Category Archives: Artificial Intelligence

When the COVID-19 pandemic forced the Medical University of South Carolina to suspend clinical rotations used to train students in health care settings, Alan Snyder had an idea.

He was third-year medical student this past spring and was in the middle of a dermatology rotation when he was sent home and required to transition to online learning. But instead of sitting back and letting the pandemic run its course, Snyder devised a new way to reach patients.

Using an artificial intelligence model developed by Jvion, a Georgia-based health care AI firm, Snyder combined Census tract information with the MUSC patient database to identify thousands of adults who faced a high risk of developing serious complications should they contract COVID-19.

But this was just the start. Snyder then mobilized more than 150 volunteers, ranging from students to retirees, across the state to call these at-risk patients and educate them about coronavirus safety protocols. Over the course of 41 days last year, these volunteers made 1,370 calls to 814 patients and were able to help more than 50 percent of "extremely high-risk patients" activate their online MyChart accounts, which are used to store electronic medical records and connect patients with their providers.

Not only that, in a few cases, the volunteers associated with the project were able to pinpoint cases of elder abuse and medical emergencies and connect patients with social workers and care almost immediately.

"Necessity brings out innovation," said Snyder, now completing his fourth and final year at MUSC. "I was scared. How could I help other people? Thats my job as a health care professional. It seems like something that was useful for my time."

Dr. Lancer Scott, the section chief of emergency medicine at the VA hospital in Charleston and a faculty member at MUSC, said when he first heard about Snyder's idea for the project, "the hairs stood up on the back of my neck."

"Find me a group in this state that is working on preventive measures on the front end of COVID instead of contact tracing on the back end," Scott said. That's what makes Snyder's idea so unique, he said.

"Its not just a model for COVID. ... It's a model for vulnerable populations," Scott said. "If there was a Nobel Prize for medical students, Alan should get it. Its really amazing."

Snyder presented his results (remotely) during the virtual American Medical Association Research Symposium in December. He is currently applying to residency programs and crunching data collecting through his project to determine if the outreach to patients did, in fact, prevent any COVID-related hospitalizations or deaths.

"There are a lot of people behind the scenes who put their heart and time into this," Snyder said. "Its something that Im really proud of."

Reach Lauren Sausser at 843-937-5598.

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MUSC student used artificial intelligence to find patients at risk for COVID complications - Charleston Post Courier

To provide the global outlook of the Artificial Intelligence in Mental Health Care Market a new statistical study has added by HealthCare Intelligence Markets to its massive database. During the analysis of this market the existing industries, as well as upcoming start-ups, have been considered. It helps to make informed decisions in the businesses. Well explained Porters five analysis and SWOT analysis have been used by a researcher of the report. The research report is comprised market trends and holistic business information that can pinpoint market pinpoint analysis along with revenue, growth, and profit over a predictable period. This provides a complete analysis of driver, paper and market opportunities.

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Key players in global Artificial Intelligence In Mental Health Care: IBM Watson AI XPRIZE. Acadia Healthcare Co., Inc., Universal Health Services, Inc., Magellan Health Inc., National Mentor Holdings Inc., Behavioural Health Services Inc., Behavioural Health Network Inc., North Range Behavioural Health, Strategic Behavioural Health, LLC,The Artificial Intelligence In Mental Health Care Market report provides:1. Comprehensive analysis of key drivers, leading players, key segments and regions.2. The experts examined various geographic areas in detail and presented a competitive scenario to help newcomers, leading market players and investors determine the emerging countries.3. The knowledge provided in the report would benefit market participants in order to formulate strategies for the future and to gain a strong position on the world market.

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The report can answer the following questions:

1. What is the global (North America, South America, Europe, Africa, Middle East, Asia, China, Japan) production, production value, consumption, consumption value, import and export of Artificial Intelligence In Mental Health Care?2. Who are the global key manufacturers of Artificial Intelligence In Mental Health Care industry? How are their operating situation (capacity, production, price, cost, gross and revenue)?3. What are the types and applications of Artificial Intelligence In Mental Health Care? What is the market share of each type and application?4. What are the upstream raw materials and manufacturing equipment of Artificial Intelligence In Mental Health Care? What is the manufacturing process of Artificial Intelligence In Mental Health Care ?5. Economic impact on Artificial Intelligence In Mental Health Care industry and development trend of Artificial Intelligence In Mental Health Care industry.6. What will the Artificial Intelligence In Mental Health Care market size and the growth rate be in 2026?7. What are the key factors driving the global Artificial Intelligence In Mental Health Care industry?8. What are the key market trends impacting the growth of the Artificial Intelligence In Mental Health Care market?

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Consistent Growth seen in Artificial Intelligence in Mental Health Care Market 2021-2027 | IBM Watson AI XPRIZE. Acadia Healthcare Co., Inc.,...

Thanks to its new processor, the Panasonic JZ2000 can analyse the picture every second and automatically adjust picture quality. Picture courtesy of Panasonic

LAS VEGAS, Jan 16 If theres one thing to take away from the 2021 edition of the Consumer Electronics Show (CES), its that artificial intelligence (AI) seemed to be omnipresent in the technologies and devices presented.

Whether a marketing argument or genuine technological progress, AI has become an inevitable part of many new applications, TVs and vehicle dashboards not to mention toothbrushes and massage chairs!

Smart TVs

TVs have been the major stars of CES for a few years now, and this edition held entirely online due to the covid-19 pandemic was no exception.

Thanks to new, ultra-high-performance processors, manufacturers are promising optimal image quality thanks to built-in artificial intelligence.

Sony, for example, unveiled a new range of TVs capable of anticipating which part of the screen viewers are likely to be looking at, in order to systematically bring out the best in this focal point of the image.

Similarly, Panasonic showcased a new TV model that automatically calibrates the image in relation to the kind of content displayed (movie, sport, concert, video game, etc.).

Samsung and LG also embraced artificial intelligence, whether for upscaling (converting HD content to 4K resolution onscreen, for example) or for recommending shows for viewers to watch.

Smart objects

As well as TVs, almost all the connected devices presented at CES seemed to boast some kind of artificial intelligence, which, in reality, is often an ability to record and convey user habits.

That, for example, is seen in products as diverse as a toothbrush, which sends brushing quality reports to your smartphone, or in a massage chair, which can adapt its technique to user needs expressed by voice command.

In-car AI

Finally, cars were also on the agenda at CES, in particular with cabin technology thats ever more spectacular and connected.

Here too, artificial intelligence has a role to play, coming to life onscreen with custom suggestions, or via driving comfort systems, not to mention entertainment for both driver and passengers.

Mercedes and BMW, for example, presented future touchscreen interfaces with integrated AI. AFP-Relaxnews

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Artificial intelligence spotted on everything (and anything!) at CES 2021 - Malay Mail

Dublin, Jan. 15, 2021 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Next Generation Networking by Infrastructure, Network Type, IoT Solution, Segment (Consumer, Enterprise, Industrial, and Government), and Industry Verticals 2021-2026" report has been added to ResearchAndMarkets.com's offering.

This report assesses the impact of AI in various networking products and solutions including embedded equipment, components, and software platforms (network automation, optimization, and transformation). The report also evaluates the role of SDN, Edge Computing, NFV, and Augmented Intelligence in the formation and support of AI driven networking ecosystems.

In addition, the impact of 5G networks, IoT technology and systems, and network analytics functions is also analyzed. The report assesses technologies, products, and solutions from key solution providers, identifying key companies in each segment of the competitive landscape.

The report also provides forecasts for the AI-driven networking market based on major market segments and subsegments, AI technology type, deployment type, network type, industry vertical and region. The report provides qualitative and quantitative analysis for AI in Next Generation Networking by Infrastructure, Network Type, IoT Solution, Segment (Consumer, Enterprise, Industrial, and Government), and Industry Vertical from 2021 through 2026.

Select Report Findings:

Artificial Intelligence is rapidly becoming an integral part of various digital technologies including software and many aspects of ICT infrastructure. For example, the AI chipset marketplace is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. With 85% of all chipsets globally shipping AI-equipped, over 63% of all electronics will have some form of embedded intelligence by 2026.

Infrastructure is anticipated to be one of the primary focus areas for AI as network operators seek to reduce costs and improve efficiencies while simultaneously reducing the incidence of errors and adverse network events. AI technology will play a key role in the transformation of network intelligence to become increasingly self-driven. Technologies like cognitive computing, machine learning, deep learning, and predictive application will be fundamental to the transformation of network configuration automation and operational autonomy.

AI-driven networking is going to impact wireless networking of all sizes for all communication service providers, improving service realization and support, and ultimately impacting every industry vertical from transportation to medical care to financial services. Furthermore, the analyst sees the convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals.

In terms of the impact of AI on wireless networks, the evolution is already underway from a standards and network topology approach with 5G service-based architectures. Implementation within public communications service providers will scale slowly due to legacy systems such as OSS/BSS. However, closed-loop private 5G wireless networks will be in the vanguard of AI network deployment.

This evolution will lead to AI-enabled functions throughout 6G networks within the 2030 to 2040 timeframe. This will include contextually agile RF networks that support event-driven adaptation and resource allocation optimization. It will also include many improvements at the device level such as AI-enabled distributed computing, which will facilitate persistent computation-oriented communications.

Target Audience:

Report Benefits:

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Unified Networks involving AI and IoT2.2 AI Driven Networks2.3 AI in Wireless Network Strategy2.4 Intent Based Networks2.5 Self-driven Networks2.6 Augmented Intelligence2.7 AI Technologies in Networking2.8 Market Drivers and Challenges

3.0 Technology and Application Analysis3.1 Networking Equipment, Platforms, and Services3.2 Enterprise Networking3.3 IoT Networking Technology3.3.1 Short Range Wireless Technologies3.3.2 Long Range Wireless Technologies3.4 IoT Application3.5 Network Optimization3.6 Network Automation3.7 Network Transformation3.8 Edge Computing and AI3.9 Software Driven Networking3.10 Software Defined Infrastructure3.11 AI-Defined Infrastructure3.12 Network Function Virtualization3.13 Telecom Infrastructure and Cloud RAN3.14 New Radio Technology and 5G Network3.15 AI Powered Network Analytics3.16 Competitive Landscape

4.0 Company Analysis4.1 Cisco Systems4.2 Hewlett Packard Enterprise4.3 IBM Corporation4.4 Samsung Electronics Co Ltd.4.5 Baidu Inc.4.6 Nvidia Corporation4.7 Google Inc.4.8 Microsoft Corporation4.9 Dell EMC4.10 Nokia Corporation4.11 ARM Limited4.12 Xively4.13 PTC Corporation4.14 Huawei Technologies Co. Ltd.4.15 ZTE Corporation4.16 Intel Corporation4.17 Ericsson AB4.18 Fujitsu Ltd.4.19 NEC Corporation4.20 H2O.ai4.21 Qualcomm Incorporated4.22 Juniper Networks, Inc.4.23 Accenture PLC4.24 Brocade Communication Systems4.25 VMware Inc.4.26 Aricent Inc.4.27 Arista Networks Inc.4.28 Extreme Networks4.29 NETSCOUT4.30 ECI Telecom4.31 Foxconn Electronics Inc.4.32 NETGEAR4.33 Riverbed Technology

5.0 Market Analysis and Forecast 2021 - 20265.1 Global AI Networking Solution Market 2021 - 20265.2 Global AI Networking Solution Market by Segment5.2.1 Global AI Networking Solution Market by Hardware5.2.1.1 Global AI Networking Solution Market by Hardware Equipment5.2.1.2 Global AI Networking Solution Market by Hardware Component5.2.2 Global AI Networking Solution Market by Software5.2.2.1 Global AI Networking Solution Market by Network Management Software5.2.2.1.1 Global AI Networking Solution Market by Network Automation Software5.2.2.1.2 Global AI Networking Solution Market by Network Optimization Software5.2.2.1.3 Global AI Networking Solution Market by Network Transformation Software5.2.3 Global AI Networking Solution Market by Service5.2.3.1 Global AI Networking Solution Market by Managed Service5.2.3.2 Global AI Networking Solution Market by Professional Service5.2.3.2.1 Global AI Networking Solution Market by Deployment, Integration, and Provisioning Service Type5.2.3.2.2 Global AI Networking Solution Market by Wireless Infrastructure Management Service Type5.2.3.2.2.1 Global AI Networking Solution Market by Telecom Infrastructure Management Service Type5.2.3.2.2.2 Global AI Networking Solution Market by 5G Infrastructure Management Service Type5.3 Global AI Networking Solution Market by AI Technology5.4 Global AI Networking Solution Market by Deployment5.5 Global AI Networking Solution Market by Industry5.6 Global AI Networking Solution Market by Organization5.7 Global AI Networking Solution Market by Network Type5.8 Global AI Networking Solution Market by Network Platform5.8.1 Global AI Networking Solution Market by IoT Network Technology Type5.8.1.1 Global AI Networking Solution Market by Short and Medium Range Wireless IoT Network Technology5.8.1.2 Global AI Networking Solution Market by Long Range Wireless IoT Network Technology5.8.2 Global AI Networking Solution Market by IoT Application Type5.9 Global AI Networking Solution Market by 5G Network Type5.10 Global AI Networking Solution Market by Industry Vertical5.11 Global AI Networking Solution Market by Region5.11.1 North America AI Networking Solution Market by Country5.11.2 Europe AI Networking Solution Market by Country5.11.3 APAC AI Networking Solution Market by Country5.11.4 MEA AI Networking Solution Market by Country5.11.5 Latin America AI Networking Solution Market by Country

6.0 Conclusions and Recommendations6.1 Artificial Intelligence Providers6.2 Broadband Infrastructure Providers6.3 Communication Service Providers6.4 Computing Companies6.5 Data Analytics Providers6.6 Networking Equipment Providers6.7 Networking Security Providers6.8 Semiconductor Companies6.9 IoT Suppliers and Service Providers6.10 Software and Platform Providers

For more information about this report visit https://www.researchandmarkets.com/r/64if4m

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Global Artificial Intelligence in Next Generation Networking Market (2021 to 2026) - by Infrastructure, Network Type, IoT Solution, Segment and...

January 14, 2021 Artificial intelligence will continue to gradually revolutionize productivity and functionality as long as policy concerns are addressed, said three separate panel of experts on Tuesday at CES 2021.

Various AI technologies will contribute $16 trillion to the economy by 2030. Better machines, better software, and an explosion of applications affecting everyday lives will continue. AI has enhanced productivity, improved safety, and made the world more accessible.

As AI does not replace but enhances human work, it presents an enormous set of curated data that must be entrusted with established guidelines and transparency.

IBM Vice President Bridget Karlin raised a concern involving ethics, and not building bias. Depending on companies purposes for using AI, models can be ethical when they are engineered to be fair and properly calibrated.

It is incumbent upon software developers to identify the requirements for ethical and non-biased data collection, she said.

What about AIs involvement in creating and spreading fake news? Kevin Guo, CEO of Hive, said this is a service still in process.

It is essential for AI engineers to research and implement data fairness and remove bias.

On health care, participants in a separate panel said AI leads to improved outcomes and lower costs. But to Christina Silcox, a digital health policy fellow, the question is: How can people trust something that cant be seen or understood?

Understanding how technologies are created helps, said Jesse Ehrenfeld from the Board of Trustees of American Medical Association. But he acknowledged that all data is in some way biased. He said he cant tell the number of times data flows have generated different meanings than expected.

Christina Silcox said that it was critical to understand how software will work overtime after being put into place.

Indeed, communication and transparency are key for trust and growth of AI, said Senior Regulatory Specialist Pat Baird. Depending on who the stakeholders are, there will need customization of such communication.

Trustworthy AI will re-humanize health care, letting computers do what they were built to be done, and allow the health care workers to work with people, he said.

Another panel during 2021 CES discussed gender and racial bias in a business setting and how AI can contribute show and reflect equal representation.

Annie Jean-Baptiste of Google said that humble inclusion fills innovation. Kimberly Sterling of ResMed declared that people are not going anywhere, and that AI will not replace peoples brains.

All three panel discussions pondered the future of AI. Most agreed that AI exists to supplement human ambition, enabling everyday businesses to become smarter, adjust to new inputs and perform human-like-tasks.

For richer data sets, one of the solutions that panelists proposed to break out the black box of AI, make sure to assemble those capabilities and understand the sources of the data, to go back and test the models, and to be able to look holistically at outcomes.

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Artificial Intelligence Aims to Enhance Human Capabilities, But Only With Caution and Safeguards - BroadbandBreakfast.com

Ive spent two decades as a researcher and educator in artificial intelligence, drawn to the field by the opportunity to explore the mysteries of perception and cognition. But life is rarely as simple as wed like, and the arc of my career has paralleled my mothers escalating health struggles, including a chronic, life-threatening cardiovascular condition. As all-consuming as the world of academia can be, it sometimes feels as if Ive spent as much time in hospitals as I have in my lab.

Im happy to report my mother continues to persevere, but her resilience hasnt been the only silver lining to this ordeal. Years spent in the company of nurses and doctorsunfailingly committed, but perpetually overworked and often sleep deprivedconvinced me that the power of AI could radically elevate the way care is delivered. Intelligent sensors could keep tireless watch over patients, automate time-consuming tasks like charting and transcription, and identify lapses in safety protocols as they happen. After all, if AI can safely guide cars along freeways at 70 miles per hour, I wondered, why cant it help caregivers keep up with the chaos of the healthcare environment?

At the heart of this idea was an obstacle, however. I was proposing research that extended beyond the limits of computer science and into an entirely different field, with decades of literature and traditions stretching back generations. It was clear I needed a collaboratornot just an authority in healthcare, but one with the patience and open-mindedness to help an outsider bring something new to the table. For the first time in my career, success would depend on more than the merits of my work; it would require the humility of researchers like me to recognize the boundaries of our knowledge, and the graciousness of experts in another discipline to help us overcome them.

Thankfully, luck was on my side. In 2012, a colleague introduced me to Arnie Milstein, a Stanford Medical School professor and member of the National Academy of Medicine with an interest in both the policy and the technology that drives healthcare. Our first real conversation on the topic turned a casual lunch at a Vietnamese pho restaurant into an impromptu, hours-long brainstorming session. The exuberance of that day never wore off, as we convened a coalition of researchers to explore the automated tracking of surgical tools during operations, privacy-preserving monitors that ensure the safety of high-risk patients and vulnerable seniors, and networks of smart sensors that help hospital staff maintain hand hygiene throughout their shifts. Finally, in September, after years of experimentation, refinements, and presentations at conferences all over the world, our research was published in Nature. And now, with the help of legal scholars, bioethicists, and even a philosopher, were partnering with select hospitals and senior homes to pilot its use in the hands of real caregivers.

The success of my collaboration with Professor Milstein demonstrates an important idea: AIs applications are vast, but technology will represent only part of any given breakthrough. The remainder will be found in the contributionseven leadershipof experts from a growing list of fields, of which healthcare is only one example. Similar partnerships await as AI intersects with economics, energy, environmental science, public health, education, and even the humanities.

For instance, its hard to talk about any application of technology in 2020 without addressing the coronavirus pandemic. This was among the motivating factors behind the launch of AI Cures, an MIT initiative that brings together researchers in machine learning and life sciences to accelerate the speed with which antivirals can be identified, evaluated, and ultimately deployed. Its applications in the face of COVID-19 are obvious, but its broader goal of elevating our defense against pathogens of all kinds will remain relevant long after the challenges of the present moment are behind us. In addition to its core research mission, the group has organized impressively inclusive events in recent months, providing a venue for presenters with backgrounds in computer science, infectious disease, cardiology, synthetic biology, and many others.

Similarly encouraging is the work of my colleague, Stanford law professor Dan Ho. His lab has published extensively on the utility of AI in the public sector, and is now working with the EPA to use machine learning to dramatically improve the tracking of ecological contamination at a national scale. The underlying technology is transformative, but its the involvement of legal scholars, policymakers, and government representatives that truly makes it applicable in the real world.

These stories are a testament to the power of humility, but the sheer scale of the challenges that remain calls for a more organized response. It was with this in mind that I partnered with Stanford professor of philosophy and former provost John Etchemendy to co-found the Stanford Institute for Human-Centered Artificial Intelligence, or HAI, in 2018. Its ongoing mission is to reframe the pursuit of AI in unequivocally human terms, to reflect its dependence on interdisciplinary alliances, and to ensure ethics, compassion, and societal responsibility are baked in from the earliest stages of our workwhether its an algorithm, a commercial product, or even legislation.

HAIs reach as an institution is helping to cross new divides as well, beyond those that separate academic worlds. Partnerships with corporations, governments, and NGOs, for instance, will be essential in building a larger community around these values. Already, for example, theyve helped us organize cross-disciplinary workshops that bring ethical, philosophical, and legal expertise to bear on contentious technologies like facial recognition, with audiences of executives and legislators at both the state and federal level. And our relationships with tech leaders like Google and Amazon allow us to offer powerful cloud computing accessa foundational but often cost-prohibitive resource for modern AI researchto young, innovative thinkers in the form of grants.

Ultimately, however, this appreciation for the power of humilityopenness, transparency, and a reverence for the expertise of otherscant be mandated from the top down. It must be built up from a cultural level, and thus requires an investment in educational efforts to instill them in the next generation of AI practitioners. Here at Stanford, political science professor Rob Reich co-created a course in the computer science department entitled Computers, Ethics and Public Policy, intended to augment the education of engineers with an awareness of their impact on people and communities, while Harvard computer science professor Barbar Grosz explores similar issues in a course called Embedded Ethics. These are encouraging signs of a shift in the way we educate not just tomorrows technologists, but business leaders, social scientists, and politicians. Its my hope that universities across the world will be inspired to follow suit.

The excitement and anxiety surrounding AI can lend it a fatalistic tone, with aggressive language like revolution, tectonic shift, and force for change all too common. But while it might seem inevitable that AI will reshape the world, collaborations like these are a chance for the worldin all its messy, complicated vibrancyto reshape AI in turn. So although Im continually excited by what were learning about intelligent machines, Im even more excited by what we can learn from each other. All it takes is the willingness to ask, and that great, understated strengthour humility.

Dr. Fei-Fei Li is the Sequoia Professor, Computer Science Department, and Denning Codirector, Stanford Institute for Human-Centered Artificial Intelligence, Stanford University. She is an elected Member of the National Academy of Engineering, and the National Academy of Medicine.

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Stanford AI scholar Fei-Fei Li writes about humility in tech - Fast Company

Since AI has become a popular technology in the current industry, Artificial Intelligence vs. Human Intelligence has led to debates.

Artificial Intelligence has come a long way from being a part of science fiction to reality. Nowadays we are equipped with many intelligent devices, including self-driving vehicles, intelligent virtual aides, chatbots, and surgical robots. Since AI has become a popular technology in the current industry and a part of the everyday life of the common man, Artificial Intelligence vs. Human Intelligence has led to debates.

Artificial Intelligence is a Computer Science Branch that aims at developing intelligent machines that carry out a broad variety of tasks, typically involving human intelligence and expertise. These smart machines are based on experience and historical information, assessing their environment, and conducting appropriate activities.

Human Intelligence refers to the cognitive capacity of human beings that enables us to think, gain from various experiences, comprehend abstract concepts, apply logic and rationality, solve problems, identify patterns, make observations and choices, retain knowledge, and interact with fellow humans. It is supported by abstract emotions such as self-confidence, enthusiasm, and motivation to enable humans to perform complex tasks.

The pace of implementation A doctor could make a diagnosis in around ten minutes, but a million could be made by the AI system simultaneously.

Less partial There are no partial views on decision making.

Operational capacity-They dont foresee an end to their work due to saturation

Accuracy-The specificity of the outcomes clearly enhances

In many activities, artificial intelligence is critical, in particular when it comes to tedious decisions.

Human Intelligence and pace of AI

Computers can handle more data at a faster rate, as opposed to humans. For instance, AI can solve 10 issues in a minute if the human mind can solve a mathematical problem in 5 minutes.

Making Decisions

In decision making, AI is highly analytical as it assesses based on strictly collected data. The judgments of humans, however, can be affected by individual traits that are not based on statistics itself.

Multiple roles

Human knowledge supports the multifunctional mission, as proved by separate and simultaneous functions, while AI can perform fewer tasks only when a machine can learn duties one by one.

Social interacting

As social beings, people can process abstract knowledge, become self-confident and sensitive to others feelings, and can interact much better. On the other hand, AI has not developed its ability to collect valuable social and emotional knowledge.

According to Pew Research Center, Experts say the rise of artificial intelligence will make most people better off over the next decade, but many have concerns about how advances in AI will affect what it means to be human, to be productive, and to exercise free will.

One example among others that they mentioned, Marina Gorbis, executive director of the Institute for the Future, said, Without significant changes in our political economy and data governance regimes, AI is likely to create greater economic inequalities, more surveillance and more programmed and non-human-centric interactions. Every time we program our environments, we end up programming ourselves and our interactions. Humans have to become more standardized, removing serendipity and ambiguity from our interactions. And this ambiguity and complexity is what is the essence of being human.

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Impact of Artificial Intelligence vs Humans and What the Future Holds - Analytics Insight

To drive American leadership in the field of AI into the future, the National Artificial Intelligence Initiative Office has been launched by the White House Office of Science and Technology Policy (OSTP).

The new agency was established under the American Artificial Intelligence Initiative Act of 2020, which was enacted and codified into law to expand many existing AI policies and initiatives throughout the federal government.

The nascentNational Artificial Intelligence Initiative Officeis charged with overseeing and implementing Americas national AI strategy, according to a statement by the White House. It will work to provide federal coordination and collaboration in AI research and policymaking across the government, as well as with private sector, academia and other stakeholders.

The National Artificial Intelligence Initiative Office will be integral to the federal governments AI efforts for many years to come, serving as a central hub for national AI research and policy for the entire U.S. innovation ecosystem,Michael Kratsios, the nations chief technology officer, said in astatementto The Hill. Kratsios is the nations fourth CTO since theoffice was created in 2009under President Barack Obama.

TheAmerican Artificial Intelligence Initiative, which was established in February 2019, identified five central goals for the nations AI direction, including increasing AI research investment, releasing federal AI computing and data resources, setting AI technical standards, building Americas AI workforce and engaging with international allies.

In addition, theSelect Committee on Artificial Intelligence, which was launched by the White House in 2018 to coordinate Federal AI efforts, is being expanded and made permanent, according to the White House. The committee will serve as the senior interagency body responsible for overseeing the National AI Initiative.

Important related efforts in the nations AI strategy were unveiled last August and September when a series of national AI research institutes were announced by the National Science Foundation.

In August of 2020,five new NSF AI instituteswere created at a cost of $100 million to expand AI to a broader range of businesses across the U.S. economy. The initiatives aim to deepen the NSFsartificial intelligenceresearch to expand the nations workforce and drive new possibilities for a wide range of businesses, educational institutions, medicine, banking and other organizations.

Those AI institutes included the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography,led by a team at the University of Oklahoma; the NSF AI Institute for Foundations of Machine Learning,led by a team at the University of Texas; the NSF AI Institute for Student-AI Teaming,led by a team at the University of Colorado; the NSF AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing (or the NSF Molecule Maker Lab),led by a team at the University of Illinois; and the NSF AI Institute for Artificial Intelligence and Fundamental Interactions,led by a team at the Massachusetts Institute of Technology.

Two related AI research institutes are also being created by theU.S. Department of Agricultureover the next five years using $40 million in funding to expand AI research in farming and food processing. They are the USDA-NIFA AI Institute for Next Generation Food Systems,led by a team at the University of California; and the USDA-NIFA AI Institute for Future Agricultural Resilience, Management, and Sustainability at the University of Illinois.

In September,eight additional NSF AI institutes were unveiledin partnership with Amazon, Google, Intel and Accenture. Those companies are contributing toward a $160 million partnership to fund the eight AI Research Institutes scheduled for creation in late 2021 by the National Science Foundation.This effort marked the first time in which direct industry funding for the AI institutes will be received by the NSF, which funded prior AI institutes on its own or with other governmental partners. Companies have participated in the NSF AI research institutes in the past with researchers, materials, content and more, but previously did not make direct monetary contributions.

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Honing In on AI, US Launches National Artificial Intelligence Initiative Office - HPCwire

Its no surprise that artificial intelligence is a key ingredient in the modern tech space. From machine learning to wearables to robotics, the AI across industries is a growing necessity for businesses looking to remain competitive in the long term. Yet there are a few common reasons why businesses often fall short in their AI strategy implementation.

Information for this eWEEK Data Points article was supplied by Dr. Charla Griffy-Brown, Professor of Information Systems and Technology Management, and Associate Dean of Executive and Part-Time Programs at Pepperdine Universitys Graziadio School of Business. Here she discusses five key reasons AI strategies fail and what businesses can do to avoid these pitfalls.

Early work on AI solutions usually involves small subsets of data, which require smaller computing resources. When AI expands into broader production systems, performance can be impacted exponentially. Insufficient attention to performance at scale creates AI systems that appear to work well during testing but quickly become unusable by the business at large.

Solution: Businesses should be accurate in computing requirements for scaling up and test, as often as possible, in a near-production environment.

There are fundamental issues that arise from decisions regarding data architecture. The wrong database can easily render a scaled AI working test system unusable. Furthermore, this is enhanced by data cleansing and preparation problems. For example, manual interventions by humans might be effective in preparing test data, but this typically cannot be scaled.

Solution: Make data architecture decisions based on not just growth but an understanding of the processes required for the data training required to build AI.

One of the biggest challenges facing implementation of new technology is human beings, and AI implementation will only be as strong as the training and support for the staff implementing it. AI solutions must also be developed with a mechanism for ensuring customer facing channels are fully prepared for customer reactions. For example, this could include a temporary spike in phone calls if chatbots arent working properly or a tsunami of emails if a phone answering service isnt getting them where they need to go.

Solution: Realizing that AI requires human work is fundamental to thinking through AI deployment. Businesses will need to implement strategies to address challenges quickly in advance of an AI initiative, including considerations for how it will impact human staff and customers.

Supporting business issues that didnt appear in testing is very challenging to scale. Scaling AI requires production systems to allow for situations not in designs or plans. Over time, new challenges may arise because of changes in the AI system itself. Machine learning is designed to improve itself over time, and usually this improves the accuracy of an algorithm. However, it can also lead to other revelations, such as identifying new patterns of customer behavior or fraud.

Solution: An important part of scaling AI means developing and working through a variety of hypothetical scenarios. Businesses should develop technical and operational contingencies, such as asking how to switch off an AI solution temporarily with minimal disruption.

One of the most important problems in scaling AI for production are the security implications. Cyber risk is an element that has to be considered from all angles when deploying AI. AI introduces new vulnerabilities and represents new risks to established cybersecurity solutions.

Solution: Before deploying AI, companies should develop a risk-based approach to implementation, identifying any points of weakness and reinforcing these appropriately. They may also consider working with a third party to test cybersecurity protections ahead of time to identify points of vulnerability.

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Reasons Why AI Projects Fail, and How to Fix Them - eWeek

HONG KONG and SHANGHAI, Jan. 14, 2021 /PRNewswire/ -- The Ping An Digital Economic Research Center (PADERC), a member of Ping An Insurance (Group) Company of China, Ltd. (HKEx:2318; SSE:601318), has created four new investment strategies for environmental, social and corporate governance (ESG) investing using Ping An's proprietary CN-ESG data for China A-shares, in light of surging demand in China for ESG ratings and data with wider coverage and a better fit for China's market.

Ping An ESG framework aligns with international standards and Chinese regulations

The investment strategies detailed in the report, "Applications of Ping An CN-ESG Data and Framework in Quantitative Investment Strategy", use the proprietary CN-ESG database and scoring framework developed by the Ping An Group. Ping An was the first asset owner in China to sign the United Nations Principles for Responsible Investment. The framework leverages Ping An's expertise in finance and technology and aligns with international standards as well as guidelines from Chinese regulators to incorporate material topics for Chinese companies.

With technologies such as web crawlers, data mining, machine learning, knowledge graphs, natural language processing (NLP) and satellite remote sensing, the CN-ESG system can verify ESG disclosure-based data as well as mine non-disclosure-based data to provide investors with richer multi-dimensional information.

PADERC's report provides an in-depth analysis on the data characteristics, effectiveness, and strategy back-testing results of the CN-ESG database and scoring framework, which covers more than 3,900 listed companies in the China A-share market with five years of historical data (2015-2019). The framework can provide quarterly results that are further adjusted based on news sentiment scores in real-time compared to annual or semi-annual updates from most ESG rating providers.

ESG factors independent of financial factors

PADERC found the Ping An's CN-ESG scores among A-share companies is close to a normal distribution. The factor correlation test results show that scores have notable performance of quality factors. The overall correlation between CN-ESG factors and traditional financial factors is generally low, showing high levels of independence of ESG factors, which indicates these can provide new data and viewpoints for investment decisions.

The results of the factor layered test show that Ping An CN-ESG factors have a relatively strong positive screening effect on the Chinese Securities Index (CSI) 300 and CSI 800 stock pools. The financial window dressing factors constructed by evaluating the quality and authenticity of the company's financial data yielded 11.61% of long-short gains since 2015.

ESG investment strategies that balance excess returns with ESG objectives

Based on CN-ESG data, PADERC constructed four types of ESG investment strategies that use artificial intelligence (AI) to balance excess investment returns and ESG investment targets:

1) Ping An AI-ESG Selected 100 Strategy: This positive screening strategy selects companies with the highest ESG scores. Based on the broader CSI 800 stock pool, it can better leverage additional information from ESG scores. This strategy achieved an annualized excess return of 4.44%. The annual weighted average ESG score quantile of the portfolio is 94.2% among the benchmark stock pool.

2) Ping An AI-ESG Enhancement Strategy: On the basis of ESG scores-based positive screening, PADERC added ESG factors to its Ping An Digital Economic Research Center 500+ No.1 AI Stock Selection Strategy and there was notable excess return. The AI stock selection strategy is based on linear and non-linear algorithms to capture complex market structures to predict the excess return of individual stocks. The Ping An AI-ESG Enhancement Strategy has an annualized excess return of 16.34%, and the annual weighted average ESG score quantile of the portfolio is 78.7% among the benchmark stock pool.

3) CSI 300 ESG Style Index Series:The CSI 300 ESG Growth Index explores the growth value of the CSI 300 stocks, while controlling its tail risks. The CSI 300 ESG Low Volatility Index reinforces the stability features of ESG investment in both the short and long term. The ESG growth index achieved annualized excess returns of 5.67% and the low volatility index achieved 8.61% relative to the benchmark. The annual weighted average ESG score quantile of the portfolios are 75.1% (ESG growth index) and 73.1% (low volatility index) relative to the benchmark stock pool.

Further testing of excess returns shows that the above active management strategies have almost all achieved excess returns in adverse market conditions, including bond crises, annual bear market downturns, Sino-US trade war, and COVID-19, verifying the effectiveness of ESG factors in challenging environments.

4) AI-ESG MAX Strategy: ESG enhancement of mainstream ETFs enables investors to gradually incorporate ESG concepts into their investing process without changing their traditional investing habits. Based on the CSI 300, controlling for sector deviation, this strategy sets tracking errors to 1%, 3% and 5%. Under different tracking error assumptions, the strategy maximizes ESG scores while achieving annualized excess returns of 3.61%, 3.40% and 3.43% respectively against the benchmark. The back-testing results of the strategy over the past five years show good performance, and excess returns were stable. This type of index enhancement strategy based on ESG factors could help drive an increase in the scale of ESG investing.

Building a richer ESG strategy portfolio to meet investors' diverse needs

Ping An's CN-ESG framework will expand to include fixed income ESG data and climate risk-related AI-driven factors. It will enable more diverse investment options, such as ESG fixed income indices and climate risk-focused indices, to meet investors' diverse needs. Ping An also developed a series of AI-ESG products focusing on corporate management, risk monitoring and analytics solutions for ESG and climate risk analysis, including portfolio sustainability footprint analysis, a portfolio adjustment tool, a sustainable funds screening tool, and climate risk asset pricing models to support ESG investment.

PADERC is a professional institution specializing in macroeconomics and policy research, using big data and artificial intelligence to provide insights on macroeconomic trends, including developments in ESG disclosures and ratings.

For the full report, click here.

About Ping An Group

Ping An Insurance (Group) Company of China, Ltd. ("Ping An") is a world-leading technology-powered retail financial services group. With over 210 million retail customers and 560 million Internet users, Ping An is one of the largest financial services companies in the world.

Ping An has two over-arching strategies, "pan financial assets" and "pan health care", which focus on the provision of financial and health care services through our integrated financial services platform and our five ecosystems of financial services, health care, auto services, real estate services and smart city services. Our "finance + technology" and "finance + ecosystem" strategies aim to provide customers and internet users with innovative and simple products and services using technology. As China's first joint stock insurance company, Ping An is committed to upholding the highest standards of corporate reporting and corporate governance. The Group is listed on the stock exchanges in Hong Kong and Shanghai.

In 2020, Ping An ranked 7th in the Forbes Global 2000 list and ranked 21st in the Fortune Global 500 list. Ping An also ranked 38th in the 2020 WPP Kantar Millward Brown BrandZTM Top 100 Most Valuable Global Brands list. For more information, please visit http://www.pingan.cn.

About Ping An Digital Economic Research Center

Ping An Digital Economic Research Center utilizes more than 50 TB high frequency data points, more than 30 years of historical data and more than 1.5 billion data points to drive research on the "AI + Macro Forecast" and provide insights and methods towards precise macroeconomic trends.

SOURCE Ping An Insurance (Group) Company of China, Ltd.

http://www.pingan.cn

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Ping An Uses Artificial Intelligence to Drive New ESG Investment Strategies - PRNewswire