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Researchers Develop Artificial Intelligence Based Tool to Tackle Mutant Variants of Coronavirus | The Weather Channel – Articles from The Weather…

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Researchers have developed a new method to counter emergent mutations of COVID-19 and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining economies.

Using Artificial Intelligence (AI), the research team at the University of Southern California's Viterbi School of Engineering developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.

According to the researchers, the method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identifiedsolutions that give humans a big advantage over the evolving contagion.

"This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety," said researcher Paul Bogdan from the varsity.

"Moreover, this can be adapted to help us stay ahead of the coronavirus as it mutates around the world," Bogdan added, in the study published in the journal Scientific Reports.

When applied to SARS-CoV-2the virus that causes COVID-19the computer model quickly eliminated 95% of the compounds that could've possibly treated the pathogen and pinpointed the best options, the study said.

The AI-assisted method predicted 26 potential vaccines that would work against the coronavirus. From those, the researchers identified the best 11 from which to construct a multi-epitope vaccine, which can attack the spike proteins that the coronavirus uses to bind and penetrate a host cell.

Vaccines target the regionor epitopeof the contagion to disrupt the spike protein, neutralizing the ability of the virus to replicate, the team said. Moreover, the engineers can construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour, they added.

By contrast, current processes to control the virus require growing the pathogen in the lab, deactivating it and injecting the virus that caused a disease. The process is time-consuming and takes more than one year; meanwhile, the disease spreads.

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Researchers Develop Artificial Intelligence Based Tool to Tackle Mutant Variants of Coronavirus | The Weather Channel - Articles from The Weather...

Which Papers Won At 35th AAAI Conference On Artificial Intelligence? – Analytics India Magazine

The 35th AAAI Conference on Artificial Intelligence (AAAI-21), held virtually this year, saw more than 9,000 paper submissions, of which, only 1,692 research papers made the cut.

The Association for the Advancement of Artificial Intelligence (AAAI) committee has announced the Best Paper and Runners Up awards. Lets take a look at the papers that won the awards.

About: Informer is an efficient transformer-based model for Long Sequence Time-series Forecasting (LSTF). A team of researchers from UC Berkeley introduced this Transformer model to predict long sequences. Informer has three distinctive characteristics:

Read the paper here.

About: Exploration-exploitation is a powerful tool in multi-agent learning (MAL). A team of researchers from Singapore University of Technology studied a variant of stateless Q-learning, with softmax or Boltzmann exploration, also termed as Boltzmann Q-learning or smooth Q-learning (SQL). Boltzmann Q-learning is one of the most fundamental models of exploration-exploitation in MAS.

Read the paper here.

About: Researchers from Dartmouth College, University of Texas and ProtagoLabs described metrics for measuring political bias in GPT-2 generation and proposed a reinforcement learning (RL) framework to reduce political biases in the generated text. Using rewards from word embeddings or a classifier, the RL framework guided the debiased generation without having access to the training data or requiring the model to be retrained. The researchers also proposed two bias metrics (indirect bias and direct bias) to quantify the political bias in language model generation.

Read the paper here.

About: Researchers from Amazon and UC Berkeley studied the problem of batch learning from bandit feedback in extremely large action spaces. They introduced a selective importance sampling estimator (sIS) operating in a significantly more favorable bias-variance regime. The sIS estimator is obtained by performing importance sampling on the conditional expectation of the reward concerning a small subset of actions for each instance.

Read the paper here.

About: Researchers from Microsoft and Beihang University proposed a self-attention attribution algorithm to interpret the information interactions inside the Transformer. As part of the research, the scientists first extracted the most salient dependencies in each layer to construct an attribution graph, which reveals the hierarchical interactions inside the Transformer. Next, they applied self attention attribution to identify the important attention head. Finally, they showed that the attribution results can be used as adversarial patterns to implement non-targeted attacks towards BERT.

Read the paper here.

About: Researchers from Harvard University and Carnegie Mellon University introduced LIZARD, an algorithm that accounts for decomposability of the reward function, smoothness of the decomposed reward function across features, monotonicity of rewards as patrollers exert more effort, and availability of historical data. According to them, LIZARD leverages both decomposability and Lipschitz continuity simultaneously, bridging the gap between combinatorial and Lipschitz bandits.

Read the paper here.

A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact: [emailprotected]

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Which Papers Won At 35th AAAI Conference On Artificial Intelligence? - Analytics India Magazine

Hospital Artificial Intelligence Industry: 2021 Global Market Size, Share, Uses, Benefits, Trends, Growth Application, Key Manufacturers and 2028…

Global Hospital Artificial Intelligence Market report presents the market analysis on the basis of several factors. Report gives the in-depth analysis on the major countries of key regions where the market is growing. In addition, report describes the wide-ranging knowledge about the major companies in this industry and the key strategies accepted by them to survive and rise in the studied industry.

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The key insights of the report:

The report includes profiles of leading companies in the Hospital Artificial Intelligence market. Some of the key players profiled include:

Intel (US)NVIDIA (US)Siemens Healthineers (Germany)Medtronic (Ireland)Micron Technology (US)IBM (US)Microsoft (US)Google Inc (US)Amazon Web Services (US)Medtronic (Ireland)Micron Technology (US)

AI in Hospital Management Breakdown Data by TypeHardwareSoftwareService

AI in Hospital Management Breakdown Data by ApplicationHealthcare ProviderPharmaceutical & Biotechnology CompanyPatientOthers

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In this study, the years considered to estimate the market size of Hospital Artificial Intelligence Market are as follows:

The Hospital Artificial Intelligence Market report includes overview, which deciphers value chain structure, industrial environment, regional examination, applications, market size, and forecast. Usually a most recent report, covering the current COVID-19 effect on the market. The pandemic of Coronavirus (COVID-19) has influenced each viewpoint of life all inclusive.

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Table of Contents

1 Study Coverage

2 Executive Summary

3 Breakdown Data by Manufacturers

4 Breakdown Data by Type

4.1 Global Hospital Artificial Intelligence Market Sales by Type

4.2 Global Hospital Artificial Intelligence Market Revenue by Type

4.3Hospital Artificial Intelligence Market Price by Type

5 Breakdown Data by Application

5.1 Overview

5.2 Global Hospital Artificial Intelligence Market Breakdown Data by Application

6 North America

7 Europe

8 Asia Pacific

9 Central & South America

10 Middle East and Africa

11 Company Profiles

12 Future Forecast

13 Market Opportunities, Challenges, Risks and Influences Factors Analysis

14 Value Chain and Sales Channels Analysis

15 Research Findings and Conclusion

16 Appendix

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At HealthCare Intelligence Markets, we supply markets intelligence reports in the domain of personalized drugs & diagnostics after going through a rigorous research process. The healthcare industry is constantly evolving as trends are getting replaced at a rapid pace. These new trends along with the changing demands of patients and healthcare organizations, are collectively contributing to the development of the global healthcare industry. The reports made by us are updated on a regular basis to cover the latest developments in the industry. Our workforce is comprised of seasoned market research professionals who can also provide customized report as per the exclusive needs. HealthCare helps clients decode the future to be more successful and innovative.

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Hospital Artificial Intelligence Industry: 2021 Global Market Size, Share, Uses, Benefits, Trends, Growth Application, Key Manufacturers and 2028...

‘Artificial Intelligence’ Integrated PET-CT launched at Yashoda Hospitals, Hyderabad on the occasion of World Cancer Day 2021 – PR Newswire India

"This year's World Cancer Day's theme, 'I Am and I Will', is all about you and your commitment to act. The new state-of-the-art artificial intelligence integrated PET-CT scanner at Yashoda Hospital Somajiguda is one more step towards our commitment to early detection of Cancer. The new scanner is now two times faster than the old generation scanners primarily due to the advanced technology known as 'Time of Flight'. The scanner provides best quality images with reduced scanning duration and lesser radiation dose," said Dr. G. Srinivasa Rao, Director of Public Health & Family Welfare, Government of Telangana.

Yashoda Hospitals Somajiguda is well equipped with a comprehensive Nuclear Medicine set up providing services like PET-CT, Gamma camera imaging and radionuclide therapy under one roof. Apart from the newly upgraded imaging of FDG PET-CT, the department provides advanced and rare imaging like Ga-68 DOTA, Ga-68 PSMA, 18F DOPA PET-CTs, DAT imaging & WBC scans, apart from routine Gamma imaging like bone scan & renal scintigraphy.

"Yashoda Hospitals Somajiguda is one of the busiest and high volume centres of radionuclide therapies for thyroid cancer, neuroendocrine tumours, and prostate cancer. The Centre also provides rare therapies like radiosynovectomy for inflammatory joint disease. Patients not only from Telangana and Andhra Pradesh, but across India, visitus for these rare therapies. NextGen PET-CT is effective in the diagnosis of Cancer, Endocrine Abnormalities and Neurodegenerative Disease," said Dr. Lingaiah Amidayala, Director - Medical Services, Yashoda Hospitals Group, Hyderabad.

The Combined PET-CT Scan at Yashoda Hospitals, Somajiguda merges PET and CT images and provides detailed information about the size, shape and differentiating cancerous lesions from normal structures with accuracy. It is a diagnostic examination that combines two state-of-the-art imaging modalities and produces 3 dimensional (3D) images of the body based on the detection of radiation from the emission of positrons. It helps in early detection of cancer and any potential health problem that reveals how the tissues and organs are functioning by identifying a variety of conditions.

Dr. Hrushikesh Aurangabadkar and Dr. A Naveen Kumar Reddy, Consultants in Nuclear Medicine while explaining about the PET-CT said, "The cancer cells require a great deal of sugar, or glucose, to have enough energy to grow. PET scanning utilizes a radioactive molecule that is similar to glucose, called fluorodeoxyglucose (FDG). FDG accumulates within malignant cells because of their high rate of glucose metabolism. Once injected with this agent, the patient is imaged on the whole body PET scanner to reveal cancer growth, which are usually difficult to characterize by conventional CT, X-Ray, or MRI."

With this new technology, motion artifacts caused by respiration can be decreased and accurate diagnosis achieved.

The use of PET scans will also help the doctors to more accurately detect the presence and location of new or recurrent cancers.

Relevant Links: https://www.yashodahospitals.com/location/somajiguda/

Nuclear Medicine: https://www.yashodahospitals.com/specialities/nuclear-medicine-hospital-in-hyderabad/

About Yashoda Hospitals Hyderabad

Yashoda Group of Hospitals has been providing quality healthcare for 3 decades for people with diverse medical needs. Under astute leadership and a strong management, Yashoda Group of Hospitals has evolved as a centre of excellence in medicine providing the highest quality standards of medical treatment. Guided by the needs of patients and delivered by perfectly combined revolutionary technology even for rare and complex procedures, the Yashoda Group hosts medical expertise and advanced procedures by offering sophisticated diagnostic and therapeutic care in virtually every specialty and subspecialty of medicine and surgery. Currently operating with 3 independent hospitals in Secunderabad, Somajiguda and Malakpet and an upcoming hospital (currently under development) in Hi-Tech city, Telangana which is expected to be one of the largest medical facilities in India and will be spread over 20 lakhs sq. ft. with a capacity of 2000 beds. With a constant and relentless emphasis on quality, excellence in service, empathy, Yashoda Group provides world-class healthcare services at affordable costs.

Photo: https://mma.prnewswire.com/media/1433696/AI_PET_CT_Launched_Yashoda.jpg

SOURCE Yashoda Hospitals Hyderabad

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'Artificial Intelligence' Integrated PET-CT launched at Yashoda Hospitals, Hyderabad on the occasion of World Cancer Day 2021 - PR Newswire India

Artificial Intelligence Is a Work in Progress, Official Says – Department of Defense

Expectations are high that artificial intelligence will be a game changer for the military and it is, in fact, one of the Defense Department's top priorities.

"We're in the very early days of a very long history of continued very rapid development in the AI field," said William Scherlis, director of the Information Innovation Office at the Defense Advanced Research Projects Agency. He spoke yesterday at a virtual panel discussion at the Defense One Genius Machines 2021 summit.

There are a lot of moving parts to AI that must come together to make it all work for the warfighter, he said.

Components include, machine learning, symbolic reasoning, statistical learning, knowledge representation, search and planning, data, cloud infrastructure, algorithms and computing, he said.

"If you want to do strategy planning, then you're gonna have a mashup of machine learning with, maybe, game theory and a few other elements. So when we talk about AI, sometimes people are referring to just machine-learning algorithms and data and training. But in the systems engineering context, we're really talking about how to build systems that, that have elements of AI capability embedded within them," he said.

Scherlis discussed the history of AI, back to the 1940s and noted that there were three waves of development.

The first wave involved symbolic AI, which has explicit rules, such as if it's raining, then bring an umbrella, he said. Commercial income tax programs operate this way, using rules, logic and reasoning to reach a conclusion.

The second wave involved neural nets, which Scherlis refers to as statistical AI. Neural nets attempt to replicate higher-order human thinking skills, such as problem solving.

All AI relies on having good data. But although data is certainly important, the real game-changer for AI will be the third wave where symbolic is meshed with statistical to get the best of both worlds, Scherlis predicted.

"This is a wide open research area, but there's a lot of good work in this area and I think it's very promising," he said, referring to third wave research.

This third wave will need to focus on how AI systems interact with humans in a productive and symbiotic way, he said.

Warriors will have to understand what it's like to have an AI as a trusted team member, he said.

Currently, AI isn't yet ready for prime time, he said. It's still fragile, opaque, biased and not robust enough, which means it does not yet have trustworthiness.

"At DARPA, we have another number of programs that are, that are addressing these challenges," he added.

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Artificial Intelligence Is a Work in Progress, Official Says - Department of Defense