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Machine learning is critical to computing and the advancements being made with artificial intelligence. All computers use algorithms to provide instructions for how to proceed with processes, solve problems, perform calculations, and more. With artificial intelligence, such operations can be done autonomously, and that requires machine learning. Basically, machine learning creates algorithms that AI systems can use to process data and learn new things without having to be programmed. For instance, AI systems with machine learning capabilities can recognise faces, detect instances of fraud, predict customer behaviour, and much more. One area that is being transformed by machine learning is the online gaming industry.

How Machine Learning Is Transforming Video Games

Machine learning is being adopted in video game development in various ways. For instance, it is being used via deep learning agents that compete with professional human players in complex strategy online games, such as Minecraft, Doom, and StarCraft. In the future, machine-learning non-playable characters in video games could get smarter and respond in unique ways based on your actions within a game. Other ways machine learning could be used in online video games in the coming years include:

Modelling complex systems, so that games can be better-modelled on the real world and become more immersive and realistic.

Making games more aesthetically pleasing, by enhancing and rendering imagery so that details become finer when they are approached in gameplay.

World creation on the fly, in which machine learning algorithms would help with pathfinding and creating game worlds.

How Machine Learning Is Transforming the Online Gambling Sector

It is not only online video games that are benefitting from machine learning. Gambling-sector games are also becoming transformed. For example, in the last few years, the Danish national lottery Danske Spil has begun using machine learning and advanced analytics algorithms that can provide a multitude of insights. Online casinos, including ones like Casumo live casino in which players play games like blackjack and roulette in real-time with real croupiers, are also becoming transformed due to the adoption of machine learning. Here are some of the key ways in which machine learning could change the online casino industry over the next decade.

Improved Customer Experience

Platforms like Netflix already use machine learning to suggest movies and shows based on users' viewing histories. Online casinos will be able to do the same thing. Player behaviour can be analysed to provide highly-customized game suggestions.

Enhanced Marketing

The marketing aspect of online casinos is due to be enhanced with the use of machine learning. Potential customers could be found more easily and customized bonuses could be used to bring those customers to a specific online gaming platform.

Better Player Protection

With the proper screening, it will be possible to identify players who have early signs of gambling problems. That simply would not be possible without machine learning. So, better player protection looks likely to become an integral part of the online casino industry's future.

Smarter Opponents

Machine learning has already enabled artificial intelligence to take on human players in all manner of casino games. As machine learning continues to become enhanced, AI bots will become more sophisticated. That means artificial opponents can become smarter, and it allows various new gaming options for the future.

Cost-cutting

As machine learning and artificial intelligence are used more and more by the online gaming industry, a number of costs can be saved and the efficiency in a multitude of spheres can be enhanced. Things like online payments, security, and customer support will become transformed in the next few years.

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How machine learning Is transforming the online gaming industry - The Munich Eye

The AI Standards Working Group, co-chairedby IBM Watson Advertising and Nielsen, today released theguideArtificial Intelligence Use Cases and BestPractices for Marketing, which is intended to help executives, marketers, and technologists get the most from artificial intelligence (AI) and machine learning (ML). The announcement was made duringthe IAB ILM annual conference.

The guide -- which provides dozens of working examples -- draws directly from the real-world experience of the co-chairs as well aspublishers, agencies, and ad-tech companies. Itincludes nine use cases that span internal robotic process automation and data migration for agencies as well as AI use cases for creative,contextual, video, and more.

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David Olesnevich, head of product at IBM Watson Advertising, and IAB AI Standards Working Group co-chair, believes the work and the guideprovide a road map through insights driven by conversations about real-world challenges faced daily by some of the industry's largest brands.

It felt natural toform this group, so we reached out to IAB to generate momentum, because with privacy forwardness, changes in identifiers, and government regulators we will need technology that canscale, he said. We believe thats AI.

AI was once deployed in advertising at the DSP level, which is very transactional, but now is beingused across the entire stack, including planning and the development of creatives.

Bias is one of the key topics the group will address, based on feedback fromindustry participants following todays release.

The reason why bias is scheduled to be the next topic of discussion is because it was on everyonesradar, he said, adding that it is an issue the group will address head on with other partners like Dentsu, GumGum, and Dun & Bradstreet.

EvenGoogle is using AI to create Cohorts.

Onegoal of the guide is to share different perspectives on how AI and ML could work, along with adoption, as well as to understand different views based on groups within the industry.

Feedback from C-suite agency executives suggests they are apprehensive about aligning AI and ML to their business priorities and objectives, how to ensure teams havethe skills they need to use the technology, and finding ways to implement solutions with a third-party partner or leveraging existing internal resources.

Despite theconcerns, C-suite agency executives do expect the technologies to create a competitive advantage, and to increase profit margins by reallocating manual effort against work by offloading criticalbusiness function, leaving the tedious tasks to machines.

Marketers have a different take on what the technologies can do, and the apprehensions that might keep themfrom using them.

Marketers look toward AI and ML to automate ad creation for addressable and non-addressable programmatic media formats, automatic media mix modelingbased on brand and performance metrics, and optimize audiences based on addressable and non-addressable programmatic media formats.

They are apprehensive about the biastheir efforts might create. When it comes to bias, they wonder whether a media vendor is using AI or ML to benefit them or their clients, and are concerned about the lack of human touch that resultsin issues at the account level, and will adoption help or hurt their career development.

Technologists are also concerned that the algorithms running the AI and MLcould be biased and assume certain behavior based only on race or gender. They also have concerns that AI-based systems might not perform fast enough or scale to their intended level, and that thedata to build a performant algorithm might not be available or affordable.

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IAB Artificial Intelligence Group To Build Standards, With Focus On AI, Machine Learning, Bias 03/10/2021 - MediaPost Communications

Organisations, regardless of size, are adopting emerging technologies like machine learning, data science, and AI to gain meaningful insights from large chunks of data in a bid to accelerate their growth. According to the Analytics and Data Science India Industry study 2020, advanced analytics, predictive modelling, and data science together account for 16% of the analytics revenues across enterprises. The rapid digital adoption has opened the skill gap wide. Many institutions across the world are now offering courses both online and offline to plug this gap.

Here are the top ten Masters in Machine Learning in the US.

(The list is in alphabetical order)

Program: Master of Science in Machine Learning

Location: Pittsburgh, Pennsylvania

Duration: Up to 2 years

About: The Master of Science in Machine Learning includes 7 Core courses, 2 Elective courses, and a practicum. Incoming students should have good analytical skills and a strong aptitude for mathematics, statistics, and programming.

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Program: Computer Science M.S. With Specialisation in Machine Learning

Location: Ithaca, New York

Duration: 2 years or more

About: The program comprises five different core areas such as algorithms and theory of computation, artificial intelligence, systems, programming languages and methodology, scientific computing and applications.

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Program: Master of Science in Computer Science With Specialisation in Machine Learning

Location: Atlanta, Georgia

Duration: 2 years

About: The Master of Science in Computer Science (M.S. CS) is a terminal degree program designed to prepare students for highly productive careers in the industry. Students can choose from 11 areas of specialisation including Machine Learning, Interactive Intelligence, Scientific Computing, High-Performance Computing, and Human-Computer Interaction.

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Program: Data Analytics & Machine Learning Masters Programs

Location: Durham, North Carolina

Duration: 1.5 2 years

About: The focus on data analysis and machine learning provides masters students with the tools to manage, interpret and gain new insights from data. Students will be exposed to mathematical foundations of Big Data, training in practical programming, and instruction in machine learning, statistics and information theory.

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Program: Master of Science in EECS with Specialisation in Machine Learning

Location: Cambridge, Massachusetts

Duration: 1 to 2 years or more

About: The Master of Science in Electrical Engineering and Computer Science (EECS) offers research possibilities in artificial intelligence, Computer Graphics and Vision, Human-Computer Interaction, Machine Learning, Natural Language and Speech Processing.

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Program: MS in Artificial Intelligence

Location: Boston

Duration: 2 years

About: In the MS in AI degree program, students will learn to apply creative thinking, algorithmic design, and coding skills to build modern AI and machine learning systems. Students will get deep technical training and expertise in machine learning, computer vision, and natural language processing.

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Program: MS in Computer Science Artificial Intelligence & Machine Learning

Location: Rochester, New York

Duration: 2 years

About: This program is for students who intend to complete their studies at the University of Rochester with an MS degree. The Artificial Intelligence & Machine Learning Masters program covers topics like data mining, computer vision, machine learning, statistical speech and language processing.

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Program: Masters in Computer Science

Location: San Diego, California

Duration: 2 years or more

About: The Masters degree is offered with the title Computer Science and Engineering or Computer Science and Engineering (Computer Engineering). Students must register for a minimum of three quarters for residency requirements.

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Program: Master of Science (MS) in Machine Learning

Location: Hoboken, New Jersey

Duration: 2 years or more

About: The machine learning masters program establishes the theoretical and practical foundations necessary to be at the forefront of the next technological revolution.

Know more here.

Program: MSCS in Artificial Intelligence

Location: Stanford, California

Duration: 2 years

About: The MSCS in Artificial Intelligence program includes the study of AI principles and machine learning techniques, as well as foundational material on topics such as logic, probability, and language. The topics in the AI course include knowledge representation and logical reasoning, robotics, machine learning, probabilistic modeling and inference, natural language processing, cognition, and applications in domains such as biology and text processing.

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Top Master's Programs In Machine Learning In The US - Analytics India Magazine

SAN FRANCISCO, Feb. 24, 2021 /PRNewswire/ -- D2iQ, provider of the leading independent Kubernetes platform built to power smarter Day 2 operations, today announced the general availability of D2iQ Kaptain, the cloud native end-to-end platform for running machine learning (ML) workloads on Kubernetes. D2iQ Kaptain combines all of the open source components required to accelerate the development, training, tuning and deployment of ML models in the enterprise, cutting the time from prototype to production from months to minutes.

With data volumes growing exponentially, machine learning is no longer an option, but a necessity for digitally-driven organizations. However, many enterprises struggle when moving from a prototype on a single machine to a scalable production deployment. According to industry research, 87 percent of all artificial intelligence (AI) projects never make it into production. D2iQ Kaptain provides data scientists with a familiar, notebook-first approach that has been fully tested and integrated with all the shared resources and data access controls required to build and share models. This enables data scientists to manage the lifecycle of their machine learning models without a need for Kubernetes or production infrastructure knowledge.

D2iQ Kaptain is powered by an opinionated subset of Kubeflow, the open source machine learning toolkit for Kubernetes, while also including all of the Day 2 ready features provided by the D2iQ Konvoy Kubernetes distribution and additional production-focused components such as Horovod and Spark. This combination empowers platform operators and data scientists with a robust and enterprise-grade Kubernetes foundation. D2iQ Kaptain dramatically reduces the friction involved in training and deploying ML models in the enterprise, increasing production success rates while speeding time to value.

"Moving ML workflows from prototype to scalable deployment is increasingly complex and challenging, often requiring significant resources and multiple months to reach production environments," said Deepak Goel, CTO, D2iQ. "D2iQ Kaptain uniquely supports both data scientists and developer teams with an enterprise-grade, end-to-end ML solution capable of running Kubernetes anywhere, from on-premises to cloud and air-gapped environments. As pioneers in helping organizations navigate cloud native journeys, D2iQ Kaptain leverages our expertise and suite of Kubernetes solutions built for security, scale, flexibility and speed to ensure successful Day 2 operations"

D2iQ Kaptain delivers significant benefits for data scientists and DevOps teams:

D2iQ Kaptain is available now. For more information on D2iQ Kaptain, visit: https://d2iq.com/products/kaptain

About D2iQ

D2iQ provides the leading independent Kubernetes platform which simplifies and automates the really difficult tasks needed for enterprise-grade production at scale, while reducing operational burden and reducing costs. As a cloud native pioneer, we have more than a decade of experience tackling the most complex, mission-critical deployments in the industry. The D2iQ Kubernetes Platform is a complete solution that includes the technology, expert services, training and support necessary to ensure your success on Day 2 and beyond. Our independence provides us the agility to meet the needs of our customers first, while always keeping TCO top of mind. D2iQ is headquartered in San Francisco and investors include Andreessen Horowitz, Hewlett Packard Enterprise, Khosla Ventures, Koch Disruptive Technologies, Microsoft, and T. Rowe Price Associates, Inc. Find us at https://d2iq.com/

SOURCE D2iQ

https://d2iq.com/

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D2iQ Introduces Cloud Native Platform to Accelerate the Deployment of Machine Learning on Kubernetes - PRNewswire

The latest report includes a thorough study of the global Machine Learning in MedicineMarket. It has successfully pointed out the key factors that have a substantial impact on theMachine Learning in Medicine Industry. This report is a result of a well-planned research methodology. The methodology employed both primary and secondary research tools. These tools aid the researchers to gather authentic data and arrive at a definite conclusion. The prevailing competitors in the global Machine Learning in MedicineMarket have also been pictured in the report, offering an opportunity to theKey Market players to measuring system their performance.

The qualitative contents for geographical analysis will cover market trends in each region and country which includes highlights of the key players operating in the respective region/country, PEST analysis of each region which includes political, economic, social, and technological factors influencing the growth of the Machine Learning in Medicine market.

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Segmental Analysis of Machine Learning in Medicine Industry:The market study contains the division of the overall market into different segments by Type and Applications according to the key geographic regions.

By Type

By Applications

By Region

Top Key Players Profiled in Machine Learning in Medicine Market Report:

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The various factors that can boost the Machine Learning in Medicine market growth in the current scenario as well as in the coming years have been discussed in detail. The interplay of demand and supply forces in this market along with the factors affecting them have been analyzed. The internal and external factors affecting the market in terms of growth have been studied by this market study. The pricing policies used and the effect they have on consumer behavior have been studied for various regions of the Machine Learning in Medicine market. The study conducted looks at the upstream as well as the downstream aspects of the market.

Key Questions Answered by Machine Learning in Medicine Market Report

Table of Contents: Machine Learning in Medicine Market

1.Overview of Machine Learning in Medicine Market2.Global Machine Learning in Medicine Market Status and Forecast by Regions3.Global Machine Learning in Medicine Market Status and Forecast by Types4.Global Machine Learning in Medicine Market Status and Forecast by Downstream Industry5.Market Driving Factor Analysis6.Market Competition Status by Major Manufacturers7.Major Manufacturers Introduction and Market Data8.Upstream and Downstream Market Analysis9.Cost and Gross Margin Analysis10.Marketing Status Analysis11.Market Report Conclusion12.Research Methodology and Reference

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Innovative Machine Learning in Medicine Market Research Report Segmented by Applications, Geography, Trends and Projection 2026 The Bisouv Network -...