The CyberGuy Kurt Knutsson presents tech toys that could be great gifts for children this holiday season. How often do you turn to Google? If you're focused on privacy, there are better options. Tap or click for alternatives to Google that work well without gathering so much of your data. Tap or click for reasons you should ditch Dr. Google.
Hogan, Aidan, Blomqvist, Eva, Cochez, Michael, d'Amato, Claudia, de Melo, Gerard, Gutierrez, Claudio, Gayo, José Emilio Labra, Kirrane, Sabrina, Neumaier, Sebastian, Polleres, Axel, Navigli, Roberto, Ngomo, Axel-Cyrille Ngonga, Rashid, Sabbir M., Rula, Anisa, Schmelzeisen, Lukas, Sequeda, Juan, Staab, Steffen, Zimmermann, Antoine
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.
Companies may be achieving only a third of the value they could be getting from data science in industry applications. In this paper, we propose a methodology for categorizing and answering 'The Big Three' questions (what is going on, what is causing it, and what actions can I take that will optimize what I care about) using data science. The applications of data science seem to be nearly endless in today's modern landscape, with each company jockeying for position in the new data and insights economy. Yet, data scientists seem to be solely focused on using classification, regression, and clustering methods to answer the question 'what is going on'. Answering questions about why things are happening or how to take optimal actions to improve metrics are relegated to niche fields of research and generally neglected in industry data science analysis. We survey technical methods to answer these other important questions, describe areas in which some of these methods are being applied, and provide a practical example of how to apply our methodology and selected methods to a real business use case.
The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a SelfLearning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated. Such architectural paradigm enables indexes to evolve naturally while accommodating the dynamic changes of user interests. It operates by continuously constructing indexes over time, while injecting progressive improvement in search performance. For search operations to be effective, convergence of index learning is of crucial importance to ensure efficiency and robustness. In this paper, we develop a Self-Learning Search Engine architecture based on reinforcement learning using a Markov Decision Process framework. The balance between exploration and exploitation is achieved through evolutionary exploration Strategies. The evolutionary index learning behavior is then studied and formulated using stochastic analysis. Experimental results are presented which corroborate the steady convergence of the index evolution mechanism. Index Term
We dive into the strategies Microsoft is pursuing across cloud, enterprise IT, AI, gaming, and more to see how the company is positioning itself for the future. As the world's most valuable company, and with a current market cap hovering around $780B, Microsoft may be the next company to reach the $1T threshold. While it may not grab as many headlines as its buzzier tech giant counterparts, the company is quietly adapting across its core business areas, led by a future-focused Satya Nadella. Since assuming the CEO role in 2014, Nadella has deprioritized the Windows offering that initially helped Microsoft become a household name, refocusing the company's efforts on implementing AI across all its products and services. That's not the only change: in addition to an increased focus on AI, cloud and subscription services have become unifying themes across products. And to maintain its dominance in enterprise technology, Microsoft is expanding in new areas -- like gaming and personal computing -- that leverage the company's own cloud infrastructure. Below, we outline Microsoft's key priorities, initiatives, investments, and acquisitions across its various business segments. The majority of Microsoft's revenue comes from its enterprise technologies, which fall under its Intelligent Cloud and Productivity & Business Processes segments. The Productivity & Business Processes segment includes software products like Office 365, Skype, LinkedIn, and Microsoft's ERP (enterprise resource planning) and CRM (customer relationship management) platform, Dynamic 365. Microsoft's Intelligence Cloud segment includes cloud platform Azure, the Visual Studio developer platform, and Windows Server, a version of Microsoft's proprietary operating system optimized for running in the cloud. Outside of enterprise technology, Microsoft generates revenue from products like Xbox and Microsoft Surface, among others areas. These products are bucketed into the company's More Personal Computing segment. In addition to its in-house efforts, Microsoft has a number of initiatives that look to support promising young businesses. These include Microsoft's venture capital arm, M12, Microsoft's accelerator, ScaleUp, and other initiatives like Microsoft for Startups.
Alphabet is broken out into its core Google business and a number of other subsidiaries, which it deems "Other Bets." The majority of Google's business comes from advertising revenues, which the company generates through its search engine as well as a number of other Google-affiliated and partnership websites. Outside of search and advertising, Google generates revenue from products including cloud and enterprise, consumer hardware, mapping, and YouTube. In addition to Google, Alphabet encompasses a host of other subsidiaries called "Other Bets." These companies are more experimental in nature, and as a result are not material to Alphabet's bottom line.