One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes Equation is a Parabolic Partial Differential Equation which provides an option pricing model. The present work proposes an approach based on Neural Networks to solve the Black-Scholes Equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes Equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Sholes Equation for a specific real-world stock options time series. The experimental results showed that the Neural network option pricing based on the Black-Sholes Equation solution can reach an option pricing forecasting more accurate than the traditional Black-Sholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. On 14 February 1946, journalists gathered at the Moore School of Engineering at the University of Pennsylvania to witness a public demonstration of one of the world's first general-purpose electronic digital computers: the Electronic Numerical Integrator and Computer (ENIAC).

We tackle the problem of authenticating high value Italian wines through machine learning classification. The problem is a seriuos one, since protection of high quality wines from forgeries is worth several million of Euros each year. In a previous work we have identified some base models (in particular classifiers based on Bayesian network (BNC), multi-layer perceptron (MLP) and sequential minimal optimization (SMO)) that well behave using unexpensive chemical analyses of the interested wines. In the present paper, we investigate the role of esemble learning in the construction of more robust classifiers; results suggest that, while bagging and boosting may significantly improve both BNC and MLP, the SMO model is already very robust and efficient as a base learner. We report on results concerning both cross validation on two different datasets, as well as experiments with models trained with the above datasets and tested with a dataset of potentially fake wines; this has been synthesized from a generative probabilistic model learned from real samples and expert knowledge.

The Bristol Robotics Laboratory (BRL) will host the first European- Commission funded European Robotics League (ERL) tournament for service robots to be held in the UK. Two teams from the BRL and Birmingham will pitch their robots against each other in a series of events from 26 and 30 June. Robots designed to support people with care-related tasks in the home will be put to the test in a simulated home test bed. The assisted living robots of the two teams will face various challenges, including understanding natural speech and finding and retrieving objects for the user. The robots will also have to greet visitors at the door appropriately, such as welcoming a doctor on their visit, or turning away unwanted visitors.

Machine learning often involves a trade-off between accuracy and interpretability. Models that are easy to understand perform poorly, while models that perform well tend to be hard to understand. This can be a show-stopper in many contexts. It can be risky or legally dubious to deploy a model you don't understand in a commercial environment. Interpreting a model is often the whole point of using machine learning in science.