An anonymous reader quotes a report from The Debrief: How do we how know to speak and to read? These essential questions led to new research from the Massachusetts Institute of Technology that uses AI models to examine how and why our brains understand language. Oddly enough, your brain may work just like your smartphone's autocorrect feature. The new study, published in the Proceedings of the National Academy of Sciences, shows that the function of these AI language models resembles the method of language processing in the human brain, suggesting that the human brain may use next-word prediction to drive language processing. In this new study, a team of researchers at MIT analyzed 43 different language models, many of which were optimized for next-word prediction.

As fears about AI's disruptive potential have grown, AI ethics has come to the fore in recent years. Concerns around privacy, transparency and the ability of algorithms to warp social and political discourse in unexpected ways have resulted in a flurry of pronouncements from companies, governments, and even supranational organizations on how to conduct ethical AI development. The majority have focused on outlining high-level principles that should guide those building these systems. Whether by chance or by design, the principles they have coalesced around closely resemble those at the heart of medical ethics. But writing in Nature Machine Intelligence, Brent Mittelstadt from the University of Oxford points out that AI development is a very different beast to medicine, and a simple copy and paste won't work. The four core principles of medical ethics are respect for autonomy (patients should have control over how they are treated), beneficence (doctors should act in the best interest of patients), non-maleficence (doctors should avoid causing harm) and justice (healthcare resources should be distributed fairly).

Despite confidence in their soft skills, including critical thinking, a majority of young professionals in the 2nd Annual State of Critical Thinking Study commissioned by Massachusetts-based educational technology company MindEdge Learning resoundingly flunked a quiz that assessed their critical thinking skills specifically when applied to digital literacy. The nine questions in the quiz targeted the respondents' ability to distinguish fake news on the internet from reliable, factual content. Getting the questions right demanded one pay attention to details of style (use of all caps, presence or absence of photo credits, words such as "promoted" or spelling errors, etc.) as well as technical aspects such as broken links and recognizable domains. I asked MindEdge if I could see the survey questions and took the quiz myself. I am pretty sure I got all 9 questions right.

Brain scans can detect autism in children long before they display symptoms, a new study says, giving hope for the development of early testing and intervention methods. MRI scans of of 148 children at six, 12 and 24 months allowed scientists to detect brain surface differences in children who were eventually diagnosed with autism. Researchers used artificial intelligence to analyze the brain scan results and were able to predict an autism diagnosis with 80 percent accuracy. The study, led by researchers from the University of North Carolina, was published Wednesday in Nature. Currently, the earliest age doctors can diagnose autism in a child is at two years old.