Elon Musk has officially introduced his xAI team to the masses with a live Twitter Spaces event - after years of claiming the tech will be the demise of humanity. The Twitter boss laid out his plans to make an artificial general intelligence (AGI) that will be'maximally curious and truth-seeking' and'won't be politically correct.' 'People will be offended,' Musk said. 'Our AI can give answers that they might find controversial even though they might be true.' But beyond the AI culture wars, Musk expressed ambitious hopes to produce an AGI with deep analytical reasoning capable of solving higher order math and science problems, including many that have eluded mankind's best thinkers. The billionaire suggested that xAI could answer questions about the nature of dark matter and dark energy: theorized but difficult to confirm components of the known universe which astrophysicists estimate constitute 95 percent of the cosmos. Musk also said he hoped xAI could help resolve the'Fermi paradox' -- a theoretical question that asks why humans have not yet encountered extraterrestrial life in a universe that is over 13 billion years old and ripe with the conditions supporting life.

Esther Shein is a freelance technology and business writer based in the Boston area.

There isn't a day that goes by without hearing about some fascinating development in artificial intelligence research, whether that might be an AI that can process and produce language in a human-like way, or an AI that can unlock the mysteries folded up within a protein, or automatically make scientific discoveries. But in the headlong rush in the to find the next breakthrough, there are legitimate concerns that the competitive nature of the "AI race" might mean that things like safety and ethics are being inadvertently overlooked, resulting in phenomena like algorithmic bias, or an escalating AI arms race between rival military powers to build lethal autonomous weapons. All of these recent developments point to a need for better regulations when it comes to engineering and implementing AI. Of course, too much regulation might stifle innovation, but too little might also bring what could have been a preventable disaster. As an international research team from Teesside University, Universidade Nova de Lisboa, and Université Libre de Bruxelles now suggest, AI can also be used to navigate this delicate balance by determining which types of AI research projects might need more regulation than others. "Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences," wrote the team in a paper that was published in the Journal of Artificial Intelligence Research.

California is evaluating whether Tesla's self-driving tests require regulatory oversight, following "videos showing a dangerous use of that technology" and federal investigations into Tesla vehicle crashes, a state regulator said. The California department of motor vehicles previous said that Tesla's full self-driving, or FSD, beta requires human intervention and therefore is not subject to its regulations on autonomous vehicles. But the agency is revisiting that decision "following recent software updates, videos showing a dangerous use of that technology, open investigations by the National Highway Traffic Safety Administration (NHTSA), and the opinions of other experts", the department said in a letter on Friday to Lena Gonzalez, chair of the state senate transportation committee. The Los Angeles Times first reported the letter. Tesla did not respond to a request for comment.

Medtronic's mission is to alleviate pain, restore health, and extend life through the application of biomedical engineering, explains Elaine Gee, PhD, Senior Principal Algorithm Engineer specializing in Artificial Intelligence at Medtronic. It's a mission Gee is well equipped for. With over 15 years' experience in modeling, bioinformatics, and engineering, she drives machine learning algorithm development and analytics to support next-generation medical devices for diabetes management. On behalf of AI Trends, Ben Lakin, from Cambridge Innovation Institute, sat down with Gee to discuss her most recent focus: algorithm development related to glucose sensing to improve the accuracy and performance of continuous glucose monitoring devices, also known as CGMs. Editor's Note: Gee will be giving a featured presentation on Advancing Continuous Glucose Monitoring Sensor Development with Machine Learning at Sensors Summit in San Diego, December 10-12.

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling.

Musk worries AI's development will outpace our ability to manage it in a safe way. "So the rate of improvement is really dramatic. We have to figure out some way to ensure that the advent of digital super intelligence is one which is symbiotic with humanity. I think that is the single biggest existential crisis that we face and the most pressing one." To do this, Musk recommended the development of artificial intelligence be regulated.

There seems to be a modern day gold rush as companies explore how to use machine learning in clinical decision support software. Unfortunately for libertarians, FDA will regulate some of that software because of its risk profile. While the 21st Century Cures Act that passed last December exempted certain CDS from regulation and indeed FDA intends to exempt even more, FDA will continue to regulate high risk CDS. The question is: how will FDA regulate high risk CDS when the software involves machine learning? Some might assume that machine learning in healthcare is so new, we have no idea how FDA will react.