Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be insufficiently effective more and more frequently, which raises risks of dangerous and emergency conditions in power systems. The paper is aimed at compensating for the shortcomings of man (a cognitive barrier, exposure to stresses and so on) and automatic devices by combining their strong points, i.e. the dispatcher's intelligence and the speed of automatic devices by virtue of development of the intelligent system "Artificial dispatcher" on the basis of deep machine learning technology. For realization of the system "Artificial dispatcher" in addition to deep learning it is planned to attract the game theory approaches to formalize work of the up-to-date power system as a game problem. The "gain" for "Artificial dispatcher" will consist in bringing in a power system in the normal steady-state or post-emergency conditions by means of the required control actions.

Legal AI doc review company, Luminance, is branching out into the regulatory world in order to expand its offering by covering areas such as Brexit impact on contracts and GDPR compliance. The move follows a recent expansion into real estate documentation review, showing the company's initial strategy of focusing only on M&A due diligence is well and truly over, with a mission now to capture a greater share of the NLP-driven doc review market across different practice areas. In other news, the firm has also bagged top New Zealand law firm, Russell McVeagh, as its client base widens to 75 around the world, and operating in 23 countries, which is not bad considering the company only launched in September 2016. Luminance already works with Chapman Tripp, New Zealand's largest full-service commercial law firm. How much each of these firms uses their Luminance review system is currently unknown, but if market feedback is accurate then not all customers are making maximum use of the AI system they have signed up to – at least not yet.

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.

Our eyes allow us to perceive the world around us, but they are also a window into the mind. In fact, an emerging body of research suggests that the way in which we move our eyes is affected by our personality. Studies looking into this topic have found people with similar traits tend to move their eyes in similar ways. For example, optimists spend less time looking at negative emotional stimuli, like images of cancer, while curious people tend to take in all regions of a scene. Recently, an international team of researchers from institutions in Australia and Germany tried to understand more about the link between personality and eye movements by developing a machine learning algorithm--a type of computer code that learns without the need to be specifically programed--New Scientist reports.

Here come the cyborg beetles. Electronic-filled backpacks have been used to create controllable flying bio-robots. Male M. torquata beetles had electrodes implanted into four of their flight muscles. Small electric pulses were then administered to steer them left or right. Their acceleration could be increased by upping the frequency of the pulses.

This technology could be put in smartphones that understand and predict our behaviour, potentially offering personalised support. They could also be used by robot companions for older people, or in self-driving cars and interactive video games. Researchers found the machine (labelled'our classifier') is currently between seven and 15 per cent better than random chance at predicting neuroticism, extroversion, agreeablesness and conscientiousness.

Artificial intelligence has moved from science fiction to science fact. OPINION: You have probably chatted with Amazon's Alexa or Apple's Siri. Autonomous vehicles are proliferating in the air, on the water and on land. And you may be familiar with the personalised recommendations of Netflix. But did you know that your bank's decision to approve or deny your loan application may rely on the judgment of predictive algorithms?

Artificial intelligence has moved from science fiction to science fact. OPINION: You have probably chatted with Amazon's Alexa or Apple's Siri. Autonomous vehicles are proliferating in the air, on the water and on land. And you may be familiar with the personalised recommendations of Netflix. But did you know that your bank's decision to approve or deny your loan application may rely on the judgment of predictive algorithms?

Kaggle is the most well known competition platform for predictive modeling and analytics. The company was founded in 2010 in Melbourne, Australia, and a year later, it moved to San Francisco after receiving funding from Silicon Valley. In 2017, it was acquired by Google. The term "data science" has gradually floated and elevated into the English lexicon over the past decade. And so, the words "data science" and "kaggle" have become inextricably linked, and many in the data science community contemplate and debate the utility of the platform: Like many people, I had some preconceived notions about Kaggle competitions.

The eyes really are a window to the soul. The way they move can reveal your personality type – a finding that could help robots better understand and interact with humans. Psychologists have long believed that personality influences the way we visually take in the world. Curious people tend to look around more and open-minded people gaze longer at abstract images, for example.