Recently, Google AI has announced that the use of a machine learning system for rapid training to game-playing agents. This framework can be used by game developers to release human game-playing agents, who can efficiently focus on other priority duties to boost productivity. Google AI will provide an open-source library to show the techniques used in this practice. Google AI wants to harness machine learning algorithms for designers to balance their games, artists to generate assets of top-notch quality within a short period of time. The models can also be used to train challenging opponents to compete at the highest level of any game efficiently.

Traditionally, game developers leverage machine learning algorithms for direct access to the source, the uniquely interactive nature of video games, and many more functionalities. But, in the current scenario, Google AI has launched a machine learning system that can be used for rapid training to game-playing agents and seeking serious bugs instantly. The modern solution can work on some popular game genres and generate game actions from a game state within one hour on a single game. Google AI is determined to produce a machine learning system that can only play the game for the game developers while detecting and fixing bugs automatically.

This updated machine learning system can allow game developers to train multiple game-playing agents instead of one super-effective agent with a single end-to-end machine learning model. Game developers experienced a most fundamental barrier in implementing machine learning to computer games- bridging the gap between the simulation-centric world of games and the data-centric world of machine learning. The current machine learning system provides efficient as well as game-developer-friendly APIs with a Dagger-inspired interactive training flow to develop user-friendly video games by describing what a player perceives and the semantic actions related to it like joysticks, 3D objects, 3D locations, buttons, and many more.

Google AI is ready to provide an open-source library for game developers with no prior knowledge of machine learning while the training of game-playing agents can be finished within an hour on a single developer machine. The reason is the effective performance of imitation learning that teaches machine learning models by observing the behaviors of professional players in the games. The imitation model is inspired by the DAgger algorithm that allows taking an advantage of the interactivity of video games.

The post GOOGLE AI: MACHINE LEARNING FOR RAPID TRAINING TO GAME-PLAYING AGENTS appeared first on Artificial Intelligence.

Artificial intelligence (AI) may offer a way to accurately determine that a person is not infected with COVID-19. An international retrospective study finds that infection with SARS-CoV-2, the virus that causes COVID-19, creates subtle electrical changes in the heart. An AI-enhanced EKG can detect these changes and potentially be used as a rapid, reliable COVID-19 screening test to rule out COVID-19 infection.

The AI-enhanced EKG was able to detect COVID-19 infection in the test with a positive predictive value — people infected — of 37% and a negative predictive value — people not infected — of 91%. When additional normal control subjects were added to reflect a 5% prevalence of COVID-19 — similar to a real-world population — the negative predictive value jumped to 99.2%. The findings are published in Mayo Clinic Proceedings.

COVID-19 has a 10- to 14-day incubation period, which is long compared to other common viruses. Many people do not show symptoms of infection, and they could unknowingly put others at risk. Also, the turnaround time and clinical resources needed for current testing methods are substantial, and access can be a problem.

“If validated prospectively using smartphone electrodes, this will make it even simpler to diagnose COVID infection, highlighting what might be done with international collaborations,” says Paul Friedman, M.D., chair of Mayo Clinic’s Department of Cardiovascular Medicine in Rochester. Dr. Friedman is senior author of the study.

The realization of a global health crisis brought together stakeholders around the world to develop a tool that could address the need to rapidly, noninvasively and cost-effectively rule out the presence of acute COVID-19 infection. The study, which included data from racially diverse populations, was conducted through a global volunteer consortium spanning four continents and 14 countries.

“The lessons from this global working group showed what is feasible, and the need pushed members in industry and academia to partner in solving the complex questions of how to gather and transfer data from multiple centers with their own EKG systems, electronic health records and variable access to their own data,” says Suraj Kapa, M.D., a cardiac electrophysiologist at Mayo Clinic. “The relationships and data processing frameworks refined through this collaboration can support the development and validation of new algorithms in the future.”

The researchers selected patients with EKG data from around the time their COVID-19 diagnosis was confirmed by a genetic test for the SARS-Co-V-2 virus. These data were control-matched with similar EKG data from patients who were not infected with COVID-19.

Researchers used more than 26,000 of the EKGs to train the AI and nearly 4,000 others to validate its readings. Finally, the AI was tested on 7,870 EKGs not previously used. In each of these sets, the prevalence of COVID-19 was around 33%.

To accurately reflect a real-world population, more than 50,000 additional normal EKGs were then added to reach a 5% prevalence rate of COVID-19. This raised the negative predictive value of the AI from 91% to 99.2%.

Zachi Attia, Ph.D., a Mayo Clinic engineer in the Department of Cardiovascular Medicine, explains that prevalence is a variable in the calculation of positive and negative predictive values. Specifically, as the prevalence decreases, the negative predictive value increases. Dr. Attia is co-first author of the study with Dr. Kapa.

“Accuracy is one of the biggest hurdles in determining the value of any test for COVID-19,” says Dr. Attia. “Not only do we need to know the sensitivity and specificity of the test, but also the prevalence of the disease. Adding the extra control EKG data was critical to demonstrating how a variable prevalence of the disease — as we have encountered with regions having widely different rates of disease at different stages of the pandemic — would impact how the test would perform.”

“This study demonstrates the presence of a biological signal in the EKG consistent with COVID-19 infection, but it included many ill patients. While it is a hopeful signal, we must prospectively test this in asymptomatic people using smartphone-based electrodes to confirm that it can be practically used in the fight against the pandemic,” notes Dr. Friedman. “Studies are underway now to address that question.”

This study was designed and conceived by Mayo Clinic investigators, and the work was made possible in part by a philanthropic gift from the Lerer Family Charitable Foundation Inc., and by the voluntary support from participating physicians and hospitals around the world who contributed in an effort to combat the COVID-19 pandemic. Technical support was donated by GE Healthcare, Philips and Epiphany Healthcare for the transfer of EKG data.

The post Artificial Intelligence for Rapid Exclusion of COVID-19 Infection appeared first on Artificial Intelligence.

This data mining tools market research report predicts the size of the market with information on key vendor revenues, development of the industry by upstream & downstream, industry progress, key companies, along with type segment & market application. All statistical and numerical data mentioned in the report is symbolized with the help of graphs and charts which eases the understanding of facts and figures. To succeed in this competitive age, it is very imperative to get well-versed about the major happenings in the market which is possible only with the excellent market report like this one.

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Data mining tools market study takes into account a market attractiveness analysis, where each segment is benchmarked based on its market size, growth rate, and general attractiveness. Geographical scope of the products is also carried out comprehensively for the major global areas such as Asia, North America, South America, and Africa which helps define strategies for the product distribution in those areas. A comprehensive market research carried out in this report puts a light on the challenges, market structures, opportunities, driving forces, scope, and competitive landscape for your business. The data mining tools report assists to achieve an extreme sense of evolving industry movements before the competitors.

Key Players: 

The renowned players in data mining tools market are IBM, Microsoft, SAS Institute, Oracle, Intel Corporation, SAP SE, RapidMiner, KNIME, Teradata, MathWorks, H2O.ai, Alteryx, FICO, Angoss, Salford Systems, BlueGranite, Megaputer,  Biomax Informatics, Frontline Systems, Dataiku (, Wolfram, Reltio, SenticNet, Business Insight among others.

Market Analysis: 

The Global Data Mining Tools Market accounted for USD 521.2 million in 2017 and is projected to grow at a CAGR of 12.3% the forecast period of 2018 to 2025. The upcoming market report contains data for historic years 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025.

Market Segmentation: Global Data Mining Tools Market

The global data mining tools market is based on component, services, business functions, deployment model, enterprise size, industry vertical and geographical segments.

Based on component, the market is segmented into tools and services.

Based on services, the market is segmented into managed services, consulting and implementation and others. The others segment can be further sub segmented into support and maintenance and training and education.

Based on business functions, the market is segmented into marketing, finance, supply chain and logistics and operations.

Based on deployment model, the market is segmented into cloud and on-premises.

Based on organization size, the market is segmented into small, medium-and large sized enterprises (SMES).

Based on industry verticals, the market is segmented into retail, banking, financial services, and insurance (BFSI), healthcare and life sciences, telecom and it, government and defense, energy and utilities, manufacturing and others (education, and media and entertainment).

Based on geography the global data mining tools market report covers data points for 28 countries across multiple geographies such as North America, South America, Europe, Asia-Pacific and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others.

Competitive Analysis: 

The global data mining tools market is fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market in order to sustain in long run. The report includes market shares of data mining tools market for global, Europe, North America, Asia Pacific and South America.

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The post Global Data Mining Tools Market 2019 to Expand at a CAGR of 12.3% by RapidMiner, KNIME, Teradata, MathWorks, H2O.ai, Alteryx, FICO, Angoss, Salford Systems & Others appeared first on Artificial Intelligence.