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Ethical AI: Five Guiding Pillars

This new white paper addresses the fact that today’s leaders are faced with a true conundrum: how can the enterprise benefit from new opportunities created through artificial intelligence while still safeguarding the well-being of employees, customers and society?
To help guide leaders through this paradox, which is growing ever-more complicated and relevant, KPMG has identified five proposed actions organizational leaders can take to create an ethical enterprise and sustain it into the future through governance and control of AI.

Qeexo AutoML Demo: Automating Machine Learning for Embedded Devices

Qeexo spun out of Carnegie Mellon University, has for a long time developed multi-touch technology for handset manufacturers which does ML on the device level. It has applied this approach to a new AutoML technology that allows companies to embed ML into hardware and conduct learning on sensor data.

The Future of the DBMS Market Is Cloud

This Gartner whitepaper, sponsored by Striim, offers a glimpse for where databases are headed: “The Future of the DBMS Market Is Cloud.” Database management system deployments and innovations are increasingly cloud-first or cloud-only. Data and analytics leaders selecting DBMS solutions must accept that cloud DBMS is the future and must plan for shifting spending, staffing and development accordingly.

DataRobot Reports that Nearly Half of AI Professionals are Very to Extremely Concerned about AI Bias

DataRobot, the leader in enterprise AI, released new research revealing that nearly half (42%) of AI professionals across the U.S. and U.K. are “very” to “extremely” concerned about AI bias. The research — based on a survey of more than 350 U.S. and U.K. executives involved in AI and machine learning purchasing decisions — uncovered that “compromised brand reputation” and “loss of customer trust” are the most concerning repercussions of AI bias, prompting 93% of respondents to say they plan to invest more in AI bias prevention initiatives in the next 12 months.

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

Interview: Tammy Wang, VP of Data Science and Engineering at Riviera Partners

I recently caught up with Tammy Wang, VP of Data Science and Engineering at Riviera Partners to discuss her company’s partnering with the University of Virginia Economics department to build an AI-powered application that can help people successfully navigate STEM career paths, with a focus on developing a long-term career trajectory in tech. The solution has the potential to impact U.S. labor supply by addressing the increasing demand for technology talent, which has resulted from the rapid digitization and automation of the labor market.

Julia: The Programming Language Of The Future

Keno Fischer, Julia Computing co-founder and CTO (Tools) discussed Julia and Julia Computing in a recent interview. Learn how he became interested in computer science and how Julia was created, and how a 16 year-old German exchange student became a 19 year-old co-founder of Julia Computing. Keno shares stories behind the creation of the popular […]

Why Music Makes Us Feel, According to AI

Your heart beats faster, palms sweat and part of your brain called the Heschl’s gyrus lights up like a Christmas tree. Chances are, you’ve never thought about what happens to your brain and body when you listen to music in such a detailed way. But it’s a question that has puzzled scientists for decades: Why does something as abstract as music provoke such a consistent response? In a new study, a team of USC researchers, with the help of artificial intelligence, investigated how music affects listeners’ brains, bodies and emotions.

AutoML in Practice

The compelling Oct. 15, 2019 presentation below is on behalf of one of my favorite Meetup groups: LA Machine Learning. The talk, “AutoML in Practice,” is by Danny D. Leybzon, a Solutions Architect at Qubole, a cloud-native big data platform. Automated Machine Learning (AutoML) is one of the hottest topics in data science today, but what does it mean? This presentation gives a broad overview of AutoML, ranging from simple hyperparameter optimization all the way to full pipeline automation.

Competition Uses AI to Combat Inequities in Early Childhood Education

Booz Allen Hamilton, Kaggle and PBS KIDS launched the fifth annual Data Science Bowl. This 90-day competition challenges thousands of data scientists and researchers from around the world to apply the power of artificial intelligence to combat inequities in early childhood education (ECE). More specifically, participants will create AI algorithms using anonymous gameplay data from the PBS KIDS Measure Up! App, which helps young children build math skills, to better understand children’s unique learning styles.