Is Anthony, CIO at a leading grocery retailer, well prepared to manage the changes in his workplace? The Age of the Wearable Anthony is looking closely at the potential for wearables to monitor the health of employees and reduce not just sickness levels, but health insurance costs. He's also considering how customers will use wearables to enhance their shopping experience. Employers will find themselves with a goldmine of information about employee performance and customer behavior, which can be used to personalize services to customer context and requirements in real time. Alongside HR, IT leaders will play a critical role in shaping how their business approaches the arrival of wearables in the workplace.

I had the pleasure of participating in Milan's FI-WARE VIP Bootcamp (4–6th May, 2016), under invitation by EBAN. There, along with several other investors and entrepreneurs, we assisted 15 startups from all across Europe to improve the structure and the presentation of their VC pitches. One of the people who actively participated, giving their full support to the event (and as a volunteer, nonetheless) was Federico Travella, founder and CEO of NoviCap, an extremely interesting venture in the "fintech" sector that came up with an innovative way of providing working capital for every kind of business. In discussion with Federico and other participants, the topic of AI was brought up, seen as a contributing factor to increasing unemployment, and through that, to social discontent and political instability. It's surely an interesting subject and is connected to the broader issue of technological progress and its social impact.

The Facebook AI Research (FAIR) is essentially putting three sets of open-sourced code on web-based repository hosting service GitHub. "We're making the code for DeepMask SharpMask as well as MultiPathNet - along with our research papers and demos related to them - open and accessible to all, with the hope that they'll help rapidly advance the field of machine vision," the social network giant said in a post on Friday. "As we continue improving these core technologies, we'll continue publishing our latest results and updating the open source tools we make available to the community," it added. DeepMask figures out if there's an object in the image. SharpMask refines the output of DeepMask, generating higher-fidelity masks that more accurately delineate object boundaries.

Like many of his bits, it became a viral phenomenon, clocking in at nearly six million views on YouTube. At around the ten-minute mark, Oliver took his verbal bat to the knees of Tronc, the new name for Tribune Publishing Company, and its parody-worthy promotional video, in which a robotic spokeswoman describes the journalistic benefits of artificial intelligence, as a string section swells underneath. Tronc is not the only company to enthusiastically embrace the term "artificial intelligence." A.I. is hot, and every company worth its stock price is talking about how this magical potion will change everything. Even Macy's recently announced that it was testing an I.B.M. artificial-intelligence tool in ten of its department stores, in order to bring back customers who are abandoning traditional retail in favor of online shopping. Much like "the cloud," "big data," and "machine learning" before it, the term "artificial intelligence" has been hijacked by marketers and advertising copywriters.

For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. There's no doubt that pretty much everything humans do can be sliced, diced, and replicated by algorithms so it's not surprising that recent work by Tom S. F. Haines, Oisin Mac Aodha, and Gabriel J. Brostow, researchers at University College London, has resulted in the fall of yet another bastion of being human: Handwriting. Their paper, called "My Text in Your Handwriting," describes software that semi-automatically analyzes a sample of a handwriting, then generates whatever text you want in what looks like the identical style of the original handwriting sample. Authors produce different glyphs to represent the same element of writing – the way one individual writes an "a" will usually be different to the way others write an "a". Although an individual's writing has slight variations, every author has a recognisable style that manifests in their glyphs and their spacing.

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Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Episode Info: Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there's good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems. The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity.

Deep learning is a branch of machine learning that uses multiple nonlinear processing layers to learn useful representations of features directly from data. Deep learning models can achieve state-of-the-art accuracy in object classification, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. The accuracy of a deep leaning model largely depends on the amount of data used to train the model. The most accurate models may require thousands or even millions of samples, which can take a very long time to train.