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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

Projected a few years ago to be a $150 billion industry by 2026, Artificial Intelligence (AI) systems are radically transforming industries around the world and healthcare is no exception to this development. New AI applications are being developed and experimented with to streamline administrative and medical processes, enhance clinical decision making and support, manage long-term care - all of which are showing great promise.

AI in healthcare refers to the use of complex algorithms designed to mimic human cognition and perform certain tasks in an automated fashion at a fraction of the time and cost. Simply put, when data is injected into the platform, algorithms, and machine learning solutions kick in, working with the data, using deep data analytics, and delivering outcomes and reports which would be as accurate if not more than human interventions.

From making more accurate diagnoses, finding links between genetic codes to powering surgical robots, maximising administrative efficiency, and understanding how patients will respond to treatment plans, there are limitless opportunities to leverage AI in healthcare.

Using machine learning in precision medicine can help predict what treatment protocols are likely to succeed based on a patients attributes, treatment history, and context, allowing more accurate and impactful interventions at the right moment in a patients care.

Similarly, the use of voice-activated Electronic Medical Records (EMRs) can go a long way towards optimising a doctors efficiency by reducing hours spent on clerical work and administration.

How AI is being used today in healthcare

Current use cases are already exhibiting AIs transformational impact in healthcare and future potential uses offer astonishing possibilities.

Here are some broad use case scenarios for current AI use:

Improving Diagnostics: It is one of AI's most exciting healthcare applications. AI solutions are helping automate image analysis and diagnosis, removing the possibility of human error in readings.

Drug Discovery: AI is being harnessed to identify new therapies from vast databases of information on existing medicines. This could help improve lengthy timelines and processes tied to discovering and taking drugs.

Predictive Patient Risk Identification: At-risk patients can be swiftly identified by algorithmic analysis of vast amounts of historic patient data. Cohesive health ecosystems that help organize and maintain patient records can play a vital role. This will also help with reducing cost and time in manual drudgery of procedures and optimising healthcare resources.

Primary Care: Direct-to-patient solutions via voice or chat-based interaction are helping provide quick, scalable access for basic medical issues. AI-based voice-to-text technologies save countless hours taken to type memos. The doctor and the patient can speak freely while a voice-enabled assistant listens in and puts down the text into EMRs, streamlining the drudgery of manually scribing patient history and easing out the problem of missing medical records.

AI Robot-Assisted Surgery: It is another area that is being explored to help with everything from minimally-invasive procedures to open-heart surgery. Working with doctors, robots have already been able to carry out complex procedures successfully with precision, flexibility, and control that goes beyond human capabilities.

Challenges

The use of AI is certainly surging in healthcare, however, it is still early days, and adoption of AI in healthcare is not without challenges that may impede its momentum.

For any AI solution to be successful, it requires a vast amount of patient data. Getting access to private medical records, however, poses the all-important issues of data privacy and ethics. Privacy is expected and enforced especially strongly when it comes to private medical data. There is room for some work around protecting patient data privacy and the answers may lie in cohesive healthcare ecosystems that will have to weave in cybersecurity as an essential component of their world view.

Regulationthis is another challenge with additional geolocation implications. Different nations will adopt different guidelines around levels of transparency in automated decision-making. Informed consent also poses questions especially when participating individuals in some cases may not be physically or mentally equipped to give consent.

Although hard to establish its parameters, transparency is vital to medical AI. A doctor needs to be able to understand and explain why an algorithm recommends a procedure or line of treatment at least until the machine itself learns to come up with more intuitive and transparent prediction-explanation tools.

Quality and usability of data is also a challenge because health data can be subjective, fragmented, and often inaccurate. While the subjectivity issue may need a cultural change, the fragmentation in legacy data can be rectified at an ecosystem level, wherein different stakeholders with access to data ingest it into a central repository.

User adoption at both patient and practitioner ends is another significant challenge. Doctors decisions are based on training, experience, and intuition, as well as problem-solving skills. For doctors to consider suggestions from machines can be difficult. Similarly, the human touch of interacting with a doctor can be lost with these types of tools. Patients may be reluctant to trust a diagnosis from an algorithm rather than humans.

Future outlook of AI

Evolving healthcare ecosystems will have to balance the use and perception of AI for both clinicians as well as patients. They must develop and use AI in hybrid models. It should be seen as an aid or amplifier of medical knowledge and not as a replacement for doctors. AI should be used and perceived as supporting diagnosis, treatment planning, and identifying risk factors, but clinicians retain final charge for a patients care. The hybrid model will help in accelerating the adoption of AI by healthcare practitioners while delivering measurable and scalable improvements in health outcomes.

Artificial intelligence is certainly pushing the envelope towards making game-changing improvements in healthcare. While efforts and advances need to be made before AI solutions can be deployed in a safe and ethical way, AI does open up limitless possibilities to accelerate the move of healthcare into a seamless ecosystem-based model that promises to drive improvements across the care continuum.

This article has been written by Aneesh Nair, Co-Founder and CIO, MyHealthcare

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How artificial intelligence is transforming the future of healthcare one step at a time - HT Tech

The world around us is changing rapidly. With the arrival of industrial revolution 4.0, businesses of all sizes and types are increasingly capitalizing on advanced, intelligent technologies. They are taking advantage of automation to reduce time-consuming, tedious tasks, especially automating assembly line work. However, as such intelligent technologies are better performing than humans, it is necessary businesses must think of knowledge management for their employees. Knowledge is significantly a crucial aspect in achieving high-quality performance for employees. The field of knowledge management consists of psychology, epistemology, and cognitive science. Gaining information provided by artificial intelligence can play a crucial role in helping business employees make timely decisions.

Knowledge grows when used and deflates when kept under lock. Thats true! Artificial intelligence provides the mechanisms that enable machines to learn. It allows them to gain, process and utilize knowledge to perform tasks. AI also enables machines to unlock knowledge that can be delivered to humans to improve the decision-making process.

Artificial intelligence has a crucial role to play in modern businesses. Every year, a fresh trove of companies emerge implementing AI-driven solutions across business processes. Many executives believe employing AI in their businesses will help both people and machines, and enable them to work together to improve operations. Furthermore, reports indicate that the increasing development and adoption of AI will boost the global GDP by up to 14% by 2030.

AI allows people in organizations to make effective business decisions, ensure customer loyalty and avert expensive production downtime. It does so as advanced forms of AI are programmed into insight engines and knowledge management systems. Insight engines emerged recently from the world of search, work with data stored in multiple silos within an organization and connect them together to populate answers in search results.

Insight engines work as an intelligent solution that makes information to be found resource-efficient and available to the user in the right context for their respective business case. These systems are equipped with artificial intelligence that helps obtain and glean existing corporate knowledge, excerpt the information, and show correlations between the individual pieces of data to provide a comprehensive overall picture. With the help of natural language processing (NLP) and natural language question answering (NLQA), insight engines can deliver search queries in more innate language and be processed directly. These intelligent solutions assess and interpret structured metadata and text content and use this to accurately determine what the user needs. Most knowledge management systems use these technologies along with the semantic processing of content that enables natural human-machine interaction.

Most companies around the world perceive knowledge management as an IT project. They try to convey information from one place to another. Nonetheless, knowledge management is more about comprehending the resource and getting aware of how to leverage it for business growth. It is like an intellectual asset for the business.

In his book, The Fifth Discipline, Peter Senge, a lecturer at the MIT Sloan School of Management, points out that learning organizations are always intensifying their knowledge, finding new ways of creating knowledge, moving it seamlessly throughout the organization, and transforming it so that people have insights into what they need to do. This requires a knowledge infrastructure involving numerous components such as databases, internal experts, libraries, research centers, outside information agents, and other knowledge-based sources for filling the knowledge gaps within the organization. There is also a need to measure and manage the value of knowledge so that it fits efficiently within the organization. Many companies have positions like Chief Knowledge Officers or Chief Learning Officers to help drive this process.

Above all, the new developments in intelligent systems will enable businesses to make effective use of enterprise search and knowledge management. Employees can use artificial intelligence that allows them to unlock knowledge to improve the decision-making process and generate high ROI.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Unleashing the Power of Artificial Intelligence to Leverage Knowledge - Analytics Insight