They may not have the ability to speak our languages directly, but over thousands of years of domestication, dogs have likely developed certain'signals' to help them communicate with humans. When they're confronted with an angry face, a new study has discovered that dogs tend to their lips as an immediate response – and, this was more often the case when a human was involved, instead of another dog. Animal behaviour researchers say this may be linked to the dogs' perception of human emotions, acting as a way for them to communicate in response to visual cues of anger. In the study, dogs were shown two facial expressions (one positive and one negative, from the same individual). When they're confronted with an angry face, the researchers discovered that dogs tend to their lips as an immediate response Historically, animal's facial expressions have been considered to be inflexible and involuntary displays, which reflect an emotional state rather than active attempts to communicate with others.

There is an extensive literature on the complexity of planning, but explicit bounds on time and space complexity are very rare. On the other hand, problems like the constraint satisfaction problem (CSP) have been thoroughly analysed in this respect. We provide a number of upper- and lower-bound results (the latter based on various complexity-theoretic assumptions such as the Exponential Time Hypothesis) for both satisficing and optimal planning. We show that many classes of planning instances exhibit a dichotomy: either they can be solved in polynomial time or they cannot be solved in subexponential time. In many cases, we can even prove closely matching upper and lower bounds. Our results also indicate, analogously to CSPs, the existence of sharp phase transitions. We finally study and discuss the trade-off between time and space. In particular, we show that depth-first search may sometimes be a viable option for planning under severe space constraints.

Philip Hammond vowed to use the Budget to push for driverless cars on the road within years. The Chancellor has made clear his crucial financial package this week will be a rallying cry for Britain to take the lead on technology. Playing down concerns about the safety of self-driving vehicles, Mr Hammond said that after Brexit the UK to be'leading the next industrial revolution'. Alongside removing obstacles to autonomous cars, Mr Hammond is set to announce investment in robotics and 5G internet. He will unveil changes to regulations that will allow developers to test self-driving cars on UK roads for the first time.

Jeremy Clarkson has warned that he could have been killed by a driverless car as Chancellor Philip Hammond is about to allow the new technology to be tested on UK roads. The former Top Gear presenter said he tested out a driverless car which made two mistakes within 50 miles, and as a result he feared for his life. He wrote in the Sunday Times magazine: "I drove a car the other day which has a claim of autonomous capability and twice in the space of 50 miles on the M4 it made a mistake, a huge mistake, which could have resulted in death. "We have to be very careful legally, so I'm not going to say which one." Writing about the technology being developed and safe enough for British roads, he said: "For now, we're miles away from it." The Grand Tour host's article came as Mr Hammond was preparing to give the green light to the autonomous car industry, allowing developers to test self-driving cars on UK roads for the first time. Changing the regulations is part of Mr Hammond's goal to see autonomous cars on British roads by 2021, boosting an industry which is predicted to be worth £28 billion by 2035 and provide 27,000 jobs. Audi will start selling advanced autonomous cars next year. Mr Clarkson wrote: "You drive one of your driverless cars over the Death Road in Bolivia and I'll buy one.

Written on 17 November 2017. About 30 percent of local institutions see AI playing an important role in their innovation plans, according to GFT Technologies' Digital Banking Expert Survey. By comparison, 23 percent of sector firms in the UK and Mexico see AI as crucial in their strategy, while only 17 percent of US banks perceive the technology as an important aspect of their overall plans, the study from the financial services vendor says. The survey covered 285 professionals from small to large retail banks based in Brazil, Germany, Italy, Mexico, Spain, Switzerland, the UK and the US. Brazilian firms may be enthusiastic about the potential of artificial intelligence for tasks such as automating customer service and achieving greater customer engagement, but the country still struggles with issues ranging from infrastructure, lack of qualified manpower and effective partnerships with AI vendors and fintechs - that means the number of real initiatives is still small.

Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase.

In our efforts to understand the Universe, we're getting greedy, making more observations than we know what to do with. Satellites beam down hundreds of terabytes of information each year, and one telescope under construction in Chile will produce 15 terabytes of pictures of space every night. It's impossible for humans to sift through it all. As astronomer Carlo Enrico Petrillo told The Verge: "Looking at images of galaxies is the most romantic part of our job. The problem is staying focused."

The risk and insurance industry is on "the cusp of significant technological change", and there are three broad trends to watch out for in the insurtech space. Roberto McQuattie, regional head of Latin America at Crawford & Company, outlined the trends in this "rapidly expanding" space. First is the use of drone technology. According to McQuattie, the technology is being used to great effect by colleagues handling claims after hurricanes Harvey, Irma and Maria. "Drones are being used in both pre- and post-loss scenarios and provide efficient and accurate handling of claims," he added.

Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task.

In the early 1980s, children at a school in Nicaragua did something remarkable: they spontaneously created a language. Brought together for the first time in a school for deaf children, they had no shared sign language and so they developed their own. The result, ISN (Idioma de Señas de Nicaragua), is a grammatically complex, expressive language that speaks to our natural need to engage with others. Those children created a way to share, learn and converse with each other because that's what humans do. Remember the last time you chatted with a good friend: it's likely that your conversation felt natural, easy.