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Deep Learning/Computer Vision Data Scientist - McLean, VA

#artificialintelligence

Booz Allen Hamilton has been at the forefront of strategy and technology for more than 100 years Today, the firm provides management and technology consulting and engineering services to leading Fortune 500 corporations, governments, and not-for-profits across the globe. Booz Allen partners with public and private sector clients to solve their most difficult challenges through a combination of consulting, analytics, mission operations, technology, systems delivery, cybersecurity, engineering and innovation expertise. Key Role: Apply technical and analytical expertise to exploring and examining data from structured, semi-structured, and unstructured data sources and types, including text, audio or signal, and image or video. Leverage a proven track record of serving as the client interface and experience with developing cutting-edge solutions using advanced machine learning, deep learning, and computer vision. Supervise the activities of others, as needed.


Microsoft Is Teaching Computers to See Like People

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Microsoft's quest to build computing systems that understand the world around them doesn't end with the company's Project Oxford machine-learning technology. Researchers at the Redmond, Wash., software maker are also developing systems that mimic how humans pull information from the things they see. "When a person is asked about something in a photo, they're taking in a lot of details--a lot of words--to answer questions about it," blogged Microsoft spokesperson Athima Chansanchai. "Now, a team of Microsoft researchers, together with colleagues from Carnegie Mellon University, has created a system that uses computer vision, deep learning and language understanding to analyze images and answer questions the same way humans would." Together, the researchers created a model that "applies multi-step reasoning to answer questions about pictures," said Chansanchai.


MIT researchers are now teaching computers to predict the future

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Using algorithms partially modeled on the human brain, researchers from the Massachusetts Institute of Technology have enabled computers to predict the immediate future by examining a photograph. A program created at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) essentially watched 2 million online videos and observed how different types of scenes typically progress: people walk across golf courses, waves crash on the shore, and so on. Now, when it sees a new still image, it can generate a short video clip (roughly 1.5 seconds long) showing its vision of the immediate future. "It's a system that tries to learn what are plausible videos -- what are plausible motions you might see," says Carl Vondrick, a graduate student at CSAIL and lead author on a related research paper to be presented this month at the Neural Information Processing Systems conference in Barcelona. The team aims to generate longer videos with more complex scenes in the future.


Why Machines Cannot Learn Mathematics, Yet

arXiv.org Artificial Intelligence

Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods. However, while mathematics is a precise and accurate science, it is usually expressed by less accurate and imprecise descriptions, contributing to the relative dearth of machine learning applications for IR in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in ML, it seems canonical to apply ML techniques to represent and retrieve mathematics semantically. In this work, we apply popular text embedding techniques to the arXiv collection of STEM documents and explore how these are unable to properly understand mathematics from that corpus. In addition, we also investigate the missing aspects that would allow mathematics to be learned by computers.


Deep learning: Why it's time for AI to get philosophical

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Catherine Stinson is a postdoctoral scholar at the Rotman Institute of Philosophy, at the University of Western Ontario, and former machine-learning researcher. I wrote my first lines of code in 1992, in a high school computer science class. When the words "Hello world" appeared in acid green on the tiny screen of a boxy Macintosh computer, I was hooked. I remember thinking with exhilaration, "This thing will do exactly what I tell it to do!" and, only half-ironically, "Finally, someone understands me!" For a kid in the throes of puberty, used to being told what to do by adults of dubious authority, it was freeing to interact with something that hung on my every word – and let me be completely in charge. For a lot of coders, the feeling of empowerment you get from knowing exactly how a thing works – and having complete control over it – is what attracts them to the job.