A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine. Machine learning is a type of artificial intelligence that involves using data to train computers to make decisions and learn from experiences, according to Pharmaphorum. It has the potential to cut costs of research and development for drugmakers by helping researchers to predict what will and won't work in clinical trials. However, the report says a lot of the data being used in drug development is not suitable for machine learning purposes. There is a phenomenon known as "garbage in, garbage out," where a machine learning system can't produce credible results because of poor data, according to Pharmaphorum.

"If you don't have an AI strategy, you are going to die in the world that's coming." Whenever there is talk about artificial intelligence, Terminator-style robots that are stealing our jobs and will eventually take our lives come to mind. This picture portrays a false image of AI; AI is much more than just robotics and war machines. The tools we use in our daily lives are getting smarter every day, from spam filtering in our inboxes to voice assistants in our homes. Google New uses AI to cluster similar news, and Nvidia uses it to create slow motion videos that outperform the Slow Mo Guys.