Google's Inbox uses machine learning to speed up email replies Pluto gets a little psychedelic in this week's space photos - Nice image produced with principal components analysis Autism cases in U.S. jump to 1 in 45 - Example of bad analysis: there are more White people with autism than from other races, because there are more Whites than other races in US. Google Maps Gets Offline Navigation And Search Tackling the Challenges of Big Data - MIT course Toyota Investing $1B in Artificial Intelligence Research Accenture invests in artificial intelligence R&D Twitter Engineering Manager Leslie Miley Leaves Company Because Of ...- classifier that attempts to guess race based on name is backfiring When I started programming - Comics 6 crazy things Deep Learning and Topological Data Analysis can do w... Harvard cracks DNA storage, crams 700 terabytes of data into a sing... Google says it's'rethinking everything' around machine learning A Year-Long US Road Trip for 70-Degree Weather Every Day The Data Science Machine, or'How To Engineer Feature Engineering' What is Machine Learning and Predictive Analytics? Google's Inbox uses machine learning to speed up email replies Pluto gets a little psychedelic in this week's space photos - Nice image produced with principal components analysis Autism cases in U.S. jump to 1 in 45 - Example of bad analysis: there are more White people with autism than from other races, because there are more Whites than other races in US. Twitter Engineering Manager Leslie Miley Leaves Company Because Of ...- classifier that attempts to guess race based on name is backfiring Harvard cracks DNA storage, crams 700 terabytes of data into a sing... Google says it's'rethinking everything' around machine learning
A study published in Science Transitional Medicine has found that doctors can predict which babies will develop autism spectrum disorder (ASD) by the age of two with an astonishing 96 percent success rate. The study took brain scans of 59 "sibs" (the younger siblings of children with ASD), who's chances of getting the disease are 20 times higher than average. The method has been applauded for its non-intrusive nature, ability to identify autism from only one scan, and potential to increase "the feasibility of developing early preventative interventions for ASD." AI and machine learning have the potential to revolutionize healthcare by identifying diseases earlier and with more accuracy, allowing doctors to perform cheaper and less intrusive preventative treatments.
To illustrate, consider Face2Gene phenotyping applications that use face recognition and machine learning to help healthcare providers in identifying rare genetic disorders. The technology innovator recently launched an AI powered Autism Test which allows providers to use eye tracking technology to identify early stages of ASD (Autism Spectrum Disorder) in children aged from 12 to 40 months. The data helps physicians identify key information in patient records and explore treatment options to create more informed treatment plans for patients. We also custom designed an algorithm with the help of Emory's Medical Researchers & Doctors for a cloud based research web application, that mimics early stage AI bots, for Emory University.
The University of Rochester Medical Center explained that the researchers designed their experiment so that a person's eyes would naturally overshoot the target as they tried to track the visual. As the experiment went on, a healthy person's eyes would adjust to overcome that design and make more precise movements, while people with autism did not -- their eyes kept missing the target. "The inability of the brain to adjust the size of eye movement may not only be a marker for cerebellum dysfunction, but it may also help explain the communication and social interaction deficits that many individuals with [autism spectrum disorder] experience." Doctors might be able to track eye movements to detect the developmental disorder autism, which would help them diagnose the condition earlier.
Autism is characterized by social and communicative difficulties, specific interests that people with autism are capable of speaking about for hours (like meteorological modelling, in Sophie's case), and stereotyped behaviors. To diagnose autism spectrum disorder (ASD), doctors and psychologists evaluate quantitative criteria using tests and questionnaires, but also qualitative criteria, like interests, stereotyped movements, difficulties with eye contact and language and isolation. But while autistic girls show similar test results to autistic boys, the clinical manifestation of their condition differs, at least in cases where language has been acquired. Since September 2016, the Francophone Association of Autistic Women (Association francophone des femmes autistes, or AFFA) has been fighting for recognition of the specific ways autism manifests in women.
An algorithm correctly found correlations between words used in updates from 28,000 of those posts and levels of depression then due to its machine learning ability the AI was able to determine depression levels in users based solely on their update posts. Additionally, the team could determine countywide mortality rates related to heart disease by using AI to analyze 148 million tweets. It turns out that words concerning negative relationships and anger could more accurately predict mortality rates than predictions relying on the 10 heart disease risk factors, including diabetes and smoking. Currently, Troyanskaya's Princeton team is examining autism patients' genomes with DeepSEA to gain a deeper understanding of the effects of the noncoding bases.
But variants in scores of genes known to play some role in autism can explain only about 20% of all cases. Finding other variants that might contribute requires looking for clues in data on the 25,000 other human genes and their surrounding DNA--an overwhelming task for human investigators. They compared those of the few well-established autism risk genes with those of thousands of other unknown genes and last year flagged another 2500 genes likely to be involved in this disorder. Now they have developed a deep learning tool to find non-coding DNA that may also play a role in autism and other diseases.
It works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. Developed by a French robotic firm, the machine will also function as a diagnostic tool by collecting data in the future. The robot works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. Developed by a French robotic firm, the machine will also function as a diagnostic tool by collecting clinical data during therapy.
As the co-founder and CEO of Affectiva, el Kaliouby is on a mission to expand what we mean by "artificial intelligence" and create intelligent machines that understand our emotions. The new AI category el Kaliouby and her team at Affectiva are spearheading is "Emotion AI," defining a new market by pursuing two goals: Allowing machines to adapt to human emotions in real-time and providing insights and analytics so organizations can understand how people engage emotionally in the digital world. Then she read Picard's Affective Computing, published in 1997, and became "super-fascinated by the idea that a computer can read people's emotions. For her dissertation, el Kaliouby used the autism research center's data to train a computer model to recognize accurately and in real-time complex mental states with "an accuracy and speed that are comparable to that of human recognition."
By training a machine learning algorithm on their behavior and earlier MRI data, the scientists built a model that predicted 9 of those 11 autism cases, with no false positives. Right now, researchers tracking autism development focus on infant siblings of people with autism; they have 1 in 5 chance of developing autism, compared to around 1 in 100 for the general population. But current autism therapies for babies and toddlers focus on their specific behavioral deficits--teaching children to communicate needs, to play with toys, and to have positive interactions with caregivers. The UNC group's next goal is to predict specific autism symptoms, correlating brain scans with future language difficulties, sensory sensitivities, social difficulties, or repetitive behaviors.