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New Survey Indicates Use of Alternative Data in Investment Community Shows No Signs of Slowing

Lowenstein Sandler announced the release of Alternative Data: The New Oil for the Digital Economy? The 2022 Lowenstein Sandler Alternative Data Report. The survey, which is the third annual survey on this market development from the firm’s Investment Management Group, finds demand increasing for alternative data—not only driven by hedge funds, but also by private equity firms and venture capital investors. Alternative data is generally defined as information not contained in company filings, press releases, analyst reports, or other traditional information sources.

Report: Audit Industry Rising to the Data Analytics Challenge

With businesses facing the strongest economic headwinds in years, the Chartered Institute of Internal Auditors (Chartered IIA) is urging internal auditors to embrace data analytics to navigate more risky, uncertain, and volatile times ahead. The new report, “Embracing data analytics: Ensuring internal audit’s relevance in a data-led world,” from Chartered IIA in partnership with AuditBoard aims to encourage internal audit to fully embrace data analytics and support the organization in doing the same.

2023 Trends in Data Governance 

In this contributed article, editorial consultant Jelani Harper offers his perspectives around 2023 trends for data governance. The valuation of data governance, both to the enterprise and to data management as a whole, is evinced in two of the most discernable trends to shape this discipline in 2023.

Research Highlights: R&R: Metric-guided Adversarial Sentence Generation

Large language models are a hot topic in AI research right now. But there’s a hotter, more significant problem looming: we might run out of data to train them on … as early as 2026. Kalyan Veeramachaneni and the team at MIT Data-to-AI Lab may have found the solution: in their new paper on Rewrite and Rollback (“R&R: Metric-Guided Adversarial Sentence Generation”), an R&R framework can tweak and turn low-quality (from sources like Twitter and 4Chan) into high-quality data (texts from sources like Wikipedia and industry websites) by rewriting meaningful sentences and thereby adding to the amount of the right type of data to test and train language models on.

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.

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. 

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.