Because the materials from a 3D printer aren't the most functional, their output has largely been limited to prototyping in the past. That should change in the near future with devices like MIT's own MultiFab, which can print up to 10 different materials at a time, but it still doesn't solve the problem of how to design such complex objects. That's where the new program called Foundry, created by MIT's Computer Science and Artificial Intelligence Laboratory comes in. According to MIT CSAIL, Foundry can import objects designed with traditional CAD programs like SolidWorks and then assign specific materials or properties to different parts of the object. While creating a multi-material object in the past might have required days of work and multiple 3D printers to create (assuming it was possible with existing technology at all), CSAIL says these sorts of designs can now be created in mere minutes.

Google has hired comedy writers from Pixar and The Onion in a bid to make its smart assistant more likeable. It hopes to use their talent to'infuse personality' into its AI helper, which will be used in the firm's new Pixel phones, Duo app and Home speaker. The ultimate goal is to make users feel more emotionally connected to their personal software agent and the firm believes a livelier disposition could make this happen. In a world of order-taking machines, Google Assistant aims to be a comedian. The search giant has recently hired comedy writers from Pixar and The Onion, a satire newspaper, in order to'infuse personality' into its virtual assistant that will live in Google Home (pictured) Earlier this month, Google unveiled its Pixel smartphones and eagerly awaited Home speaker that will both be designed with the smart assistant.

Just days after threatening to jail a political opponent should he win the presidency, Donald Trump's got a new campaign website that's so very … Trumpian. "Together, we are making waterboarding part of the Republican Party again," it declares. Truer words have never come out of Trump's mouth. A Trump AI generated all of the site's copy on its own. This is the work of MIT researcher Brad Hayes, who's taken his tweeting AI called DeepDrumpf and turned up its political aspirations.

Additionally, computer processing power has grown at an incredible rate while the cost of processing this data has decreased significantly, making AI more accessible. In fact, AI is everywhere, in nearly every app and device that we use every day. Apple's Siri leverages natural language processing to recognize voice commands. Facebook's deep learning facial recognition algorithm can instantly identify a person with nearly 98 percent accuracy. And Amazon, Netflix and Spotify all utilize machine learning to understand how each item in their massive catalogs not only relate to one another, but also each customer's preferences.

Kover is an out-of-core implementation of the Set Covering Machine algorithm that has been tailored for genomic biomarker discovery. It produces highly interpretable models of phenotypes. The models are rule-based and rely on the presence/absence of k-mers. The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a new reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models.

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.