In the book, Pinker argues that war and poverty are in decline and that humanity's progress can be attributed to reason and science, central aspects of the Enlightenment, an intellectual movement in the 17th and 18th century. However, critics accuse Pinker of cherry-picking data. They point out that the statistics he uses to support declining death rates begin from 1945, thus excluding the millions killed the decades before, including during World War I and II. "The claim about the Enlightenment is not that it instantaneously brought an end to war, but the Enlightenment did bring the first ideas on how to reduce war out in the open," says Pinker. "The decline in war is really a post-1945 phenomenon."

When exploring marine environments, underwater robots tend to be a bull trout in a china shop, disturbing marine life with their bulk and disruptive propulsion. Enter SoFi, the soft, agile robotic fish with a delicate demeanour. Scientists said they have created a remote-controlled robot that swims quietly through coral reefs and schools of fish and uses a fisheye lens – of course – to capture high-resolution photos and video with a camera built into its nose. Dubbed SoFi, it can swim forward, move up and down, turn and change speeds, propelling itself by wiggling its tail side to side like a real fish, a motion created by pumping water with a small motor into two balloon-like tail chambers. SoFi, built with a generic fish design, is white, weighs less than 1.6kg and is about 47cm long.

China's capital city has given the green light to tech giant Baidu Inc to test self-driving cars on city streets, indicating strong support for the budding sector even as the industry reels from a fatal accident in the United States. Beijing's move is an important step as China looks to bolster its position in the global race for autonomous vehicles, where regulatory concerns have come into the spotlight since the crash earlier this month. The accident in Tempe, Arizona, involving an Uber self-driving car, was the first death attributed to a self-driving car operating in autonomous mode, and has ramped up pressure on the industry to prove its software and sensors are safe. China's capital city has given the green light to tech giant Baidu Inc to test self-driving cars on city streets, indicating strong support for the budding sector even as the industry reels from a fatal accident in the United States. Beijing has given Baidu, best known as China's version of search engine Google, a permit to test its autonomous vehicles on 33 roads spanning around 105 kilometres (65 miles) in the city's less-populated suburbs, the firm said in a statement.

Network growth processes can be understood as generative models of the structure and history of complex networks. This point of view naturally leads to the problem of network archaeology: Reconstructing all the past states of a network from its structure---a difficult permutation inference problem. In this paper, we introduce a Bayesian formulation of network archaeology, with a generalization of preferential attachment as our generative mechanism. We develop a sequential importance sampling algorithm to evaluate the posterior averages of this model, as well as an efficient heuristic that uncovers the history of a network in linear time. We use these methods to identify and characterize a phase transition in the quality of the reconstructed history, when they are applied to artificial networks generated by the model itself. Despite the existence of a no-recovery phase, we find that non-trivial inference is possible in a large portion of the parameter space as well as on empirical data.

It is a fundamental, but still elusive question whether methods based on quantum mechanics, in particular on quantum entanglement, can be used for classical information processing and machine learning. Even partial answer to this question would bring important insights to both fields of both machine learning and quantum mechanics. In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by quantum matrix product states (MPS). Classical machine learning algorithm is then applied to these quantum states. We explicitly show how quantum features (i.e., single-site and bipartite entanglement) can emerge in such represented images; entanglement characterizes here the importance of data, and this information can be practically used to improve the learning procedures. Thanks to the low demands on the dimensions and number of the unitary matrices, necessary to construct the MPS, we expect such numerical experiments could open new paths in classical machine learning, and shed at same time lights on generic quantum simulations/computations.

BENGALURU: Imagine you walk into aretail store wearing a blue striped shirt and by the time you go to the store help, he is already prepared with half a dozen striped shirts that you could consider buying. This might very well be possible, thanks to a suite of artificial intelligence (AI) based retail in-store products of SaaS provider Capillary Technologies. The company, which recently raised $20-million, has been working on using AI to understand the demographics, footfall and conversations within a retail store to give these stores a helping hand in an era where online players are aggressively expanding their operations. By using these solutions, stores can now attempt to do targeted advertising, produce heat maps, analyse conversations and build unique customer profiles. Capillary is piloting these solutions with an initial few customers. For Capillary, this is opening up a new suite of products other than its existing omnichannel customer engagement products.

This week, China gave tech company Baidu, Inc., which wants to be known as China's version of Google, the go-ahead to start autonomous driving tests in Beijing's suburbs, according to Reuters. China is looking to compete with other countries with self-driving technology, so this is an important move that indicates support for the technology at a somewhat contentious time. This green light comes on the heels of the first fatal accident involving a self-driving car in the United States. An Uber vehicle using autonomous technology (but with a human operator in the car) struck and killed a pedestrian in Tempe, AZ, earlier this month. That investigation is still ongoing, and it's unclear what ramifications it might have for self-driving technology as a whole in the US.

Artificial intelligence – or AI – has been around for at least 50 years, but there's a huge amount of buzz about it now. According to Joshua Sullivan, senior vice president at Booz Allen Hamilton, huge advances in machine learning in the last few years are driving the AI hype and practicality of what's possible. Sullivan is the co-author of "The Mathematical Corporation: Where Machine Intelligence and Human Ingenuity Achieve the Impossible." He was the featured speaker at the Chamber Strome Business Series event March 21. Sullivan's book, which he wrote with Angela Zutavern, was released last June.

Sophia, the worlds most advanced humanoid released to date was granted an honorary citizenship a few months ago by Saudi Arabia. In a move that set the net flooding with awe and dismay, this act probably triggered the first step towards recognising artificial intelligence being in the room and not at door step. The UN joined to recognise Sophia as the world's first UN Innovation Champion by UNDP. While these moves were music to many, artificial intelligence is raising a lot of divided opinions across the best of brains in science and technology. A quote widely in circulation on the social media on Einstein's premonition of a world having a generation of idiots may have its fair share of laughs. Einstein had indeed written a letter to his friend, psychiatrist Otto Juliusburger, in 1948 where he believed that the abominable deterioration of ethical standards stemmed primarily from the mechanisation and depersonalisation of our lives, a disastrous byproduct of science and technology.

The first pieces of the brain's "inner GPS" started coming to light in 1970. In the laboratories of University College London, John O'Keefe and his student Jonathan Dostrovsky recorded the electrical activity of neurons in the hippocampus of freely moving rats. They found a group of neurons that increased their activity only when a rat found itself in a particular location.1 They called them "place cells." Building on these early findings, O'Keefe and his colleague Lynn Nadel proposed that the hippocampus contains an invariant representation of space that does not depend on mood or desire.