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Falls Risk Classification Using Smartphone Based Inertial Sensors and Deep Learning. (Conference)

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There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps inmore » terms of a budget for the number of sensors or on where, when, or how data can be collected. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall.


Using AI, people who are blind are able to find familiar faces

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Cambridge, United Kingdom – Theo, a 12-year-old boy who is blind, is seated at a table in a crowded kitchen on a gray and drippy mid-December day. A headband that houses cameras, a depth sensor and speakers rings his sandy-brown hair. He swivels his head left and right until the camera in the front of the headband points at the nose of a person on the far side of a counter. Theo hears a bump sound followed by the name "Martin" through the headband's speakers, which are positioned above his ears. "It took me like five seconds to get you, Martin," Theo says, his head and body fixed in the direction of Martin Grayson, a senior research software development engineer with Microsoft's research lab in Cambridge.


Google AI chief Jeff Dean interview: Machine learning trends in 2020

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At the Neural Information Processing Systems (NeurIPS) conference this week in Vancouver, Canada, machine learning took center stage as 13,000 researchers explored things like neuroscience, how to interpret neural network outputs, and how AI can help solve big real-world problems. With more than 1,400 works accepted for publication, you have to choose how to prioritize your time. For Google AI chief Jeff Dean, that means giving talks at workshops about how machine learning can help confront the threat posed by climate change and how machine learning is reshaping systems and semiconductors. VentureBeat spoke with Dean Thursday about Google's early work on the use of ML to create semiconductors for machine learning, the impact of Google's BERT on conversational AI, and machine learning trends to watch in 2020. This interview has been edited for brevity and clarity.


To Understand The Future of AI, Study Its Past

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Dr. Claude Shannon, one of the pioneers of the field of artificial intelligence, with an electronic ... [ ] mouse designed to navigate its way around a maze after only one'training' run. A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to "solve" intelligence, with differing research agendas and sometimes bitter relations. Today, connectionism dominates the world of AI.


To Understand The Future of AI, Study Its Past

#artificialintelligence

Dr. Claude Shannon, one of the pioneers of the field of artificial intelligence, with an electronic ... [ ] mouse designed to navigate its way around a maze after only one'training' run. A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to "solve" intelligence, with differing research agendas and sometimes bitter relations. Today, connectionism dominates the world of AI.


Research Scientist - Computer Vision and Machine Learning ai-jobs.net

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Lead research efforts in developing computer vision/machine learning capabilities for novel Human AI collaboration/interaction mechanisms Perform world-wide scouting around people-AI partnership research trends in academia and industries to shape Bosch's research efforts in human-centric machine intelligence Perform cutting-edge research to accelerate Bosch's data-driven AI efforts (e.g., utilize existing computer vision/machine learning algorithms themselves to further expedite large-scale crowdsourced data annotations for improving perception tasks such as semantic segmentation, pedestrian detection/tracking, object annotation on LIDAR for Autonomous Driving) Work with international research teams and business unit partners to elicit requirements, propose technical directions, develop/prototype solutions, and transfer the results to the business units as a next generation product Generate high quality patents and/or academic publications in the areas of applied ML, computer vision and human computer interaction (e.g. Perform world-wide scouting around people-AI partnership research trends in academia and industries to shape Bosch's research efforts in human-centric machine intelligence Perform cutting-edge research to accelerate Bosch's data-driven AI efforts (e.g., utilize existing computer vision/machine learning algorithms themselves to further expedite large-scale crowdsourced data annotations for improving perception tasks such as semantic segmentation, pedestrian detection/tracking, object annotation on LIDAR for Autonomous Driving) Generate high quality patents and/or academic publications in the areas of applied ML, computer vision and human computer interaction (e.g.


Artificial Intelligence is Ramping up in Drug Development BioSpace

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AstraZeneca announced a long-term collaboration deal with BenevolentAI, a UK-based company focused on combining computational medicine and advanced artificial intelligence. The two companies will focus on using AI and machine learning to discover and develop new drugs for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF). "The vast amount of data available to research scientists is growing exponentially each year," stated Mene Pangalo, AstraZeneca's executive vice president and president BioPharmaceuticals R&D. "By combining AstraZeneca's disease area expertise and large, diverse datasets with BenevolentAI's leading AI and machine learning capabilities, we can unlock the potential of this wealth of data to improve our understanding of complex disease biology and identify new targets that could treat debilitating diseases." No financial details were disclosed.


Where AI is today and where it's going. Richard Socher TEDxSanFrancisco

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Richard Socher is an adjunct professor at the Stanford Computer Science Department where he obtained his PhD working on deep learning with Chris Manning and Andrew Ng. He won the best Stanford CS PhD thesis award. He is now Chief Scientist at Salesforce where he leads the company's research efforts in artificial intelligence. He previously founded MetaMind, a deep learning AI platform that analyzes, labels and makes predictions on image and text data. Richard Socher is Chief Scientist at Salesforce and an adjunct professor at the Stanford Computer Science Department.


Where AI is today and where it's going. Richard Socher

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Richard Socher is an adjunct professor at the Stanford Computer Science Department where he obtained his PhD working on deep learning with Chris Manning and Andrew Ng. He won the best Stanford CS PhD thesis award. He is now Chief Scientist at Salesforce where he leads the company's research efforts in artificial intelligence. He previously founded MetaMind, a deep learning AI platform that analyzes, labels and makes predictions on image and text data. Richard Socher is Chief Scientist at Salesforce and an adjunct professor at the Stanford Computer Science Department.


Scientists Begin Work on Reverse-Engineering the Brain

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Researchers at Carnegie Mellon University have a new project: Reverse-engineer the brain. Ultimately, their goal is to "make computers think more like humans." Now, their five-year research effort has been funded by the U.S. Intelligence Advanced Research Projects Activity (IARPA) for $12 million. The research effort, through IARPA's Machine Intelligence from Cortical Networks (MICrONS) research program, is part of the U.S. BRAIN Initiative to revolutionize the understanding of the human brain. It's being led by Tai Sing Lee, a professor in the Computer Science Department and the Center for the Neural Basis of Cognition (CNBC).