Integrating social determinants of health into machine learning models helped mitigate bias when predicting long-term outcomes for heart failure patients, according to a Northwestern Medicine study published in Circulation: Heart Failure. The study found that integrating 15 measures of social determinants of health into select machine learning models noticeably reduced disparities observed in predicting the probability of long-term hospitalization or in-hospital mortality for heart failure patients. "We show that for minority populations, the machine learning models actually performed worse than for white individuals. We also show that for people with poor socioeconomic status, let's say for those uninsured or for people that have Medicaid, the model also performed worse and missed people that are at a higher risk of dying or have a higher risk of staying in the hospital longer," said Yuan Luo, PhD, associate professor of Preventive Medicine, of Pediatrics, chief AI officer at the Northwestern Clinical and Translational Sciences (NUCATS) Institute and the Institute for Augmented Intelligence in Medicine, and senior author of the study. Machine learning can be a powerful tool for predicting long-term patient outcomes, especially for diagnosed with chronic conditions such as heart failure.

According to IDC MarketScape, the SAP Hybris Marketing Cloud solution provides customers with a unique opportunity to employ artificial intelligence (AI) and machine learning (ML) across many use cases and levels of complexity. "The AI and ML use cases available from SAP Hybris Marketing Cloud are invaluable to digital marketers, particularly those operating at scale where rapid decision-making is crucial to conversion rates, average sale value and customer experience and loyalty," said Gerry Murray, director, IDC Marketing and Sales Technology Research. "It's currently all baked in at no additional cost, so customers of SAP Hybris solutions should embrace this technological windfall and accelerate to the forefront of AI-enabled marketing." According to Marcus Ruebsam, senior vice president and global head of SAP Hybris solution management at SAP, enterprises recognize the value of a sustainable customer experience infrastructure that combines efficiencies such as AI-enabled recommendation engines, attribution analysis, interest affinity and social sentiment analysis. He believes that in the era of the customer, today's businesses must apply these types of capabilities in a single platform to truly optimize customer engagements and drive loyalty.

Now, a UT Dallas researcher is giving the fantasy of robotic friends a practical edge with a robot that teaches social skills to children with Autism Spectrum Disorder (ASD). "It's not to replace therapy with humans, but you can deliver a social skills lesson in a less threatening way, and the robot can deliver the same lesson multiple times," Rollins said. During a lesson, the robot explains a social situation to the child with ASD. Media Contact: Ben Porter, UT Dallas, (972) 883-2193, [email protected] or the Office of Media Relations, UT Dallas, (972) 883-2155, [email protected].

Robots and humans socialize frequently in pop fiction -- think of Wall-E and Star Trek: The Next Generation. Now, a UT Dallas researcher is giving the fantasy of robotic friends a practical edge with a robot that teaches social skills to children with Autism Spectrum Disorder (ASD). Dr. Pamela Rollins, associate professor in the School of Behavioral and Brain Sciences, explained that individuals with ASD often have social anxiety. Learning social interactions via a less threatening interface -- a robot -- may help patients better identify emotions and use specific social skills with humans, like holding a conversation. "Some preliminary data has shown that individuals with autism start talking to the robots when they don't talk to other people," Rollins said. Rollins, who conducts research at the Callier Center for Communication Disorders, is working with a team of autism experts and robotics designers at the company Robokind to create Robots4Autism.

Researchers at Facebook Artificial Intelligence Research (FAIR) published a paper introducing AI-based dialog agents that can negotiate and compromise. In a new blog post, Facebook explains how existing chatbots can hold short conversations and perform simple tasks such as booking a restaurant – but building machines that can hold meaningful conversations with people is challenging because it requires a bot to combine its understanding of the conversation with its knowledge of the world, and then produce a new sentence that helps it achieve its goals. To help build their training set, the team created an interface with multi-issue bargaining scenarios and crowdsourced humans on Amazon Mechanical Turk to negotiate in natural language to divide a random set of objects. Using CUDA and NVIDIA GPUs, they trained their recurrent neural network by teaching it to imitate people's actions. The models were trained end-to-end from the language and decisions that humans made, meaning that the approach can easily be adapted to other tasks.

Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger. A number of techniques already exist, but they are expensive and time consuming. Using CUDA, Tesla K40 GPUs and cuDNN for the deep learning work, they trained their neural network on 5,000 faces from each age group (0-18, 19- 29, 30-39, 40-49, 50-59, and 60 years old) taken from the Internet Movie Database and from Wikipedia and then labeled with the person's age -- this helped the system learn the characteristic signature of faces in each age group. A second neural network, called the face discriminator, looks at the synthetically aged face to see whether the original identity can still be picked out. If it can't, the image is rejected, which they call the process in their paper, Age Conditional Generative Adversarial Network.

A group of Chinese ophthalmologists and scientists developed a deep learning algorithm to identify congenital cataracts, a rare eye disease responsible for nearly 10 percent of all vision loss in children worldwide. The researchers suggest the algorithm would assist humans, instead of replacing them. "For doctors, technology is not sufficient to determine the best course of treatment with 100 percent certainty, and doctors should therefore make good use of the machine's suggestion to identify and prevent the potential misclassification and complement their own judgment," said study co-author Haotian Lin, a professor of ophthalmology at Sun Yat-sen University. "The results of our comparative analysis showed that both artificial intelligence and human intelligence have strengths and limitations." Using CUDA, TITAN X Pascal GPUs and the cuDNN-accelerated Caffe deep learning framework, the researchers algorithm was able to catch the disease with more than 90% accuracy.

A new deep learning app called Face2Gene lets doctors snap an image of a patient and receive a suggested diagnosis from thousands of genetic disorders. "One in 10 people suffer from a rare disease," said Dekel Gelbman, FDNA CEO, the company who developed the app. "Many of these are life threatening, and often extremely difficult to diagnose. These patients and their families suffer a great burden while trying to find answers to their symptoms, averaging seven years and seven doctors. FDNA helps healthcare providers find answers faster in hopes of saving lives and improving patient quality of life. We accomplish this through using the best technology in the world -- based on a combination of facial analysis, deep learning, and artificial intelligence and partnering with clinicians, researchers, labs, and ultimately with drug development companies that share our goal."

A developer spent a couple of days over his winter break training an artificial neural network to play the classic racing game Mario Kart 64 and documented his results to share what he learned in the process. "It had been a few years since I'd done any serious machine learning, and I wanted to try out some of the new hotness (aka TensorFlow) I'd been hearing about," said Kevin Hughes who works as a developer at Shopify. Using a GeForce GTX 1060 GPU and the cuDNN-accelerated TensorFlow deep learning framework, Hughes trained his model on only four races on Luigi Raceway, two races on Kalimari Desert and two races on Mario Raceway. He mentioned that even with a small training set, the model was able to drive a new untrained section of the Royal Raceway (below). Hughes' adds that he plans on adding a reinforcement layer to the project so the AI can start to teach itself.

Ronald Summers, Senior Investigator at the National Institutes of Health (NIH) shares how they are trying to improve patient care by increasing the accuracy of radiologic diagnosis with advanced computer techniques. His group is using deep learning and NVIDIA GPUs to assist physicians make a more accurate diagnosis by developing software that improves diagnosis, reduce the chance of errors, and help underserved patients that have limited access to advanced radiology services. "With deep learning and GPU acceleration we've had a substantial improvement in the performance of all these computer programs to the point where the programs are getting pretty close to performing as well as the average physician," says Summers. Watch more scientists and researchers share how accelerated computing is benefiting their work at http://nvda.ws/2dbscA7