Why are we on the verge of creating a technology that will combine the computer with the human nervous system into a single complex? Can a computer system handle the flood of data from billions of living neurons? I will try to answer these questions in this article. In the previous article "Individual artificial intelligence: A new technology that will change our world", we talked about the fact that a new type of artificial intelligence will become a bioelectronic hybrid in which a living human brain and a computer will work together. Thus, a new type of AI will be born – individual artificial intelligence.
Researchers at Memorial Sloan Kettering Cancer Center (MSK) have developed a sensor that can be trained to sniff for cancer, with the help of artificial intelligence. Although the training doesn't work the same way one trains a police dog to sniff for explosives or drugs, the sensor has some similarity to how the nose works. The nose can detect more than a trillion different scents, even though it has just a few hundred types of olfactory receptors. The pattern of which odor molecules bind to which receptors creates a kind of molecular signature that the brain uses to recognize a scent. Like the nose, the cancer detection technology uses an array of multiple sensors to detect a molecular signature of the disease.
Purpose: A reliable tool for outcome prognostication in severe traumatic brain injury (TBI) would improve intensive care unit (ICU) decision-making process by providing objective information to caregivers and family. This study aimed at designing a new classification score based on magnetic resonance (MR) diffusion metrics measured in the deep white matter between day 7 and day 35 after TBI to predict 1-year clinical outcome. Methods: Two multicenter cohorts (29 centers) were used. MRI-COMA cohort (NCT00577954) was split into MRI-COMA-Train (50 patients enrolled between 2006 and mid-2014) and MRI-COMA-Test (140 patients followed up in clinical routine from 2014) sub-cohorts. These latter patients were pooled with 56 ICU patients (enrolled from 2014 to 2020) from CENTER-TBI cohort (NCT02210221).
Image Classification is one of the most fundamental tasks in computer vision. It has revolutionized and propelled technological advancements in the most prominent fields, including the automobile industry, healthcare, manufacturing, and more. How does Image Classification work, and what are its benefits and limitations? Keep reading, and in the next few minutes, you'll learn the following: Image Classification (often referred to as Image Recognition) is the task of associating one (single-label classification) or more (multi-label classification) labels to a given image. Here's how it looks like in practice when classifying different birds-- images are tagged using V7. Image Classification is a solid task to benchmark modern architectures and methodologies in the domain of computer vision. Now let's briefly discuss two types of Image Classification, depending on the complexity of the classification task at hand. Single-label classification is the most common classification task in supervised Image Classification.
Artificial intelligence does all kinds of things….genomics Genetic engineering has always been a go-to plot twist in sci-fi movies and TV shows. The idea of genetically mutated humans with superior abilities and unique DNAs still has ripple effects on Marvel fans and box offices. But what if we can alter genes in real life? CRISPR gene editing has been doing that since 2012 (no Wolverine or Magneto though). In 2022, this powerful genetic engineering technique is complemented with artificial intelligence.
A 3D rendering of a protein complex structures predicted from protein sequences by AF2Complex. From the muscle fibers that move us to the enzymes that replicate our DNA, proteins are the molecular machinery that makes life possible. Protein function heavily depends on their three-dimensional structure, and researchers around the world have long endeavored to answer a seemingly simple inquiry to bridge function and form: if you know the building blocks of these molecular machines, can you predict how they are assembled into their functional shape? This question is not so easy to answer. With complex structures dependent on intricate physical interactions, researchers have turned to artificial neural network models – mathematical frameworks that convert complex patterns into numerical representations – to predict and "see" the shape of proteins in 3D.
Nuance is a technology pioneer with market leadership in conversational AI and ambient intelligence, and a full-service partner of 77 percent of U.S. hospitals and trusted by over 500,000 physicians daily. Microsoft provides trusted and secure cloud and AI capabilities with the goal to empower people and organizations to address the complex challenges facing the healthcare industry today. With a long-term commitment to leveraging cloud and AI technologies to enhance patient engagement and outcomes, reduce clinician burnout, improve clinical quality and safety, and enhance financial performance, Nuance and Microsoft are leaders in the future-focused healthcare ecosystem and well-equipped to ensure The AI Collaborative members are at the front-end of education and learning on the evolution of AI in healthcare. "The key to successful healthcare innovation using AI is understanding at a deep level the problems that you're trying to solve and focusing on the outcomes you want to achieve," said Peter Durlach, Chief Strategy Officer of Nuance. "With the combined engineering, market and domain expertise of Nuance and Microsoft, The AI Collaborative can bring together multiple technical, business and clinical stakeholders to prioritize deployment of solutions for clinician burnout, patient engagement and health system financial stability, while accelerating innovation in precision medicine, drug discovery, clinical decision support and other promising use cases across the entire healthcare ecosystem."
Fujifilm and the National Center of Neurology and Psychiatry (NCNP) have just released new research which shows that AI technology could help to predict whether or not someone is likely to get Alzheimer's disease. By monitoring brain activity, Fujifilm and NCNP say that they are able to predict whether a patient with mild cognitive impairment (MCI) will progress to having dementia within two years with an accuracy of up to 88%. Alzheimer's disease is the most common cause of dementia and it is estimated that 55 million people worldwide have the neurological condition that causes loss of memory. As the population ages, it's expected that by 2050, more than 139 million people will suffer from the life-changing condition. Using advanced image recognition technology, Fujifilm and NCNP have developed a way in which they are able to monitor the progression of Alzheimer's from three-dimensional MRI scans of the brain.
We are presently living in an age of "artificial intelligence" -- but not how the companies selling "AI" would have you believe. According to Silicon Valley, machines are rapidly surpassing human performance on a variety of tasks from mundane, but well-defined and useful ones like automatic transcription to much vaguer skills like "reading comprehension" and "visual understanding." According to some, these skills even represent rapid progress toward "Artificial General Intelligence," or systems which are capable of learning new skills on their own. Given these grand and ultimately false claims, we need media coverage that holds tech companies to account. Far too often, what we get instead is breathless "gee whiz" reporting, even in venerable publications like The New York Times.