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

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.

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.

Missing data is quite common when dealing with real world datasets. There are several ways to improve prediction accuracy when missing data in some predictors without completely discarding the entire observation. This example shows how decision trees with surrogate splits can be used to improve prediction accuracy in the presence of missing data. Bagging (bootstrap aggregating), is an ensemble approach which involves training several weak learners to create a strong classifier. Decreasing value with number of trees indicates good performance.

OpenAI, Elon Musk's artificial intelligence research company, just became the proud owner of the first ever DGX-1 supercomputer. Made by NVIDIA, the rig boasts a whopping 170 teraflops of computing power, equivalent to 250 usual servers -- and OpenAI is gonna use it all to read Reddit comments. OpenAI's researchers gather around the first AI supercomputer in a box, NVIDIA DGX-1. OpenAI is a non-profit AI research company whose purpose is to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." And now, NVIDIA CEO CEO Jen-Hsun Huang just delivered the most powerful tool the company has ever had at its disposal, a US 2 billion supercomputer.

Scientists in Japan reportedly saved a woman's life by applying artificial intelligence to help them diagnose a rare form of cancer. Faced with a 60-year-old woman whose cancer diagnosis was unresponsive to treatment, they supplied an AI system with huge amounts of clinical cancer case data, and it diagnosed the rare leukemia that had stumped the clinicians in just ten minutes. The Watson AI system from IBM matched the patient's symptoms against 20m clinical oncology studies uploaded by a team headed by Arinobu Tojo at the University of Tokyo's Institute of Medical Science that included symptoms, treatment and response. The Memorial Sloan Kettering Cancer Center in New York has carried out similar work, where teams of clinicians and data analysts trained Watson's machine learning capabilities with oncological data in order to focus its predictive and analytic capabilities on diagnosing cancers. IBM Watson first became famous when it won the US television game show Jeopardy in 2011.