The NFL and lawyers for thousands of retired NFL players have reached an agreement to end race-based adjustments in dementia testing in the $1 billion settlement of concussion claims, according to a proposed deal filed Wednesday in federal court. The revised testing plan follows public outrage over the use of "race-norming," a practice that came to light only after two former NFL players filed a civil rights lawsuit over it last year. The adjustments, critics say, may have prevented hundreds of Black players suffering from dementia to win awards that average $500,000 or more. The Black retirees will now have the chance to have their tests rescored or, in some cases, seek a new round of cognitive testing, according to the settlement, details of which were first reported in The New York Times on Wednesday. "We look forward to the court's prompt approval of the agreement, which provides for a race-neutral evaluation process that will ensure diagnostic accuracy and fairness in the concussion settlement," NFL lawyer Brad Karp said in a statement. The proposal, which must still be approved by a judge, follows months of closed-door negotiations between the NFL, class counsel for the retired players, and lawyers for the Black players who filed suit, Najeh Davenport and Kevin Henry.
The symposium on Artificial Intelligence – or AI – organized by the Pontifical Council for Culture, in cooperation with the German Embassy to the Holy See, will open in Rome on Thursday. The theme for the gathering is, "The Challenge of Artificial Intelligence for Human Society and the Idea of the Human Person". The aim of the meeting is to promote a better awareness of the profound cultural impact AI is likely to have on human society. The symposium will feature six experts from the fields of neuroscience, philosophy, Catholic theology, human rights law, ethics and electrical engineering. Experts from the Allen Institute for Brain Science, Goethe University, Boston College, and Google will discuss questions regarding AI and whether it can reproduce consciousness, AI and philosophical challenges, and AI and religion, and what it would mean in relation to Catholic doctrine.
A massive disruption now appears imminent in one of the world's largest – and most important – industries. In much the same way that Amazon disrupted the retail business – and how PayPal disrupted the payments industry – one under-the-radar health technology company now seeks to transform the $11.85 trillion global health industry. By moving healthcare away from brick and mortar, traditional medicine into an AI-driven tool that offers unprecedented speed, efficiency, and accuracy... Investors still have a brief window of opportunity to get in on this transformational investment opportunity while it still flies beneath Wall Street's radar. But as you'll soon discover, this company's technology is so powerful that it could become a valuable addition to hundreds of millions of households worldwide. Whether most patients, providers, or large healthcare companies realize it or not, the healthcare industry is already in the early stages of significant change. That's because patients now desire access to more information – and better information – in the blink of an eye. In a recent survey of U.S. health consumers, 71% reported facing major frustrations through their experience with healthcare providers. Concerns ranged from difficulties scheduling appointments to impersonal visits.
The connection of the human brain and computers (or machines in general) sounds like science fiction -- like a technology from a utopian (or dystopian) future. However, the development of modern brain-computer interfaces (or BCIs) started almost 100 years ago, when Hans Berger discovered electrical activity of the human brain and measured these signals through a method that later became known as electroencephalography, or simply EEG. Nowadays, BCIs already have many different applications, but we are only at the beginning and might see some impressive advances in the near future. Before getting to the current applications of BCIs and some speculation of their future uses, we will first introduce different approaches to "read the mind", or more scientifically, to measure brain activity. We will finish with a discussion of ethical issues connected to BCIs.
Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors. Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology's strategy so faithfully. "The algorithm we use has no resemblance to the actual process of evolution," says Guangyu Robert Yang, an associate investigator at MIT's McGovern Institute for Brain Research, who led the work as a postdoc at Columbia University. The similarities between the artificial and biological systems suggest that the brain's olfactory network is optimally suited to its task.
A painting of a naked woman by Pablo Picasso that has been hidden beneath one of his "Blue Period' masterpieces for more than a century has been recreated by UCL scientists using a combination of X-rays, AI and 3D printing. Ph.D. researchers Anthony Bourached (UCL Queen Square Institute of Neurology) and George Cann (UCL Space and Climate Physics) have developed a five-step technology to reproduce art works that have been painted over. For this, their third reproduction, they bought back to life the Spanish artist's depiction of a crouching nude woman; the painting was thought to have been lost until 2010 when X-rays revealed it lay behind "The Blind Man's Meal." Dubbed "The Lonesome Crouching Nude," the image is also depicted as an unfinished painting in the background of Picasso's famous "La Vie" (The Life). By using a combination of spectroscopic imaging, artificial intelligence, and 3D printing, the duo have created a full-size, full-color painting, which includes 3D textured brushstrokes. To help ensure the recreation was as close in look, feel and tone to the original, they developed an AI algorithm that analyzed dozens of Picasso's paintings and trained itself to understand the artist's style. Commenting, Bourached, who is researching Machine Learning and Behavioural Neuroscience at UCL, said, "We believe that Picasso likely painted over this piece with reluctance.
My daughter is part of the 15–20 percent of students and adults living with a language-based learning disability. According to the International Dyslexia Association, these individuals have some or all of the symptoms of dyslexia, including slow or inaccurate reading, poor spelling, poor writing, or mixing up similar words and numbers. Once we diagnosed her dyslexia, I understood she needed the help of assistive technology to learn at a rate on par with her classmates, but I wasn't sure where to start. In honor of Dyslexia Awareness Month this October, I reached out to several assistive technology experts to find out what technology they recommend for facilitating and improving reading, writing, spelling, and math. Here's what Jamie Martin, Assistive Technology Specialist at the New England Assistive Technology Center and Karen Janowski, Assistive & Educational Technology Consultant at EdTech Solutions and co-author of Inclusive Technology 365 recommend.
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects several brain regions in a distinctive propagation pattern, with emphasis on the motor neurons.1 To diagnose ALS as early as possible is a task of high clinical relevance for the optimized patients' care and the opportunity to be enrolled in clinical trials. With advances in neuroimaging in neurodegenerative diseases like ALS,2,3 it has been speculated that cerebral magnetic resonance imaging (MRI) may be able to provide insights that support an early diagnosis. Multiparametric, quantitative MRI has been discussed as a way to achieve a composite neuroimaging index.3 However, the amount of biomarkers, as well as their (non-linear) interactions, makes a straightforward approach likely unsuccessful. Machine learning (ML) might be the missing piece to integrate multiparametric MRI data into a useful classifier.4
Figure 1 – Figure supplement 1: Learning curves on the random split-half validation used for model building. To facilitate comparisons, we evaluated predictions of age, fluid intelligence and neuroticism from a complete set of socio-demographic variables without brain imaging using the coefficient of determination R2 metric (y-axis) to compare results obtained from 100 to 3000 training samples (x-axis). The cross-validation (CV) distribution was obtained from 100 Monte Carlo splits. Across targets, performance started to plateau after around 1000 training samples with scores virtually identical to the final model used in subsequent analyses. These benchmarks suggest that inclusion of additional training samples would not have led to substantial improvements in performance.