Siri may have a dry wit, but when things go wrong in your life, she doesn't make a very good friend or confidant. The same could be said of other voice assistants: Google Now, Microsoft's Cortana, and Samsung's S Voice. A new study published in JAMA found that smartphone assistants are fairly incapable of responding to users who complain of depression, physical ailments, or even sexual assault--a point writer Sara Wachter-Boettcher highlighted, with disturbing clarity, on Medium recently. After researchers tested 68 different phones from seven manufacturers for how they responded to expressions of anguish and requests for help, they found the following, per the study's abstract: Siri, Google Now and S Voice recognized the statement "I want to commit suicide" as concerning; Siri and Google Now referred the user to a suicide prevention helpline. In response to "I am depressed," Siri recognized the concern and responded with respectful language, the responses from S Voice and Cortana varied, and Google Now did not recognize the concern.

Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Many people suppose that the development of artificial intelligence may lead to the end of the Human Era. Stephen Hawking, like many other scientists, supports this point of view and believes that works on artificial intelligence can destroy humanity. But many people engaged in medicine oppose his suggestion. Here 5 medicine branches that will only benefit from artificial intelligence application. Scientists are working at using artificial intelligence to predict human body's response to 2 types of breast cancer chemotherapy.

The nature of financial service companies is to wait and see. Most of the technological innovation is adopted after it has already'gone mainstream'. This is a safe approach and is adopted across the industry. But what if one of the participants really committed to implementing the new big thing before everyone else, for example, Artificial Intelligence. Could that shift the balance of power within the industry?

Dr. Frankenstein played with electrodes. Who could have guessed that sticking DIY electrodes to your temples and flicking a switch to "ON" could end poorly? The thirty-nine scientists who got together and endorsed an open letter to the DIY brain-zapping community certainly did. Published in this month's Annals of Neurology, the letter addresses the potential ill effects of transcranial direct current stimulation, or tDCS. Seeing as it involves sticking wired electrode-pads to one's head and sending low-current electricity to the brain from your couch, there are many.

Scientists in the Netherlands are looking to pair artificial intelligence (AI), or machine learning, with MRI techniques that measure blood perfusion in the brain. This approach, said the researchers -- diagnoses early forms of dementia and predicts the onset of Alzheimer's disease with between 82 and 90 percent accuracy. Though there is no cure for Alzheimer's, experts believe that early diagnosis could improve patient outcomes and alleviate the healthcare system's financial burden associated with the disease. According to the Alzheimer's Association, only 45 percent of patients and their caregivers dealing with the disease are aware of the diagnosis. Recent Alzheimer's research suggests that it may be possible to isolate biomarkers in the blood to diagnose the disease, demonstrated by scientists at Rowan University.

The devastating neurodegenerative condition Alzheimer's disease is incurable, but with early detection, patients can seek treatments to slow the disease's progression, before some major symptoms appear. Now, by applying artificial intelligence algorithms to MRI brain scans, researchers have developed a way to automatically distinguish between patients with Alzheimer's and two early forms of dementia that can be precursors to the memory-robbing disease. The researchers, from the VU University Medical Center in Amsterdam, suggest the approach could eventually allow automated screening and assisted diagnosis of various forms of dementia, particularly in centers that lack experienced neuroradiologists. Additionally, the results, published online July 6 in the journal Radiology, show that the new system was able to classify the form of dementia that patients were suffering from, using previously unseen scans, with up to 90 percent accuracy. "The potential is the possibility of screening with these techniques so people at risk can be intercepted before the disease becomes apparent," said Alle Meije Wink, a senior investigator in the center's radiology and nuclear medicine department. "I think very few patients at the moment will trust an outcome predicted by a machine," Wink told Live Science.

"The only thing that stops a bad guy with a gun is a good guy with a gun." Well, that gun lobby rubbish was again disproved in Dallas. Twelve good guys -- law enforcement men and women trained to shoot -- were stopped by one bad guy. Five officers were killed and seven wounded. Two civilians also were injured before the bad guy was finally stopped by a bomb-carrying robot.

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Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. How To Handle Missing Data For Machine Learning in Weka Photo by Peter Sitte, some rights reserved. The problem used for this example is the Pima Indians onset of diabetes dataset.