Scientists taught a deep learning system to learn intuitive physics the same way human babies do. This mechanism could be key in bridging the gap between humans and AI, as well as inform future psychology studies about cognition. By definition, "human intuition" seems to denote a barrier between us and artificial intelligence. It's why we have gut feelings and reflexive -- sometimes impulsive -- reactions inexplicable by logic, and therefore not simply transferrable to computers. I mean, we can hardly parse our own reasoning for instinctive behavior, so how could we develop algorithms to encode it?

Dogs are able to pick up on individual words in sentences spoken to them using similar computations and brain regions as human babies, a study has found. When we are infants, we learn to spot new words in a stream of speech first, before we actually learn what each individual word means. To tell where each word ends and another begins, babies use complex calculations that keep track of which syllables appear together -- and thus likely form words. By using a combination of brain imaging techniques, experts led from Hungary's Eötvös Loránd University have shown that dogs are capable of similar feats. This is the first time that the capacity to apply so-called statistical learning has been shown to be demonstrated in a non-human mammal.

This is the Frankenstein breakthrough that the medical world has long been dreading. A Chinese scientist yesterday declared that he has changed the fundamental genetic code of human babies, using methods that are banned in most of the world. The potential consequences are as alarming as they are unpredictable. No less an authority than Professor Stephen Hawking feared such experiments would one day create a race of'super-humans', ending mankind as we know it. Researchers have already discovered that gene editing may cause a host of cancers as a result of interfering in a genetic code so complex we will perhaps never be capable of understanding it fully.

Robots are becoming more and more useful. Thanks to Carnegie Mellon's approach, robots could become even smarter by learning as a human would. Google is betting big on artificial intelligence. Robots have come a long way over the past decade or so, but they're still not as good at interacting with humans as they could be. In fact, robots still struggle to do some basic tasks. A team at Carnegie Mellon, however, is trying to fix that.

The nightmare of robot babies continues apace in Japan, a rapidly aging society where human babies are going out of style. Babyloid is the latest cyber-tot to spring from the minds of engineers with little apparent regard for how scary their progeny are. Creator Masayoshi Kano of Nagoya's Chukyo University and Ifbot fame has been showing off Babyloid, developed two years ago, in presentations sponsored by the local government. He recently explained the robot in a talk at the Artificial Intelligence Research Promotion Foundation. Inspired by a baby beluga whale, Babyloid is designed to be a therapeutic robot for depressed seniors, similar to Takanori Shibata's robot seal Paro.

Researchers at Carnegie Mellon University are teaching robots to learn by touch, much like human babies. The experiment one day could allow artificial intelligence to learn about the physical environment through senses, including touch. The development draws robotics and AI closer together, paving the way for potential unified applications in factories, automated deliveries of goods, or household assistants. The research is set out in a paper by CMU students Lerrel Pinto, Dhiraj Gandhi, and Yuanfeng Han; and professors Yong-Lae Park and Abhinav Gupta. The paper, titled "The Curious Robot: Learning Visual Representations via Physical Interactions (pdf)," describes the experiment's goal: to use physical robotic interactions to teach an AI to recognize objects.