There has never been so much interest in legal technology as right now. Conferences about technology and the law are now oversubscribed and people are being turned away because they are full. Legal Geek's 2016 conference in London on 18th October, is a case in point, generating the kind of excitement one normally sees for the arrival of a stadium rock band. At the same time the mainstream legal media now covers what are in effect software supplier deals on their front pages, even if that software is Artificial Intelligence (AI) software. We are even seeing dedicated legal AI sites such as ArtificialLawyer.com

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and \textgreater{}300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean \textgreater{}300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.

Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.

In our today's information society more and more data emerges, e.g.~in social networks, technical applications, or business applications. Companies try to commercialize these data using data mining or machine learning methods. For this purpose, the data are categorized or classified, but often at high (monetary or temporal) costs. An effective approach to reduce these costs is to apply any kind of active learning (AL) methods, as AL controls the training process of a classifier by specific querying individual data points (samples), which are then labeled (e.g., provided with class memberships) by a domain expert. However, an analysis of current AL research shows that AL still has some shortcomings. In particular, the structure information given by the spatial pattern of the (un)labeled data in the input space of a classification model (e.g.,~cluster information), is used in an insufficient way. In addition, many existing AL techniques pay too little attention to their practical applicability. To meet these challenges, this article presents several techniques that together build a new approach for combining AL and semi-supervised learning (SSL) for support vector machines (SVM) in classification tasks. Structure information is captured by means of probabilistic models that are iteratively improved at runtime when label information becomes available. The probabilistic models are considered in a selection strategy based on distance, density, diversity, and distribution (4DS strategy) information for AL and in a kernel function (Responsibility Weighted Mahalanobis kernel) for SVM. The approach fuses generative and discriminative modeling techniques. With 20 benchmark data sets and with the MNIST data set it is shown that our new solution yields significantly better results than state-of-the-art methods.

Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite increasing interest in FMs, there exists to date no efficient training algorithm for higher-order FMs (HOFMs). In this paper, we present the first generic yet efficient algorithms for training arbitrary-order HOFMs. We also present new variants of HOFMs with shared parameters, which greatly reduce model size and prediction times while maintaining similar accuracy. We demonstrate the proposed approaches on four different link prediction tasks.

Carl Vondrick is a doctoral candidate and researcher at MIT, where he studies computer vision and machine learning. His research focuses include leveraging large-scale data with minimal annotation and its applications to predictive vision and scene understanding. Recently his work has received a lot of media attention, including features in Forbes, Wired, CNN and PopSci, and other media outlets worldwide. As part of his work with MIT CSAIL, Carl built a deep learning vision system for AI to learn and understand human behaviour and interactions, using popular TV shows like The Office, Desperate Housewives, and YouTube videos. The resulting algorithm analyzes videos, then uses what it learns to predict how humans will behave.

Elon Musk famously called it "our greatest existential threat". Physicist Stephen Hawking said that, limited by slow biological evolution, humans wouldn't be able to compete and would be superseded. But the technology that sparked those fears - artificial intelligence - is also being touted as the biggest potential advance in our history. A recent international study found that 50 per cent of experts questioned believe that artificial intelligence - or AI - will be smarter than humans within the next 24 years. And 90 per cent of those surveyed believed that milestone would be reached within 60 years.

Modern neural networks are good at making quick, reactive decisions and recognizing patterns, but they're not very skilled at the careful, deliberate thought that you need for complex choices. Google's DeepMind team may have licked that problem, however. Its researchers have developed a memory-boosted neural network (a "differentiable neural computer") that can create and work with sophisticated data structures. If it has a map of the London Underground, for example, it could figure out the quickest path from stop to stop or tell you where you'd end up after following a route sequence. The key is how the AI uses its memory.

We are looking for a Data Scientist to join our team and help us push our data driven organisation to even higher levels. We are looking for someone with a strong academic and/or business background in machine learning, statistics and programming, and with a keen interest in applying machine learning to real business problems. The Data Scientist will be leading a variety of projects that aim to automate decision making in core business processes. What you'll be doing: Use historical data to build accurate models of KPIs for our performance-based marketing campaigns: customer value, competition, etc. Optimize the targeting and pricing of our SEM campaigns. Use Machine Learning to make our website as effective as possible: e.g. by providing visitors the recommendations that are most relevant to them.

Google seems to have taken another step forward with their progress in artificial intelligence as their new AI program can now navigate the London Underground system without repetitive feeding of data. Most AI programs can do the same but the difference with the new Google AI agent is that it can learn the ropes in just one try. In addition, the same program also has the capability to answer several questions regarding a family tree. Google DeepMind researchers developed the program without having to pre-program it to know what and how to learn. Once the map of the London Underground subway was given, it took care of the rest.