Despite an era of supercomputing and increasingly precise instrumentation, many biological phenomena remain misunderstood. One approach to understanding such events is the elaboration of case-control studies, where a group of individuals that exhibit a given biological state (phenotype) is compared to a group of individuals that do not. In this setting, one seeks biological characteristics (biomarkers), that are predictive of the phenotype. Such biomarkers can serve as the basis for diagnostic tests, or they can guide the development of new therapies and drug treatments by providing insight on the biological processes that underlie a phenotype [1–4]. With the help of computational tools, such studies can be conducted at a much larger scale and produce more significant results.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis. DL-Learner is used for data analysis tasks within other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern-based and evolutionary techniques for learning on structured data. It also offers a plugin for Protégé, which can give suggestions for axioms to add.

Saul is a modeling language implemented as a domain specific language (DSL) in Scala. The flexibility in designing above components helps rapid development of intelligent AI systems with one or more learned functions that interact with each other. Saul offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application. With Saul, the details of feature extraction, learning, model evaluation, and inference are all abstracted away from the programmer, leaving him to reason more directly about his application. The project contains three modules.

Last week, Salesforce pitched its sales, marketing, e-commerce, and customer service software as a treasure trove of smarts because of the artificial intelligence it is layering across that portfolio. This week, it's Microsoft's turn to claim the better and smarter mantle with the official launch of Dynamics 365, a previously announced suite of business applications for handling back-office processes. Actually the applications, already announced once, have been announced again on Tuesday, with availability slated for November 1. Dynamics 365 converges a bunch of previously separate products--including Dynamics CRM and at least three enterprise resource planning (ERP) software applications--into a single code base that also incorporates AI features. Microsoft's pitch is that while Salesforce is adding AI cobbled together via a set of acquisitions over the past year, Microsoft is "building in" technology that it's been working with for years, much of which was built in-house. ERP software is used to manage inventory, manufacturing processes, and finances.

Bioelectronics has recently hit the headlines as Google has teamed up with GlaxoSmithKline, mixing chemistry and biology to develop implantable electronic devices that can help treat chronic diseases. The whole area of Artificial Intelligence (AI) is developing at a rapid pace but is still often built around auto-bot style tools, which provide an intelligent question and answer interaction. However, what happens when you have more complex business processes that require more than a simple'yes' or'no' response? Most information and content repositories, and their subsequent business process flows, are based on modelling years of experience to provide semi-automated and compliant business processes. They enable organisations to adhere to standards and operate as efficiently as possible, or so it would seem.

During a recent interview with Erik Lindberg, Senior Director for Westlaw Product Management, not only did I get the scoop on the latest emerging technologies from Westlaw, but I also got to pick his brain about how younger generations are influencing advancements in legal technology and the future of the legal industry. Take a look at his thoughts, and share your own ideas and experiences with us on social media. How are Gen Y and Gen Z attorneys driving advancements in legal technology? They're used to having the world in their pocket and being able to get information much more quickly. They consume and interact with information sources and technology in a completely different way than generations before.

Two of the technology world's most powerful billionaires are concerned we are living in a Matrix-style simulated world and are working with scientists to break us out. In an article in The New Yorker, writer Ted Friend explains that the idea of the "simulation hypothesis" has been on the rise among tech's elite. "Many people in Silicon Valley have become obsessed with the simulation hypothesis, the argument that what we experience as reality is in fact fabricated in a computer; two tech billionaires have gone so far as to secretly engage scientists to work on breaking us out of the simulation," Friend claimed. Neither of the billionaires were named in the piece. The idea has gained traction in recent years with the advancement of artificial intelligence (AI) technologies.

We're excited by Zymergen's latest funding of 130 million to aggressively scale its machine learning and applied robotics platform. This platform enables precision engineering biology at industrial scale and quality, optimizing existing products and producing breakthrough new ones in agriculture, life-sciences, and manufacturing. It was an easy decision for us to continue to invest in Zymergen, which we've backed since its seed. Their technology lead is huge and self-reinforcing, and they have an impressive number of global Fortune 500 customers, outstanding financial metrics, and a world-class team. We're pleased that this success attracted the support of SoftBank, which brings Deep Nishar to join us on the Zymergen Board.

Artificial Intelligence (AI) has already made its mark in areas like technology and manufacturing. Technology has progressed by such leaps and bounds that we have computers that untangle spoken language, recognize faces, and even turn down our thermostat when we're away from home. But what does all this have to do with your customer's experience? Unless you closely follow tech trends, you might be surprised to learn that AI is already playing a role in customer service, user experience, inventory planning, and other areas. First, let's consider how AI is changing and improving things on the business side.

In his recent book, The Inevitable, former Wired editor Kevin Kelly lists Marketing among the fields that will soon become cognified, his term for being infused with and transformed by Artificial Intelligence. "The amount of attention an individual reader or watcher spends on an advertisement can be multiplied by their social influence … in order to optimize their attention and influence per dollar. Done at the scale of millions, this is a job for AI." Although The Inevitable's aim is to describe technologies that will shape our future, it's clear that the first tendrils of that future reach back to today and that advertising is going to get cognified sooner than most suspect. In some ways, of course, it already has been.