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.

The standard state-of-the-art backend for text-independent speaker recognizers that use i-vectors or x-vectors, is Gaussian PLDA (G-PLDA), assisted by a Gaussianization step involving length normalization. G-PLDA can be trained with both generative or discriminative methods. It has long been known that heavy-tailed PLDA (HT-PLDA), applied without length normalization, gives similar accuracy, but at considerable extra computational cost. We have recently introduced a fast scoring algorithm for a discriminatively trained HT-PLDA backend. This paper extends that work by introducing a fast, variational Bayes, generative training algorithm. We compare old and new backends, with and without length-normalization, with i-vectors and x-vectors, on SRE'10, SRE'16 and SITW.

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.

Researchers at analytics firm SAS claim to have created an artificial intelligence (AI) program that can rank the best places to live in the world using a range of publicly available data sources. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Welcome to your living room, the latest battleground for tech companies vying for your allegiance. What started as Amazon staking its claim with the Echo line of smart speakers now includes competition from Google and Apple, too. It's not just tiny smart speakers, either -- all three companies have launched Hi-Fi systems in the past few months in an attempt to appeal to audiophiles. But while the Sonos One with Alexa and the Apple HomePod have mustered a ton of press since their debuts, the Google Home Max hasn't picked up quite as much traction. It's not that the Home Max is underwhelming -- it's just that the Sonos One is surprisingly affordable, while the HomePod launched to much fanfare because it's Apple's first foray into the smart-speaker space.

Athens, Greece - Propped up on the radiator next to a battered door is a half-charred plaque that welcomes visitors to the office of the Afghan Community in Greece. The door, also burned, hangs loosely from the hinges. Inside, a wooden desk is collapsed in front of a soot-blackened wall, a shattered computer monitor toppled sideways on the ground next to it. Far-right attackers waited until office workers left for their lunch break on Thursday to break into the Afghan Community in Greece's single-room workspace, and smash computers, speakers and framed photos on the wall, before dousing the office in gasoline and setting it ablaze. "It is good that no one was here, otherwise we would have had victims," Yonous Muhammadi, former president of the Afghan Community in Greece and head of the Greek Forum of Refugees, tells Al Jazeera.

Artificial intelligence (AI) has enormous potential for the care of Alzheimer's disease (AD) patients. Jesse Hoey, PhD from the University of Waterloo, Ontario, Canada explains how his work in AI development is aiming to create a more emotional system, and why this would be beneficial for the people it is intended for. Dr Hoey also talks to us about presentations given by the Technology Professional Interest Area which highlighted recent work in technology and dementia care. This interview was recorded at the 2017 Alzheimer's Association International Conference (AAIC) held in London, UK.

With accelerating advances in artificial intelligence, the world of AI is weaving itself -- almost imperceptibly at times -- into the fabric of our lives, affecting how we move and live and relate. But as AI extends human agency, intelligence and action in the world -- sometimes replacing human beings entirely for certain tasks -- it raises increasingly complex issues of ethics and responsibility. Paula Boddington, senior researcher at Oxford University's Department of Computer Science, tackled the subject head on, addressing a packed audience at the Mobile World Congress in Barcelona last month. For a topic that presents many more questions than answers, she offered a glimpse into how technology brushes up against philosophy. "The power of AI is pushing us towards questions about the very limits and grounds of our human values," Boddington says.

Eight years ago, when neurosurgeon Marcelo Galarza saw images from jazz guitarist Pat Martino's cerebral MRI, he was astonished. "I couldn't believe how much of his left temporal lobe had been removed," he said. Martino had brain surgery in 1980 to remove a tangle of malformed veins and arteries. At the time he was one of the most celebrated guitarists in jazz. Yet few people knew that Martino suffered epileptic seizures, crushing headaches, and depression. Locked in psychiatric wards, he withstood debilitating electroshock therapy. It wasn't until 2007 that Martino had an MRI and not until recently that neuroscientists published their analyses of the images.

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.