Experimental Study


5 Ways AI is Helping Improve Omnichannel Fulfillment - Watson Customer Engagement

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Today's retailers have to find profitable strategies to deliver on their customers' expectations, amid significant fulfillment challenges and complexities. Customers don't understand the massive omnichannel fulfillment operation that is set into motion when they click "Buy Now." Whether they purchase a product on your e-commerce site for home delivery or order a product to be delivered to your nearest brick and mortar location, customers want immediate inventory availability, fast shipping and a seamless end-to-end purchasing experience. Here's a closer look at how AI and cognitive technologies are helping improve omnichannel fulfillment in a world of rising customer expectations, free 2-day delivery from the competition, and rapidly shrinking margins. What does intelligent fulfillment look like?


Improving Clinical Trials With Machine Learning

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Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference--from common functional maps to individual behaviour--is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain.


An artificial intelligence designed for the end of human life is already among us

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Chatbots are used for a variety of tasks: ordering pizza, getting product suggestions via Facebook Messenger and receiving online customer support. But can they cope with death? A three-year clinical study with financial backing of more than $1 million from the National Institutes of Health is exploring whether a chatbot can help terminally ill, geriatric patients with their end-of-life care. Over the next three years, Northeastern University professor Timothy Bickmore and Boston Medical Center doctor Michael Paasche-Orlow will distribute Microsoft Surface tablets preloaded with a chatbot to about 360 patients who have been told they have less than a year to live. Designed in consultation with experts from Boston Medical Center and programmed by Bickmore and other Northeastern University researchers, the chatbot -- which takes the form of a middle-age female digital character -- is preloaded with a number of capabilities.


How sensors enabled Eli Lilly to improve the patient experience

ZDNet

Global pharmaceutical company Eli Lilly set itself a "big, hairy audacious" goal to engage more people in clinical trials, but after visiting caregivers and their patients, the company soon realised more was needed than just developing medication and having it trialled. Dave Crumbacher, Technology Architect at Eli Lilly and Company, told the AWS re:Invent conference in Las Vegas on Monday that his company tweaked its initial goal to one that makes clinical trials an option for all. To do that, Crumbacher said the focus of Eli Lilly needed to be one alleviating the burden of participating in a clinical trial. "Before we jumped to a technical solution, we wanted to see what we could learn," he explained. "We needed to gain empathy by going to an adult care centre for patients with dementia."


World's First Artificial Intelligence Politician Developed In New Zealand

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Scientists in New Zealand have developed a robot politician whose "brain" is powered by artificial intelligence. This politician, whose name is SAM, is able to answer a citizen's questions about housing, education, and immigration, NDTV reports. According to NDTV, this AI politician was created by a 49-year-old entrepreneur in New Zealand named Nick Gerritsen. "There is a lot of bias in the'analog' practice of politics right now," said Gerritsen about his new invention."There SAM answers questions via Facebook messenger.


Brain training doesn't improve your general intelligence

Daily Mail

From doing Sudoku every morning to playing more chess to learning a musical instrument, lots of people try different ways to become smarter and improve their memory. Thirty-five years after a landmark memory training experiment in 1982, have scientists really found any foolproof way to make us more intelligent? In a new paper, researchers have looked through several cognitive training programmes and find they actually don't improve our general cognitive and academic skills. Writing for The Conversation, PhD Candidate Giovanni Sala and Professor Fernand Gobet from the University of Liverpool say the general public should be fully aware of the benefits - and limits - of training the brain. Music instruction does not seem to exert any true effect on skills outside of music.


Oxytocin controls how dogs read smiling human faces

Daily Mail

Oxytocin, a hormone involved in social bonding, influences what dogs see and how they experience the world around them, a new study has found. Normally, dogs focus on the most remarkable aspect of a situation, for example threatening stimuli in scary situations - an important skill for survival. But the new study found that dogs under the influence of oxytocin were more likely to focus on smiling human faces than angry ones. Pictured top left are examples of two images shown to the dogs treated with oxytocin during the experiment. Left is an angry face, and right is a happy face.


Can Artificial Intelligence Really Identify Suicidal Thoughts? Experts Aren't Convinced

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Australian experts have spoken out about a recent US study that claimed to show artificial intelligence can identify people with suicidal thoughts - by analysing their brain scans. It sounds promising - but it's worth pointing out only 79 people were studied, so are the results enough to show this is a path worth pursing? The research, published in Nature, studied brain activity in subjects when presented with a number of different words - like death, cruelty, trouble, carefree, good and praise. A machine-learning algorithm was then trained to see the nureal response differences between the two groups involved - those with suicidal thoughts, and those with non-suicidal thoughts. And it showed promise - the algorithm correctly identified 15 of 17 patients as belonging to the suicide group, and 16 of 17 healthy individuals as belonging to the control group.


Can Artificial Intelligence Really Identify Suicidal Thoughts? Experts Aren't Convinced

#artificialintelligence

Australian experts have spoken out about a recent US study that claimed to show artificial intelligence can identify people with suicidal thoughts - by analysing their brain scans. It sounds promising - but it's worth pointing out only 79 people were studied, so are the results enough to show this is a path worth pursing? The research, published in Nature, studied brain activity in subjects when presented with a number of different words - like death, cruelty, trouble, carefree, good and praise. A machine-learning algorithm was then trained to see the nureal response differences between the two groups involved - those with suicidal thoughts, and those with non-suicidal thoughts. And it showed promise - the algorithm correctly identified 15 of 17 patients as belonging to the suicide group, and 16 of 17 healthy individuals as belonging to the control group.


Improving clinical trials with machine learning

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Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published in Brain. "Current statistical models are too simple. They fail to capture complex biological variations across people, discarding them as mere noise. We suspected this could partly explain why so many drug trials work in simple animals but fail in the complex brains of humans. If so, machine learning capable of modelling the human brain in its full complexity may uncover treatment effects that would otherwise be missed," said the study's lead author, Dr Parashkev Nachev (UCL Institute of Neurology).