This year, we have seen an acceleration of Silicon Valley tech giants opening AI research labs around the world as they seek to gain traction among researchers and fulfill their global ambitions. In the past six months or so, Google brought labs to China and France, Facebook opened labs in Pittsburgh and Seattle, and Microsoft announced plans to open labs near universities in Berkeley, California and Melbourne, Australia. This trend shows no signs of slowing down. Last month, Samsung announced labs in Cambridge, Moscow, and Toronto. This week, Nvidia announced plans to open a new lab in Toronto, while Google shared plans to open a lab in Accra, Ghana, Google's first in Africa and perhaps the first of any tech giant in Africa.

It may sound like a stalkers dream come true, but dating app Happn is adding a new'creepy' feature that will let potential love matches revisit your past movements. Starting next month, if you remember crossing paths with someone that took your fancy, you will be able to retrace your steps to try and find them again. If they are also a user of the popular dating app, which matches people through their device's geolocation, their profile will appear on the new map tool at that spot. Budding romantics may find the feature appealing, giving them the chance to tap locations they've visited over the past week to track down lost connections. However, some may find the idea of strangers tracing their movements more than a little creepy.

Billionaire Elon Musk has said cash handouts'will be necessary' if robots take human jobs, in his latest flurry of tweets. Musk made the comment in response to a question from a Twitter user about whether he supported universal basic income (UBI) - a cash handout that could be given to people irrespective of their employment. Musk believes UBI could be a possible solution for unemployment caused by machines taking over the workforce. Billionaire Elon Musk has said cash handouts'will be necessary' if robots take human jobs, in his latest flurry of tweets A universal basic income would give a standard amount of money to every citizen to cover basic expenses like food and living costs each month. Musk first joined the growing list of tech executives supporting the payment system in 2016 when he spoke about the concept in an interview.

By means of the CTBC systems, the exact position of a train is known more precisely than with the regular signaling systems. This results in a more efficient and safe way to manage the railway traffic. Metros and other railway systems are able to improve headways while maintaining or even improving safety. The main objective of the CTBC is to increase capacity by reducing the time interval (headway) between trains. Traditional signaling systems detect trains in discrete sections of the track called'blocks', each protected by signals that prevent a train from entering an occupied block.

Biometrics emerged as a robust solution for security systems. However, given the widespread of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks). Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection. However, these methods do not exploit the importance of learning deep local features from each facial region, even though it is known from face recognition that different face regions have much different visual aspects, that can also be exploited for face spoofing detection. In this work we propose a novel CNN architecture trained in two steps for such task. Initially, each part of the neural network learns features from a given facial region. After, the whole model is fine-tuned on the whole facial images. Results show that such pretraining step allows the CNN to learn different local spoofing cues, improving the performance and convergence speed of the final model, outperforming the state-of-the-art approaches.

A group of Amazon shareholders is asking CEO Jeff Bezos to stop selling and marketing facial recognition technology to governments after civil liberties groups warned of the potential for abuse. Earlier this year, a group of advocacy organisations led by the American Civil Liberties Union (ACLU) published a report detailing how Amazon was marketing its Rekognition tool to American law enforcement agencies. In addition to touting the technology as helping to find suspects, Amazon has said it could be used to preemptively identify "persons of interest" and prevent crimes. A letter signed by 19 shareholders - and provided to The Independent by the ACLU - urges Mr Bezos to halt the tool's expansion until those concerns can be addressed. Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Amazon told to stop selling facial recognition tools to police Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Furnishing police and sheriff's departments with the tool would bolster "government surveillance infrastructure technology" and could drive down Amazon's value, the letter warned. It also echoed concerns about the potential for misuse. "While Rekognition may be intended to enhance some law enforcement activities, we are deeply concerned it may ultimately violate civil and human rights", the letter said.

The 1985 deal has less than 8,000 words and contains just 22 articles, mostly dedicated to tariffs, agricultural restrictions, import licensing and rules of origin – what Harvard economist Dani Rodrik calls conventional trade topics . While these issues are also covered in the US-Singapore deal, most of its 20 chapters and 70,000 or so words deal with other topics such as anti-competitive business conduct, e-commerce, intellectual property, investment rules, labour rights and the environment. AI has already proved its worth in the comparable field of law. A two-month test pitting 20 lawyers against LawGeek's AI showed that humans were no match for a robot in spotting risks within the legal documentation for non-disclosure agreements – deals meant to protect confidential information such as new manufacturing processes and marketing schemes. In terms of accuracy, the lawyers scored an average of 85%, compared to the robot's 94%.

Visa Canada, a unit of Visa Inc. V, recently announced a strategic collaboration with Finn Al, to add new stimulus to its conversational banking chatbots and artificial intelligence (AI), powered by the Visa Developer Platform. Stocks to Consider Investors interested in the Financial Transaction Services industry might take a look at some better-ranked stocks, namely Cardtronics plc CATM and WEX Inc. WEX, both sporting a Zacks Rank #1 (Strong Buy). You can see the complete list of today's Zacks #1 Rank stocks here . Cardtronics offers automated consumer financial services through its network of automated teller machines (ATMs) and multi-function financial services kiosks. The company managed to come up with an average four-quarter positive surprise of 27.17%.

Gating is a key technique used for integrating information from multiple sources by long short-term memory (LSTM) models and has recently also been applied to other models such as the highway network. Although gating is powerful, it is rather expensive in terms of both computation and storage as each gating unit uses a separate full weight matrix. This issue can be severe since several gates can be used together in e.g. an LSTM cell. This paper proposes a semi-tied unit (STU) approach to solve this efficiency issue, which uses one shared weight matrix to replace those in all the units in the same layer. The approach is termed "semi-tied" since extra parameters are used to separately scale each of the shared output values. These extra scaling factors are associated with the network activation functions and result in the use of parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions. Speech recognition experiments using British English multi-genre broadcast data showed that using STUs can reduce the calculation and storage cost by a factor of three for highway networks and four for LSTMs, while giving similar word error rates to the original models.

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation.