps integration company-wide, further solidifying their value.

These and many other fascinating findings are from Kount's 2020 Bot Landscape & Impact Report published earlier this week. The report's methodology is based on interviews with online retail and eCommerce business employees with full-time roles related to fraud prevention, customer experience, payments and management. Please see page 3 of the study for additional details on the methodology. The findings bring to light new insights into how businesses are using good bots, the breadth of the threat posed by different types of malicious bots and the state of bot mitigation and management. It is a fascinating read for anyone involved in cybersecurity in general and bots specifically.