Washington State University researchers have developed a novel way to identify previously unrecognized antibiotic-resistance genes in bacteria. By employing machine learning and game theory, the researchers were able to determine with 93 to 99 percent accuracy the presence of antibiotic-resistant genes in three different types of bacteria. The increasing prevalence of antibiotic-resistant bacteria is a growing problem around the world. Every year, millions of people in the U.S. are infected with drug-resistant pathogens, and thousands of people die from pneumonia or bloodstream infections that become impossible to treat. In recent years, researchers have been working to make use of genome sequencing to identify antibiotic-resistant genes, looking for similar sequences of genes in public databases.

I've been spending some time lately trying to understand how machine learning is used in marketing attribution. After some research, I found that most of the content out there either focuses exclusively on the business side or discusses very simple models (like the first click model which is used in Google Analytics). I recently wrote a technical blogpost in which I present how Machine Learning and Game theory are used to attribute the purchases to the right marketing channels.

Africa's wildlife is in a constant state of danger. Between 2009 and 2015, Tanzania and Mozambique lost more than half of their elephants, many of them to poaching for ivory smuggling. The decline has propelled African vulture populations, who feed on elephant carcasses, toward extinction too. And attempts at curtailing poaching and ivory smuggling haven't helped the dwindling elephant population. In South Africa, rhinos are a prized poaching target too, for their horns.