As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other facets of research, such as popularity of hate tweets.

To choose a suitable multi-winner rule, i.e., a voting rule for selecting a subset of k alternatives based on a collection of preferences, is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an "optimal" subset. In this paper, we offer a new perspective to measure the quality of such subsets and--consequently-- multi-winner rules. We provide a quantitative analysis using methods from the theory of approximation algorithms and estimate how well multi-winner rules approximate two extreme objectives: diversity as captured by the (Approval) Chamberlin-Courant rule and individual excellence as captured by Multi-winner Approval Voting. With both theoretical and experimental methods we classify multi-winner rules in terms of their quantitative alignment with these two opposing objectives.

LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. In this paper, we study how to automatically extract such relationship through a sentence-level relation classifier and aggregating the scores of entity pairs from a large corpus. Also, we release two benchmark datasets for evaluation and future research.

Scientists have revealed a radical plan to grow miniature Neanderthal'brains' in the lab. A team of researchers who have previously inserted Neanderthal genes into mice and frogs' eggs are now using the technique to understand how humans became'cognitively special' compared to our ancient relatives, according to the Guardian. The lab-grown mini brains will only be about the size of a lentil, and cannot achieve thoughts or feelings – but, by mimicking the basic structure of the developed brain, they could reveal key differences in how the nerve cells function. A team of researchers who have previously inserted Neanderthal genes into mice and frogs' eggs are now using the technique to understand how humans became'cognitively special' compared to our ancient relatives, according to the Guardian. The work is led by Professor Svante Pääbo, director of the genetics department at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, who previously unraveled the Neanderthal genome, the Guardian reports.

Is mutual love with a robot possible? And if it is possible, would it make relationships between human beings less desirable? Those are the questions examined by Lily Eva Frank, a philosophy professor at the Technical University of Eindhoven in the Netherlands who wrote an essay with Sven Nyholm for the new book Robot Sex. We already have sex robots, but the technology is still limited. Eventually, the machines will become sufficiently lifelike that the line between person and robot will be blurred.

There is no questioning the true potential that AI has to offer. However, as AI technology continues to develop and grow in sophistication, we are beginning to become more aware of the possible ethical and moral consequences associated with machines that are designed to think for themselves. The Tunnel Problem is one popular ethical thought experiment involving an autonomous car. Let's say that there is a self-driving car carrying a passenger and it is driving into a tunnel. Suddenly, a child runs across the road and trips, forcing the car into an impossible situation – either swerve over to the side of the tunnel, which will most definitely kill the occupant of the car, or run over the child.

The development of new Artificial Intelligence (AI) technology is often subject to bias, and the resulting systems can be discriminatory, meaning more should be done by policymakers to ensure its development is democratic and socially responsible. This is according to Dr Barbara Ribeiro of Manchester Institute of Innovation Research at The University of Manchester, in On AI and Robotics: Developing policy for the Fourth Industrial Revolution, a new policy report on the role of AI and Robotics in society, being published today. Dr Ribeiro adds because investment into AI will essentially be paid for by tax-payers in the long-term, policymakers need to make sure that the benefits of such technologies are fairly distributed throughout society. She says: "Ensuring social justice in AI development is essential. AI technologies rely on big data and the use of algorithms, which influence decision-making in public life and on matters such as social welfare, public safety and urban planning."

Duncan Higgins, head of electronic products at ITG, said using a machine learning approach in the broker's implementation shortfall algorithm in the US has been a'game changer'. Higgins told Markets Media that the industry needs to finish with MiFID II changes and move to business as usual, with reinvestment in algorithms and infrastructure. MiFID II regulations went live in the European Union at the start of this year after a multi-year investment and implementation process. He added that ITG has a big program of work including changing its implementation shortfall algorithm to use a machine learning approach. "The algo is much less constrained in its decision making and uses the state of the market and past experience to decide on the best approach to execute an order, determining how much and how to trade across lit and dark markets and auctions," said Higgins.

Today, young people are in pole position to steer the best possible future of the development of artificial intelligence (AI). As Douglas Adams famously said: "Anything that's invented between when you're fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it. Anything invented after you're thirty-five is against the natural order of things." The next generation are coming of age as the most exciting chapter in the development of AI is written. And there is a huge opportunity for organisations to harness the power of this younger generation to play a guiding role in how this technology is used and develops.

China, India, Japan, France and the European Union are crafting bold plans for artificial intelligence (AI). They see AI as a means to economic growth and social progress. Meanwhile, the U.S. disbanded its AI taskforce in 2016. The U.S. technology sector has long been a driver of global economic growth. From the PC to the Internet, the greatest advancements of the past 50 years were spawned in the U.S.