Can machines think?" asked Alan Turing, known as the father of artificial intelligence (AI), in a seminal paper on the topic of computing machinery and intelligence in 1950. Turing did not coin the term'Artificial Intelligence' but his work laid the foundations for a new research area to be termed'Artificial Intelligence' by John McCarthy, one of the organizers of the 1956 conference held at Dartmouth College, UK to delve into the fundamental task of developing an electronic brain. However, by 1973, disappointed by the progress of work, funding dried up in the UK and USA and AI plunged into a long'winter'. In the 20th century, AI was an idea for the future. It needed much more computing power and a greater variety of digital data sources than was available at that time. Today, the picture has changed. Computing power has reached petaflops levels, distributed on the Cloud and accessible to personal devices like smartphones. As much as 90% of the existing data has been created in the last two years; 2.5 quintillion of data is generated per day from sensors, mobiles, online transactions and social media. The challenge is how to harness this huge data flow to return actionable information without storing this data for future analysis because the growth of storage capacity has long been surpassed by the growth of data volume and there is no possibility of the gap being covered. This is why AI has again gained prominence. Big Data Analytics enables the analysis of data as it streams and stores only the intelligence gathered for future reference. Big Data Analytics is one of the applications of Artificial Intelligence. Atanu Sinha, Director – India and SAARC, Hexagon Geospatial, says: "Big Data Analytics is more to do with past analysis and future trends based on which an organization can make informed decisions.

A mysterious self-driving car company has been quietly expanding in recent years in the world's largest country. Now, Moscow-based Cognitive Technologies has hired a slew of new recruits and is ready to move to the U.S. in the coming months. "The big R&D center will stay in Russia, but the main engineers and business guys will be sent to U.S. soil to set up a proper office," Roman Tarasov, the company's VP for global business, tells Inverse. Cognitive Technologies was founded in 1993 by the guys who created Kaissa, the world's first computer chess champion. For decades it worked on image and voice recognition applications, selling products to Intel, Yandex, and others.

It was just a friendly little argument about the fate of humanity. Demis Hassabis, a leading creator of advanced artificial intelligence, was chatting with Elon Musk, a leading doomsayer, about the perils of artificial intelligence. They are two of the most consequential and intriguing men in Silicon Valley who don't live there. Hassabis, a co-founder of the mysterious London laboratory DeepMind, had come to Musk's SpaceX rocket factory, outside Los Angeles, a few years ago. They were in the canteen, talking, as a massive rocket part traversed overhead. Musk explained that his ultimate goal at SpaceX was the most important project in the world: interplanetary colonization. Hassabis replied that, in fact, he was working on the most important project in the world: developing artificial super-intelligence. Musk countered that this was one reason we needed to colonize Mars--so that we'll have a bolt-hole if A.I. goes rogue and turns on humanity. Amused, Hassabis said that A.I. would simply follow humans to Mars. This did nothing to soothe Musk's anxieties (even though he says there are scenarios where A.I. wouldn't follow). An unassuming but competitive 40-year-old, Hassabis is regarded as the Merlin who will likely help conjure our A.I. children. The field of A.I. is rapidly developing but still far from the powerful, self-evolving software that haunts Musk. Facebook uses A.I. for targeted advertising, photo tagging, and curated news feeds. Microsoft and Apple use A.I. to power their digital assistants, Cortana and Siri. Google's search engine from the beginning has been dependent on A.I. All of these small advances are part of the chase to eventually create flexible, self-teaching A.I. that will mirror human learning. Some in Silicon Valley were intrigued to learn that Hassabis, a skilled chess player and former video-game designer, once came up with a game called Evil Genius, featuring a malevolent scientist who creates a doomsday device to achieve world domination.

I'm always amazed at people who tell me they would never trust a driverless car to take them somewhere but then happily get into a car driven by their teenager. Talk about preferring the devil you know. Driverless vehicles are likely to be much safer than those driven by humans. The safety differential is so large that insurance companies are already looking at alternative business models to make up for the fact that premiums will likely plummet once robots are driving us everywhere. The barriers to our transition to driverless vehicles, and to other forms of robot intervention into our daily lives, then, are not just technical but social, political and psychological.

Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR application. Even though DL-based approaches now outperform the state-of-the-art in a number of recognitions tasks of the field, yet substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.

Natural Language Inference is an important task for Natural Language Understanding. It is concerned with classifying the logical relation between two sentences. In this paper, we propose several text generative neural networks for generating text hypothesis, which allows construction of new Natural Language Inference datasets. To evaluate the models, we propose a new metric -- the accuracy of the classifier trained on the generated dataset. The accuracy obtained by our best generative model is only 2.7% lower than the accuracy of the classifier trained on the original, human crafted dataset. Furthermore, the best generated dataset combined with the original dataset achieves the highest accuracy. The best model learns a mapping embedding for each training example. By comparing various metrics we show that datasets that obtain higher ROUGE or METEOR scores do not necessarily yield higher classification accuracies. We also provide analysis of what are the characteristics of a good dataset including the distinguishability of the generated datasets from the original one.

Problems such as predicting a new shading field (Y) for an image (X) are ambiguous: many very distinct solutions are good. Representing this ambiguity requires building a conditional model P(Y|X) of the prediction, conditioned on the image. Such a model is difficult to train, because we do not usually have training data containing many different shadings for the same image. As a result, we need different training examples to share data to produce good models. This presents a danger we call "code space collapse" - the training procedure produces a model that has a very good loss score, but which represents the conditional distribution poorly. We demonstrate an improved method for building conditional models by exploiting a metric constraint on training data that prevents code space collapse. We demonstrate our model on two example tasks using real data: image saturation adjustment, image relighting. We describe quantitative metrics to evaluate ambiguous generation results. Our results quantitatively and qualitatively outperform different strong baselines.

Artificial intelligence has been the secret sauce for some of the biggest technology companies. But technology giant Alphabet Inc.'s Google is betting big on'democratising' artificial intelligence and machine learning and making them available to everyone -- users, developers and enterprises. From detecting and managing deadly diseases, reducing accident risks to discovering financial fraud, Google said that it aimed to improve the quality of life by lowering entry barriers to using these technologies. These technologies would also add a lot of value to self-driving cars, Google Photos' search capabilities and even Snapchat filters that convert the images of users into animated pictures. "Google's cloud platform already delivers customer applications to over a billion users every day," said Fei-Fei Li, chief scientist of AI and machine learning at Google Cloud.

A self-driven Volvo SUV owned and operated by Uber Technologies Inc. is flipped on its side after a collision in Tempe, Arizona, U.S. on March 24, 2017. Picture taken on March 24, 2017. Uber Technologies Inc. on Saturday halted a pilot program for self-driving vehicles following an accident on Saturday in Arizona. The accident took place in the city of Tempe after the driver of a second vehicle made a turn and failed to yield to a self-driving Uber, police said. Two drivers were sitting in the front seats of the Uber car when the crash took place, the company told Reuters.

This week's milestones in the history of technology include the birth of Apple Computer, the first release of Gmail, and IBM signing an agreement with Harvard to build one of the earliest computers, the Automatic Sequence Controlled Calculator (ASCC), later called Mark I. Guglielmo Marconi receives the first wireless signal transmitted across the English Channel, sent from Wimereux, France, to his ship-to-shore station at the South Foreland Lighthouse outside Dover, England. The signal was a test held at the request of the French Government which was considering licensing the invention in France. Bell Telephone Laboratories announces the invention of the phototransistor, a transistor operated by light rather than electric current, invented by John Northrup Shive. An entirely new type of "electric eye" much smaller and sturdier than present photo-electric cells and possibly cheaper-has been invented at the Laboratories. During the past quarter century, electric eyes have found widespread use in electronics because of their ability to control electric currents by the action of light.