Penn State College of Medicine and Huck Institutes of the Life Sciences are launching a new Grand Rounds topic: "Artificial Intelligence in Health."

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Despite nurturing its R&D capacity, the city of Braunschweig lags its German peers in attracting FDI. Now it hopes a focus on the mobility sector will mean its technical skills are matched with investment.

Naga Lalitha has become visible as someone who is changing the face of technology for industry verticals like supply chain and manufacturing.

National Bureau of Economic Research

VJ Lakshmi’s nuanced presentation was rich in manodharma aspects.

The joint statement emphasised collaboration in technology exchange, joint research, and development initiatives to optimize SAF production processes and create frameworks encouraging investment in SAF production.

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Developing countries that are interested in geothermal energy may see transformational results by approaching World Bank and other institutions, as Djibouti did. In an interview, the World Bank Djibouti geothermal project team talks about the project investment, lessons from Kenya’s experience, and preparations that are being made for the private sector to take up the next steps.

Full Text:

Machine learning algorithms can sometimes do a great job with a little help from human expertise, at least in the field of materials science. In many specialized areas of science, engineering and medicine, researchers are turning to machine learning algorithms to analyze data sets that have grown too large for humans to understand. In materials science, success with this effort could accelerate the design of next-generation advanced functional materials, where development now usually depends on old-fashioned trial and error. By themselves, however, data analytics techniques borrowed from other research areas often fail to provide the insights needed to help materials scientists and engineers choose which of many variables to adjust -- and the techniques can't account for dramatic changes such as the introduction of a new chemical compound into the process. In a new study, researchers explain a technique known as dimensional stacking, which shows that human experience still has a role to play in the age of machine intelligence. The machines gain an edge at solving a challenge when the data to be analyzed are intelligently organized based on human knowledge of what factors are likely to be important and related. "When your machine accepts strings of data, it really does matter how you are putting those strings together," said Nazanin Bassiri-Gharb, the paper's corresponding author and a scientist at the Georgia Institute of Technology. "We must be mindful that the organization of data before it goes to the algorithm makes a difference. If you don't plug the information in correctly, you will get a result that isn't necessarily correlated with the reality of the physics and chemistry that govern the materials."

Image credit: Rob Felt/Georgia Tech

Full Text:

Machine learning algorithms can sometimes do a great job with a little help from human expertise, at least in the field of materials science. In many specialized areas of science, engineering and medicine, researchers are turning to machine learning algorithms to analyze data sets that have grown too large for humans to understand. In materials science, success with this effort could accelerate the design of next-generation advanced functional materials, where development now usually depends on old-fashioned trial and error. By themselves, however, data analytics techniques borrowed from other research areas often fail to provide the insights needed to help materials scientists and engineers choose which of many variables to adjust -- and the techniques can't account for dramatic changes such as the introduction of a new chemical compound into the process. In a new study, researchers explain a technique known as dimensional stacking, which shows that human experience still has a role to play in the age of machine intelligence. The machines gain an edge at solving a challenge when the data to be analyzed are intelligently organized based on human knowledge of what factors are likely to be important and related. "When your machine accepts strings of data, it really does matter how you are putting those strings together," said Nazanin Bassiri-Gharb, the paper's corresponding author and a scientist at the Georgia Institute of Technology. "We must be mindful that the organization of data before it goes to the algorithm makes a difference. If you don't plug the information in correctly, you will get a result that isn't necessarily correlated with the reality of the physics and chemistry that govern the materials."

Image credit: Rob Felt/Georgia Tech

A passion for data is a prerequisite to pursue a career in data science — datasets should instantly inspire you to infer, analyse and visualise information.

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