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Data Revenue: Machine Learning Engineer (Python) – Medical Research

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The roleTired of data science roles that end up being mostly data engineering? Or spending years on edge cases for a single application?Join our 100% remote, full-time, Machine Learning team, and work on exciting ML solutions every day.Take responsibility for the entire ML project cycle: From re...


NLPBT 2020 - Call for Papers

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Humans interact with each other through several means (e.g., voice, gestures, written text, facial expressions, etc.) and a natural human-machine interaction system should preserve the same modality. However, traditional Natural Language Processing (NLP) focuses on analyzing textual input to solve language understanding and reasoning tasks, and other modalities are only partially targeted. This workshop aims to be a forum for both academia and industry researchers where new and unfinished research in the area of Multi/Cross-Modal NLP can be discussed. In particular, the focus of this workshop are (i) studying how to bridge the gap between NLP on spoken and written language and (ii) exploring how NLU models can be empowered by jointly analyzing multiple input sources, including language (spoken or written), vision (gestures and expressions) and acoustic (paralingustic) modalities. All deadlines must be considered at 11.59pm GMT-12 (anywhere on Earth).


Artificial Intelligence detects collision-prone roadways

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A Winnipeg-based start up is using Artificial Intelligence to look at near miss traffic collisions in order to get ahead of the accident and stop it before it can happen. Mark Neufeld reports on the new technology that could make roads safer for cars and people.



Artificial Intelligence detects collision-prone roadways - Video - 660 NEWS

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A Winnipeg-based start up is using Artificial Intelligence to look at near miss traffic collisions in order to get ahead of the accident and stop it before it can happen. Mark Neufeld reports on the new technology that could make roads safer for cars and people.


Victory! Court Orders CA Prisons to Release Race of Parole Candidates

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In a win for transparency, a state court judge ordered the California Department of Corrections and Rehabilitation (CDCR) to disclose records regarding the race and ethnicity of parole candidates. This is also a win for innovation, because the plaintiffs will use this data to build new technology in service of criminal justice reform and racial justice. In Voss v. CDCR, EFF represented a team of researchers (known as Project Recon) from Stanford University and University of Oregon who are attempting to study California parole suitability determinations using machine-learning models. This involves using automation to review over 50,000 parole hearing transcripts and identify various factors that influence parole determinations. Project Recon's ultimate goal is to develop an AI tool that can identify parole denials that may have been influenced by improper factors as potential candidates for reconsideration.


An Experiment in Deep Learning with Wild Animal Trail Camera Data

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Trail cameras are automatically triggered by animal movements. They are used by ecologists and wildlife managers around the world to study wild animal behavior and help preserve endangered species. I want to see if MATLAB image processing and deep learning can be used to identify individual animal species that visit trail cameras. We are going to start with off-the-shelf functionality--nothing specialized for this particular task. My partners on this project are Heather Gorr and Jim Sanderson. Heather is a machine learning expert at MathWorks.


Atomwise raises $123 million to accelerate drug discovery with AI

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Atomwise, a startup using AI to accelerate drug discovery, today secured $123 million in funding. A spokesperson said the funds will enable the startup to scale its technology and team as it expands its portfolio of joint ventures with researchers at the University of Toronto, Duke University School of Medicine, Charles River, Bayer, Eli Lilly, Merck, and others. Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America. Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in under two days, expediting a process that would normally take months or years.


The Top 10 Digital Transformation Trends Of 2020: A Post Covid-19 Assessment

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Every year, I make my predictions for 10 of the biggest trends in digital transformation. With Covid-19, we saw massive change in a short period of time. In this post we look back at the predictions for 2020, and how the pandemic impacted the adoption of new tech and digital trends.


The AI & Machine Learning Imperative

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"The AI & Machine Learning Imperative" offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.