In a joint effort between the Massachusetts Institute of Technology (MIT) and other U.S. institutions of higher education, scientists have shown off an AI system with the ability to pick up on the nuances of human language with relative ease. In addition to being able to learn lower-level language patterns automatically, this model is also capable of automatically learning higher-level language patterns that can apply to numerous languages. The researchers trained and evaluated the model using problems from 58 distinct language-specific linguistics textbooks. Each task contained a list of words with appropriate word-form modifications. For 60 per cent of the problems, the model was able to provide a correct set of rules to explain these word-form modifications.

Summary: A newly developed artificial intelligence model can detect Parkinson's disease by reading a person's breathing patterns. The algorithm can also discern the severity of Parkinson's disease and track progression over time. Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing--i.e., breathing patterns that occur while sleeping.

A relationship between Parkinson's and breathing was noted as early as 1817, in the work of Dr. James Parkinson,

This is something that we've done our entire life and still we haven't mastered it, this is due to countless variables, but one of those we could say that is the fast life we have in big cities. If we don't actively understand breathing, how could we simulate this for robots? Let's take a look into how breathing patterns works and how doctors actually learn in school. If you take a look in the video below, you'll see a few breathing patterns along with specific situations for each one of those. Those are some of the names gathered in medicine along the years to describe breathing patterns that we have as humans, but they're not exactly ready for usage as they do not relate specifically to a current emotion, and more likely to how our body is chemically/hormone related responding.