Watson is going to solve poverty, illiteracy and Global Crisis says IBM

One project is named Emergency Food Best Practice: The Digital expertise, that plans to compile emergency food distribution best practices and share it with nonprofits through an interactive digital tool. IBM can partner with noncommercial St. John’s Bread & Life to develop the tool supported the nonprofit’s distribution model, that helps the organization seamlessly serve over two,500 meals daily in new york town.

Another project is named Overcoming Illiteracy, which can use AI to permit low-literate adults to “navigate the information-dense world with confidence.” The project hopes to rewrite advanced texts (such as product descriptions and manuals), extract the fundamental message, and gift it to users through visuals and straightforward spoken messages. whereas this project does not solve the worldwide literacy crisis, it’ll permit low-literate adults interact with text independently.

“The projects were chosen for this year’s Social sensible program cover a vital range of topics — as well as predicting new diseases, promoting innovation, assuaging illiteracy and hunger, and serving to individuals out of poorness,” Arvind Krishna, director of IBM research, aforesaid during a statement. “What unifies all is that, at the core, they necessitate major advances in science and technology. Armed with the experience of our partners and drawing on a wealth of recent knowledge, tools, and experiences, Science for Social-smart offers new solutions to the issues our society is facing.”
IBM hopes the initiative can build off the success of the company’s noted mainframe, Watson, which has helped address health care, education, and environmental challenges since its development.

Six pilot projects were conducted in 2016 so as to develop the Science for the Social good initiative. These projects lined broad vary of topics, like health care, humanitarian relief, and international innovation.

A particularly successful project used machine learning techniques to better understand the spread of the Zika virus. Using complex data, the team developed a predictive model that identified which primate species should be targeted for Zika virus surveillance and management. The results of the project are now leading new testing in the field to help prevent the spread of the disease.