At the Intel Developer Forum in San Francisco this week, Intel Senior Vice President and General Manager Diane Bryant announced the launch of Intel's Silicon Photonics product line and teased a brand-new Phi product, codenamed "Knights Mill," aimed at machine learning workloads. With the introduction of Silicon Photonics, Intel is debuting two new 100G optical transceivers. Sixteen years in the making, the small form-factor design fuses optical components with silicon integrated circuits to provide 100 gigabits per second over a distance of two kilometers. Initial target applications include connectivity for cloud and enterprise datacenters as well as Ethernet switch, router, and client-side telecom interfaces. Microsoft is adopting the technology for its scale-loving Azure datacenters.

Can FinTech and AI solve the ESG data puzzle?

Johann Rupert, head of the powerful Richemont luxury goods company whose brands include Cartier, Piaget and Dunhill, takes a bleak view of how the new technologies arising from artificial intelligence and robots will affect employment and social stability. Rupert says hundreds of millions of jobs will be lost as they are introduced. Existing social inequalities, on which the luxury industry thrives, will be reinforced, he says. If much greater structural unemployment results, the social fabric of developed societies will be torn apart by envy, hatred and social warfare as many middle class jobs are destroyed. That would make the luxury sector unsustainable.

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Apple Inc. acquired artificial intelligence startup Turi Inc. for about 200 million, according to people familiar with the situation, in the latest deal by the iPhone maker to accumulate advanced computing capabilities for its products and services. Turi helps developers create and manage software and services that use a form of AI called machine learning. It also has systems that let companies to build recommendation engines, detect fraud, analyze customer usage patterns and better target potential users, according to the Seattle-based startup's website. Apple could use this to more rapidly integrate the technology with future products. Apple's move Friday is part of a broader battle among Google, Facebook Inc. and Amazon.com

Technology companies are hurriedly snapping up startups in the field of artificial intelligence, and Intel Corp. INTC 0.77 % is the latest to join a buying spree fueled by one of the hottest trends in the tech sector. The chip maker on Tuesday announced plans to pay an undisclosed amount for Nervana Systems, a 48-employee company working on semiconductors, software and services to exploit a popular AI technique called deep learning. Intel's move follows a deal disclosed Friday by Apple Inc. AAPL 0.26 % to purchase Turi Inc., a Seattle-based specialist in the field. The two acquisitions add to a string of 31 purchases since 2011 of AI startups by large companies, according to venture-capital research firm CB Insights. Factoring in smaller acquirers, PricewaterhouseCoopers LLP counts 29 related acquisitions so far this year, suggesting the total deal count for 2016 will top the 37 deals announced last year.

BBC Worldwide has teamed up with artificial intelligence start-up Thoughtly to explore how machine learning can help it understand which genres of content are most in demand in which territories. Following an initial trial of Thoughtly's technology, the pair have completed a detailed analysis looking at synopses and descriptions of programming alongside data mining to figure out how best to categorise individual programme titles. Thoughtly's flagship platform, Ellipse, is designed to map themes, generate summaries and identify anomalies in text. It was originally designed to assist researchers in academic institutions to draw insights from very large volumes of text, such as helping medical researchers identify unexpected anomalies in large clinical data sets or help scientific researchers navigate unstructured text for automated screening of'noisy' data sets. BBC Worldwide is using the technology to identify themes that are under or over-represented in its content, to identify recurring and possibly unseen patterns across the various genres in it catalogue, identify which themes have grown and which have declined over the years and generally build a deeper understanding of its content with the objective of matching it with the most relevant audiences both for the BBC itself and for its client broadcasters, according to David Boyle, EVP of insight.

Have you ever wondered what's the magic behind the tutorials on Large-scale Linear Models and Wide & Deep Learning? I hope this post would at least point you to the right direction. Please take a look at my previous blog posts to understanding some basics of TensorFlow Learn and its integration with other high-level TensorFlow modules. The purpose of this post is to help you better understand the underlying principles of estimators in TensorFlow Learn and point out some tips and hints if you ever want to build your own estimator that's suitable for your particular application. This post will be helpful when you ever wonder how everything works internally and gets overwelmed by the large codebase.

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.

Dyson's 360 Eye robot vacuum is going global, with retail availability in Canada today, and a U.S. launch following soon. I spoke to Dyson's Lead Robotics Engineer Mike Aldred about the vacuum, which has been in development at the company long before its Japanese debut last year – in fact, the project dates back 18 years to 1998. But true advances takes time, and the tech behind Dyson's first robot vacuum is nothing if not advanced. The 360 Eye vacuum boasts a sophisticated 360-degree vision system that combines a top-mounted spherical camera with a pair of advanced sensors flanking the robot's'face,' and is designed to be much smarter than the competition from Roomba and others, as well as just offering better basic vacuum capabilities in terms of being able to pick up dirt, hair and dust. "Vision is absolutely critical, but it was a completely new technology [when development began]," Aldred told me, explaining the early days of 360 Eye's development.