Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.

Artificial Intelligence may conjure up images of robots stealing all our jobs, but AI isn't futuristic. AI is as old as technology itself – the original AI systems operated more as search engines, where you inputted information and organised data comes back. It essentially is data-driven, but requires human expertise to apply answers and discoveries to solve problems with it. AI technology has become widely used as elements of larger systems, but the field is rarely credited for these successes. Think of the latest advances in medical diagnosis, aviation, stock trading or robot control, and you're likely thinking of an innovation using AI at its core.

The State of European Tech report points to the success of tech-based businesses or acquisitions across the continent, but at the core of this success is deep tech. When it comes to advances in AI, machine learning, VR and AR, drones, robotics and 3D printing, Europe is either leading the way, or is on par with the likes of the US. Since the start of 2015, $2.3 billion has been invested into deep tech in Europe and 2016 is on track for $1 billion – four times the amount in 2011. Even Norway, the lowest ranking country of those studied, has received $56 million in the past five years. These figures also don't represent the recent acquisition of Skyscanner by China's Ctrip travel firm.

A book purporting to prove that a comet caused Noah's Flood became a best-seller. Five editions were published by 1737, and it was translated into German. "Late in the autumn of 1680 the good people of Manhattan were overcome with terror at a sight in the heavens such as has seldom greeted human eyes," history tells us. That terrible sight was a comet so bright that it could be seen in daytime. Mathematician William Whiston was determined to reconcile apparent contradictions between mathematical laws and Biblical scripture.

Domain Mondo's weekly review of technology news: Feature • Tech Story of the Year: Tay, Microsoft's Artificial Intelligence (AI) Chatterbot: "As many of you know by now, on Wednesday [March 23, 2016] we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values ... The logical place for us to engage with a massive group of users was Twitter. Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay. Although we had prepared for many types of abuses of the system, we had made a critical oversight for this specific attack. We take full responsibility for not seeing this possibility ahead of time. We will take this lesson forward as well as those from our experiences in China, Japan and the U.S. Right now, we are hard at work addressing the specific vulnerability that was exposed by the attack on Tay."--Learning from Tay's introduction blogs.microsoft.com

Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunatelyin applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming ofNN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models.To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation beforeproducing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.

Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don't have to be symmetric with the weights used for propagation the activation forward. In fact, random feedback weights work evenly well, because the network learns how to make the feedback useful. In this work, the feedback alignment principle is used for training hidden layers more independently from the rest of the network, and from a zero initial condition. The error is propagated through fixed random feedback connections directly from the output layer to each hidden layer. This simple method is able to achieve zero training error even in convolutional networks and very deep networks, completely without error back-propagation. The method is a step towards biologically plausible machine learning because the error signal is almost local, and no symmetric or reciprocal weights are required. Experiments show that the test performance on MNIST and CIFAR is almost as good as those obtained with back-propagation for fully connected networks. If combined with dropout, the method achieves 1.45% error on the permutation invariant MNIST task.

The stochastic block model (SBM) has long been studied in machine learning and network science as a canonical model for clustering and community detection. In the recent years, new developments have demonstrated the presence of threshold phenomena for this model, which have set new challenges for algorithms. For the detection problem in symmetric SBMs, Decelle et al. conjectured that the so-called Kesten-Stigum (KS) threshold can be achieved efficiently. This was proved for two communities, but remained open for three and more communities. We prove this conjecture here, obtaining a general result that applies to arbitrary SBMs with linear size communities. The developed algorithm is a linearized acyclic belief propagation (ABP) algorithm, which mitigates the effects of cycles while provably achieving the KS threshold in O(n ln n) time. This extends prior methods by achieving universally the KS threshold while reducing or preserving the computational complexity. ABP is also connected to a power iteration method on a generalized nonbacktracking operator, formalizing the spectral-message passing interplay described in Krzakala et al., and extending results from Bordenave et al.

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach which allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we, first, propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.

In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers a much less biased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns. Our code is freely available in Brain Imaging Analysis Kit (Brainiak) (https://github.com/IntelPNI/brainiak), a python toolkit for brain imaging analysis.