Intel unveils its first AI-driven neural network chip

Agencies
August 21, 2019

San Francisco, Aug 21: Intel has unveiled its first high-performance Artificial Intelligence (AI)-driven neural network chip "Nervana" to meet the growing high-speed computing and robotics demand.

The chip-maker showcased the Nervana neural network processors, with the NNP-T for training and the NNP-I for inference at the "Hot Chips 2019" conference at Stanford University in California on Tuesday.

"To get to a future state of 'AI everywhere,' we'll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it's collected when it makes sense and making smarter use of their upstream resources," said Naveen Rao, Intel Vice President and General Manager, Artificial Intelligence Products Group.

Built from the ground up to train deep learning models at scale, Intel Nervana NNP-T (Neural Network Processor) pushes the boundaries of deep learning training.

It is built to prioritise two key real-world considerations: training a network as fast as possible and doing it within a given power budget.

This deep learning training processor is built with flexibility in mind, striking a balance among computing, communication and memory, said Intel.

Intel Nervana NNP-I, or Springhill, is purpose-built for inference and is designed to accelerate deep learning deployment at scale, introducing specialised deep learning acceleration while leveraging Intel's 10nm process technology with Ice Lake cores to offer industry-leading performance.

Additionally, the Intel Nervana NNP-I offers a high degree of programmability without compromising performance or power efficiency.

"Data centres and the cloud need to have access to scalable general purpose computing and specialized acceleration for complex AI applications," said Rao.

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News Network
March 6,2020

Mar 6: The spread of the new coronavirus is shining the spotlight on a little-discussed gender split: men wash their hands after using the bathroom less than women, years of research and on-the-ground observations show.

Health officials around the world advise that deliberate, regular handwashing is one of the best weapons against the virus which causes a flu-like respiratory illness that can kill and has spread to around 80 countries.

The Centers for Disease Control and Prevention's online fact sheet "Handwashing: A corporate activity," cites a 2009 study that finds "only 31% of men and 65% of women washed their hands" after using a public restroom.

Social media comments about men's handwashing lapses forced an august British institution to caution visitors about bathroom behaviour this week.

After author Sathnam Sanghera complained on Twitter about "grown," "educated" men in the British Library toilets not washing their hands, the library responded, putting up additional signs reminding patrons to wash their hands in men's and women's bathrooms.

Thanks to "visitor feedback," a spokesman told Reuters, "we have increased further the number of posters in public toilets so that visitors are reminded of the importance of good hygiene at exactly the point where they can wash their hands."

Men and women approach handwashing after using the restroom differently, according to multiple surveys and field studies.

"Women wash their hands significantly more often, use soap more often, and wash their hands somewhat longer than men," according to a 2013 Michigan State University field study conducted by research assistants who observed nearly 4,000 people in restrooms around East Lansing, Michigan.

The study found 14.6% of men did not wash their hands at all after using the bathroom and 35.1% wet their hands but did not use soap, compared to 7.1% and 15.1% of women, respectively.

"If you stand in the men's bathroom at work, and watch men leave, they mostly don't wash their hands if they used the urinal," said one New York City public relations executive, who did not want to be identified for fear of alienating his colleagues.

Since the virus's spread, he's seen an uptick in men's handwashing at work, he noted. "I, for the record, do wash my hands all the time," he added.

Female medical staff in critical care units "washed their hands significantly more often than did their male counterparts after patient contact," a 2001 study published in the American Journal of Infection Control found.

Middle-aged women with some college education had the highest level of knowledge about hand hygiene, a survey published in 2019 by BMC Public Health, an open access public health journal, found.

Early information about coronavirus infection in China shows that men may be more susceptible to the disease. Just over 58% of the more than 1,000 COVID-19 patients reported in China through Jan. 29, 2020, were male, research published in the New England Journal of Medicine shows.

Researchers have not linked the difference to hand hygiene.

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Agencies
May 25,2020

Singapore, May 25: COVID-19 patients are no longer infectious after 11 days of getting sick even though some may still test positive, according to a new study by infectious disease experts in Singapore.

A positive test "does not equate to infectiousness or viable virus," a joint research paper by Singapore's National Centre for Infectious Diseases and the Academy of Medicine, Singapore said. The virus "could not be isolated or cultured after day 11 of illness."

The paper was based on a study of 73 patents in the city-state.

The latest findings may have implications on the country's patient discharge policy. The discharge criteria is currently based on negative test results rather than infectiousness.

Singapore's strategy on managing COVID-19 patients is guided by the latest local and international clinical scientific evidence, and the Ministry of Health will evaluate if the latest evidence can be incorporated into its patient clinical management plan, according to a report by the Straits Times.

So far, 13,882, or about 45% of the total 31,068 Covid-19 patients in Singapore have been discharged from hospitals and community facilities. Singapore reported 642 new Covid-19 cases as of noon on Saturday.

The government has been actively screening pre-school staff as it prepares to reopen pre-schools from June 2. On Friday, two pre-school employees tested positive for the novel coronavirus, bringing the total number of confirmed cases among pre-school staff to seven, according to the Ministry of Health.

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News Network
February 26,2020

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.

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