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South Africa has joined its global counterparts in synthetic intelligence (AI) and device learning (ML) to combat the Covid-19 pandemic, says Swami Sivasubramanian, Vice President of Amazon Machine Learning at Amazon Web Services (AWS).
He says that more than ever, innovation in artificial intelligence and device learning is critical because of his perspective for communities and businesses to triumph over the coronavirus pandemic.
According to Sivasubramanian, in months, organizations covering all sectors of the public sector, pharmaceuticals, non-profit organizations and more, have deployed ML to respond to this unprecedented situation.
“Lately we’re seeing 3 important spaces that ML is impacting: expanding customer communications, how Covid-19 is spreading, and accelerating studies and processing,” he says.
In “new normality,” says Sivasubramanian, organizations are also creating a new cloud to satisfy visitors’ desires and ensure business continuity.
For him, ML generation plays a vital role in enabling this replacement by providing the necessary equipment for remote communication, enabling telemedicine and protecting food security.
“In South Africa, we have noticed how important access to complex technologies such as artificial intelligence and machine learning is to prevent the spread of Covid-19 and help others temporarily locate medical care when they are sick. “
GovChat, South Africa’s largest citizen engagement platform, introduced a Covid-19 chatbot in less than 2 weeks, Amazon Lex, a synthetic intelligence service that creates conversation interfaces in any voice and text application.
The chatbot provides recommendations and fitness recommendations on whether to perform a Covid-19 check, data on the nearest Covid-19 verification facility, the ability to obtain verification effects, and the ability of citizens to report Covid-19 symptoms by themselves, their circle of family members, or household members.
Another example is the spouse of apN – A2D24. In just 3 days, A2D24 was able to expand and implement an automated artificial intelligence platform that Amazon Lex used for a personal hospital organization to inform anyone in one of its hospitals of the imaginable exposure to a showed Covid. -19 patient.
The formula automatically sends an alert message and writes it via SMS where, day, patients are asked about the symptoms they are experiencing and, according to the answers, the chatbot recommends what to do and whether to seek more medical help.
This app has provided comprehensive care to thousands of patients across the country and has potentially prevented many new infections.
Another tool developed through A2D24 is a Covid-19 self-declaration, WhatsApp has incorporated the Amazon Lex chatbot for a well-known South African mine to connect directly with workers through reliance on floating reminiscence in the workplace. before returning to work, whether they or members of their family circle have attended giant meetings, marriages, or other conditions that would possibly increase their vulnerability to Covid-19.
Those who are allowed to return to the mine are placed their temperature in the WhatsApp chatbot and allowed to enter the mine. The AI-driven self-reporting tool also provides mine workers with convenient, timely medical care and reduces potential exposure to Covid-19 for others.
Information overload
Sivasubramanian believes that researchers and fitness care providers face an exponentially expanding volume of data on Covid-19, making it difficult to download data that may indicate treatment.
In response, he says, AWS has introduced CORD-19 Search, a new online study page driven by device learning, that allows researchers to temporarily and seamlessly search for work and study documents for answers to questions.
The search for CORD-19 produces accurate reactions as well as source documents. “Based on the Allen Institute for AI’s cord-19 open study dataset containing more than 128,000 study articles and other documents, this device learning solution can extract applicable medical data from unstructured text and provides a strong ability to wonder in herbal language, helping to accelerate the speed of discovery , where the progression of the immediate reaction and the remedy of Covid-19 disease is essential ».
Sivasubramanian builds on the innovation already underway in Africa in relation to the use of AI and ML to combat coronavirus.
He explains that organizations on the continent are exploiting complex technologies such as artificial intelligence, The Internet of Things ML, cellular services, etc. to drive innovation.
“And the incorporation of the AWS Africa (Cape Town) region has made innovation imaginable for more corporations of all sizes and helped drive AI and ML adoption. “
He explains that the AWS Africa region is also organizing organizations to provide reduced latency to end users in Sub-Saharan Africa and to launch permanent remote painting platforms or distance learning systems.
“We’ve seen how providing access to a scalable, reliable, and highly secure computing force is critical to moving businesses forward. “
Sivasubramanian points out that one example in Africa is Aella Credit, a money-generating start-up founded in Lagos, Nigeria, which provides simple access to credit to others who do not have banking services around the world.
Aella uses Amazon Rekognition to help determine the identity of new consumers to deliver instant monetary loans, all without human intervention. Such a pioneering advertising solution has an apparent application in the pandemic, he says.
One of the ultimate tactics vital to patient care and the drive for clinical research, he says, is to perceive and analyze the concepts and relationships ‘trapped’ in a free-form medical text, adding admission notes to the hospital and the patient’s medical history. Amazon Comprehend Medical is an herbal language processing service that makes it easy to use device learning to extract applicable medical data from unstructured text.
South Africa’s National Department of Health is running to create paperless hospitals in the country. Paper clinical records in fitness services lead to long waiting times for patients (sometimes 60-80 minutes); reduced clinical care due to loss of files in overcrowded file rooms; and the accumulation of legal costs.
With Amazon Comprehend Medical, regional public hospitals have implemented indexing systems to facilitate the retrieval of digitized patient records. Called hybrid e-scripting, the solution allows for an electronic knowledge garage without input, an electronic sketchbook for electronically available medical diagrams and easy-to-label automatic pharmaceutical use to reduce drug delivery time. The implementation of this solution has resulted in 90% relief in patient wait times for prescriptions, 10% relief in patient waiting times in the hospital and a saving of 12 million rand in software licenses.
Sivasubramanian also notes that well-established money-facilities organizations in Africa are turning to AWS to become more agile, modernize legacy systems, and drive innovation. For example, he says that Africa’s oldest and largest money-facilities provider, Old Mutual, you’ve selected AWS the way you like it. cloud provider and is migrating its programs, adding more than 1,000 critical insurance programs and product management systems, to AWS, completing its data centers by early 2022.
Old Mutual uses a variety of AWS device learning services, adds Amazon Lex, and expands a chatbot for your oldmutual. co. za website to immediately provide visitor responses, the channel visitor likes: voice, email, Internet, or text. The insurer has also begun exploring other AWS ML technologies, such as Amazon SageMaker, to expand automated ML-based investment features to help consumers take the right resolution when they save to achieve their monetary goals.
“At AWS, we have strived to provide our consumers with over 20 years of device learning skills and knowledge,” says Sivasubramanian.
“Our project is to put device learning in the hands of each and every developer and knowledge scientist. Our consumers have responded in turn, as the vast majority of cloud device learning is now done on AWS.
Notes that with a broad 3-layer portfolio of the build stack, more consumers use AWS for device learning than any other provider.
“With the broadest and most comprehensive portfolio of device learning facilities available in the cloud, AWS drives its innovation with feedback from Amazon as well as thousands of other consumers on their device learning capabilities. In 2019, AWS introduced more than 250 devices, features, and learning capabilities. »
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