Monthly Archives: March 2020

The newly-formed C3.ai Digital Transformation Institute has an open call for proposals to mitigate the COVID-19 epidemic using artificial intelligence and machine learning.

With the coronavirus impacting most of the world, the medical community is hard at work trying to come up with some type of magic bullet that will stop the pandemic from propagating. Can artificial intelligence (AI) and machine learning (ML) help nurture a solution? That's what Microsoft and a host of top universities are hoping.

In a blog post published last week, Microsoft detailed the creation of the C3.ai Digital Transformation Institute (C3.ai DTI), a consortium of scientists, researchers, innovators, and executives from the academic and corporate worlds whose mission it is to push AI to achieve social and economic benefits. As such, C3.ai DTI will sponsor and fund scientists and researchers to spur the digital transformation of business, government, and society.

Created by Microsoft, AI software provider C3.ai, and several leading universities, C3.ai DTI already has the first task on its agenda--to harness the power of AI to combat the coronavirus.

SEE:Coronavirus: Critical IT policies and tools every business needs(TechRepublic Premium)

Known as "AI Techniques to Mitigate Pandemic," C3.ai DTI's first call for research proposals is asking scholars, developers, and researchers to "embrace the challenge of abating COVID-19 and advance the knowledge, science, and technologies for mitigating future pandemics using AI." Researchers are free to develop their own topics in response to this subject, but the consortium outlined 10 different areas open for consideration:

"We are collecting a massive amount of data about MERS, SARS, and now COVID-19," Condoleezza Rice, former US Secretary of State, said in the blog post. "We have a unique opportunity before us to apply the new sciences of AI and digital transformation to learn from these data how we can better manage these phenomena and avert the worst outcomes for humanity."

This first call is currently open with a deadline of May 1, 2020. Interested participants can check the C3.ai DTI website to learn about the process and find out how to submit their proposals. Selected proposals will be announced by June 1, 2020.

The group will fund as much as $5.8 million in awards for this first call, with cash awards ranging from $100,000 to $500,000 each. Recipients will also receive cloud computing, supercomputing, data access, and AI software resources and technical support provided by Microsoft and C3.ai. Specifically, those with successful proposals will get unlimited use of the C3 AI Suite, access to the Microsoft Azure cloud platform, and access to the Blue Waters supercomputer at the National Center for Super Computing Applicationsat the University of Illinois Urbana-Champaign (UIUC).

To fund the institute, C3.ai will provide $57,250,000 over the first five years of operation. C3.ai and Microsoft will contribute an additional $310 million, which includes use of the C3 AI Suite and Microsoft Azure. The universities involved in the consortium include the UIUC; the University of California, Berkeley; Princeton University; the University of Chicago; the Massachusetts Institute of Technology; and Carnegie Mellon University.

Beyond funding successful research proposals, Microsoft said that C3.ai DTI will generate new ideas for the use of AI and ML through ongoing research, visiting professors and research scholars, and faculty and scholars in residence, many of whom will come from the member universities.

More specifically, the group will focus its research on AI, ML, Internet of Things, Big Data Analytics, human factors, organizational behavior, ethics, and public policy. This research will examine new business models, develop ways for creating change within organizations, analyze methods to protect privacy, and ramp up the conversations around the ethics and public policy of AI.

"In these difficult times, we need--now more than ever--to join our forces with scholars, innovators, and industry experts to propose solutions to complex problems," Gwenalle Avice-Huet, Executive Vice President of ENGIE, said. "I am convinced that digital, data science, and AI are a key answer. The C3.ai Digital Transformation Institute is a perfect example of what we can do together to make the world better."

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Microsoft teams up with leading universities to tackle coronavirus pandemic using AI - TechRepublic

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but theyve been limited in their ability to optimize across multiple goals, including the chips power draw, computational performance, and area.

Intelligent design: In response to these challenges, Google researchers Anna Goldie and Azalia Mirhoseini took a new approach: reinforcement learning. Reinforcement-learning algorithms use positive and negative feedback to learn complicated tasks. So the researchers designed whats known as a reward function to punish and reward the algorithm according to the performance of its designs. The algorithm then produced tens to hundreds of thousands of new designs, each within a fraction of a second, and evaluated them using the reward function. Over time, it converged on a final strategy for placing chip components in an optimal way.

Validation: After checking the designs with the electronic design automation software, the researchers found that many of the algorithms floor plans performed better than those designed by human engineers. It also taught its human counterparts some new tricks, the researchers said.

Production line: Throughout the field's history, progress in AI has been tightly interlinked with progress in chip design. The hope is this algorithm will speed up the chip design process and lead to a new generation of improved architectures, in turn accelerating AI advancement.

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Google is using AI to design chips that will accelerate AI - MIT Technology Review

A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, werent very accurate even when trained on 13,000 data points from over 4,000 families. They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs.

Heres a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate, said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. These results show us that machine learning isnt magic; there are clearly other factors at play when it comes to predicting the life course.

The study, which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data. Over 457 groups applied, of which 160 were selected to participate, and their predictions were evaluated with an error metric that assessed their ability to predict held-out data (i.e., data held by the organizer and not available to the participants).

The Challenge was an outgrowth of the Fragile Families Study (formerly Fragile Families and Child Wellbeing Study) based at Princeton, Columbia University, and the University of Michigan, which has been studying a cohort of about 5,000 children born in 20 large American cities between 1998 and 2000. Its designed to oversample births to unmarried couples in those cities, and to address four questions of interest to researchers and policymakers:

When we began, I really didnt know what a mass collaboration was, but I knew it would be a good idea to introduce our data to a new group of researchers: data scientists, said Sara McLanahan, the William S. Tod Professor of Sociology and Public Affairs at Princeton. The results were eye-opening.

The Fragile Families Study data set consists of modules, each of which is made up of roughly 10 sections, where each section includes questions about a topic asked of the childrens parents, caregivers, teachers, and the children themselves. For example, a mother who recently gave birth might be asked about relationships with extended kin, government programs, and marriage attitudes, while a 9-year-old child might be asked about parental supervision, sibling relationships, and school. In addition to the surveys, the corpus contains the results of in-home assessments, including psychometric testing, biometric measurements, and observations of neighborhoods and homes.

The goal of the Challenge was to predict the social outcomes of children aged 15 years, which encompasses 1,617 variables. From the variables, six were selected to be the focus:

Contributing researchers were provided anonymized background data from 4,242 families and 12,942 variables about each family, as well as training data incorporating the six outcomes for half of the families. Once the Challenge was completed, all 160 submissions were scored using the holdout data.

In the end, even the best of the over 3,000 models submitted which often used complex AI methods and had access to thousands of predictor variables werent spot on. In fact, they were only marginally better than linear regression and logistic regression, which dont rely on any form of machine learning.

Either luck plays a major role in peoples lives, or our theories as social scientists are missing some important variable, added McLanahan. Its too early at this point to know for sure.

Measured by the coefficient of determination, or the correlation of the best models predictions with the ground truth data, material hardship i.e., whether 15-year-old childrens parents suffered financial issues was .23, or 23% accuracy. GPA predictions were 0.19 (19%), while grit, eviction, job training, and layoffs were 0.06 (6%), 0.05 (5%), and 0.03 (3%), respectively.

The results raise questions about the relative performance of complex machine-learning models compared with simple benchmark models. In the Challenge, the simple benchmark model with only a few predictors was only slightly worse than the most accurate submission, and it actually outperformed many of the submissions, concluded the studys coauthors. Therefore, before using complex predictive models, we recommend that policymakers determine whether the achievable level of predictive accuracy is appropriate for the setting where the predictions will be used, whether complex models are more accurate than simple models or domain experts in their setting, and whether possible improvement in predictive performance is worth the additional costs to create, test, and understand the more complex model.

The research team is currently applying for grants to continue studies in this area, and theyve also published 12 of the teams results in a special issue of a journal called Socius, a new open-access journal from the American Sociological Association. In order to support additional research, all the submissions to the Challenge including the code, predictions, and narrative explanations will be made publicly available.

The Challenge isnt the first to expose the predictive shortcomings of AI and machine learning models. The Partnership on AI, a nonprofit coalition committed to the responsible use of AI, concluded in its first-ever report last year that algorithms are unfit to automate the pre-trial bail process or label some people as high-risk and detain them. The use of algorithms in decision making for judges has been known to produce race-based unfair results that are more likely to label African-American inmates as at risk of recidivism.

Its well-understood that AI has a bias problem. For instance, word embedding, a common algorithmic training technique that involves linking words to vectors, unavoidably picks up and at worst amplifies prejudices implicit in source text and dialogue. A recent study by the National Institute of Standards and Technology (NIST) found that many facial recognition systems misidentify people of color more often than Caucasian faces. And Amazons internal recruitment tool which was trained on resumes submitted over a 10-year period was reportedly scrapped because it showed bias against women.

A number of solutions have been proposed, from algorithmic tools to services that detect bias by crowdsourcing large training data sets.

In June 2019, working with experts in AI fairness, Microsoft revised and expanded the data sets it uses to train Face API, a Microsoft Azure API that provides algorithms for detecting, recognizing, and analyzing human faces in images. Last May, Facebook announced Fairness Flow, which automatically sends a warning if an algorithm is making an unfair judgment about a person based on their race, gender, or age. Google recently released the What-If Tool, a bias-detecting feature of the TensorBoard web dashboard for its TensorFlow machine learning framework. Not to be outdone, IBM last fall released AI Fairness 360, a cloud-based, fully automated suite that continually provides [insights] into how AI systems are making their decisions and recommends adjustments such as algorithmic tweaks or counterbalancing data that might lessen the impact of prejudice.

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Researchers find AI is bad at predicting GPA, grit, eviction, job training, layoffs, and material hardship - VentureBeat

Reinforcement learning algorithms may be the next best thing since sliced bread for engineers looking to improve chip placement.

Researchers from Google have created a new algorithm that has learned how to optimize the placement of the components in a computer chip, so as to make it more efficient and less power-hungry.

SEE ALSO: WILL AI AND GENERATIVE DESIGN STEAL OUR ENGINEERING JOBS?

Typically, engineers can spend up to 30 hours configuring a single floor plan of chip placement, or chip floor planning. This complicated 3D design problem requires the configuration of hundreds, or even thousands, of components across a number of layers in a constrained area. Engineers will manually design configurations to minimize the number of wires used between components as a proxy for efficiency.

Because this is time-consuming, these chips are designed to only last between two and five years. However, as machine-learning algorithms keep improving year upon year, a need for new chip architectures has also arisen.

Facing these challenges, Google researchers Anna Goldie and Azalia Mirhoseini, have looked into reinforcement learning. These types of algorithms use positive and negative feedback in order to learn new and complicated tasks. Thus, the algorithm is either "rewarded" or "punished" depending on how well it learns a task. Following this, it then creates tens to hundreds of thousands of new designs. Ultimately, it creates an optimal strategy on how to place these chip components.

After their tests, the researchers checked their designs with the electronic design automation software and discovered that their method's floor planning was much more effective than the ones human engineers designed. Moreover, the system was able to teach its human workers a new trick or two.

Progress in AI has been largely interlinked with progress is computer chip design. The researchers' hope is that their new algorithm will assist in speeding up the chip design process and pave the way for new and improved architectures, which would ultimately accelerate AI.

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Google Researchers Create AI-ception with an AI Chip That Speeds Up AI - Interesting Engineering

Artificial intelligence (AI) is informing just about every facet of society, from detecting fraud and surveillanceto helping countries battle the current COVID-19 pandemic. But AI is a thorny subject, fraught with complex terminology, contradictory information, and general confusion about what it is at its most fundamental level. This is why the Oxford Internet Institute (OII), the University of Oxfords research and teaching department specializing in the social science of the internet, has partnered with Google to launch a portal with a series of explainers outlining what AI actually is including the fundamentals, ethics, its impact on society, and how its created.

The Oxford Internet Institute is a multidisciplinary research and teaching department of the University of Oxford, dedicated to the social science of the Internet.

At launch, the A-Z of AI covers 26 topics, including bias and how AI is used in climate science, ethics, machine learning, human-in-the-loop, and Generative adversarial networks (GANs).

Googles People and AI Research team (PAIR) worked with Gina Neff, a senior research fellow and associate professor at OII, and her team to select the subjects they felt were pivotal to understanding AI and its role today.

The 26 topics chosen are by no means an exhaustive list, but they are a great place for first-timers to start, the guides FAQ section explains. The team carefully balanced their selections across a spectrum of technical understanding, production techniques, use cases, societal implications, and ethical considerations.

For example, bias in data sets is a well-documented issue in the development of AI algorithms, and the guide briefly explains how the problem is created and how it can be addressed.

Typically, AI forms a bias when the data its given to learn from isnt fully comprehensive and, therefore, starts leading it toward certain outcomes, the guide reads. Because data is an AI systems only means of learning, it could end up reproducing any imbalances or biases found within the original information. For example, if you were teaching AI to recognize shoes and only showed it imagery of sneakers, it wouldnt learn to recognize high heels, sandals, or boots as shoes.

You can peruse the guide in its full A-Z form or filter content by one of four categories: AI fundamentals, Making AI, Society and AI, and Using AI.

Those with a decent background in AI will find this guide simplistic, but its a good starting point for anyone looking to grasp the key points they will be hearing about as AI continues to shape society in the years to come.

Its also worth noting that this isnt a static resource the plan is to update it as AI evolves.

The A-Z will be refreshed periodically as new technologies come into play and existing technologies evolve, the guide explains.

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Google and the Oxford Internet Institute explain artificial intelligence basics with the A-Z of AI - VentureBeat

behold.ai and Wellbeing Software collaborate onnational solution for rapid COVID-19 diagnosis using AI analysis of chest X-rays

Companies working to fast-track programme for UK-wide rollout

LONDON, UK, March 31, 2020 Two British companies at the leading edge of medical imaging technology are working together on a plan to fast-track the diagnosis of COVID-19 in NHS hospitals using artificial intelligence analysis of chest X-rays.

behold.ai has developed the artificial intelligence-based red dot algorithm which can identify within 30 seconds abnormalities in chest X-rays. Wellbeing Software operates Cris, the UKs most widely used Radiology Information System (RIS), which is installed in over 700 locations.

A national roll-out combining these two technologies would enable a large number of hospitals to quickly process the significant volume of X-rays, currently being used as the key diagnostic test for triage of COVID-19 patients, thereby speeding up diagnosis and easing pressure on the NHS at this critical time. This solution will also find significant utility in dealing with the backlog of cases that continue to mount, such as suspected cancer patients.

Simon Rasalingham, Chairman and CEO of behold.ai, said:

behold.ai and Wellbeing are a great fit in terms of expertise and technology. We are able to prioritise abnormal chest X-rays with greater than 90% accuracy and a 30-second turnaround. If that were translated into a busy hospitals coping with COVID-19, the benefits to healthcare systems are potentially enormous.

Chris Yeowart, Director at Wellbeing Software, said:

Our technology provides the integration between the algorithm and the hospitals radiology systems and working processes, addressing the technical challenges to clearing the way for accelerated national rollout. It is clear from talking to radiology departments that chest X-rays have become one of the primary diagnostic tools for COVID-19 in this country.

https://www.behold.ai

https://www.wellbeingsoftware.com/

Ends

For further information, please contact:Consilium Strategic Communications Tel: +44(0)20 3709 5700 beholdai@consilium-comms.com

About behold.ai and radiology

behold.ai provides artificial intelligence, through its red dot cognitive computing platform, to radiology departments. This technology augments the expertise of radiologists to enable them to report with greater clinical accuracy, faster and more safely than they could before. This revolutionary combination helps to deliver greater performance in radiology reporting at a fraction of the price of outsourced reporting.

Radiology departments play an essential role in the diagnostic process; however, a consequence of fewer radiologists and a growing demand for images has left services stretched beyond capacity across many trusts, resulting in reporting delays - in some cases impacting cancer diagnosis. These service issues have been highlighted by the Care Quality Commission and the Royal College of Radiologists.

Our solution seamlessly integrates into local trust workflows augmenting clinical practice and delivering state-of-the-art, safe, Artificial Intelligence.

The behold.ai algorithm has been developed using more than 30,000 example images, all of which have been reviewed and reported by highly experienced consultant radiology clinicians in order to shape accurate decision making. The red dot prioritisation platform is capable of sorting images into normal and abnormal categories in less than 30 seconds post image acquisition.

About behold.ai and quality

Apart from its FDA clearance,behold.aiis also CE approved and is gaining further approval for a CE mark Class IIa certification.

In June 2019 the Company was awarded ISO 13485 QMS certification for an AI medical device the gold standard of quality certification.

About Wellbeing Software

Wellbeing Software is a leading healthcare technology provider with a presence in more than 75% of NHS organisations. The company has combined its extensive UK resources and unparalleled experience in its specialist divisions radiology, maternity, data management and electronic health records - to form Wellbeing Software, uniting their core businesses to enable customers to build on existing investments in IT as a way of delivering connected healthcare records and better patient care. Wellbeings ability to connect its specialist systems with other third-party software enables healthcare organisations to achieve key objectives, such as paperless working and the creation of complete electronic health records. Through their established footprint, specialist knowledge and significant development resources, the company is building the foundations for connectivity within NHS organisations and beyond.

Wellbeing media contact : Jenni Livesley, Context Public Relations, wellbeing@contextpr.co.uk

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behold.ai and Wellbeing Software collaborate on national solution for rapid COVID-19 diagnosis using AI analysis of chest X-rays - GlobeNewswire

Advanced computers have defeated chess masters and learned how to pick through mountains of data to recognize faces and voices. Now, a billionaire developer of software and artificial intelligence is teaming up with top universities and companies to see if A.I. can help curb the current and future pandemics.

Thomas M. Siebel, founder and chief executive of C3.ai, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweeping the globe.

I cannot imagine a more important use of A.I., Mr. Siebel said in an interview.

Known as the C3.ai Digital Transformation Institute, the new research consortium includes commitments from Princeton, Carnegie Mellon, the Massachusetts Institute of Technology, the University of California, the University of Illinois and the University of Chicago, as well as C3.ai and Microsoft. It seeks to put top scientists onto gargantuan social problems with the help of A.I. its first challenge being the pandemic.

The new institute will seek new ways of slowing the pathogens spread, speeding the development of medical treatments, designing and repurposing drugs, planning clinical trials, predicting the diseases evolution, judging the value of interventions, improving public health strategies and finding better ways in the future to fight infectious outbreaks.

Condoleezza Rice, a former U.S. secretary of state who serves on the C3.ai board and was recently named the next director of the Hoover Institution, a conservative think tank on the Stanford campus, called the initiative a unique opportunity to better manage these phenomena and avert the worst outcomes for humanity.

The new institute plans to award up to 26 grants annually, each featuring up to $500,000 in research funds in addition to computing resources. It requires the principal investigators to be located at the consortiums universities but allows partners and team members at other institutions. It wants coronavirus proposals to be submitted by May and plans to award its first grants in June. The research findings are to be made public.

The institutes co-directors are S. Shankar Sastry of the University of California, Berkeley, and Rayadurgam Srikant of the University of Illinois, Urbana-Champaign. The computing power is to come from C3.ai and Microsoft, as well as the Lawrence Berkeley National Laboratory at the University of California and the National Center for Supercomputing Applications at the University of Illinois. The schools run some of the worlds most advanced supercomputers.

Successful A.I. can be extremely hard to deliver, especially in thorny real-world problems such as self-driving cars. When asked if the institute was less a plan for practical results than a feel-good exercise, Mr. Siebel replied, The probability of something good not coming out of this is zero.

In recent decades, many rich Americans have sought to reinvent themselves as patrons of social progress through science research, in some cases outdoing what the federal government can achieve because its goals are often unadventurous and its budgets unpredictable.

Forbes puts Mr. Siebels current net worth at $3.6 billion. His First Virtual Group is a diversified holding company that includes philanthropic ventures.

Born in 1952, Mr. Siebel studied history and computer science at the University of Illinois and was an executive at Oracle before founding Siebel Systems in 1993. It pioneered customer service software and merged with Oracle in 2006. He founded what came to be named C3.ai in 2009.

The first part of the companys name, Mr. Siebel said in an email, stands for the convergence of three digital trends: big data, cloud computing and the internet of things, with A.I. amplifying their power. Last year, he laid out his thesis in a book Digital Transformation: Survive and Thrive in an Era of Mass Extinction. C3.ai works with clients on projects like ferreting out digital fraud and building smart cities.

In an interview, Eric Horvitz, the chief scientist of Microsoft and a medical doctor who serves on the spinoff institutes board, likened the push for coronavirus solutions to a compressed moon shot.

The power of the approach, he said, comes from bringing together key players and institutions. We forget who is where and ask what we can do as a team, Dr. Horvitz said.

Seeing artificial intelligence as a good thing perhaps a lifesaver is a sharp reversal from how it often gets held in dread. Critics have assailed A.I. as dangerously powerful, even threatening the enslavement of humanity to robots with superhuman powers.

In no way am I suggesting that A.I. is all sweetness and light, Mr. Siebel said. But the new institute, he added, is a place where it can be a force for good.

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A.I. Versus the Coronavirus - The New York Times

TOKYO--(BUSINESS WIRE)--NEC Corporation today announced the conclusion of a strategic partnership agreement with Kagome Co., Ltd. to launch agricultural management support services utilizing AI for leading tomato processing companies.

The new service uses NECs AI-enabled agricultural ICT platform, CropScope, to visualize tomato growth and soil conditions based on sensor data and satellite images, and to provide farming management recommendation services. This AI enables the service to provide data on the best timing and amounts of irrigation and fertilizer for healthy crops. As a result, farms are able to achieve stable yields and lower costs, while practicing environmentally sustainable agriculture without depending on the skill of individual growers.

Tomato processing companies can obtain a comprehensive understanding of the most effective growing conditions for tomato production on their own farms, as well as their contract growers. Also, they can optimally manage crop harvest orders across all fields based on objective data, which helps to reduce yield loss and improve productivity.

NEC and Kagome began agricultural collaboration in 2015, and by 2019 they had conducted demonstrations in regions that include Portugal, Australia and the USA. An AI farming experiment in Portugal in 2019 showed that the amount of fertilizer used for the trial was approximately 20% less than the average amount used in general, yielding 127 tons of tomatoes per hectare, approximately 1.3 times that of the average Portuguese grower, and almost the same as that of skilled growers.

Kagome will establish a Smart Agri Division in April 2020, first targeting customers in Europe, then aiming to expand the business to worldwide markets.

Kagome has been developing agricultural management support technologies using big data in collaboration with NEC since 2015, with the aim of realizing environmentally friendly and highly profitable agricultural management in the cultivation of tomatoes for processing on a global basis, said Kengo Nakata, General Manager, Smart Agri Division, Kagome. By combining Kagomes farming know-how with NEC's AI technology, we will realize sustainable agriculture, he added.

NEC is pleased to have signed a strategic partnership agreement with Kagome, said Masamitsu Kitase, General Manager, Corporate Business Development Division, NEC. NEC aims to realize a sustainable agriculture that can respond flexibly to global social issues on climate change and food safety, he added.

About NEC Corporation: For more information, visit NEC at http://www.nec.com.

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NEC and Kagome to Provide AI-enabled Services That Improve Tomato Yields - Business Wire

HEFEI, China--(BUSINESS WIRE)--Asias leading artificial intelligence (AI) and speech technology company, iFLYTEK has partnered with the South Korean technology company, Hancom Group, to launch the joint venture Accufly.AI in South Korea. Accufly.AI launched its AI Outbound Calling System to assist the South Korean government at no cost and provide information to individuals who have been in close contact with or have had a confirmed coronavirus case.

The AI Outbound Calling System is a smart, integrated system that is based on iFLYTEK solutions and Hancom Groups Korean-based speech recognition. The technology saves manpower and assists in the automatic distribution of important information to potential carriers of the virus and provides a mechanism for follow up with recovered patients. iFLYTEK is looking to make this technology available in markets around the world, including North American and Europe.

The battle against the Covid-19 epidemic requires collective wisdom and sharing of best practices from the international community, said iFLYTEK Chief Financial Officer Mr. Dawei Duan. Given the challenges we all face, iFLYTEK is continuously looking at ways to provide technologies and support to partners around the world, including in the United States, Canada, the United Kingdom, New Zealand, and Australia.

In February, the Hancom Group donated 20,000 protective masks and 5 thermal devices to check temperatures to Anhui to help fight the epidemic.

iFLYTEKs AI technology helped stem the spread of the virus in China and will help the South Korean government conduct follow-up, identify patients with symptoms, manage self-isolated residents, and reduce the risk of cross-infection. The system also will help the government distribute important health updates, increase public awareness, and bring communities together.

iFLYTEK is working to create a better world through artificial intelligence and seeks to do so on a global scale. iFLYTEK will maximize its technical advantages in smart services to support the international community in defeating the coronavirus, said Mr. Duan.

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iFLYTEK and Hancom Group Launch Accufly.AI to Help Combat the Coronavirus Pandemic - Business Wire

New York, March 31, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global AI in Telecommunication Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871938/?utm_source=GNW

The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global AI in Telecommunication Market including are AT&T Inc., Google LLC, IBM Corporation, Intel, Microsoft Corporation, Cisco Systems, H2O.ai, Infosys Limited, Nuance Communications, Nvidia Corporation, Salesforce.com, Inc., and Sentient Technologies.

On the basis of Technology, the Global AI in Telecommunication Market is studied across Machine Learning & Deep Learning and Natural Language Processing.

On the basis of Component, the Global AI in Telecommunication Market is studied across Service and Solution.

On the basis of Application, the Global AI in Telecommunication Market is studied across Customer Analytics, Network Optimization, Network Security, Self-Diagnostics, and Virtual Assistance.

On the basis of Deployment, the Global AI in Telecommunication Market is studied across On-Cloud and On-Premise.

For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.

In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.

Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global AI in Telecommunication Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global AI in Telecommunication Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global AI in Telecommunication Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global AI in Telecommunication Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global AI in Telecommunication Market

The report answers questions such as:1. What is the market size of AI in Telecommunication market in the Global?2. What are the factors that affect the growth in the Global AI in Telecommunication Market over the forecast period?3. What is the competitive position in the Global AI in Telecommunication Market?4. Which are the best product areas to be invested in over the forecast period in the Global AI in Telecommunication Market?5. What are the opportunities in the Global AI in Telecommunication Market?6. What are the modes of entering the Global AI in Telecommunication Market?Read the full report: https://www.reportlinker.com/p05871938/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Global AI in Telecommunication Market is expected to grow from USD 347.28 Million in 2018 to USD 2,145.39 Million by the end of 2025 at a Compound...