Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.

High-resolution satellite imagery have been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Even though, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this dataset is used to train deep-learning-based models in order to accurately classify satellite images that contains or not zebra crossings. A novel dataset with more than 240,000 images from 3 continents, 9 countries and more than 20 cities was used in the experiments. Experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.

Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if the other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. Here we show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements with a new robust private regression method in the accuracy of private drug sensitivity prediction. The method combines two key properties not present even in recent proposals, which can be generalised to other predictors: we prove it is asymptotically consistently and efficiently private, and demonstrate that it performs well on finite data. Good finite data performance is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields, such as mobile sensing and social media, in addition to the badly needed precision medicine solutions.

Catalan nanotechnology engineer Sergi Santos holds the head of Samantha, a sex doll packed with artificial intelligence providing her the capability to respond to different scenarios and verbal stimulus, in his house in Rubi, north of Barcelona, Spain, March 31, 2017.

When it comes to effectiveness of machine learning, more data almost always yields better results--and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments.

Scientists are using artificial intelligence (AI) to help predict which embryos will result in IVF success. In a new study, AI was found to be more accurate than embryologists at pinpointing which embryos had the potential to result in the birth of a healthy baby. Experts from Sao Paulo State University in Brazil have teamed up with Boston Place Clinic in London to develop the technology in collaboration with Dr Cristina Hickman, scientific adviser to the British Fertility Society. They believe the inexpensive technique has the potential to transform care for patients and help women achieve pregnancy sooner. During the process, AI was "trained" in what a good embryo looks like from a series of images.

Women have long claimed their monthly period makes them more irritable or stressed. Now, however, scientists have said the idea that a woman's menstrual cycle affects her thinking is nothing more than a myth. According to some previous studies, women are more impulsive and moody before their period, and more'rational' afterwards. Scientists have said the idea that a woman's menstrual cycle affects her thinking is nothing more than a myth But the latest research says that while women may feel their thinking became altered, this was not the case when studied scientifically. It seems that despite hormone levels fluctuating enormously in a woman's body, they have no effect on her ability to remember or make decisions.

Artificial intelligence is getting pretty good at besting humans in things like chess and Go and dominating at trivia. Now, AI is moving into the arts, aping van Gogh's style and creating a truly trippy art form called Inceptionism. A new AI project is continuing to push the envelope with an algorithm that only produces original styles of art, and Chris Baraniuk at New Scientist reports that the product gets equal or higher ratings than human-generated artwork. Researchers from Rutgers University, the College of Charleston and Facebook's AI Lab collaborated on the system, which is a type of generative adversarial network or GAN, which uses two independent neural networks to critique each other. In this case, one of the systems is a generator network, which creates pieces of art.

"Traditional rule-based KYC-AML technology necessitates significant dependence on manual efforts particularly in alert investigation stage, which is costly, error-prone, and inefficient" The ultimate aim of any Financial Institution (FI) is to earn the confidence and faith of their customers but equally important to verify the information customers provide back to them. The regulators are increasingly concentrating on ensuring that banks have robust and effective controls in place for customer due diligence (CDD). Multinational banks need to ensure compliance not only in their home country but also in environments that are more complex and have fewer infrastructures. For Example, Deloitte has highlighted in its "Meeting new expectation" report that AML sanctions-related fines and penalties imposed in 2013 and 2014 quadrupled the total for the previous nine years. Artificial Intelligence (AI) takes KYC and AML compliance to the next level.

Snapchat's controversial new feature allows other people to see exactly where you are in real time, and parents are being advised to take steps to protect their children's privacy. Snap Map uses data such as your location, speed of travel and phone usage to work out where you are and what you're doing, and shares this information with your friends on an interactive map. The map, which you can launch by pinching the Snapchat camera home screen, is precise enough to show not only what street you're on, but also whereabouts on that street you are. Parents are being urged to read up on it, and to ensure their children are aware of the risks that can come with sharing too much information through social media. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.