evaluation


Department of Energy Announces $15 Million for Development of Artificial Intelligence and Machine Learning Tools

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WASHINGTON, D.C. – Today, the U.S. Department of Energy's (DOE's) Advanced Research Projects Agency-Energy (ARPA-E) announced $15 million in funding for 23 projects to accelerate the incorporation of machine learning and artificial intelligence into the energy technology and product design processes as part of the Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program. Launched in April of this year, the DIFFERENTIATE program aims to develop streamlined solutions to next-generation energy challenges. The program identified three general mathematical optimization problems that are common to many design processes. The selected projects then conceptualized machine learning and artificial intelligence-based solutions to help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation. "The incorporation of AI and Machine Learning into our energy technology design and engineering processes has great potential to increase the productivity of our nation's engineers and scientists," said Secretary of Energy Rick Perry.


Causality-based Feature Selection: Methods and Evaluations

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Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection.


Early AI adopters beginning to see success predicting readmissions, ED visits

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PHOENIX--Early adopters of artificial intelligence solutions are beginning to see success in clinical areas such as predicting readmissions and avoidable emergency department visits, according to a joint report from KLAS Research and the College of Healthcare Information Management Executives (CHIME). KLAS and CHIME polled early adopter healthcare organizations using AI software, specifically machine learning and natural language processing, to evaluate the gains they've achieved in clinical, financial and operational areas. "The most exciting insight from our research is that artificial intelligence (machine learning and natural language processing) has truly begun to make a difference in healthcare. It's not all just smoke," Ryan Pretnik, director of research and strategy at KLAS and co-author of the study, said via email. "Artificial intelligence is driving outcomes, saving patient lives, and driving operational and financial efficiencies for providers and payers."


Radiology, News, Education, Service

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The vast number of CT presentations at RSNA 2019 is a testament to the modality's resilience and its value to the medical imaging community. Of the numerous factors that have contributed to CT's long-standing relevance, its adaptability certainly ranks high among them. This year's RSNA meeting looks to serve as a reminder of just how adaptable CT continues to be: Presentations will reaffirm the utility of tried-and-true imaging techniques and also feature relatively new technologies that have already begun reshaping the approach radiologists take to common clinical applications. Perhaps one of the best examples of this theme lies in the diagnostic evaluation of heart disease. Researchers will discuss the benefits of traditional coronary CT angiography (CCTA), one of the most reliable noninvasive methods for examining patients suspected of having coronary artery disease.


74 Summaries of Machine Learning and NLP Research - Marek Rei

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My previous post on summarising 57 research papers turned out to be quite useful for people working in this field, so it is about time for a sequel. Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). They cover a wide range of different topics, authors and venues. These are not meant to be reviews showing my subjective opinion, but instead I aim to provide a blunt and concise overview of the core contribution of each publication. Given how many papers are published in our area every year, it is getting more and more difficult to keep track of all of them. The goal of this post is to save some time for both new and experienced readers in the field and allow them to get a quick overview of 74 research papers in about 30 minutes reading time. I set out to post 60 summaries (up from 50 compared to last time). At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74). A transformer architecture that is trained as a language model on a large corpus, then fine-tuned for individual text classification and similarity tasks. Multiple sentences are combined together into a single sequence using delimiters in order to work with the same model.


Applying NLP in Java, all from the command-line

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Here's what we need to be able to get started: The how part of this task is not hard once we have our goals and requirements clear, we will expand on this in the following sections. We have all of the code and instructions needed to get started with this post, captured for you on GitHub.


KLAS-CHIME report offers AI insights from early adopters

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A new joint report from KLAS and CHIME polled some early adopters of artificial intelligence and machine learning tools, and asked how the technology is impacting their clinical, financial and operational goals. WHY IT MATTERS The study is based on interviews with IT leaders at 57 organizations – CIOs, CMIOs, data scientists and more – that are using AI across a variety of cases, from clinical decision support to patient engagement to revenue cycle management. It asked them about some tangible gains the technology has helped them achieve. It also gleaned some insights about a handful of leading vendors, and found some common best practices for AI adoption. KLAS focused on purpose-built AI vendors – those focused primarily on analytics and AI, with dedicated, standalone product – and analytics platforms with AI infrastructure.


How would a robot or AI make a moral decision?

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The first question is philosophical: a matter of moral theory. The second is technical: a matter of practical engineering. Philosophical analysis of the theoretical problem of practical action (moral theory) informs software design. Software design informs moral theory. As Lewin (1943) puts it: "There's nothing so practical as a good theory." My solution to the problem of right and wrong, succinctly stated, consists of five steps.


Artificial Intelligence in Healthcare: The Hope, The Hype, The Promise, The Peril - Stanford Center for Continuing Medical Education - Continuing Education (CE)

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Registration for this conference is now closed. This conference is anchored and building on the preview of the Special National Academy of Medicine (NAM) publication titled: "Artificial Intelligence in Healthcare: The Hope, The Hype, The Promise, The Peril." Co-led by Michael Matheny and Sonoo Thadaney Israni. Registration includes course materials, certificate of participation, breakfast and lunch. CME Certificate Fee: $25.00 Note: If you would like to receive CE Credit for your attendance, there will be a $25.00 fee option after the conference evaluation is completed and your conference attendance is verified. Your email address is used for critical information, including registration confirmation, evaluation, and certificate.


L'Oréal and ModiFace: An Artificial Intelligence-powered skin diagnostic - L'Oréal Group World Leader in Beauty Official Website

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L'Oréal's recently acquired Augmented Reality and Artificial Intelligence entity, ModiFace, and L'Oréal Research & Innovation, have announced the launch of a digital skin diagnostic for consumers based on 15 years of scientific research on skin aging by L'Oréal R&I evaluation teams. This new technology is based on an Artificial Intelligence-powered algorithm developed by ModiFace and nourished by L'Oréal's skin aging expertise and photo database. Using deep learning, the algorithm has been trained on 6000 clinical images from L'Oréal's R&I evaluation and knowledge studies conducted with Skin Aging Atlases, and then a new model has been created on over 4500 smartphones selfies for 3 groups of women (Asian, Caucasian and Afro-American) in 4 different lighting conditions. The results, which were developed with dermatologists, achieved a high level of skin assessment precision. Accurate results were obtained with different facial expressions and photo taking conditions (light, phone position) similar to those used by consumers.