We invest in early-stage companies that develop tools, applications, content, and services to improve education opportunities for all children. 2 2017 Reach Capital. About Reach Capital 3. 3 At Reach, we believe in... Learning that... Technology that... Have a sense of purpose and actively pursue it Are empathetic, caring, and connected Work together to solve problems and improve the world Enables and respects a person's agency and voice Exposes one to broad perspectives, places, and challenges Enables meaningful human interaction Minimizes boundaries and deepens connections between people Enhances and scales effective practices Increases access to quality education Communities where people... 2017 Reach Capital. Today's students are mobile and always connected Photo sources: Express Newspapers 2015, Mr. Martin's Web Site, MacStories 2017, Independent Digital News & Media 2017 6. 6 2017 Reach Capital. Then Now 67%of millennials agree they can find a YouTube video on anything they want to learn Learning is now bite-sized, on-demand, and accessible anywhere Think with Google Photo sources: Amazon, Buzzfeed 7. a K-12 schools are making headway 8. 8 2017 Reach Capital. PC Revolution Begins: first computers in school 1:5 Computer:Student 2:3 Computer/Tablet:Student 1977 2000 2016 NCES Schools are moving rapidly to one device per child Photo sources: Computer History Museum, Ben Schumin, Google 9. 9 2017 Reach Capital.

In this video, you will learn the newly released Azure Machine Learning tools that help professional data scientists to build Azure ML apps. The tools include Azure ML Experimentation, Model Management Services, Azure ML Workbench, VS Code Tools for AI.

Humankind has done a great deal in terms of making exquisite art, whether through its paintings, songs, or performance art. Museums are packed with such work, and ordinary people have libraries full of fantastic literary achievements--both physical and digital. For the longest time, creativity seemed like an exclusively human forte. However, now many budding artists are learning to express their creativity in the realm of artificial intelligence. Recently, AIs have been trying to outperform their human counterparts in a number of endeavors, particularly when it comes to playing games.

From its first incarnation in 2000, to its online launch in 2005, up through today, Pandora [Music] set-out to differentiate itself -- a music discovery service hand-built on a scientific and proprietary matching engine. In 2000, 80% of the music industry's revenues came from less than 3% of the releases [2]. Tim Westergren, a musician and composer, saw an untapped market opportunity to bridge this gap -- changing the music industry paradigm and dynamics between artists and consumers. Tim saw an opportunity to match undiscovered artists and their music to listeners who would enjoy their sound. Matching would create value for the artists, listeners and the intermediary facilitating this process.

Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.

In this Zetabyte Era, the abundance of information calls for personalization systems to ease the navigation of users. Among these systems, recommenders are becoming mainstream, and are used by major service providers such as Facebook, Amazon and Netflix. Some recommenders make use of the content of the items: these include popularity-based, knowledge-based or demographic-based schemes [8]. Others are content-agnostic: these are mainly collaborative filtering (CF) [14], [44] schemes, and are predominant today for they achieve good recommendation quality without requiring any prior knowledge of the content of the items recommended. Recommenders typically collect user preferences using explicit feedback [32], such as numerical ratings (star ratings in Imdb, Netflix, Amazon), binary preferences (likes/dislikes in Youtube), or unary preferences (retweets in Twitter). Yet, relying on explicit feedback raises issues regarding feedback sparsity (in systems where the item catalog is large, users tend to give feedback on a trace amount of those items, impacting the quality of recommendations [8]), and limited efficiency for recommending fresh items in reaction to recent user actions [37]. A few implicit recommenders have been proposed to answer those shortcomings.

Two tech giants are in a messy streaming video fight right now, leaving consumers squeezed in the middle. Google on Tuesday said it would pull its YouTube apps from Amazon's Echo Show, which is an Alexa-powered device with a screen, and Fire TV starting next month. Why? Google pointed a finger at Amazon, which hasn't been selling some products from Google and Nest, which is also owned by Google's parent company. Amazon also doesn't allow Google products to have access to its Prime Video streaming service, the statement said. "Amazon doesn't carry Google products like Chromecast and Google Home, doesn't make Prime Video available for Google Cast users, and last month stopped selling some of Nest's latest products," the company said in a statement to The Post. "Given this lack of reciprocity, we are no longer supporting YouTube on Echo Show and Fire TV. We hope we can reach an agreement to resolve these issues soon."

This post is authored by Erika Menezes, Software Engineer at Microsoft. Using deep learning to learn feature representations from near-raw input has been shown to outperform traditional task-specific feature engineering in multiple domains in several situations, including in object recognition, speech recognition and text classification. With the recent advancements in neural networks, deep learning has been gaining popularity in computational creativity tasks such as music generation. There has been great progress in this field via projects such as Magenta, an open-source project focused on creating machine learning projects for art and music, from the Google Brain team, and Flow Machines, who have released an entire AI generated pop album. For those of you who are curious about music generation, you can find additional resources here.

Advent is upon us, and the season of overconsumption begins! My wife and I attended our first Christmas party of the year yesterday and the tree and decorations are going up this evening, in a solid attempt to be better organised this year! Whilst many of us might be starting to ease off for Christmas now, the LawTech sector has had another busy week. Legal Futures' Dan Bindman has written a great in depth piece on the Ailira chatbot that we mentioned in last weeks LawTech Review. Artificial Intelligence may help you win your next court case!

Our vast experience with planning data science conferences across a multitude of industries has enabled us to host, listen and learn valuable insights into the industry's most ambitious goals and research advancements. As the data science community heads towards 2018, we asked our top speakers to comment on 2017's most impactful achievements in Artificial Intelligence and make a few predictions for 2018. We summarize the most notable insights in this post, and offer expert commentary on the advancements, predictions and lessons learned regarding machine learning algorithms and deep learning systems. Daniel Monistere, SVP-Client Solutions at Nielsen points out the technology advancement electronic devices have met and the increase in their storage and data gathering capabilities. Also, applications have become intelligent being able to collect user data.