British people are using Wi-Fi hotspots to watch porn in public

The Independent - Tech

One in 12 British people use public Wi-Fi to access pornographic content, according to a new report. Some of the most popular places to watch pornography in public are restaurants, train stations, offices and libraries, though the street is also a common choice. Norton by Symantec has warned public Wi-Fi users that they risk exposing themselves to hackers, unless they start taking proper precautions. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.


Efficient Incremental Learning for Mobile Object Detection

arXiv.org Artificial Intelligence

Object detection models shipped with camera-equipped mobile devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized mobile object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, IMOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of IMOD is a novel incremental learning algorithm that trains end-to-end for one-stage object detection deep models only using training data of new object classes. Specifically, to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented IMOD under both mobile-cloud and mobile-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.


Watson Will Soon Be a Bus Driver In Washington D.C.

#artificialintelligence

IBM has teamed up with Local Motors, a Phoenix-based automotive manufacturer that made the first 3D-printed car, to create a self-driving electric bus. Named "Olli," the bus has room for 12 people and uses IBM Watson's cloud-based cognitive computing system to provide information to passengers. In addition to automatically driving you where you want to go using Phoenix Wings autonomous driving technology, Olli can respond to questions and provide information, similar to Amazon's Echo home assistant. The bus debuts today in the Washington D.C. area for the public to use during select times over the next several months, and the IBM-Local Motors team hopes to introduce Olli to the Miami and Las Vegas areas by the end of the year. By using Watson's speech to text, natural language classifier, entity extraction, and text to speech APIs, the bus can provide several services beyond taking you to your destination.


FASTER: Fusion AnalyticS for public Transport Event Response

arXiv.org Machine Learning

Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal responses. The FASTER platform that we describe in this work, deployed at nation scale and handling 1.5 billion public transport trips a year, offers such a full stack of techniques for this large-scale, real-time problem. FASTER provides fine-grained situational awareness and real-time decision support with the objective of improving the public transport commuter experience. The methods employed range from statistical machine learning to agent-based simulation and mixed-integer optimization. In this work we present an overview of the challenges and methods involved, with details of the commuter movement prediction module, as well as a discussion of open problems.


BMW's autonomous concept car of the future was cool, I guess

Mashable

As I approached San Francisco International Airport, my expectations for BMW's new concept car were as big as the looming Boeing 777F Lufthansa cargo jet waiting for me. I had surrendered my cellphone and everything in my purse but my drivers license to see BMW's iNext vehicle. Its tour started in Munich a few days earlier; it came to the Bay Area after a stop at New York's JFK airport, and was scheduled to continue on to Beijing. SEE ALSO: BMW makes sure we can't escape voice assistants while driving After passing a final security check, I climbed up the rickety staircase with fellow media members and entered the cavernous aircraft. We had been told very little about what we were going to see, except it was not only the "car of the future" but the "idea of the future."