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Memory-Based Learning


Using machine learning to improve student success in higher education

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Many higher-education institutions are now using data and analytics as an integral part of their processes. Whether the goal is to identify and better support pain points in the student journey, more efficiently allocate resources, or improve student and faculty experience, institutions are seeing the benefits of data-backed solutions. This article is a collaborative effort by Claudio Brasca, Nikhil Kaithwal, Charag Krishnan, Monatrice Lam, Jonathan Law, and Varun Marya, representing views from McKinsey's Public & Social Sector Practice. Those at the forefront of this trend are focusing on harnessing analytics to increase program personalization and flexibility, as well as to improve retention by identifying students at risk of dropping out and reaching out proactively with tailored interventions. Indeed, data science and machine learning may unlock significant value for universities by ensuring resources are targeted toward the highest-impact opportunities to improve access for more students, as well as student engagement and satisfaction.


Using Machine Learning To Improve Targeting Of Humanitarian Aid

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As cell phones have grown increasingly prevalent worldwide, with a projected global penetration level of 73 percent in 2020, research on wealth forecasting from digital trail data has concentrated on mobile phone metadata (GSMA, 2017). Machine learning algorithms based on call detail records (CDR) have recently been proved to yield meaningful estimations of prosperity and well-being at a fine geographical resolution. Machine Learning and Artificial Intelligence can be used to target poor populations effectively for humanitarian aid using digital indicators. The challenge of assessing who is qualified for humanitarian help and who is not is a key cause of problems in anti-poverty programme management. Typically, programmes target people based on administrative records like tax records or survey-based asset or consumption measurements.


Amazon Uses Machine Learning to Improve Video Quality on Prime Video

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Because streaming video might be harmed by flaws introduced during recording, encoding, packing, or transmission, most subscription video services, such as Amazon Prime Video, monitor the quality of the content they stream regularly. Manual content review, often known as eyes-on-glass testing, doesn't scale well and comes with its own set of issues, such as discrepancies in reviewers' quality judgments. The use of digital signal processing to detect anomalies in the video signal, which are typically associated with faults, is becoming more popular in the business. To validate new program releases or offline modifications to encoding profiles, Prime Video's Video Quality Analysis (VQA) division began employing machine learning three years ago to discover faults in collected footage from devices such as consoles, TVs, and set-top boxes. More recently, Amazon has used the same techniques to solve problems like real-time quality monitoring of our thousands of channels and live events, as well as large-scale content analysis.


Insitro, Genomics England Collaborate on Machine Learning to Improve Database Searches

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Insitro will apply its machine learning technology to the Genomics England database to create a multimodal representation of clinical data.


How to Use Machine Learning to Improve Cryptocurrency Mining Profitability

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In 2008, Satoshi Nakamoto introduced bitcoin to the world. Many speculated that bitcoin would not gain popularity and eventually disappear. It has grown faster than anyone expected, even the most staunch supporters. The trend towards bitcoin has been set by advances in AI and machine-learning technology. Since cryptocurrencies were first introduced to the public, it's been more than a decade. And they have become more popular every day.


MIT-IBM Watson AI Lab Tackles Power Grid Failures with AI

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Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.


MIT-IBM Watson AI Lab Tackles Energy Grid Failures with AI - Channel969

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Subsequent time your energy stays on throughout a extreme climate occasion, you could have a machine studying mannequin to thank. Researchers on the MIT-IBM Watson AI Lab are utilizing synthetic intelligence to resolve energy grid failures. The supervisor of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine studying mannequin that works to investigate knowledge collected from tons of of 1000's of sensors situated throughout the U.S. energy grid. The sensors, parts of what's referred to as synchrophasor expertise, compile huge quantities of real-time knowledge associated to electrical present and voltage in an effort to monitor the well being of the grid and find anomalies that would trigger outages. Synchrophasor evaluation requires intensive computational sources as a result of dimension and real-time nature of the info streams the sensors produce.


MIT-IBM Watson AI Lab Tackles Power Grid Failures with AI

#artificialintelligence

Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.


IBM Watson-Powered AI Virtual Assistant Helps Visitors on the TD Precious Metals Digital Store

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Investors looking to diversify their portfolios and coin collectors looking to add a new treasure to their collection are familiar with the benefits and value that precious metals can offer. To help make the purchasing process easier, IBM worked with TD Securities to launch an AI-based virtual assistant powered by IBM Watson Assistant that can help customers with inquiries on the TD Precious Metals digital store, including frequently asked questions. The TD Precious Metals digital store allows customers to buy physical gold, silver and platinum bullion and coins online from the comfort of their home. The new virtual assistant, now available as a feature on the TD Precious Metals digital store, provides customers with a convenient self-service option, available 24/7, for frequently asked questions about TD Precious Metals. Customers type their questions into the virtual assistant and receive an instant written response, along with links to help further assist them.


IBM「Watson」搭載AIバーチャルアシスタント、貴金属デジタル店舗で顧客体験向上を支援

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「IBM Watson Assistant」を搭載した人工知能(AI)ベースの新しいバーチャルアシスタントがTD Securitiesの貴金属事業のデジタルストアで利用可能になっている。