Deep learning will radically change aspects of our medical care. How well do we need to understand how AI tools work? In clinics around the world, a type of artificial intelligence called deep learning is starting to supplement or replace humans in common tasks such as analyzing medical images. Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data.
Interested in helping the millions of Americans with chronic conditions get better care? OM1 is a leading real-world outcomes and technology company leveraging big clinical data and AI to better understand, compare, and predict patient outcomes. Our products are built to accelerate research, measure and benchmark health outcomes and to personalize patient care. Were looking for Machine Learning Engineers to help design, build, test, deploy, and monitor our platform that seeks to understand clinical text at large scale, as a means to measuring and predicting patient outcomes. Our product-focused team embraces creative, rigorous, innovative approaches and experimentation, while emphasizing high quality code, user-friendliness (data is a first-class product here), and rapid iteration.
We have the largest annotated dataset for the construction industry ever assembled with all of its real world attributes: dirty, unexplored, and rich. This role is for you if you want hands-on experience with ML on image, speech, and video data. We are looking for someone excited to design, train, apply and evaluate the latest deep learning models on customer data within our cloud based research and production environments. The goal is to generate an automated assessment of job site safety risks and feed the data to a predictive pipeline that will help our clients better manage their workforce and ultimately save lives. Most of our programming is done in Python3 using AWS resources.
Is my organisation a member? The Whitehall & Industry Group's AI Collaboration Forum will bring together a wide audience from our 230 members, spanning the private, public and not-for-profit sectors, as well as academic institutions. Supported by the Office for Artificial Intelligence and kindly hosted by EY. The agenda will explore the vital role of cross-sector collaboration to ensure the endless possibilities of AI are harnessed and regulated effectively, generating maximum positive economic and societal impacts for the UK. Holding a BSc in Computer Science and an MBA from the Massachusetts Institute of Technology, Sana Khareghani has over 20 years' experience in technology and business across the private and public sectors.
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complete and valid quantification of model uncertainty. We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty. It has a theoretical guarantee in that it robustly estimates the uncertainty patterns in the data distribution, and can decompose its overall predictive uncertainty into distinct components that are due to different sources of noise and error. We show that our method achieves accurate uncertainty estimates under complex observational noise, and illustrate its real-world utility in terms of uncertainty decomposition and model bias detection for an ensemble in predict air pollution exposures in Eastern Massachusetts, USA.
Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable -- hidden inside a so-called black box -- how can it be trusted?
BioSig Technologies, Inc. (NASDAQ: BSGM) ("BioSig" or the "Company"), a medical technology company developing a proprietary biomedical signal processing platform designed to improve signal fidelity and uncover the full range of ECG and intra-cardiac signals, today announced that the Company entered into a technical collaboration with Reified Capital, a provider of advanced artificial intelligence-focused technical advisory services to the private sector. Reified was co-founded by Dr. Alexander D. Wissner-Gross and Timothy M. Sullivan, the founders of Gemedy. The new collaboration with Cambridge, Massachusetts-based Reified will focus on developing a foundational artificial intelligence platform on the basis of integrated healthcare datasets, beginning with ECG and EEG data acquired by BioSig's first product, PURE EP(tm) System - a novel real-time signal processing platform engineered to provide electrophysiologists with high fidelity cardiac signals. Electrophysiology focused technological solutions developed under the terms of this collaboration will be integrated into the PURE EP(tm) technology platform. Reified is led by Harvard- and MIT-trained computer scientist and physicist Dr. Wissner-Gross, an award-winning computer scientist, physicist, entrepreneur and author.
Dakuo Wang is a Research Scientist at IBM Research AI, Cambridge, Massachusetts. His research lies in the intersection between human-computer interaction (HCI) and artificial intelligence (AI). He is now leading a team of researchers, engineers, and designers to conduct research and design user experience for IBM AutoAI, a solution to automate the end-to-end machine learning pipeline. From studying how users work with various AI systems such as automated machine learning (AutoML/AutoAI), chatbots, and clinical decision support systems (CDSS), he proposes "Human-AI Collaboration" as a new framework to examine and design AI systems to work together with humans. Before joining IBM Research, Dakuo Wang got his Ph.D. and M.S. in Information and Computer Science from the University of California Irvine, a Diplôme d'Ingénieur (M.S.) in Information System from École Centrale d'Électronique Paris, and a B.S. in Computer Science from Beijing University of Technology.
A hot trend in artificial intelligence in recent years has been the rise of impressive fakes -- fake headshots, fake videos, fake text. Deep learning techniques, part of machine learning, have gotten better and better at taking real-world data and using it to make something artificial, such as a picture, seem incredibly convincing. Researchers at the Massachusetts Institute of Technology on Monday announced an AI approach that goes in the opposite direction: it takes something real and makes it artificial. The application is somewhat surprising: knitted garments that need to be reproduced. The system studies a picture of a garment and computes a series of stitches to give to an automated knitting machine.