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Designing societally beneficial Reinforcement Learning (RL) systems

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Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind's work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential for real world applications of RL should also come with a healthy dose of caution – for example RL policies are well known to be vulnerable to exploitation, and methods for safe and robust policy development are an active area of research. At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. The focus of these research efforts to date has been to account for shortcomings of datasets or supervised learning practices that can harm individuals.


5 Trends in Medical Health Technology in 2022

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It is predicted that technologies such as artificial intelligence (AI), cloud computing, extended reality and the Internet of Things (IoT) will be introduced further among related workers, leading to the development and provision of new and better treatments and services. In the months following the outbreak of the COVID-19 outbreak, the proportion of telemedicine consulting has risen sharply from 0.1% to 43.5%, and is expected to rise further in the future, as this trend could save more patients' lives, said Deloitte Accounting Firm analyst. . To achieve this goal, the next-generation portable device, heart rate, stress, and blood oximetry, enables doctors to accurately determine the patient's condition in real time. During the COVID-19 period, doctors built'virtual hospital rooms' in some areas to observe the treatment status of patients in various areas through the central communication infrastructure. The Pennsylvania Emergency Medical Center is developing a high-quality'virtual emergency room'.


Markov Chain

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Markov chains are used to model probabilities using information that can be encoded in the current state. Each state has a certain probability of transitioning to each other state, so each time you are in a state and want to transition, a markov chain can predict outcomes based on pre-existing probability data. More technically, information is put into a matrix and a vector - also called a column matrix - and with many iterations, a collection of probability vectors makes up Markov chains. To determine the transition probabilities, you have to "train" your Markov Chain on some input corpus.


Technique protects privacy when making online recommendations

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Algorithms recommend products while we shop online or suggest songs we might like as we listen to music on streaming apps. These algorithms work by using personal information like our past purchases and browsing history to generate tailored recommendations. The sensitive nature of such data makes preserving privacy extremely important, but existing methods for solving this problem rely on heavy cryptographic tools requiring enormous amounts of computation and bandwidth. MIT researchers may have a better solution. They developed a privacy-preserving protocol that is so efficient it can run on a smartphone over a very slow network.


Global Big Data Conference

#artificialintelligence

Algorithms recommend products while we shop online or suggest songs we might like as we listen to music on streaming apps. These algorithms work by using personal information like our past purchases and browsing history to generate tailored recommendations. The sensitive nature of such data makes preserving privacy extremely important, but existing methods for solving this problem rely on heavy cryptographic tools requiring enormous amounts of computation and bandwidth. MIT researchers may have a better solution. They developed a privacy-preserving protocol that is so efficient it can run on a smartphone over a very slow network.


Introducing the New Intelligent SAP Service Cloud

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We love it when people exceed expectations. Whether it's an athlete who steps up to replace an injured starter or a team that pulls together to deliver exceptional results, it is inspiring to see long-held assumptions about potential turned upside down. Now, service organizations have an opportunity to exceed traditional expectations in the same way. Instead of being considered simply a means of connection and cost containment post-customer purchase, intelligent service teams can become a strategic driver to direct value back to the business. Focusing on speed, insights, and accuracy, SAP Service Cloud resolves customer issues at unmatched speed -- protecting the brands promise and securing future growth.


Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring

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This guidance explains how algorithms and artificial intelligence can lead to disability discrimination in hiring. The Department of Justice enforces disability discrimination laws with respect to state and local government employers. The Equal Employment Opportunity Commission (EEOC) enforces disability discrimination laws with respect to employers in the private sector and the federal government. The obligation to avoid disability discrimination in employment applies to both public and private employers. Employers, including state and local government employers, increasingly use hiring technologies to help them select new employees.


The Future of Artificial Intelligence in Manufacturing Industries

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Artificial intelligence (AI) is now transforming the manufacturing industry. AI can extend the sheer reach of potential applications in the manufacturing process from real-time equipment maintenance to virtual design that allows for new, improved, and customized products to a smart supply chain and the creation of new business models. Artificial intelligence (AI) in the manufacturing industry is being used across a variety of different application cases. It is being used as a way to enhance defect detection through sophisticated image processing algorithms that can then automatically categorize defects across any industrial object that it sees. The term artificial intelligence is used because these machines are artificially incorporated with human-like to perform tasks as we do.


12 examples of artificial intelligence in everyday life

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In the article below, you can check out twelve examples of AI being present in our everyday lives. Artificial intelligence (AI) is growing in popularity, and it's not hard to see why. AI has the potential to be applied in many different ways, from cooking to healthcare. Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. They work and continue to advance by using lots of sensor data, learning how to handle traffic and making real-time decisions.


What Are the Most Important Preprocessing Steps in Machine Learning and Data Science?

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Data Science and Machine Learning has been the latest talk right now and companies are looking for data scientists and machine learning engineers to handle their data and make significant contributions to them. Whenever data is given to data scientists, they must take the right steps to process them and ensure that the transformed data can be used to train various machine learning models optimally while ensuring maximum efficiency. It is often found that the data that is present in real-world is oftentimes incomplete and inaccurate along with containing a lot of outliers which some machine learning models cannot handle, leading to suboptimal training performance. It is also important to note that there might be duplicate rows or columns in the data which must be dealt with before giving it to machine learning models. Addressing these issues along with many others can be crucial, especially when one wants to improve model performance and generalizing ability of the model.