A new training model, dubbed "KnowNo," aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much. Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door.

Artificial intelligence has enjoyed some remarkable online-gaming victories in recent years, though usually in slower games with clear--if incredibly multi-threaded--rule systems. On Monday, the robots pushed ahead with a slightly more remarkable online-gaming victory over their puny human masters when an AI program won big at Texas Hold'Em poker. A lengthy tournament of Hold'Em--specifically, the heads-up, no-limit variety--ended with victory for Libratus, an AI program developed by a professor and PhD candidate at Carnegie Mellon University. Libratus emerged victorious after 120,000 combined hands of poker played against four human online-poker pros. Libratus' $1.7 million margin of victory, combined with so many hands, clears the "Brains Vs.