Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.

Taxi destination prediction is a very important task for optimizing the efficiency of electronic dispatching systems, thus allowing relevant advantages for both taxi companies and customers. In fact, during periods of high demand, there should be a taxi whose current ride will end near a requested pick up location from a new customer. If an electronic dispatcher is able to know in advance where all taxi drivers will end their current ride, it will also be able to better allocate its resources, identifying which taxi to assign to each call. Moreover, automatic systems for the taxi mobility monitoring collect data that, integrated with other information sources, can help in understanding daytime human mobility routines. In this paper, we introduce a novel approach for addressing the taxi destination prediction problem, based on Recurrent Neural Networks (RNNs) applied to a regression setting. RNNs are trained based on the individual drivers' history and on geographical information (i.e., points of interest), using only the starting point of each ride (with no knowledge about the whole trajectory). The proposed approach was tested on the dataset of the ECML/PKDD Discovery Challenge 2015 - based on the city of Porto - obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.

Taking a look back at another week of news from Cupertino, this week's Apple Loop includes FaceID for every new iPhone in 2018, the corners being cut for a cheaper iPhone X, secrets inside the new MacBook Pro, falling iPhone sales in India, the lack of fast chargers for the iPhone, Photoshop for the iPad, and iOS 12's updated USB security. Apple Loop is here to remind you of a few of the very many discussions that have happened around Apple over the last seven days (and you can read my weekly digest of Android news here on Forbes). Thanks to a regulatory listing made by Apple in the Eurasian database, we now know there are three new models of iPhone on the way, with a number of'sub models' in each range. Spotted by Consomac, Apple has chosen to publicly file identifiers for all its new iPhones in the Eurasian database, and it confirms three distinct designs will be coming to market. There are three clear runs here: A19, A20 and A21.

SoftBank Vision Fund is seeking to invest almost $1 billion in China's SenseTime Group Ltd. as it seeks a stake in the world's most valuable artificial intelligence startup, people familiar with the matter said. The fund, created by SoftBank Group Corp., and SenseTime are still finalizing terms of the deal and the details could still change, the people said, asking not to be identified as the details are private. The company has already announced two other funding rounds this year as it raised more than $1.2 billion. SenseTime, a four-year-old startup, makes systems that analyze faces and images on a huge scale and contributes to China's vast surveillance system. The company is one of a host of local startups at the forefront of Beijing's goal of making the country the world leader in AI by 2030.

The graph represents a network of 2,727 Twitter users whose tweets in the requested range contained "#iot OR "internet of things"", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 July 2018 at 20:39 UTC. The requested start date was Thursday, 19 July 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 0-hour, 11-minute period from Thursday, 05 July 2018 at 07:32 UTC to Wednesday, 18 July 2018 at 07:43 UTC.

The graph represents a network of 2,727 Twitter users whose tweets in the requested range contained "#iot OR "internet of things"", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 July 2018 at 20:39 UTC. The requested start date was Thursday, 19 July 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 0-hour, 11-minute period from Thursday, 05 July 2018 at 07:32 UTC to Wednesday, 18 July 2018 at 07:43 UTC.

The International Space Station should prepare for the arrival of its first android crew members, Russian state media says. The Roskosmos space agency has approved a preliminary plan to send a pair of humanoid robots called FEDOR into space in August 2019, according to "a source in the space and rocket industry" quoted by the RIA Novosti website. Robots in space have become commonplace for space superpowers: the U.S. has two operational Mars rovers, China has a lunar lander on the moon and more on the way, and Russia has several now-defunct rovers on both the moon and Mars. In 2011, NASA sent Robonaut 2, a 330-pound manually controlled "humanoid" robot, to the ISS to look into how such robots might be used to perform simple, repetitive, or especially dangerous tasks. But while previous robots were shot into space on as cargo, Russia's pair of FEDORs -- the acronym stands for Final Experimental Demonstration Object Research -- will "fly for the first time to the ISS as crew members, and not as cargo in the transport compartment," RIA Novosti wrote, adding that the robots will fly in an otherwise unmanned Soyuz rocket.

Last month, two parliamentary reports on the involvement of the British intelligence services in torture and rendition were released last week. What has been hypothesized by several journalists is now confirmed: that British functionaries--to include soldiers, civil servants and intelligence officers with MI5 and MI6--knew about and participated in a vast array of human rights abuses committed during the capture and interrogation of terrorism suspects. The Guardian's Peter Beaumont writes with great contempt for what has transpired since 2001, "[A]s it is now quite clear, it was all a bloody lie. The answers given to journalists at the Observer over the years, as well as colleagues at The Guardian and those at other news organisations, as they investigated these allegations, were rotten with untruth and evasion." Governments' lying to their citizens about covert wars is hardly new, nor is the pervasive use of kidnapping of terrorism suspects by the CIA to include its many "black sites."