Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings of these words are usually a mixture of multiple meanings. In this paper, we acknowledge multiple identities of the same word in different contexts and learn the \textbf{identity-sensitive} word embeddings. Based on an identity-labeled text corpora, a heterogeneous network of words and word identities is constructed to model different-levels of word co-occurrences. The heterogeneous network is further embedded into a low-dimensional space through a principled network embedding approach, through which we are able to obtain the embeddings of words and the embeddings of word identities. We study three different types of word identities including topics, sentiments and categories. Experimental results on real-world data sets show that the identity-sensitive word embeddings learned by our approach indeed capture different meanings of words and outperforms competitive methods on tasks including text classification and word similarity computation.

The future of Education is always a hot topic anywhere in the globe, as we universally want the best for the next generation. As I get older, I see a rapidly growing gap between how I learnt at school and at University, and what the current working world expects. Recently, I was on a speaking panel for a conference about Emerging Trends in Learning and working tackling the practical impacts of the digital disruption starting to hit the Education sector. Greg Prior from NSW Education, drove the point that literacy and numeracy are still a fundamental core. Collaboration and personalised learning is emerging as a key approach and governments will allow students to progressively take leadership of their own learning.

The past two decades has seen machine learning (ML) transformed from an academic curiosity to a multi-billion dollar industry, and a centerpiece of our economic, social, scientific, and security infrastructure. Much work in machine learning has drawn on research in optimization, motivated by large-scale applications requiring analysis of massive high-dimensional data. In this talk, I'll argue that the growing importance of networked data environments, from the Internet to cloud computing, requires a fundamental rethinking of our basic analytic tools. My thesis will be that ML needs to shift from its current focus on optimization to equilibration, from modeling the world as uncertain, but stationary and benign, to one where the world is non-stationary, competitive, and potentially malicious. Adapting to this new world will require developing new ML frameworks and algorithms.

After Thursday's annual shareholder meeting in downtown Los Angeles, LA Times' parent Tribune Publishing announced it would be changing its name to tronc Inc. and moving its shares from the New York Stock Exchange to the Nasdaq, effective June 20. In the release, the future consonant-heavy media organization describes itself as a "a content curation and monetization company focused on creating and distributing premium, verified content across all channels," or a news organization, in other words. It also "plans to launch www.tronc.com, "Our industry requires an innovative approach and a fundamentally different way of operating," Ferro said in the release. Earlier in the day, Tribune Chairman Michael Ferro won a big victory when he had his slate of board members confirmed.

Network analysis offers a new set of techniques to tackle the persistent and growing problem of complex fraud. Network analysis supplements traditional techniques by providing a mechanism to bridge investigative and analytics methods. Beyond base visualization, network analysis provides a standardized platform for complex fraud pattern storage and retrieval, pattern discovery and detection, statistical analysis, and risk scoring. This article gives an overview of the main challenges and demonstrates a promising approach using a hands-on example. With swelling globalization, advanced digital communication technology, and international financial deregulation, fraud investigators face a daunting battle against increasingly sophisticated fraudsters.

Speaking after the first day of the ICT meeting, Takaichi said she introduced eight basic principles Tokyo believes important when developing computer science that gives machines human-like intelligence, and that she was generally supported in calling for further discussion. The eight principles include making AI networks controllable by human beings and respect for human dignity and privacy. "The development of AI is expected to progress at a tremendous pace of speed, and it should be amazing technology that does not give anxiety to people," the minister of internal affairs and communications told reporters, noting the need to deepen international discussion about establishing a basic set of rules. The first G-7 ICT ministerial meeting in nearly two decades comes at a time when cyberattacks have become a global reality and the development of such potentially revolutionary technologies as artificial intelligence and the "Internet of Things" (IoT) -- the concept of connecting various products to the Internet -- continues apace. With cyberattacks having become a global reality, participants from Britain, Canada, France, Germany, Italy, Japan and the United States discussed at the G-7 meeting ways to utilize advances in the field to drive economic growth while ensuring data security.

The popularity of tagging systems provides a great opportunity to improve the performance of item recommendation. Although existing approaches use topic modeling to mine the semantic information of items by grouping the tags labelled for items, they overlook an important property that tags link users and items as a bridge. Thus these methods cannot deal with the data sparsity without commonly rated items (DS-WO-CRI) problem, limiting their recommendation performance. Towards solving this challenging problem, we propose a novel tag and rating based collaborative filtering (CF) model for item recommendation, which first uses topic modeling to mine the semantic information of tags for each user and for each item respectively, and then incorporates the semantic information into matrix factorization to factorize rating information and to capture the bridging feature of tags and ratings between users and items.As a result, our model captures the semantic correlation between users and items, and is able to greatly improve recommendation performance, especially in DS-WO-CRI situations.Experiments conducted on two popular real-world datasets demonstrate that our proposed model significantly outperforms the conventional CF approach, the state-of-the-art social relation based CF approach, and the state-of-the-art topic modeling based CF approaches in terms of both precision and recall, and it is an effective approach to the DS-WO-CRI problem.

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

GettyFacebook CEO Mark Zuckerberg's 2016 New Years resolution is to create a virtual assistant for his home. Facebook is investing heavily in what many in the tech industry believe to be the next frontier of innovation, artificial intelligence. The largest social network on earth has a division of AI experts it calls FAIR. There's also a separate team called Applied Machine Learning, which focuses on "giving people communication superpowers through AI." Facebook clearly believes that AI is important to the company's future. Its employees are running 50x more AI experiments per day compared to last year.

In theory, chatbots on Messenger would allow businesses to provide customer service without involving human workers. If you wanted to reach out to a business, you could do so via your Messenger app, rather than by looking up a phone number and calling. For example, Facebook showed off a chatbot for 1-800-Flowers.com that automatically takes orders via Messenger. It says things like: "White is a great choice! What is the recipient's name?"