A machine learning algorithm that can flag papers that may have come from paper mills could help publishers fight fake scientific studies. Paper mills may be the biggest organised fraud perpetrated on scientific journals ever. While there have been instances of individual researchers manipulating images or simply inventing data, paper mills serve up professional fakery on an industrial scale. Buyers can purchase a paper, or authorship of one, on any topic based on entirely made-up results. Biochemical and biomedical journals have been hit particularly hard, flooded with hundreds of fake research manuscripts.

Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Wimbledon is one of the most prestigious major events in the world. With over 675 matches played and over 147,000 tennis points played, its size and scale are substantial. In fact, even if fans diligently watch their favorite players, they will miss a high proportion of the played points. Wimbledon uses IBM digital and AI capabilities to provide rapid access to match highlights to serve up the best content to fans.

Leave it to the folks at Google to devise AI capable of predicting which machine learning models will produce the best results. In a newly-published paper ("Off-Policy Evaluation via Off-Policy Classification") and blog post, a team of Google AI researchers propose what they call "off-policy classification," or OPC, which evaluates the performance of AI-driven agents by treating evaluation as a classification problem. The team notes that their approach -- a variant of reinforcement learning, which employs rewards to drive software policies toward goals -- works with image inputs and scales to tasks including vision-based robotic grasping. "Fully off-policy reinforcement learning is a variant in which an agent learns entirely from older data, which is appealing because it enables model iteration without requiring a physical robot," writes Robotics at Google software engineer Alexa Irpan. "With fully off-policy RL, one can train several models on the same fixed dataset collected by previous agents, then select the best one."

Fantasy Premier League is a popular platform for football fans to compete during the season. But what if artificial intelligence could find a way of beating the human competition?