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AI Under the Hood: Mixing Things Up – Optimizing Fluid Mixing with Machine Learning

Fluid mixing is an important part of several industrial processes and chemical reactions. However, the process often relies on trial-and-error-based experiments instead of mathematical optimization. While turbulent mixing is effective, it cannot always be sustained and can damage the materials involved. To address this issue, researchers from Japan (Tokyo University of Science) have now proposed an optimization approach to fluid mixing for laminar flows using machine learning, which can be extended to turbulent mixing as well.

Research Highlights: Interactive continual learning for robots: a neuromorphicapproach

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

Climate Change is an Existential Threat, and Businesses Need Data to Fight It

In this contributed article, Or Lenchner, CEO, Bright Data, examines how public web data collection is essential to ESG efforts in 2022. The recent International Panel on Climate Change report warned that we aren’t doing enough to avoid the dire impacts of climate change – businesses must use every tool at their disposal to support the fight against climate change, especially quality data.

Franz’s Allegro CL® Used for Scheduling the Hubble Space Telescope Discovery of Earendel

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, announced that its Allegro CL (Allegro Common Lisp) dynamic object-oriented development system used by the Space Telescope Science Institute to develop the SPIKE Hubble Space Telescope observation scheduler has aided in the discovery of “Earendel,” the farthest star ever seen in the universe.

State of Data Report Emphasizes Emerging Shift to a Decentralized Model

New market research commissioned by Starburst and Red Hat uncovers that 55% of organizations claim the pandemic has made data access more critical, a slight increase from the 2021 study. As a result, enterprises plan to prioritize multi-cloud flexibility and ease of use when it comes to selecting data infrastructure solutions. The second annual report, “The State of Data and What’s Next,” conducted by independent research firm Enterprise Management Associates (EMA), found that the shift to quick and flexible deployments is imperative for driving the business functions and insights required to deliver valuable customer experiences in today’s fast-paced, distributed environment.

New Quantum Computing Research Shows Promising Path to Commercialization

Agnostiq, Inc., the quantum computing SaaS startup, announced its latest benchmark research which analyzed the state of quantum computing hardware to determine its current and future practicality as a mainstream solution. The findings show that quantum computing hardware has improved over time and that application-specific benchmarks can serve as a more practical yardstick for comparing the capabilities of alternative types of quantum hardware.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2021

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.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – November 2021

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.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2021

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.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2021

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.