A total of 51 institutions submitted a Letter of Intent (LOI) to the Fund in Inaugural Competition 2. Of these, 30 were invited to submit a full proposal. Ultimately, 29 proposals were submitted, representing a total Fund request of 2.38 billion. The proposals underwent a multilevel peer review process that included an evaluation of scientific merit and strategic relevance by external experts, and merit assessment by expert review panels, based on three selection criteria. Each proposal was assigned to one of four review panels composed of leading Canadian and international experts. Subsequently, proposals underwent a strategic review by the selection board, which is composed of distinguished Canadian and international leaders.

Stewart Butterfield (left), CEO of Slack, talks about the future of work with Box CEO Aaron Levie. "We need self-driving Ubers, like now," said Levie, 30, referencing the ride-sharing company's Thursday announcement that it would soon begin testing autonomous Volvos in Pittsburgh. Apparently, Levie's Uber driver got lost en route to this lunchtime session Friday on the future of work, a discussion organized for a few journalists featuring Levie and Stewart Butterfield, 43, CEO of messaging platform Slack. Over the next hour, the two startup entrepreneurs discussed how workplace expectations have been upended by increasingly easy access to information; how work-life balance rules may need to be rewritten; and how artificial intelligence will be able to bail us out of information overload. "I'd say right now (workplace tools) are a net cultural positive, but we're also going to have to learn how to handle it," said Butterfield, who added that his company instituted a Do Not Disturb feature to help create a work-life divide.

Businesses are increasingly adopting connected technologies such as wearables and office sensors to streamline operations and boost productivity, and as a result, the Internet of Things (IoT) industry is on the up. By 2020, there'll be more than 50 billion internet-connected objects in use, and the industry will be turning over trillions in profit annually, according to Cisco. At the same time, these devices will be generating large amounts of data. Clearly, the industry is entering a golden age. But while there's major potential here, there are fears that cybercriminals are eyeing up IoT as a lucrative opportunity.

This post is authored by Jennifer Marsman, Principal Software Development Engineer at Microsoft. The first Microsoft Data Science Summit will take place on Sept 26-27, 2016 in Atlanta, GA, at the same time as Ignite. This means that you have access to the amazing Ignite Expo hall, in addition to the fabulous speakers and content catered just to data scientists, machine learning developers, and big data engineers. For more information on how the co-location with Ignite will work and what you have access to, please see here. I will be speaking on the Microsoft Cognitive Services.

Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today's biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the public. Researchers have been playing with the idea of deep learning for decades, but it has only blossomed in recent years. With companies like Facebook and Google pouring funds and resources into research, consumers are finally seeing the results of deep learning for themselves.

This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models DPMM: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model which is used to cluster documents. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.clustering to see the implementation of Dirichlet Process Mixture Models in Java.

We offer a contextual authentication API that really has one job and that's to tell you if a user is who you think they are. The answer we return must be on point as our customers will use it to make decisions, allow access, or process transactions within their applications. A question that comes up often is "how well does your system work with limited data?". In other words, how do we confirm identity and return a risk score for a user on the very first time we've seen them? A reliable answer to this question enables better user experiences, improved security, and assists in detecting fake or fraudulent new account registrations.

Abstract: With the mission "leave no valuable data behind", we developed techniques for knowledge fusion to guarantee the correctness of the knowledge. This talk starts with describing a few crazy ideas we have tested. The first, known as "Knowledge Vault", used 15 extractors to automatically extract knowledge from 1B Webpages, obtaining 3B distinct (subject, predicate, object) knowledge triples and predicting well-calibrated probabilities for extracted triples. The second, known as "Knowledge-Based Trust", estimated the trustworthiness of 119M webpages and 5.6M websites based on the correctness of their factual information. We then present how we bring the ideas to business in filling the gap between the knowledge at Google Knowledge Graph and the knowledge in the world.

Amazon's boosting the features of Fire TV and Fire TV Stick with several convenient tweaks designed to make it easier to find the shows you actually want to watch. On Wednesday, the company announced you will soon be able to find content with your voice on more than 75 apps and channels. This includes big name services such as Netflix and HBO Go. HBO Now support is coming soon. Perhaps even more handy than the new voice search are new interface convenience features for Fire TV devices.

The ScanSnap S1300i from Fujitsu is the smallest multi-page double-sided ScanSnap scanner for one-button ease of use at the desk or on the road. Just stack up to 10 pages into the feeder and press the blue button. The S1300i features a powerful set of automated image processing functions including auto skew correction, auto orientation, and auto color detection -- all working behind the scenes so your scanned content looks great without effort. Scanning to the cloud lets you access your documents from just about anywhere. The unit is powered by USB or the included AC power cord.