As data becomes a driving force in the business and IT world, data center artificial intelligence and machine learning tools can be the answer to leveraging data to solve business outcomes. Machine learning (ML) and artificial intelligence (AI) solutions are helping organizations today stay ahead of the ever-increasing amount of data storage needed and processing requirements. New innovative AI and ML tools are being used in data centers across the globe to improve operational efficiency and productivity as well as solve some of the industry's most glaring problems, such as data security and a data center's carbon footprint. CRN breaks down eight ways AI and ML tools are being used in data centers to solve some of the biggest technical, cybersecurity and data-related issues in the IT world today.

According to a recent press release, "Today, the U.S. Department of Energy's (DOE's) Advanced Research Projects Agency-Energy (ARPA-E) announced $15 million in funding for 23 projects to accelerate the incorporation of machine learning and artificial intelligence into the energy technology and product design processes as part of the Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program. Launched in April of this year, the DIFFERENTIATE program aims to develop streamlined solutions to next-generation energy challenges. The program identified three general mathematical optimization problems that are common to many design processes. The selected projects then conceptualized machine learning and artificial intelligence-based solutions to help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation." The release goes on, "Following the initial round of Phase I funding for the DIFFERENTIATE program, additional funding will be available to qualifying awardees at a future date… DIFFERENTIATE projects include: Iowa State University – Ames, Iowa. Iowa State University will develop machine learning tools to accelerate the inverse design of new microstructures in photovoltaics. The team will create a new deep generative model to combat challenges in real-world inverse design problems. The proposed inverse design tools, if successful, will produce novel, manufacturable material microstructures with improved electromagnetic properties relative to existing technology."

Across Visual Studio Code and Azure Notebooks, January brought numerous exciting updates to the AI and Machine Learning tooling for Python! The Python extension for VS Code first introduced an interactive data science experience in the last Oct update. With this release, we brought the power of Jupyter Notebooks into VS Code. Many feature additions have been released since, including remote Jupyter support, ability to export Python code to Jupyter Notebooks, etc. The most noticeable enhancement in the Jan 2019 update allows code to be typed and executed directly in the Python Interactive window.

It's a non-traditional application of neural networks where the network is used to'hallucinate' a desirable image,