Hellblade: Senua's Sacrifice, a dark mythological adventure that follows a young warrior suffering from psychosis, was the big winner at the 2018 Bafta video game awards on Thursday night at Tobacco Dock, London. The game, which was developed in conjunction with psychologists and neuroscientists to ensure its accurate depiction of mental illness, was nominated in nine categories and won for best British game, best performance, artistic achievement, audio achievement and a new category, games beyond entertainment, which celebrates new releases with a political or social message. Accepting the latter prize, psychologist Paul Fletcher, a professor of neuroscience at Cambridge University who worked closely with the game's Cambridge-based developer, Ninja Theory, said: "Mental illness is usually characterised by the fact that it's invisible. Working with Ninja Theory has shown me something valuable: games can aspire to and achieve a remarkable exploration of state of the mind and mental suffering." However, the night's biggest award, best game, also provided its biggest shock.

The brain is complex; in humans it consists of about 100 billion neurons, making on the order of 100 trillion connections. It is often compared with another complex system that has enormous problem-solving power: the digital computer. Both the brain and the computer contain a large number of elementary units--neurons and transistors, respectively--that are wired into complex circuits to process information conveyed by electrical signals. At a global level, the architectures of the brain and the computer resemble each other, consisting of largely separate circuits for input, output, central processing, and memory.1 Which has more problem-solving power--the brain or the computer? Given the rapid advances in computer technology in the past decades, you might think that the computer has the edge.

Prospects of universal AI ethics seem slim. However the five design pillars will serve organizations well beyond the social fads and fears. The goal – build controls that will identify biases, show attribution, and enable course correction as needed. Ready to roll out your plans for AI? Do you understand the business model implications? Who will you partner with for AI? Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org. Please let us know if you need help with your Digital Business transformation efforts. Here's how we can assist: Reprints can be purchased through Constellation Research, Inc. To request official reprints in PDF format, please contact Sales .

The US and China have imposed tariffs on each other's goods. But will a skirmish between the world's two biggest economies turn into a full-on trade war? Perhaps it has already started. Both sides have struck initial blows. The US has imposed tariffs on imports of steel and aluminium.

A Chinese fugitive was arrested after an AI-powered facial recognition system alerted authorities to his presence in a crowd of 60,000 people attending a pop concert. Welcome to the age of robot snitches. Wanted for "economic crimes," the 31 year-old man was reportedly surprised when police apprehended him. He'd traveled nearly 100 km (about 60 miles) with his wife and friends to attend the event, a concert headlined by Cantopop star Jacky Cheung, before authorities nabbed him on a tip from a venue camera. Chinese authorities have entirely embraced facial recognition systems and AI-powered surveillance monitoring.

One-class Support Vector Machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and widely adopted in both research as well as industrial applications. The biggest issue for OC-SVM is, however, the capability to operate with large and high-dimensional datasets due to inefficient features and optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or auto-encoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class SVM (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descend to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.

The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.

Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This paper explores the characteristics, capabilities and intelligence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). It will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.

Previously referred to as `miraculous' in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.

With this course, you'll have the keys to the entire R Machine Learning Kingdom!