The database landscape is much more diverse than it once was, thanks in large part to big data, and on Tuesday, one of today's newer contenders unveiled an upcoming release featuring a major boost in security. Version 9 of MarkLogic's namesake NoSQL database will be available at the end of this year, and one of its key new features is the inclusion of Cryptsoft's KMIP (Key Management Interoperability Protocol) technology. MarkLogic has placed its bets on companies' need to integrate data from dispersed enterprise silos -- a task that has often required the use of so-called ETL tools to extract, transform and load data into a traditional relational database. Aiming to offer an alternative approach, MarkLogic's technology combines the flexibility, scalability, and agility of NoSQL with enterprise-hardened features like government-grade security and high availability, it says. Now coming up in the next generation of the software will be a variety of improvements in data integration, manageability and security, the company says, but certainly most notable among them is the addition of Cryptsoft's KMIP.

Microsoft's Azure Machine Learning is one of many changes making its way into Dynamics CRM 2016, the customer engagement software set for release later this year, the company announced Nov. 5. We first learned about the development of Dynamics CRM 2016 in September, when Microsoft promised the upcoming version would be more mobile-friendly and include better transitions across apps like email, Excel, and OneDrive for Business. The push to improve Dynamics CRM continued in late September 2015, when Microsoft acquired technologies and assets from partner Adxstudios. Web portals from Adxstudios are built into Dynamics CRM so sales and customer service can be conducted online; its tools are also designed to improve customer communications. For the upcoming release of Dynamics CRM 2016, Microsoft plans to release related updates in waves as the launch grows closer.

We have been convinced for many years that machine learning, the kind of artificial intelligence that actually works in practice, not in theory, would be a key element of the next platform. In fact, it might be the most important part of the stack. And therefore, those who control how we deploy machine learning will, to a large extent, control the nature of future applications and the systems that run them. Machine learning is the killer app for the hyperscalers, just like modeling and simulation were for supercomputing centers decades ago, and we believe we are only seeing the tip of the machine learning iceberg as Google, Facebook, Baidu, Amazon, Microsoft, and other titans of the Internet, who have enough data to make machine learning not only practical, but necessary, build out their expertise and embody it in the development and production platforms that support their empires. Google's first machine learning platform, called DistBelief, was rolled out in 2011 and used to train deep neural networks using tens of thousands of CPU cores across thousands of servers, By its own admission, DistBelief was difficult to use and tied very tightly to Google's own infrastructure, and so the company created a better, more generic machine learning platform called TensorFlow, which was unveiled and open source last November.

Machine learning is a technology that has recently matured enough to show business benefits, and those benefits are incredible. By training machines to understand and perform certain tasks, businesses can reap the rewards of human ability combined with machine speed. One place where machine learning can be a gamechanger is the medical field. To shed some light on these developments, Jeff Frick (@JeffFrick), cohost of theCUBE, from the SiliconANGLE Media team, joined Scarlett Spring, president and chief commercial officer at VisionGate, Inc., during the Girls in Tech Catalyst Conference 2016 event. The conversation opened up as Spring explained the technology her company was developing.

In particular, I checked out the k-Nearest Neighbors (k-NN) and logistic regression algorithms and saw how scaling numerical data strongly influenced the performance of the former but not that of the latter, as measured, for example, by accuracy (see Glossary below or previous articles for definitions of scaling, k-NN and other relevant terms). The real take home message here was that preprocessing doesn't occur in a vacuum, that is, you can prepocess the heck out of your data but the proof is in the pudding: how well does your model then perform? Scaling numerical data (that is, multiplying all instances of a variable by a constant in order to change that variable's range) has two related purposes: i) if your measurements are in meters and mine are in miles, then, if we both scale our data, they end up being the same & ii) if two variables have vastly different ranges, the one with the larger range may dominate your predictive model, even though it may be less important to your target variable than the variable with the smaller range. What we saw is that this problem identified in ii) occurs with k-NN, which explicitly looks at how close data are to one another but not in logistic regression which, when being trained, will shrink the relevant coefficient to account for the lack of scaling. As the data we used in the previous articles was real-world data, all we could see was how the models performed before and after scaling.

Machine types that accept non-regular languages 20.

Dag Kittlaus and Adam Cheyer created the artificial intelligence behind Siri, Apple's iconic digital assistant, and one of the first modern apps to capably handle natural language queries on a smartphone. Today the pair showed off their newest creation, Viv, a next generation AI assistant that they have been developing in stealth mode for the last four years. The goal was to create a better version of Siri, one that connected to a multitude of services, instead of routinely shuffling queries off to a basic web search. During a 20-minute demo onstage at Disrupt NYC, Viv flawlessly handled a number of complex requests, not just in terms of comprehension, but by connecting with third-party merchants to purchase goods and book reservations. Viv's approach is much closer to Amazon's Alexa or Facebook's Messenger bots, offering the ability to connect with third-party merchants and vendors so that it can execute on requests to purchase goods or book reservations.

In the previous blog post, we discussed the nature of sequential data and why we need a robust separate modeling technique to analyze that data. Traditionally, people have been using Hidden Markov Models (HMMs) to analyze sequential data, so we will center the discussion around HMMs in this blog post. HMMs have been implemented for many tasks such as speech recognition, gesture recognition, part-of-speech tagging, and so on. But HMMs place a lot of restrictions as to how we can model our data. HMMs are definitely better than using classical machine learning techniques, but they don't fully cover the needs of all the modern data analysis.

For newbies this is the best place to start; introductions, FAQs and a glossary of terms. Information on the different types of learning algorithms used in AI and ML systems and applications. A list of different software tools, used to simulate AI techniques, both free open source and commercial. A list of free data sets that can be used for research and testing of AI learning algorithms. Find out how different hardware can be used to host and accelerate the performance of AI applications.

For the IT sector, the concept of digital transformation represents a time for evolution, revolution and opportunity, according to Information Technology Association of Canada (ITAC) president Robert Watson. The new president for the technology association made the statements at last week's IDC Directions and CanadianCIO Symposium in Toronto. The tech trends event was co-hosted by ITWC and IDC with support from ITAC. Notable sessions included the ITWC-moderated Digital Transformation panel -- which featured veteran CIOs discussing the digital transformation opportunities and challenges-- and IDC Canada's Nigel Wallis outlining why Canadian business models should shift to reap IoT rewards. Digital transformation refers to the changes associated with the application of digital technology in all aspects of human society; the overarching event theme focused on digital transformation as more than mere buzzword, but as process that tech leaders and organizations should already be adopting.