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Memory-Based Learning


Scientists turn to deep learning to improve air quality forecasts

#artificialintelligence

Air pollution from the burning of fossil fuels impacts human health but predicting pollution levels at a given time and place remains challenging, according to a team of scientists who are turning to deep learning to improve air quality estimates. Results of the team's study could be helpful for modelers examining how economic factors like industrial productivity and health factors like hospitalizations change with pollution levels. "Air quality is one of the major issues within an urban area that affects people's lives," said Manzhu Yu, assistant professor of geography at Penn State. "Yet existing observations are not adequate to provide comprehensive information that may help vulnerable populations to plan ahead." Satellite and ground-based observations each measure air pollution, but they are limited, the scientists said.


IBM Watson: How is it used for AI research & projects - datamahadev.com

#artificialintelligence

A number of web APIs enable developers to develop applications using IBM Watson, Watson Machine Learning infrastructure, and capabilities running on IBM Cloud Services to build analytical models and neural networks, deploy AI, and more. Watson Analytics is a natural language-based cognitive service from IBM Watson that can provide real-time analysis, machine learning, and artificial intelligence (AI) capabilities. Watson Analytics, which includes IBM Cloud Services, an IBM cloud-based service that runs on both desktop and mobile devices, is available in a range of languages including English, French, German, Spanish, English – as – a – Second – Language (EASL) and Mandarin Chinese (Mandarin), as well as English and French. Watson is an IBM supercomputer that combines the best of both worlds – a high-performance computing platform and artificial intelligence (AI) for the optimal performance of an answering machine. This expert guide(IBM Watson) is designed to help you better understand the design and maintenance considerations of your infrastructure machine that support your initiative.


Machine learning to improve the mobile game UI, UX design

#artificialintelligence

Machine learning keeps on revolutionizing almost every industry and sector, from crop planning in agriculture to cancer diagnosis in the healthcare industry. These topics are often more broadly addressed because they have made an impact that is tangible and beneficial for humanity. Machine learning in games, particularly in mobile game design, is also hitting the headlines in the same way. To grasp the scale of the gaming industry, according to Newzoo's Global Games Market Report, the video game industry has reached a global market value of $139 billion by the end of 2018 and is already far more significant than the Film and Music industry put together. Across all platforms combined, the gaming industry now has over 3 billion gamers worldwide.


IBM Watson: Why Is Healthcare AI So Tough?

#artificialintelligence

UKRAINE - 2021/02/19: In this photo illustration an IBM logo is seen on a smartphone screen. A pivotal event for AI happened when IBM's Watson beat two all-time champions of Jeopardy! in 2011. This showed that the technology was far from being experimental. IBM would soon go on to make Watson the centerpiece of its AI strategy. And a big part of this was to focus on healthcare.


Improve Your Sales & Product with this AI Pattern

#artificialintelligence

Many organizations struggle with both identifying and prioritizing what sales leads to pursue. Where do you start when you have a large stack of leads to go through? What do you when your leads have gone cold? For Product Leaders, it's often a challenge to get a broad spectrum of feedback from their customers. How do they know where to focus next?


IBM's Watson is AI's greatest ambassador

#artificialintelligence

When I heard the 60th annual Grammy Awards show was going to feature artificial intelligence, I immediately thought "this is a marketing ploy." But then I found out IBM's Watson was the AI in question. Watson, you see, doesn't have a problem rolling up its non-existent sleeves and doing some good old fashioned hard work. Don't expect a silly robot rolling around doing a human impersonation on the red carpet, IBM's machines show up to solve problems and optimize workflows. And while that isn't very sexy – hard work seldom is – it's incredibly important.


Potential IBM Watson Health Sale Puts Focus on Data Challenges

WSJ.com: WSJD - Technology

Even so, some experts found that it can be difficult to apply AI to treating complex medical conditions. Having access to data that represents patient populations broadly has been a challenge, experts told the Journal, and gaps in knowledge about complex diseases may not be fully captured in clinical databases. "I believe that we're many years away from AI products that really positively impact clinical care for many patients," said Bob Kocher, a partner at venture-capital firm Venrock who focuses on healthcare IT and services investments and who was a White House health adviser under President Barack Obama. Software that makes recommendations on personal medical treatments needs data on what actions have worked in the past. But data on medical histories and treatment outcomes aren't always complete, may be recorded in different formats, and may be sitting in various systems owned by insurance carriers, health providers and other organizations.


IBM Explores Sale of IBM Watson Health

WSJ.com: WSJD - Technology

International Business Machines Corp. is exploring a potential sale of its IBM Watson Health business, according to people familiar with the matter, as the technology giant's new chief executive moves to streamline the company and become more competitive in cloud computing. IBM is studying alternatives for the unit that could include a sale to a private-equity firm or industry player or a merger with a blank-check company, the people said. The unit, which employs artificial intelligence to help hospitals, insurers and drugmakers manage their data, has roughly $1 billion in annual revenue and isn't currently profitable, the people said. Its brands include Merge Healthcare, which analyzes mammograms and MRIs; Phytel, which assists with patient communications; and Truven Health Analytics, which analyzes complex healthcare data. It isn't clear how much the business might fetch in a sale, and there may not be one. IBM, with a market value of $108 billion, has been left behind as cloud-computing rivals Microsoft Corp. and Amazon.com


Using AI-enhanced music-supported therapy to assist stroke patients

AIHub

Stroke currently ranks as the second most common cause of death and the second most common cause of disability worldwide. Motor deficits of the upper extremity (hemiparesis) are the most common and debilitating consequences of stroke, affecting around 80% of patients. These deficits limit the accomplishment of daily activities, affect social participation, are the origin of significant emotional distress, and cause profound detrimental effects on quality of life. Stroke rehabilitation aims to improve and maintain functional ability through restitution, substitution and compensation of functions. The restoration of motor deficits and improvements in motor function typically occurs during the first months following a stroke and therefore, major efforts are devoted to this acute stage.


THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment

arXiv.org Artificial Intelligence

We participated in the two case law tasks, i.e., the legal case retrieval task and the legal case entailment task. Task 1 (the retrieval task) aims to automatically identify supporting cases from the case law corpus given a new case, and Task 2 (the entailment task) to identify specific paragraphs that entail the decision of a new case in a relevant case. In both tasks, we employed the neural models for semantic understanding and the traditional retrieval models for exact matching. As a result, our team ("TLIR") ranked 2nd among all of the teams in Task 1 and 3rd among teams in Task 2. Experimental results suggest that combing models of semantic understanding and exact matching benefits the legal case retrieval task while the legal case entailment task relies more on semantic understanding.