Chris Winfield, founder of Understanding A.I., tells'Fox & Friends Weekend' host Will Cain about a study showing patients preferred medical answers from artificial intelligence over doctors. Artificial intelligence fails to match humans in judgment calls and is more prone to issue harsher penalties and punishments for rule breakers, according to a new study from MIT researchers. The finding could have real world implications if AI systems are used to predict the likelihood of a criminal reoffending, which could lead to longer jail sentences or setting bail at a higher price tag, the study said. Researchers at the Massachusetts university, as well as Canadian universities and nonprofits, studied machine-learning models and found that when AI is not trained properly, it makes more severe judgment calls than humans. Human participants then labeled the photos or text, with their responses used to train AI systems.

… of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT.

Images in this blind light transport factorization example are projected onto a wall behind the camera. A group of scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a method that can reconstruct, using no special equipment, hidden video from the subtle shadows and reflections on an observed pile of clutter. With a video camera turned on in a room, the scientists can reconstruct a video of an unseen corner of the room, even if it falls outside the camera's field of view. By observing the interplay of shadow and geometry in video, the team's algorithm predicts the way that light travels in a scene, which is known as "light transport." The system then uses that to estimate the hidden video from the observed shadows -- and it can even construct the silhouette of a live-action performance.

Welcome to the September edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI surveillance, Deepfake, a documentary from the 60s and much more. Let's kick off with the comic of the month: Let's jump in 1960, we are ten years from HAL 9000 and the first personal computers but people are already thinking about the emergence of Artificial Intelligence. From the late 1950s to the early 1960s, newspapers were full of articles about it.

Recently, a team of researchers from the MIT-IBM Watson AI Lab created a method of displaying what a Generative Adversarial Network leaves out of an image when asked to generate images. The study was dubbed Seeing What a GAN Cannot Generate, and it was recently presented at the International Conference on Computer Vision. Generative Adversarial Networks have become more robust, sophisticated, and widely used in the past few years. They've become quite good at rendering images full of detail, as long as that image is confined to a relatively small area. However, when GANs are used to generate images of larger scenes and environments, they tend not to perform as well. In scenarios where GANs are asked to render scenes full of many objects and items, like a busy street, GANs often leave many important aspects of the image out.

MIT graduate student Justin Swaney is applying machine learning to music production. Rhythmic flashes from a computer screen illuminate a dark room as sounds fill the air. The snare drum sample comes out crisp and clean by itself, but turns muddy in the mix, no matter how the levels are set. Welcome to the world of modern music-making -- and its discontents. Today's digital music producers face a common dilemma: how to mesh samples that may sound great on their own but do not necessarily fit into a song like they originally imagined.

Imagine a highly sophisticated body armor that is a tough as it is flexible, a shield that consists largely of water, but remains strong enough to prevent mechanical penetration. Now imagine that this armor is not only strong, but also soft and stretchy, so much so that the wearer is able to move their body parts with ease, whether they're swimming in water, walking across the ground or rushing to escape danger. That description might sound like a suit worn by a fictional hero in the DC Comics franchise, but it actually describes portions of a lobster's exoskeleton. Researchers at the Massachusetts Institute of Technology and Harvard believe the soft membrane covering the animal's joints and abdomen ---- a material that is as tough as the industrial rubber used to make car tires and garden hoses ---- could guide the development of a new type of flexible body armor for humans, one designed to cover joints like knees and elbows. The researchers' findings appeared in a recent edition of the journal Acta Materialia.

Researchers at the Massachusetts Institute of Technology have developed an artificial intelligence (AI) system that can isolate small, nearly transparent imperfections in poorly lit images in order to reproduce objects. A blog post published by MIT News today describes a deep neural network -- layered mathematical functions loosely mimicking the behavior of neurons in the brain -- that can erase target artifacts from grainy images. George Barbastathis, professor of mechanical engineering at MIT, believes this might have applications in medicine. "In the lab, if you blast biological cells with light you burn them, and there is nothing left to image," he told MIT News. "When it comes to X-ray imaging, if you expose a patient to X-rays, you increase the danger they may get cancer. What we're doing here is -- you can get the same image quality but with a lower exposure to the patient. And in biology, you can reduce the damage to biological specimens when you want to sample them."

Many technology companies are working on artificial intelligence systems that can analyze medical data to help diagnose or treat health problems. Such systems raise the question of whether this kind of technology can perform as well as a human doctor. A new study from MIT computer scientists suggests that human doctors provide a dimension that, as yet, artificial intelligence does not. By analyzing doctors' written notes on intensive-care-unit patients, the researchers found that the doctors' "gut feelings" about a particular patient's condition played a significant role in determining how many tests they ordered for the patient. The researchers describe their findings in "How is the Doctor Feeling? ICU Provider Sentiment is Associated with Diagnostic Imaging Utilization," presented at the IEEE's 40th International Engineering in Medicine and Biology Conference.

MIT has created a system likened to X-ray vision, but the AI can track a person through walls -- or identify one specific person out of a group of 100 people -- by using wireless signals. MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) calls it RF-Pose. How could they ignore the blaring red alert of potential privacy and spying issues and continue to develop artificial intelligence (AI) that can monitor a person's movements through a solid wall using wireless radio waves? The team says that RF-Pose could be used to monitor diseases like Parkinson's, multiple sclerosis (MS), and muscular dystrophy, providing a better understanding of disease progression and allowing doctors to adjust medications accordingly. It could also help elderly people live more independently, while providing the added security of monitoring for falls, injuries and changes in activity patterns.