With a black bowler hat and a chiffon white scarf, Berenson certainly looks the part of a stuffy art connoisseur -- so long as you ignore the neural network poking out from his suit. Meet the Art Critic 2.0, built from gleaming metal and sleek sensors, with equal parts smarts and snob. The sage art critic once commanded considerable power in creative spheres, making or breaking an artist's career with a simple smirk of disapproval or a punishing review in next day's paper. But today, as the number of full-time art critics dwindles in newsrooms, a growing force of high-tech art experts is starting to pick up the slack by methodically decoding art's finest details. In Canada, the Roomba-esque kulturBOT snaps photos at exhibitions and uses an algorithm-powered "stream of consciousness" to tweet out the images with often nonsensical captions like "panting with love of danger" or "streaked with the nocturnal vibration."

In last week's recap, I recounted how Alec Ross, author and technology policy advisor, had speculated that robot design, development and manufacturing would be critical to future economic well-being worldwide. What I didn't share was some of the background he provided that calls into question whether United States is positioning itself to be a leader in the robotics industry and the impact robotics will have on U.S. manufacturing jobs going forward. John Hitch has authored two recent articles on the robotics industry--a July 17 article, Reconciling Robot-Induced Anxiety and Admiration and an August 14 article, Manufacturing Obscurity is a Fate Worse than the Robopocalypse. These articles provide detail on these two questions above, and I highly recommend you read them. Here is some of the information they present.

Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. A Kansas City doctor is performing groundbreaking research on vaping, using a robot. Dr. Matthias Salathe spends a lot of time with e-cigarettes. "The notion was it's safe, and frankly we did not believe this," said Salathe.

Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. A Kansas City doctor is performing groundbreaking research on vaping, using a robot. Dr. Matthias Salathe spends a lot of time with e-cigarettes. "The notion was it's safe, and frankly we did not believe this," said Salathe.

Artificial intelligence is at the center of many emerging technologies today, and perhaps nowhere are the implications more meaningful than in healthcare. So where is AI making an impact in healthcare today? What will the future bring, and how should healthcare providers and technologists get ready? On the Season 4 premiere of GeekWire's Health Tech Podcast, we address all of those questions with three guests: Linda Hand, CEO of Cardinal Analytx Solutions, a venture-backed company that uses predictive technology to identify people at high risk of declining health, and match them with interventions; Colt Courtright, who leads Corporate Data & Analytics at Premera Blue Cross; and Dr. David Rhew, Microsoft's new chief medical officer and vice president of healthcare. This episode was recorded on location at the dotBlue conference in Seattle, hosted by the returning sponsor of the show, Premera Blue Cross.

The real robotics revolution is not having robots take care of tasks but having them available to businesses as a service. And so another acronyms to represent the expanding world of as a service is added to today's business vocabulary. The business world has introduced a number of different functions as a service, including software-as a-service (SaaS), platform-as-a-service (PaaS) and Infrastructure-as-a-service (Iaas) among others. But another as a service category has come on the scene in the past couple of years: robotics-as-a-service (RaaS). RELATED: WHY ARE WE SO SCARED OF ROBOTS? 15 EXPERTS WEIGH IN ON WHAT THE REAL DANGERS ARE The video above explains RaaS "is a cloud computing unit that facilitates the seamless integration of robot and embedded devices into Web and cloud computing environment."

Brent is a PhD Candidate in the Department of Computer Science and a member of the Φ Lab and Insight Lab. He was previously the instructor for MMASc 9251A: Professional Computing for Applied Scientists and presently the Teaching Assistant for Unstructured Data. As of March 1st 2019, Brent will also be a Mitacs Accelerate Intern. This work is with the Parkwood Institute and IBM with the target of improving mental health resources for Canadian Veterans. His research interests are two-fold.

Vilynx is working with the largest media and publisher companies in the US and Europe to drive their growth and change the way the data is being understood. We are developing leading edge AI and Machine Learning technologies that help drive customer engagement and brand loyalty. You would be joining a young team with of some of the top talent in areas like machine learning, deep learning, computer vision, natural language processing and analytics. We are building a rocking front-end team, capable of doing such things like a single plugin that modifies the DOM of thousands of pages, and brokes nothing, working with the latest stable technologies such ES6 and Typescript. We are looking for people capable of pushing his own limits, that fears nothing and embrace the unknown.

If you're one of the majority of Americans who have shopped online, you're already familiar with retailers' tendencies to make buying suggestions for you based on your past purchases and on-site behavior. You might also like: this air mattress, this camping grill and this sleeping bag. And if the suggestions are spot on, you're probably thankful that the company saved you a couple extra clicks. This is just one example of artificial intelligence and machine learning using data to identify patterns and relationships, then offer intelligent suggestions based on these findings. Unsurprisingly, forward-thinking businesses across every industry are harnessing AI and ML in an attempt to derive maximum value from their stored data.

We used longitudinal data from linked electronic health records of 4.6 million patients aged 18–100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions.