Researchers use AI to predict Alzheimer’s disease 7 years before clinical diagnosis

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IBM and Pfizer developed an AI that looks at speech patterns over time for markers of the crippling degenerative disease.

Alzheimer’s is a crippling degenerative disease, but the answer to early diagnosis might lie in speech.

I was afraid my grandmother wouldn’t remember who I was the last time I saw her in person. She looked small and frail in the wheelchair but I could still see the sparkle in her eyes. Our relationship was complicated, but when she said she remembered me, none of it mattered any more.

I sat by her wheelchair and tried to cram in a decade of memories and happenings. Every few moments, it was like she’d reset, and would ask what I’d been up to all these years. We’d go through everything again. We only had a few precious hours before she didn’t remember who I was anymore, no matter how many stories I told her.

Alzheimer’s is a degenerative brain disease that over 5 million Americans live with, and that number is only expected to grow, according to the Alzheimer’s Association. But IBM Research and Pfizer have developed a new AI model that uses quick speech tests to help predict the onset of the disease in healthy people, the companies said Thursday. The AI’s accuracy is about 70%, potentially giving people up to seven years’ notice before symptoms of cognitive decline.

The disease can seem like it sneaks in, beginning with symptoms that may be misinterpreted as typical age-related changes. These early warning flags are important to recognize, as they’re a sign of coming cognitive decline. The sooner clinicians can detect Alzheimer’s disease, the more that can be done to help a patient, even though there’s no cure to date.

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Top tech trends for 2021: Gartner predicts hyperautomation, AI and more will dominate business technology

Top strategic technology trends for the enterprise

Operational resiliency is key as the COVID-19 pandemic continues to change how companies will do business next year.

There are nine top strategic technology trends that businesses should plan for in 2021 as the pandemic continues, according to Gartner’s analysts. Their findings were presented on Monday at the virtual Gartner IT Symposium/Xpo Americas conference, which runs through Thursday.

Organizational plasticity is key to these trends. “When we talk about the strategic technology trends, we actually have them grouped into three different themes, which is people centricity, location independence, and resilient delivery,” said Brian Burke, research vice president at Gartner. “What we’re talking about with the trends is how do you leverage technology to gain the organizational plasticity that you need to form and reform into whatever’s going to be required as we emerge from this pandemic?”

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Harnessing deep neural networks to predict future self-harm based on clinical notes

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According to the American Foundation for Suicide Prevention, suicide is the 10th leading cause of death in the U.S., with over 1.4 million suicide attempts recorded in 2018. Although effective treatments are available for those at risk, clinicians do not have a reliable way of predicting which patients are likely to make a suicide attempt.

Researchers at the Medical University of South Carolina and University of South Florida report in JMIR Medical Informatics that they have taken important steps toward addressing the problem by creating an artificial intelligence algorithm that can automatically identify patients at high risk of intentional self-harm, based on the information in the clinical notes in the electronic health record.

The study was led by Jihad Obeid, M.D., co-director of the MUSC Biomedical Informatics Center, and Brian Bunnell, Ph.D., formerly at MUSC and currently an assistant professor in the Department of Psychiatry and Behavioral Neurosciences at the University of South Florida.

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An AI analysis of 500,000 studies shows how we can end world hunger

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An Indian farmer dries harvested rice from a paddy field in Assam.

Ending hunger is one of the top priorities of the United Nations this decade. Yet the world appears to be backsliding, with an uptick of 60 million people experiencing hunger in the last five years to an estimated 690 million worldwide.

To help turn this trend around, a team of 70 researchers published a landmark series of eight studies in Nature Food, Nature Plants, and Nature Sustainability on Monday. The scientists turned to machine learning to comb 500,000 studies and white papers chronicling the world’s food system. The results show that there are routes to address world hunger this decade, but also that there are also huge gaps in knowledge we need to fill to ensure those routes are equitable and don’t destroy the biosphere.

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6 future trends everyone has to be ready for today

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I had the pleasure of talking with futurist and the managing partner of ChangeistScott Smith recently about some of the biggest macro trends everyone should be aware of today. While these trends had already begun prior to the coronavirus pandemic, in many ways, they accelerated as the world fought to deal with the pandemic and now as we begin to build our post-COVID-19 world. Here are the six future trends he believes everyone should be ready for.

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The next generation of Artificial Intelligence

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AI legend Yann LeCun, one of the godfathers of AI

 The field of artificial intelligence moves fast. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless.

If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. Study up now.

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This AI lyrics generator strings your random words into songs

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The system provides a new cure for songwriter’s block

 Could keyword lyrics provide a new cure for songwriter’s block?

Songwriter‘s block can be a problem for even the world’s most successful musicians. They can sometimes overcome it by taking breaks, seeking new forms of inspiration, or simply pushing through. And if none of that works, they could try out a new AI lyrics generator called keyword2lyrics.

The system was created by Mathi Gatti, a data scientist from Argentina, who told TNW he got the idea from his own songwriting struggles:

Sometimes I have a few ideas that I want to turn into a song, but I’m too lazy for that, so I thought it would be cool to make a program that generates lyrics from isolated keywords or phrases.

Gatti developed the tool by training OpenAI‘s GPT-2 language model on songs that Google lists when you search for “top artists 20th century” and “top artists 21st century,” and extracted keywords from them using a tool called yake.

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AI tool could predict how drugs will react in the body

 

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“The safety of a drug does not depend only on the drug itself but also on the metabolites
that can be formed when the drug is processed in the body,” says Eleni Litsa

A new deep learning-based tool called Metabolic Translator may soon give researchers a better handle on how drugs in development will perform in the human body.

When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do.

Metabolic Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes could help improve the process.

The new tool takes advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do. The method is unconstrained by rules that companies use to determine metabolic reactions, opening a path to new discoveries.

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Neural network trained to control anesthetic doses, keep patients under during surgery

To define how the world should look, neural networks are making up their own rules

 Researchers demonstrate how deep learning could eventually replace traditional anesthetic practices.

Academics from the Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have demonstrated how neural networks can be trained to administer anesthetic during surgery.

Over the past decade, machine learning (ML), artificial intelligence (AI), and deep learning algorithms have been developed and applied to a range of sectors and applications, including in the medical field.

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9 soft skills every employee will need in the age of Artificial Intelligence (AI)

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Technical skills and data literacy are obviously important in this age of AI, big data, and automation. But that doesn’t mean we should ignore the human side of work – skills in areas that robots can’t do so well. I believe these softer skills will become even more critical for success as the nature of work evolves, and as machines take on more of the easily automated aspects of work. In other words, the work of humans is going to become altogether more, well, human.

With this in mind, what skills should employees be looking to cultivate going forward? Here are nine soft skills that I think are going to become even more precious to employers in the future.

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Self-driving cars will hit the Indianapolis Motor Speedway in a landmark A.I. race

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Take a look at the ‘Road of the Future’

Next year, a squad of souped-up Dallara race cars will reach speeds of up to 200 miles per hour as they zoom around the legendary Indianapolis Motor Speedway to discover whether a computer could be the next Mario Andretti.

The planned Indy Autonomous Challenge—taking place in October 2021 in Indianapolis—is intended for 31 university computer science and engineering teams to push the limits of current self-driving car technology. There will be no human racers sitting inside the cramped cockpits of the Dallara IL-15 race cars. Instead, onboard computer systems will take their place, outfitted with deep-learning software enabling the vehicles to drive themselves.

In order to win, a team’s autonomous car must be able to complete 20 laps—which equates to a little less than 50 miles in distance—and cross the finish line first in 25 minutes or less. At stake is a $1 million prize, with second- and third-place winners receiving a $250,000 and $50,000 award, respectively.

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