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Shanghai, Sept. 30, 2022 (GLOBE NEWSWIRE) -- The 2022 WLA Prize Laureates are:

--"For fundamental contributions to the foundations of machine learning and its application."

About the Laureate

Prof. Michael I. Jordan has been a world-leading researcher in the field of statistical machine learning for nearly four decades. His contributions to the interface between computer science and statistics include the variational approach to statistical inference and learning, inference methods based on graphical models and Bayesian non-parametrics, and characterizations of trade-offs between statistical risk and computational complexity.

He has also worked at the interface between optimization and machine learning, where he is well known for his development of continuous-time models of gradient-based optimization and sampling, and his work on distributed systems for optimization. He has built bridges between machine learning and control theory, contributing to the theory of reinforcement learning, learning-based model predictive control, and optimality principles for human motor control.

He has also led the way in bringing microeconomic concepts into contact with machine learning, developing learning methods that incentivize learners to share data, showing how contract theory can be employed for statistical inference, and contributing to the study of learning-based matching markets.He has also pursued numerous high-impact applications of machine learning in domains such as single-molecule imaging, protein modeling, genetic admixture modeling, and natural language processing.

Prof. Jordan's contributions to computer science are also evident in education. He has mentored over 80 PhD students and over 60 postdoctoral researchers, an influential cohort who are now professors at the world's leading academic institutions and leaders in the industry.

--"For key discoveries elucidating the mechanism and selectivity of protein transport between the cytoplasm and nucleus."

About the Laureate

Dr. Dirk Grlich was born in Halle/Saale in Germany and studied biochemistry at Martin Luther University in Halle. For his Ph.D., he joined Tom Rapoport's lab in Berlin, where he identified the heterotrimeric Sec61 complex as a receptor for translating ribosomes and protein-conducting channels of the endoplasmic reticulum (ER). He also succeeded in reconstituting a fully functional "translocon" from purified components and demonstrated its capacity to transport secretory proteins across the ER membrane and to integrate type I and type II membrane proteins into the lipid bilayer.

In 1993, Dr. Grlich joined Ron. Laskey's lab at the University of Cambridge, where he discovered the first importins as mediators of protein import into the cell nucleus.

In 1996, he became an independent group leader and later a professor of molecular biology at the ZMBH (University of Heidelberg). During this time, he developed the RanGTP-gradient model to explain the directionality and energetics of nuclear transport. His group first described chaperone functions of importins and was instrumental in discovering and characterizing exportins that mediate export from the cell nucleus.

Dr. Grlich is now a director at the Max Planck Institute for Multidisciplinary Sciences in Gttingen, focusing on the question of how nuclear pore complexes function as highly efficient transport machines. His team discovered that intrinsically disordered FG domains assemble into a condensed (selective) FG phase that serves as a highly selective permeability barrier of extreme transport capacity. His group also develops nanobodies as cell biological tools and, more recently, also as therapeutics for treating diseases such as Covid-19, malaria, bacterial infections, sepsis, and autoimmune conditions.

Roger KORNBERG, Chairman of the WorldLaureatesAssociation and 2006 Nobel Laureate in Chemistry, said that The WLA Prize advocates for original basic science and encourages scientific researchers to be better committed to the common well-being of human beings. It is believed that the WLA Prize, established in China through multi-lateral efforts, will become one of the world's globally influential awards.

Michael LEVITT, Vice Chairman of the World Laureates Association and 2013 Nobel Laureate in Chemistry announced the 2022 WLA Prize laureates.YANG Wei, Member of the Chinese Academy of Sciences, Foreign Member of the National Academy of Engineering (USA), and Fellow and Treasurer of the World Academy of Sciences (TWAS) also attended the press conference.

Global technological development has brought revolutionary and far-reaching impact since the start of this century, and the pace of development is only accelerating, said Neil SHEN, StewardofSequoiaCapitalFoundingandManagingPartnerofSequoiaChina, which is the exclusive sponsor of the WLA Prize. Sequoia China is committed to supporting cutting-edge technology and fundamental research and encouraging those who push forward the frontiers of science.

WU Xiangdong , Executive Director of the World Laureates Association and Chairman of the WLA Prize Management Committee announced that the Award Ceremony of the inaugural WLA Prize will be solemnly held at the Opening Ceremony of the 5th WLA Forum in early November 2022; both of the laureates are on schedule to Shanghai to accept their awards.

About the WLA Prize

The World Laureates Association Prize (WLA Prize) is an international science prize established in Shanghai, in 2021, initiated by World Laureates Association (WLA), managed by the WLA Foundation, and exclusively funded by Sequoia China.

The WLA Prize aims to recognize and support eminent researchers and technologists worldwide for their contributions to science. It is intended to support global science and technology advancement, address the challenges to humanity, and promote society's long-term progress.

Each year, the WLA Prize is awarded in two categories: the "WLA Prize in Computer Science or Mathematics" and the "WLA Prize in Life Science or Medicine."

The total award for each Prize, which may be divided among up to four laureates, is RMB 10 million.

More info about the WLA Prize:

http://www.wlaprize.org

About Us

World Laureates Association

The World Laureates Association (WLA) is a non-governmental and non-profit international organization. It is one of the world's highest profile organizations of laureates with three missions: advocacy for basic science, promotion of international cooperation, and support for young scientists. Upholding the vision of "Science and Technology for the Common Destiny of Mankind," the WLA is committed to enhancing the academic exchange of ideas and research among scientists and scholars in China and the world.

Follow Us for updates on Facebook and Twitter@wlaforum

WLA Foundation

The WLA Foundationsupports all WLA activities and is funded entirely by donations from private sources.

Sequoia China

Sequoia China helps daring founders build legendary companies. In partnering with Sequoia, companies benefit from its unmatched community and the lessons Sequoia has learned over the years. As "The Entrepreneurs Behind The Entrepreneurs", Sequoia China focuses on three sectors: tech, consumer and healthcare. Over the past 17 years, Sequoia China has had the privilege of working with more than 900 companies. It is committed to advancing innovation in science and technology. It actively supports scientists and entrepreneurs, promotes technological innovation, and nurtures leading tech-enabled enterprises, with a concern for corporate social responsibility. As the exclusive sponsor of the World Laureates Association (WLA) Prize, Sequoia China hopes that its support will encourage the pursuit of innovative scientific developments and drive growth across the world.

Link:
Winners of the Inaugural WLA Prize Announced RMB 10 Million for Each Prize - GlobeNewswire

From taking home gold in the Delaware Valley Science Fair competition his sophomore year to having one of his most time-consuming and difficult research papers rejected at a regional competition Yash Prabhu turns his failure into a success story.

Yash Prabhu starts the school year strong with one of his research papers being published at a conference called IEEE. IEEE is an organization dedicated to assisting humanity through enhancing technology. The organization sponsors over 2,000 events yearly and organizes top-tier conferences with published papers being recognized by academia and industries. Prabhu recently had a paper published called A CNN Based Automated Stuttering Identification System, where he uses machine learning to detect different types of stuttering in audio segments.

Stuttering affects a lot of people around the world and its a surprisingly ignored issue. Some people who stutter receive speech therapy but a lot of people dont. Stuttering can damage the quality of life for people. It makes it harder to speak and harder to interact with people. You could get bullied, teachers could get frustrated speaking to you [and] it gets worse when youre older because you have to give meetings and presentations, explained senior Yash Prabhu.

The goal for his research paper revolves around his wish to make low-cost automation available to people worldwide, mainly in developing countries such as India where resources are scarce.

I am trying to design a model that can classify stuttering. By classifying stuttering it can help keep data. In an area where theres a lack of speech pathologists like in India, this model can have a big impact by keeping diagnostics on stuttering and also helping speech pathologists do a better and faster job so they can treat more people, Prabhu said.

The model Prabhu used is called a machine learning model. Machine learning creates functions to help make predictions.

I found a data set provided by Apple called SEP-28k and this data set [consists] of many examples of stuttering. I chose 5 different speech disfluencies and I trained my model to detect the speech disfluencies, Prabhu added.

Prabhu took the initiative to explore a data set that had not been extensively researched leading to hardships.

I emailed professors, I called doctors, I asked for access to data sets. Nothing really [worked] out because it was hard to find someone who could help me get access to data. A lot of doctors and professors are busy, they dont have time to talk to you, Prabhu said disappointed.

Nonetheless, his chances of success improved when Dr. Naeem Seliya, a professor at the University of Wisconsin Eau Claire, agreed to assist him.

I emailed him my credentials and asked him if I could work with him. He is actually a stutterer and he told me about a device he uses for stuttering. From there this idea was born in my mind. I can use my machine learning skills to have an impact in this area. At that point, I had no clue how to do it. I had absolutely no clue, I just knew I wanted to. For months I did research. I tried finding different ways to do it, but I failed a lot of times, Prabhu explained.

The difficulties he encountered while working with audio data resulted in poor models that produced faulty results.

When I submitted this to the local science fair it didnt go through which was a disappointment. I thought this was the most difficult project I have ever done to this day, Prabhu said disheartened.

He reflected back on his Sophomore year, where he designed a Covid-19 screener using machine learning and hardware. The project was inspired by the pandemic and received gold at the Delaware Valley Science Fair. However, his recent paper was rejected by the local science fair.

Being rejected by the local science fair was a big bruisebut I think it was a blessing in disguise because I got to make this whole paper. This paper was born from the science fair project failing. I made my models better, I trained them with more data, [and] I ended up submitting them to the conference, Prabhu added.

In the future, Prabhu plans to attend a four year university and continue doing research while immersing himself in robotics, engineering, and programming.

Continued here:
Yash Prabhu celebrating victory after publication of research paper - Knight Crier

Cybernetics will drive the future of robotics by empowering them with human intelligence

Robotics Industry is constantly rising in this automation world. According to reports, the Indian industrial robotics market is predicted to grow at a CAGR of 13.3% between 2019-2024. With its rising industry applications and productivity benefits, the study of cybernetics is likely to be a vital element in the advancement of robotics.

The craving for gadgets or machines that can keep up with the challenges of the present world and largely function in simpler and smarter ways is evident. Automation and autonomy have offered this by producing and delivering products and services that contain the least amount of human intervention, making certain jobs more convenient than ever before even when information is incomplete and uncertain. The appearance of new service robots and their wide evolution into new applications has further facilitated the world of automation. Due to the dynamic nature of robotics, numerous application sectors are now using robotics to perform predetermined tasks and enhance human efforts in both physical and analytic ways. Robotics has enhanced task efficiency, dependability, and quality, all of which were earlier, products of a laborious procedure. Being a critical component of automation, robotics is currently used in an ever-growing variety of fields, like manufacturing, transportation, healthcare & medical care, utilities, defence, facilities, operations, and more recently, information technology. Here Cybernetics enters as a primary element as robots need to be advanced.

Cybernetics is a study of science that focuses on developing technologies that act or think like humans by researching how electrical devices or machines and the human brain function to enhance the value of the job to be performed. Cybernetics is the best workaround physical embodiment of Artificial Intelligence (AI), Machine Learning (ML), and predictive analysis and control, investigating underlying systems/structures, possibilities, and limitations of complex mechanisms, including robotics, and generating an autonomous environment that uses minimal to no human interaction. AI and cybernetics are two dissimilar perspectives on intelligent systems or systems that may act to achieve an aim. Making computers imitate intelligent behavior using pre-stored world representations is the primary goal of AI. In general, cybernetics tells us how systems control themselves and can take actions autonomously based on environmental signals even when the information is minimal and subject to significant uncertainty or noise. These systems go beyond simple computation; they can also control biological (body temperature regulation), mechanical (engine speed regulation), social (managing a huge workforce), and economic (controlling a national economy) systems.

Every cybernetic systems aim is to be set up so that its operations are linked in a variety of input-output system configurations which are normally driven with reference control signs. This is achieved by processing feedback-based automatic closed-loop control systems that can decide which behaviors should be changed, which actions should be tracked, how to compare the actions to the reference, and how to adapt the application behaviors in the most effective way. In natural cybernetic systems, this regulatory mechanism generates or organizes by itself with the help of self-learning. On the other hand, artificial cybernetic systems behave or are influenced by human-implemented automatic control systems. Essential elements of cybernetic systems are sensors, the controller, actuators and the system to be controlled.

Cybernetics in robotics systems main objective is to use AI and machine learning in the sense-plan-act paradigm normally used to develop robots so they can operate productively in real-world scenarios. Developing a robot to understand and differentiate complex situations every day is highly demanding and getting the situation awareness correctly identified is crucial to ensuring the desired reference control signal can be identified for implementation. This can make sure an industrial robot recognizes and picks up the correct item for the next stage of the manufacturing process from a selection of parts to ensure the requests of the human to be served a variety of beverages will get the correct drink. Sensors and sensor systems that are perfectly calibrated are necessary for ensuring the situation awareness is achieved perfectly and in real-time using AI-based models which can be learned and applied in various situations such as driverless cars, medical robots, automated manufacturing, and home care robots.

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Cybernetics is the Only Way Robots Can Achieve Human Intelligence - Analytics Insight

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BELLEVUE, Wash. and WATKINSVILLE, Ga., Sept. 13, 2022 (GLOBE NEWSWIRE) -- AI Dynamics and PETTIGREW Medical announced today the formation of a joint venture, with the working name mAIcode, designed to accelerate the automation of medical record coding by applying advanced machine learning. AI Dynamics is an organization founded on the belief that everyone should have access to the power of artificial intelligence (AI) to change the world. PETTIGREW Medical is a pioneer in providing revenue cycle management services and has expanded into a diversified and accredited industry leader on a global scale.

The joint venture with AI Dynamics will enable us to create an automated coding solution that provides an order of magnitude improvement in productivity, efficiency and accuracy, while also reducing costs, said David Young, president and chief financial officer, PETTIGREW Medical. Once the joint venture is fully operational, we look forward to serving a larger percentage of the $18 billion annual medical coding market, which is growing at a compounded annual growth rate (CAGR) of eight percent.

Today, the medical coding market is highly complex, with more than 68,000 diagnostic codes and over 10,000 Current Procedural Terminology (CPT) codes. Current coding approaches are expensive, with the median salary of a medical coder in the U.S being more than $50,000. The typical coder can code at most a few hundred medical records a day, with each record costing between $2 to $20 to code. The volume of content to code is enormous and growing, meaning costs will grow as well.

Unlike other AI coding companies that are focused primarily on the cloud, we have developed mAIcode to be equally efficient for customers that want to manage their data on premise or in a more secure environment. We are also orienting the solution to audit-level accuracy, backed by the NeoPulse Platform, said Rajeev Dutt, founder and CEO of AI Dynamics. Our solution relies on multiple deep learning models built on the NeoPulse Platform, that provides the joint venture with the unique ability to continuously improve its own medical coding capabilities based on experience the AI solution is learning continuously.

The solution is built on a SaaS model that can be run in the cloud or at the customers location. At the core of the solution is AI Dynamics NeoPulse Framework, which will enable customers to manage their entire AI workflow and infrastructure from one place. NeoPulse enables lower cost and faster design and deployment of AI solutions. Customers can adopt the solution to their own medical chart formats. It also features clear explainability, which is necessary for audits, increasing confidence in decisions, and providing peace of mind to clients and auditors. It incorporates federated learning data privacy technology; data never leaves the data owners firewall but the solution enables data users to generate insights from the data, ensuring all parties remain in compliance with HIPAA, U.S state data privacy regulations and data residency requirements. As mAIcode learns from customer use, it will quickly outperform manual solutions.

About AI Dynamics:AI Dynamics aims to make artificial intelligence (AI) accessible to organizations of all sizes. The company's NeoPulse Framework is an intuitive development and management platform for AI, which enables companies to develop and implement deep neural networks and other machine learning models that can improve key performance metrics. The company's team brings decades of experience in the fields of machine learning and artificial intelligence from leading companies and research organizations. For more information, please visit aidynamics.com.

About Pettigrew Medical:PETTIGREW Medical specializes in billing, coding, accounts receivable management and contact center solutions for healthcare billing companies, hospitals, private practices and insurers with large central business office operations. Since 1989, PETTIGREW has provided superior, aggressive, and compliant services to our clients. PETTIGREW is continuously seeking ways to make the experience of running a facility or group easier on owners and medical directors, and of making their practice's information easily accessible. For more information, please visit pettigrewmedical.com.

Media Contact:Madi Oliv / Valeria CarrilloUPRAISE Marketing + PR for AI Dynamics and PETTIGREW Medicalaidynamics@upraisepr.com

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AI Dynamics and PETTIGREW Medical Announce Joint Venture that Applies Advanced Machine Learning to Accelerate Automation of Medical Record Coding -...