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HONG KONG and SHANGHAI, April 19, 2021 /PRNewswire/ -- Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318) is pleased to announce that AskBob Doctor, a consultation and treatment assistance tool, has ranked first in one of the three tasks in MEDIQA 2021, an artificial intelligence (AI) healthcare question-and-answer contest hosted by the Association for Computational Linguistics (ACL). This achievement confirms Ping An's world-leading research capability in natural language processing (NLP) in the healthcare industry, which is a key driver of Ping An's healthcare ecosystem.

ACL is one of the most authoritative international academic institutions in the NLP field. The MEDIQA 2021 competition tests the performance of competitors' algorithms on the automatic summarization of medical text with three tasks: consumer health question summarization, multi-answer summarization (MAS), and radiology report summarization. Ping An's AskBob Doctor ranked first in the MAS task, tackling the challenges of combining and summarizing multiple answers to a medical question.

Doctors typically spend 10 to 15 hours a week on data query, collation and interpretation. Entering a medical question in a search engine may lead to multiple answers, which can be complementary or repetitive. The MAS technology automatically summarizes lengthy medical answers toconvey the main idea of the original texts. The technology is integrated into Ping An's AskBob Doctor to provide intelligent literature analysis and cutting-edge scientific research insights to help doctors make diagnoses and treatment decisions.

Ping An's AskBob Doctor was launched in 2019 by the smart healthcare team of Ping An Smart City, which offers services for health departments, doctors, medical imaging and chronic disease research and treatment. It has served 37,000 medical institutions in mainland China and 740,000 doctors to date.

Ping An Group focuses on two domains of business, "pan financial assets" and "pan health care". It applies innovative technologies to its ecosystems in healthcare, financial services, auto services and smart city services. The Group has been building its healthcare ecosystem over the past decade as a new engine for growth. The healthcare ecosystem includes medical regulators, patients, service providers, payers, and technologies to support the national "Healthy China" initiative.

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 218 million retail customers and 598 million internet users, Ping An is one of the largest financial services companies in the world. Ping An focuses on two over-arching domains of activity, "pan financial assets" and "pan health care", covering the provision of financial and health care services through our integrated financial services platform and our ecosystems; in financial services, health care, auto services and smart city services. Our "finance + technology" and "finance + ecosystem" transformation 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.group.pingan.com and follow us on LinkedIn - PING AN.

About Ping An Smart City

Ping An Smart City, a member of the Ping An Group, is a technology company that focuses on the construction of smart cities. Ping An Smart City is at the cutting edge of infrastructure development, using technologies such as big data, cloud computing, blockchain, and artificial intelligence to enhance governance systems, the business environment and public services. Ping An Smart City solutions covers life, education, healthcare, government affairs, smart transportation, and business operations. To date, Ping An Smart City has launched above more than 230 programs, cooperating with more than 151 cities across China and six countries and regions outside of China.

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

http://www.pingan.cn

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Ping An's AskBob Doctor Top-ranked in Global Artificial Intelligence Competition, Supporting Research, Diagnoses and Treatment Decisions - PRNewswire

While every lab focuses on different domains of artificial intelligence, commercial AI labs like Google, Facebook, Amazon, Apple, and Microsoft, the U.S Big Tech, have set up dedicated AI labs too. Theres also DeepMind by Googles parent company Alphabet and OpenAI, which has Elon Musk as a founding investor. These are the obvious contenders in the top AI labs ranking.

At one of CNBCs conferences, when Mark Riedl, associate professor at the Georgia Tech School of Interactive Computing was asked to pick his standouts, he said promptly, Wow, I hate this question. He further added, Reputationally, there is a good argument to say DeepMind, OpenAI, and FAIR (Facebook AI research) are the top three.

Another AI expert (who requested to remain anonymous due to lack of approval from his company to speak publicly) told that DeepMind, OpenAI, and FAIR were probably the top three pure AI research labs as their fundings is known while IBM has the reputation to push out more patents. The unknown question is the Baidus and Tencents of the world, he said, talking about the Chinese technology companies whose activities are rather secret.

Googles parent company Alphabet gives DeepMind hundreds of millions of dollars each year to conduct research, while Microsoft has invested $1 billion in OpenAI apart from the $1 billion it received from the founding investors. While these fundings are known, FAIRs funding is unknown because Facebook doesnt reveal it clearly.

AlphaGo is DeepMinds best-known AI which defeated the best human players in the world at the ancient Chinese board game, Go. The program gained so much attraction that Netflix made a documentary that talks about AlphaGos victory over South Korean Go legend Lee Sedol. After this renowned success, the company is now shifting its focus to using AI to solve humanitys biggest scientific problems. This attempt had a breakthrough last year in a biological field called protein folding.

Open AI has developed game-playing AI software that can defeat humans at games like Dota II. But instead of this, Open AI caught more eyeballs for its AI text generator GPT-3 and its fancy AI image generator Dall-E.

On the other hand, FAIR doesnt have an AI thats as famous as AlphaGo or a GPT-3. But what FAIR has to its name is several published academic papers on areas that relate to Facebook like computer vision, natural language processing, and conversational AI.

Publishing academic papers at two of the biggest AI conferences, NeurIPS and ICML is one way to measure the impact of an AI lab. In 2020, Google published 178 papers at NeurIPS, while Microsoft had 95, DeepMind had 59, Facebook had 58, and IBM had 3.8 while Amazon had less than 30.

In the same year, at ICML, Google had 114 papers published, DeepMind had 51, Microsoft 49, Facebook 34, IBM had 19, and Amazon had 18.

Artificial Intelligence has been touted as a technology that has the potential to start a new industrial revolution and change the way our society functions. But currently, as the technology is still in its initial phase the use cases are quite narrow.

Read the rest here:
Researchers Rank These Artificial Intelligence Labs As The Best In World - Analytics Insight

BEIJING, April 19, 2021 /PRNewswire/ -- Ambow Education Holding Ltd. ("Ambow" or "the Company") (NYSE American: AMBO), China's leading provider of educational and career enhancement services, today announced an expanded strategic partnership with Amazon with the launch of Artificial Intelligence ("AI") training for teachers.

The deepened partnership is part of the Company's ongoing efforts to collaborate with prestigious enterprises to carry out AI education and training for teachers. Since 2018, Ambow has collaborated with Amazon Web Services ("AWS") for in-depth training courses. Combining their respective strengths and advantages in educational expertise and industry practices, Ambow and AWS recently launched live streaming courses on AI education and training for teachers to help teachers improve their educational skills. In the AI landscape, the collaborated courses will further facilitate related talent cultivation, curricula design and a shared platform for innovative educational resources. The cooperation will also help the Company to enrich its emerging engineering courses to address growing job placement needs.

Dr. Jin Huang, President and Chief Executive Officer of Ambow, commented, "Further cooperation with Amazon is a great testament to our strong capabilities in providing high-quality professional education and training. Leveraging our industry-leading AI Panorama Digital Teaching System, we will regularly launch AI training courses and host various events for new skills, shared experience and project research. In collaboration with influential enterprises, we are committed to delivering effective education services that will closely integrate talent training and industry development in the AI space."

About Ambow Education Holding Ltd.

Ambow Education Holding Ltd. is a leading national provider of educational and career enhancement services in China, offering high-quality, individualized services and products. With its extensive network of regional service hubs complemented by a dynamic proprietary learning platform and distributors, Ambow provides its services and products to students in 15 out of the 34 provinces and autonomous regions within China.

Follow us on Twitter:@Ambow_Education

Safe Harbor Statement

This announcement contains forward-looking statements. These statements are made under the "safe harbor" provisions of the U.S. Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates" and similar statements. Among other things, the outlook and quotations from management in this announcement, as well as Ambow's strategic and operational plans, contain forward-looking statements. Ambow may also make written or oral forward-looking statements in its reports filed or furnished to the U.S. Securities and Exchange Commission, in its annual reports to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statements, including but not limited to the following: the Company's goals and strategies, expansion plans, the expected growth of the content and application delivery services market, the Company's expectations regarding keeping and strengthening its relationships with its customers, and the general economic and business conditions in the regions where the Company provides its solutions and services. Further information regarding these and other risks is included in the Company's filings with the U.S. Securities and Exchange Commission. All information provided in this press release is as of the date of this press release, and Ambow undertakes no duty to update such information, except as required under applicable law.

For investor and media inquiries please contact:

Ambow Education Holding Ltd.Tel: +86 10-6206-8000

The Piacente Group | Investor RelationsTel: +1 212-481-2050 or +86 10-6508-0677Email:[emailprotected]

SOURCE Ambow Education Holding Ltd.

http://www.ambow.com

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Ambow Education Expands Partnership with Amazon in Artificial Intelligence Training for Teachers - PRNewswire

That glass of fine whisky you sip at the end of a long day? It may have been created with the help of AI.

Mackmyra, an award-winning Swedish distillery, has launched Intelligens, the world's first whisky created using an artificial intelligence program.

Artificial Intelligence And Whisky Making: The Perfect Blend?

The Fine Art of Creating a Top-Quality AI Whisky

Mackmyra partnered with Finnish technology company Fourkind to develop an AI system that augments and automates some of the tasks of the distillerys master blender, who is responsible for whisky flavor and product development.

Master blenders spend their time meticulously tasting and experimenting to create the best flavors possible, and that process can be time-consuming. Mackmyra wanted machine learning to work its magic in sifting through massive amounts of data to find new combinations.

Fourkind created their AI system using Machine Learning Studio and Microsoft Azure, then fed the system datasets that included:

Existing whisky recipes from Mackmyra

Wooden cask information (each cask gives the whisky a distinct flavor)

Ratings from consumers

Sales data

Evaluations from whisky experts

The AI system analyzed 70 million possible combinations and created a framework for creating innovative new recipes that taste great.

After the first batch of recipes, Macmyra's master blender, Angela D'Orazio, provided feedback so the AI system could learn more about whisky combinations that work for the palette and sell well in the market.

In each round, the distillery narrowed down their options and increased the quality of the recipes. At the end of the process, D'Orazio selected recipe #36 as the final pick for their innovative new product.

AI Can't Replace the Human Touch

Master blenders aren't going anywhere, though. Even the most sophisticated AI system cannot replicate or replace the intelligence and discernment of the human senses. The human side of whisky-making is here to stay.

Instead, Mackmyra embarked on their AI-created whisky experiment to see if they could augment the skills of their master blender to create an innovative new recipe.

The AI technology that allowed Fourkind and Mackmyra to sift through vast amounts of data to develop new formulas and recipes could have all kinds of other applications. Be on the lookout for new AI software in industries like perfumes, desserts, medicines, and clothing.

Companies are looking for ways to partner with AI technology companies to augment and expand what their internal experts can do, so they can get the most out of every new product they develop, and reduce some of the heaviest data analysis and learning from their employees' shoulders.

The end result of the next consumer AI experiment? It might just have notes of toffee, pear, apples, and creamy vanilla with a light tone of oak.

Want to try Intelligens? You can order a bottle here.

And you can watch my interview with Macmyra's Chief Nose Officer Angela D'Orazio here

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Artificial Intelligence And Whisky Making: The Perfect Blend? - Forbes

In the race to out-compete other companies artificial intelligence (AI) design is lacking a deep understanding of what data about humans mean and its relation to equity. Two Drexel University sociologists suggest we pay greater attention to the societal impact of AI, as it is appearing more frequently than ever before.

The coronavirus pandemic has sped up the use of AI and automation to replace human workers, as part of the effort to minimize the risks associated with face-to-face interactions, saidKelly Joyce, PhD,a professor in theCollege of Arts and Sciencesand founding director of theCenter for Science, Technology and Societyat Drexel. Increasingly we are seeing examples of algorithms that are intensifying existing inequalities. As institutions such as education, healthcare, warfare, and work adopt these systems, we must remediate this inequity.

In a newly published paper inSocius,Joyce,Susan Bell, PhD, a professor in theCollege of Arts and Sciences,and colleagues raise concerns about the push to rapidly accelerate AI development in the United States without accelerating the training and development practices necessary to make ethical technology. The paper proposes a research agenda for a sociology of AI.

Sociology's understanding of the relationship between human data and long-standing inequalities is needed to make AI systems that promote equality, explained Joyce.

The term AI has been used in many different ways and early interpretations associate the term with software that is able to learn and act on its own. For example, self-driving cars learn and identify routes and obstacles just as robotic vacuums do the perimeter or layout of a home, and smart assistants (Alexa or Google Assistant) identify the tone of voice and preferences of their user.

AI has a fluid definitional scope that helps explain its appeal, said Joyce. Its expansive, yet unspecified meaning enables promoters to make future-oriented, empirically unsubstantiated, promissory claims of its potential positive societal impact.

Joyce, Bell and colleagues explain that in recent years, programming communities have largely focused on developing machine learning (ML) as a form of AI. The term ML is more commonly used among researchers than the term AI, although AI continues to be the public-facing term used by companies, institutes, and initiatives. ML emphasizes the training of computer systems to recognize, sort, and predict outcomes from analysis of existing data sets, explained Joyce.

AI practitioners, computer scientists, data scientists and engineers are training systems to recognize, sort and predict outcomes from analysis of existing data sets. Humans input existing data to help train AI systems to make autonomous decisions. The problem here is that AI practitioners do not typically understand how data about humans is almost always also data about inequality.

AI practitioners may not be aware that data about X (e.g., ZIP codes, health records, location of highways) may also be data about Y (e.g., class, gender or race inequalities, socioeconomic status), said Joyce, who is the lead author on the paper. They may think, for example, that ZIP codes are a neutral piece of data that apply to all people in an equal manner instead of understanding that ZIP codes often also provide information about race and class due to segregation. This lack of understanding has resulted in the acceleration and intensification of inequalities as ML systems are developed and deployed."

Identifying correlations between vulnerable groups and life chances, AI systems accept these correlations as causation, and use them to make decisions about interventions going forward. In this way, AI systems do not create new futures, but rather replicate the durable inequalities that exist in a particular social world, explains Joyce.

There are politics tied to algorithms, data and code. Consider the search engine Google. Although Google search results might appear to be neutral or singular outputs, Googles search engine recreates the sexism and racism found in everyday life.

Search results reflect the decisions that go into making the algorithms and codes, and these reflect the standpoint of Google workers, explains Bell. Specifically, their decisions about what to label as sexist or racist reflect the broader social structures of pervasive racism and sexism. In turn, decisions about what to label as sexist or racist trains an ML system. Although Google blames users for contributing to sexist and racist search results, the source lies in the input.

Bell points out in contrast to the perceived neutrality of Googles search results, societal oppression and inequality are embedded in and amplified by them.

Another example the authors point out are AI systems that use data from patients' electronic health records (EHRs) to make predictions about appropriate treatment recommendations. Although computer scientists and engineers often consider privacy when designing AI systems, understanding the multivalent dimensions of human data is not typically part of their training. Given this, they may assume that EHR data represents objective knowledge about treatment and outcomes, instead of viewing it through a sociological lens that recognizes how EHR data is partial and situated.

"When using a sociological approach," Joyce explains, "You understand that patient outcomes are not neutral or objective these are related to patients socioeconomic status, and often tell us more about class differences, racism and other kinds of inequalities than the effectiveness of particular treatments."

The paper notes examples such asan algorithm that recommended that black patients receive less health care than white patientswith the same conditions and a report showing thatfacial recognition software is less likely to recognize people of color and womenshowed thatAI can intensify existing inequalities.

A sociological understanding of data is important, given that an uncritical use of human data in AI sociotechnical systems will tend to reproduce, and perhaps even exacerbate, preexisting social inequalities, said Bell. Although companies that produce AI systems hide behind the claim that algorithms or platform users create racist, sexist outcomes, sociological scholarship illustrates how human decision making occurs at every step of the coding process.

In the paper, the researchers demonstrate that sociological scholarship can be joined with other critical social science research to avoid some of the pitfalls of AI applications.By examining the design and implementation of AI sociotechnical systems, sociological work brings human labor and social contexts into view, said Joyce.Building on sociologys recognition of the importance of organizational contexts in shaping outcomes, the paper shows that both funding sources and institutional contexts are key drivers of how AI systems are developed and used.

Joyce, Bell and colleagues suggest that, despite well-intentioned efforts to incorporate knowledge about social worlds into sociotechnical systems, AI scientists continue to demonstrate a limited understanding of the social prioritizing that which may be instrumental for the execution of AI engineering tasks, but erasing the complexity and embeddedness of social inequalities.

Sociologys deeply structural approach also stands in contrast to approaches that highlight individual choice, said Joyce. One of the most pervasive tropes of political liberalism is that social change is driven by individual choice. As individuals, the logic goes, we can create more equitable futures by making and choosing better products, practices, and political representatives. The tech world tends to sustain a similarly individualistic perspective when its engineers and ethicists emphasize eliminating individual-level human bias and improving sensitivity training as a way to address inequality in AI systems.

Joyce, Bell and colleagues invite sociologists to use the disciplines theoretical and methodological tools to analyze when and how inequalities are made more durable by AI systems. The researchers emphasize that the creation of AI sociotechnical systems is not simply a question of technological design, but also raises fundamental questions about power and social order.

Sociologists are trained to identify how inequalities are embedded in all aspects of society and to point toward avenues for structural social change. Therefore, sociologists should play a leading role in the imagining and shaping of AI futures, said Joyce.

Originally posted here:
The Future of Artificial Intelligence Requires the Guidance of Sociology - DrexelNow - Drexel Now