The Sheffield Institute for Translational Neuroscience (SITraN) and AI start-up BenevolentAI have announced a potentially major breakthrough in the treatment of motor neuron disease, thanks to artificial intelligence. The groundbreaking development for the disease, also known as amyotrophic lateral sclerosis (ALS), came about as scientists from SITraN assessed the efficacy of a drug candidate proposed by BenevolentAI's AI technology. The scientists, led by Dr. Richard Mead and Dr. Laura Ferraiuolo, found there are significant and reproducible indications that the drug prevents the death of motor neurones in patient cell models, and delayed the onset of the disease in the gold standard model of ALS. Ken Mulvany, founder and chairman at BenevolentAI, added: "We understand from SITraN their research demonstrates that the hypothesis and drug candidate that our technology identified has delayed the onset of cell death in the gold standard model of ALS.
Men tended to speak at a faster pace, averaging about 2.88 words per second compared to the average 2.79 seconds for women, the study found. Men also paused longer -- averaging about 1.5 seconds compared to women's 1.3 seconds. To carry out the research, Gong used artificial intelligence and machine learning technology that was developed to recognize sales conversation patterns. "The danger is that there can be an over reliance on machine learning to create the right formula for a sales call, which is really as much an art as it is science," Local Search Association's Sterling added.
Sogaard said that these deep learning techniques have shown promise in finding disease patterns across large groups of people, but the ultimate goal is to eventually help individual patients. Sogaard believes a handful of cloud computing providers will have AI technologies that drug companies could eventually use for research and development. Federal regulations have not yet caught up to the rapid pace of innovation that could one day help predict and diagnose diseases using a combination of genomic, protein, and medical imaging data. But Sogaard is hopeful, and based on Pfizer's meetings with regulators, he believes the Federal Drug Administration is "open-minded" to AI-assisted medical treatment.
Healthcare AI expert Peter Borden, managing director at consulting and services firm Sapient Health, helps healthcare organizations apply innovative AI technologies to their ecosystems. In this Q&A with SearchHealthIT, Borden talks about how such AI in healthcare applications helps with clinical trials, customizing post-discharge instructions using patients' personal characteristics and population health. How will new forms of AI in healthcare affect transitional care when patients leave the hospital for other settings? How could emotional intelligence help AI in healthcare applications?
To figure out whether random AI can help people coordinate, Hirokazu Shirado, a sociologist and systems engineer, and Nicholas Christakis, a sociologist and physician, both at Yale University, asked volunteers to play a simple online game. In some networks, every 1.5 seconds the bots picked whatever color differed from the greatest number of neighbors--generally a good strategy among people playing the game. The noise level of bots influenced the noise level in people--even those several nodes away, suggesting a ripple effect. If you see a neighbor (bot or human) change color frequently, you might decide to do so, too.
A number of distinguished speakers and organizations have committed to participating in the Summit, including: Rupert Stadler CEO and Chairman of the Board of Management of AUDI AG Margaret Chan Director-General of World Health Organization (WHO) Peter Norvig Director of Research at Google Professor at University of Padova, and Research Scientist at IBM Watson Corporate Vice President of Microsoft AI and Research Head of Montreal Institute for Learning Algorithms (MILA), and Canada Research Chair in Statistical Learning Algorithms Fei-Fei Li Director of Stanford Artificial Intelligence Lab (SAIL), and Chieft Scientist of AI at Google Cloud Professor in Computer Science and Robotics at Carnegie Mellon University, and Former President of AAAI Vicki Hanson President of ACM, and Distinguished Professor of Computing at the Rochester Institute of Technology Salil Shetty Secretary General of Amnesty International Pedro Domingos Professor of Computer Science and Engineering at University of Washington Gary Marcus Professor of Psychology and Neural Science at James Martin Research Fellow, Future of Humanity Institute, at University of Oxford Director of United Nations Interregional Crime and Justice Research Institute (UNICRI) Jü rgen Schmidhuber Scientific Director, Swiss AI Lab, IDSIA; Professor of AI, USI & SUPSI, Switzerland; President of NNAISENSE Eric Horvitz (Remote) Technical Fellow and Managing Director of Microsoft Research Founder and CEO of Hanson Robotics Stuart Russell Professor of Electrical Engineering and Computer Sciences at UC-Berkeley, and Adjunct Professor of Neurological Surgery at UC-San Francisco Click here for full list The event will convene representatives of government, industry, UN agencies, civil society and the AI research community to explore the latest developments in AI and their implications for regulation, ethics and security and privacy. Breakout sessions will invite participants to collaborate and propose strategies for the development of AI applications and systems to promote sustainable living, reduce poverty and deliver citizen-centric public services. The event will convene representatives of government, industry, UN agencies, civil society and the AI research community to explore the latest developments in AI and their implications for regulation, ethics and security and privacy. Breakout sessions will invite participants to collaborate and propose strategies for the development of AI applications and systems to promote sustainable living, reduce poverty and deliver citizen-centric public services.
But new research suggests that learning to read does more than make life easier: it literally changes how the brain works by increasing connectivity between its regions. "We're trying to understand the basic principle of how the brain works," says Falk Huettig, a researcher in the department of psychology of language at the Max Planck Institute for Psycholinguistics. To understand how reading affects the brain, Huettig and a team of researchers took two groups of illiterate Hindi-speaking Indian adults in their thirties and matched them for gender, handedness (right handed vs left), income, number of literate family members, and intelligence. Afterwards, one group undertook six months of literacy instruction in the Devanagari script, a writing system used for several languages including Hindi.
Here's a more realistic prediction: Self-correcting machine learning models and auto-generated code will change the way statisticians, programmers, and data scientists work. On the front end of the process, every machine learning model requires training data. All of these very different kinds of data have one thing in common: Human beings worked to gather the data, structure the data, and provide access to it. The reason data exist is because human beings decided to gather the data, often at significant cost, if not in terms of cash then in terms of time and effort.
Monkey brains have sections dedicated solely to social interactions, a new finding that researchers say could help us better understand the human mind. Scientists scanning the brains of rhesus macaques found that certain parts were active when the monkeys watched videos of social interactions between other monkeys, but that same network was largely inactive in response to other images. With this new research about the social network in rhesus monkey brains, scientists may have found a brain structure of which humans have a more evolved version -- what the study called "an evolutionary forerunner of human mind-reading capabilities." Rhesus monkeys have an entire brain network that is dedicated to social interactions.
Phinney and Volek wrote two books together about low-carbohydrate diets and published scientific papers describing how constant adjustments to diet and lifestyle can reverse diabetes in many patients. On the back end, Virta hires doctors who get streams of updates from Virta's software and use the data to help them make decisions about how to adjust each patient's diet and medications or anything else that might affect that person's health. AI software learns about the patient and sends a stream of tips and information intended to help the diabetic manage the disease and stay out of hospitals. "If we want to massively lower health care costs, we need to figure out how to address metabolic health issues [like diabetes] at their core," Inkinen says.