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CATALOG Achieves Historic DNA Computing Milestone

Catalog Technologies, Inc., a leader in DNA-based digital data storage and computation, has made a historic breakthrough in DNA computation by demonstrating the ability to search data stored in DNA in a massively parallel and scalable manner with resource usage almost independent of the data size. 

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.

What to Avoid When Solving Multilabel Classification Problems

In this contributed article, April Miller, a senior IT and cybersecurity writer for ReHack Magazine, suggests that If you are working with a model with a multilabel classification problem, there is a likely chance you will run into something in need of fixing. Here are a few common issues you may encounter and what to avoid when solving them.

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.

AWS Announces 10 New AI Features at AWS re:Invent 2022

At AWS re:Invent 2022, Amazon Web Services (AWS) announced 10 new features to its portfolio of AI services, and is excited to expand its offerings to more than 100,000 customers who currently rely on AWS for AI and ML initiatives. Please see below for a high-level overview of these new features.

The Key Role Missing in Most Data Science Teams

In this contributed article, Wendy Lynch, Founder of Analytic-Translator.com, shares her experience of working with small to large global clients on how to break down the communication barriers in an organization to deliver results. This often happens between the analyst teams and the business teams.

The Anyscale Platform™, built on Ray, Introduces New Breakthroughs in AI Development, Experimentation and AI Scaling

Anyscale, the company behind Ray open source, the unified compute framework for scaling any machine learning or Python workload, announced several new advancements on the Anyscale Platform™ at AWS re:Invent in Las Vegas, NV. The new capabilities extend beyond the advantages of Ray open source to make AI/ML and Python workload development, experimentation, and scaling even easier for developers.

2023 Trends in Artificial Intelligence and Machine Learning: Generative AI Unfolds  

In this contributed article, editorial consultant Jelani Harper offers his perspectives around 2023 trends for the boundless potential of generative Artificial Intelligence—the variety of predominantly advanced machine learning that analyzes content to produce strikingly similar new content.

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.

d-Matrix Unlocks New Potential with Reinforcement Learning based Compiler for at Scale Digital In-Memory Compute Platforms

d-Matrix, a leader in high-efficiency AI-compute and inference, announced a collaboration with Microsoft using its low-code reinforcement learning (RL) platform, Project Bonsai, to enable an AI-trained compiler for d-Matrix’s unique digital in memory compute (DIMC) products. The user-friendly Project Bonsai platform accelerates time to value, with a product-ready solution that cuts down on development efforts using an AI-based compiler that leverages ultra-efficient DIMC technology from d-Matrix.