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Microsoft AI news: Making AI easier, simpler, more responsible

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Today is a big day for AI announcements from Microsoft, both from this week's Build conference and beyond. But one common theme bubbles over consistently: For AI to become more useful for business applications, it needs to be easier, simpler, more explainable, more accessible and, most of all, responsible. Responsible AI is actually at the heart of a lot of today's Build news, John Montgomery, corporate vice president of Azure AI, told VentureBeat. Most notable is Azure Machine Learning's preview of a responsible AI dashboard, which brings together capabilities in use over the past 18 months, such as data explorer, model interpretability, error analysis, counterfactual and causal inference analysis, into a single view.


Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture - BMC Geriatrics

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Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com . External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003 ).


Staff Technical Product Manager, Machine Learning Platform

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We're Cruise, a self-driving service designed for the cities we love. We're building the world's most advanced, self-driving vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many. Cruisers have the opportunity to grow and develop while learning from leaders at the forefront of their fields. With a culture of internal mobility, there's an opportunity to thrive in a variety of disciplines.


Machine Learning Researcher - Saalfeld Lab

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Janelia Research Campus is a pioneering research center in Ashburn, Virginia, where scientists pursue fundamental questions in neuroscience and imaging. The Howard Hughes Medical Institute (HHMI) launched Janelia in 2006, establishing an intellectually distinctive environment for scientists to do creative, collaborative, hands-on work. Our integrated teams of biologists, computational scientists, and tool-builders pursue a small number of scientific questions with potential for transformative impact. We share our methods, results, and tools with the scientific community. It is a uniquely innovative and collaborative atmosphere that reflects HHMI's reputation for excellence.


Emery Brown wins a share of 2022 Gruber Neuroscience Prize

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David Orenstein | Picower Institute for Learning and Memory … physics, and machine learning to create theories, mathematical models, …


Advancing Machine Intelligence: Why Context Is Everything

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Most of us have heard the phrase, "Image is everything." But when it comes to taking AI to the next level, it's context that is everything. Contextual awareness embodies all the subtle nuances of human learning. It is the'who', 'why', 'when', and'why' that inform human decisions and behavior. Without context, the current foundation models are destined to spin their wheels and ultimately interrupt the trajectory of expectation for AI to improve our lives.


A glimpse into the future of radiation therapy – Physics World

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Which innovations will have the greatest impact in radiotherapy by 2030? That was the question posed in the closing session of last week's ESTRO 2022 congress; and five experts stepped up to respond. As often seen in debate-style ESTRO sessions, competition was intense and gimmicks were plentiful, with all talk titles based on movies and a definite sci-fi twist. Before battle commenced, the audience voted for their preferred innovation based on the presentation titles. This opening vote put personalized inter-fraction adaptation as the winner.


The $2 Billion Emoji: Hugging Face Wants To Be Launchpad For A Machine Learning Revolution

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When Hugging Face first announced itself to the world five years ago, it came in the form of an iPhone chatbot app for bored teenagers. It shared selfies of its computer-generated face, cracked jokes and gossiped about its crush on Siri. It hardly made any money. The viral moment came in 2018--not among teens, but developers. The founders of Hugging Face had begun to share bits of the app's underlying code online for free.


Top 5 smart personal home robots you can buy in 2022

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Robots are not limited to industrial works anymore! Thanks to the integration of artificial intelligence and voice recognition, robots are slowly invading our smart homes embedded with devices like wireless security cameras, Smart TVs, Amazon's Alexa, Amazon Echo, Google Assistant, Philips Hue lightbulbs, Ecobee4, etc. And it is not a secret that machine learning software development is on rise now. A lot of clients are coming to develop personalized ML solutions for their businesses. ABI Research predicts that this integration will grow, and by 2024 that over 79 million homes in the world will have a robot in the house.


Access and Action: Healthcare Systems Put Big Data to Work

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Across all industries, organizations are now managing more data, nearly 14 petabytes on average, according to Dell Technologies' 2020 Global Data Protection Index (1 petabyte is just over 1 million gigabytes). In healthcare, providers and patients want to see more done with all that data. Some 75 percent of healthcare consumers want to work together with providers on wellness goals, according to Deloitte research, and 85 percent of physicians expect interoperability and data sharing to become standardized. The pandemic has highlighted the value of innovative technologies to gather, manage and gain insights from the vast stores of data that hospitals collect, guiding them toward improved care and adaptive clinical workflows. "The pandemic has been a huge validation of the path we were on and the investments we've made in data management," Lamm says.