Sign up for our newsletter and get the latest big data news and analysis.

Chung-Ang University Researchers Develop Algorithm for Optimal Decision Making under Heavy-tailed Noisy Rewards

Researchers from South Korean Chung-Ang University propose methods that theoretically guarantee minimal loss for worst case scenarios with minimal prior information for heavy-tailed reward distributions.

Podcast Highlights: “Intel on AI”: Learning with AI

With over 15 years of experience in machine learning, neural networks, and computer vision, Microsoft’s Milena Marinova knows a thing or two about using AI for professional growth. In this podcast episode of “Intel on AI,” Milena shares lessons she’s learned during her career, including the challenges of developing AI products and the importance of data policy by design.

Video Highlights: Google Engineer on His Sentient AI Claim

Google Engineer Blake Lemoine (who worked for the company’s Responsible AI unit) joins Emily Chang of Bloomberg Technology in the video below to talk about some of the experiments he conducted that led him to believe that LaMDA (a Large Language Model) was a sentient AI, and to explain why he was placed on administrative leave and ultimately fired.

AI Hiring Experts on President Biden’s AI Bill of Rights

A recent interdisciplinary study from NYU Tandon researchers explores the issue of accountable AI. The study reveals how resume format, LinkedIn URLs and other unexpected factors can influence AI personality prediction and affect hiring.

The Move Toward Green Machine Learning

A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture, upgrading hardware, and using efficient data centers. 

ACM Global Technology Policy Council Releases Joint Statement on Principles for Responsible Algorithmic Systems by US and Europe Policy Committees 

The Association for Computing Machinery’s global Technology Policy Council (TPC) has released a new Statement on Principles for Responsible Algorithmic Systems authored jointly by its US (USTPC) and Europe Technology Policy Committees (Europe TPC). Recognizing that algorithmic systems are increasingly used by governments and companies to make or recommend decisions that have far-reaching effects on individuals, organizations and society, the ACM Statement lays out nine instrumental principles intended to foster fair, accurate, and beneficial algorithmic decision-making.

AI For the Future of Work.

AI has been a buzzword and businesses are all at different stages of implementing it. But is it actually solving anything? MIT Sloan and Boston Consulting Group (BCG) answer that question in a new report Achieving Individual – and Organization- Value with AI. Among respondents who report that their organization obtains moderate, significant, or extensive value from AI, the vast majority (85%) claim that they personally obtain value from AI. 

Video Highlights: Modernize your IBM Mainframe & Netezza With Databricks Lakehouse

In the video presentation below, learn from experts how to architect modern data pipelines to consolidate data from multiple IBM data sources into Databricks Lakehouse, using the state-of-the-art replication technique—Change Data Capture (CDC).

Capital One + Forrester Survey Reveals Key Challenges that Inhibit ML Deployment Across the Enterprise

Capital One’s new Forrester study, “Operationalizing Machine Learning Achieves Key Business Outcomes,” reveals the biggest challenges, concerns and opportunities data executives experience when leveraging machine learning to improve business performance. While the report finds that data management decision-makers are concerned about key operational challenges that could slow ML deployments and maturity, the data also reveals that adoption continues to rise, with 67% of leaders planning to increase their use of ML across their business within the next three years.

Discover the Secret to Building Effective ML Teams

What does it take to run an efficient ML team? Many wonder why some teams fail and a few others succeed. Though there is no one answer to this question, our friends over at Comet found three major success factors: visibility, reproducibility, and collaboration. Read Comet’s paper on “Building Effective Machine Learning Teams,” and gain deeper insights on how you can apply three ML components to your teams.