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Tisch researchers team with Deloitte to bring AI to bear on MS

#artificialintelligence

Tisch Multiple Sclerosis Research Center of New York is using artificial intelligence, machine learning and data science to find patterns that may relate to the cause of the disease. The disease affects more than 2.3 million individuals, and while researchers make progress in understanding MS, the cause remains unknown. However, research techniques are improving thanks to data science and technology, says Saud Sadiq, MD, director and chief research scientist at Tisch. The research center is getting help from Deloitte, Sadiq adds. "Given the complexity of MS and the urgent need to help patients living with this diagnosis, we wanted to explore new ways to infuse technology into our research. We met with Deloitte and discussed the possibility of applying tools like AI and machine learning to narrow down molecules that may be correlated to MS, as well as accelerate the discovery process."


Senior Deep Learning Research Scientist (Multiple Roles) Logikk

#artificialintelligence

Salary is £80,000 – £150,000, however, this is up to £200,000 for absolute superstars… we are talking about multiple 1st author publications at NIPS, TPAMI, CVPR, ICML et al. and/or multiple SOTA results across relevant data sets. We are looking for several world-class Senior R&D Scientist's to apply deep learning on a range of bleeding-edge projects that will make AI accessible to the world. This global company's platform has been labelled the most accurate on the planet which has secured them a spot on the big table as a market leader in not only the design but the development of next-gen AI and Computer Vision technology. Working within this environment would allow you to join forces with a team that has 20 years' experience and is recognised as world leading talent when it comes to deep learning & computer vision research The research team is central to making this organisation the best AI company on the planet and work on the core underpinning AI, they continuously aim to push the bar and work with bleeding edge technology & techniques to achieve the impossible. This team look beyond the latest techniques in Deep Learning to what is next.


Tutorials

AAAI Conferences

The tutorials presented at ICWSM 2013 included Advanced Methods for Collecting Social Science Data in the Social Media Field, presented by Riki Conrey; Information-Theoretic Tools for Social Media Analysis, presented by Greg Ver Steeg and Aram Galstyan; Crisis Mapping, Citizen Sensing, and Social Media Analytics: Leveraging Citizen Roles for Crisis Response, presented by Amit Sheth, Patrick Meier, Carlos Castillo, and Hemant Purohit; Multiple Network Models for Complex Online Social Network Analysis, presented by Matteo Magnani and Luca Rossi; and Pulse of Virtual Worlds: Behavioral Mining and SNA in Massive Online Games, presented by Muhammad Ahmad and Jaideep Srivastava.


Dear Colleague Letter: Accelerating Development of and Research Impacts from Quantum Information Science (QIS)

@machinelearnbot

The following Dear Colleague letter was recently released by the Department of Energy's Office of Science. The letter recognizes the importance of quantum information science, expounds upon the level of interest that exists among all of the Office's programs in the rapidly developing field, and encourages innovative research ideas.


Crowdsourcing Multiple Choice Science Questions

arXiv.org Machine Learning

We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.