Making targeted weather forecasts highly available

TWC acquisition perfect complement to IBM’s Deep Thunder

Editor’s note: This article is by Lloyd Treinish, IBM Distinguished Engineer, Chief Scientist -- Environmental Modelling, Weather and Deep Thunder, IBM Research

Blue skies. Not just a perfect day for a picnic. But it’s also the term utilities use to describe a grid that’s perfectly balanced with energy from solar, wind, and other sources, or free of disruptions from a storm. With IBM’s acquisition of The Weather Company closing last month, utility companies could soon use TWC’s size, scale and expertise, and my team’s Deep Thunder precision forecasting, to predict those blue skies – and what they can do to rebalance their loads for days that aren’t so perfect. And that’s just one industry example.


Smarter Energy

Special Issue of the IBM Journal of Research and Development

Current issue of IBM Journal of R&D
Large amounts of data are transforming industries, and this has a particularly important and significant effect in the energy domain. Energy stands at the nexus of areas relating to the environment, economic development, and national security. This crucial industry is also positioned at the tipping point of multiple disruptive trends.

As noted by our guest editors IBM Fellow Chandu Visweswariah, director of the IBM Smarter Energy Research Institute, and Brad Gammons, Jr.,  general manager of IBM’s Global Energy and Utilities Industry, we also face an unprecedented amount of uncertainty in planning, managing, and orchestrating energy systems. For example, consider weather uncertainty, uncertainty in supply and demand, intermittency due to renewable energy adoption, an uncertain regulatory environment, and uncertainty in energy prices.


Data Privacy Day Webinar: Protecting Personal Information Can Be Easy

Editor’s Note: This article is by Dr. Jan Camenisch, cryptographer, IBM Research
Your personal identity data was worth several hundred dollars on the black market 10 years ago. Today, it’s worth pocket change. Why? Because it’s easier to steal this information than ever before.

As more pieces of our lives go online, we regularly have to authenticate ourselves for service providers, including social media tools and e-commerce sites. Today, identity authentication is realized by mirroring the paper-based processes in the electronic realm, but this often exposes an excessive amount of personal information. For example, when you show your driver’s license to prove your age, you also unintentionally share your address.


30 Years of Atomic Force Microscopy: IBM Scientists Trigger and Observe Reactions in an Individual Molecule

Once again, IBM scientists are opening the eyes of the world to objects that exist only at the atomic scale.

In a new paper appearing today in the peer-reviewed journal Nature ChemistryIBM researchers, in collaboration with CiQUS at the University of Santiago de Compostela, have observed a fascinating molecular rearrangement reaction known as a Bergman cyclisation which was first described in 1972 by American chemist Robert George Bergman. The paper will be featured on the cover of the March issue.


Data Science for a Better World

Saška Mojsilović led the creation of the Ebola Open Data Jam in 2014. A community effort which helped to identify, inventory and classify all open data sources related to the Ebola outbreak, it provided governments, aid agencies and researchers with free and open access to valuable open data related to the epidemic on the platform EbolaData.org.

IBM Research scientist Kush Varshney
Kush Varshney is a data ambassador for DataKind, an organization that brings together leading data scientists with high impact social organizations to better analyze, model and visualize data in the service of humanity. He has led projects with NGO GiveDirectly and Simpa Networks. GiveDirectly delivers cash directly to extremely poor villagers in Kenya and Uganda.

Using a combination of volunteers, satellite imagery, image processing and a machine learning-based algorithm, the team trained a system to identify the poorest villages based on the proportion of thatch to metal roofed homes, a simple yet effective proxy for poverty. A resulting paper on the research was awarded the best social good paper at the 2014 KDD Conference.

IBM's Privacy-Friendly Identity Mixer Meets the Internet of Things

Prof. Gomez Skarmeta 
When IBM scientist Dr. Jan Camenisch co-invented Identity Mixer, a cryptographic algorithm to protect personal data, his goal was to help people take back control of their data, like date of birth, age, and address.

After more than a decade of research and development, Identity Mixer is now available as an easy to use cloud service. And while Dr. Camenisch’s original goal was protecting people, the Internet of Things (IoT) is presenting another opportunity for his invention protecting the privacy of sensor data.

Sensors are collecting all kinds of data, some of it public, like the amount of rainfall or highway traffic; some of it personal, like a heart beat. This data should also be kept anonymous, yet still be sharable in some form, if the user allows it.


From Knowledge Graphs to Cognitive Computing

Editor’s note: This article was written by Marco Luca Sbodio, Research Staff Member at IBM Research - Ireland, and originally appeared on 01net’s website as Dati di pubblica utilità: dai knowledge graph al cognitive computing; re-printed here with permission. 

We live in the era of Big Data, and we use data for everything: from predicting which goods a customer may buy next, to forecasting the weather, and analyzing traffic in cities, or the spreading of diseases in the population of a country. Indeed data is an extremely powerful resource, an enabler for a large portion of our current technology.

However, we should not forget that though data is the foundation underpinning information processing, the real value is in the information that we are able to surface by processing the data, in the patterns that we are able to identify and recognize, and, ultimately, in the knowledge that we extract. So, we can say that from Big Data we usually distill Small Knowledge, which has Big Value.