Uber made two big announcements on Monday: It is acquiring Geometric Intelligence, a New York-based startup, and using that 15-person team to help launch a new artificial intelligence division within Uber, called Uber AI Labs. Uber AI Labs will focus on improving both ride-hailing software and the company's self-driving car software, according to a blog post by Uber's product chief Jeff Holden. "In spite of notable wins with machine learning in recent years, we are still very much in the early innings of machine intelligence," Holden wrote in the blog post. "The formation of Uber AI Labs, to be directed by Geometric's Founding CEO Gary Marcus, represents Uber's commitment to advancing the state of the art, driven by our vision that moving people and things in the physical world can be radically faster, safer and accessible to all." According to The Wall Street Journal, Uber AI Labs will start aggressively hiring and plans to open an office in the UK.

The explosive growth of ESI has made for more costly, complex litigation and driven changes to the Federal Rules of Civil Procedure (FRCP). These changes will continue to shape e-discovery, and roles in e-discovery, technology and litigation support will continue to emerge. Median annual spend on corporate litigation expenditures Norton Rose "2016 Litigation Trends Annual Survey" $1,000,000 Special Eurobarometer 431 - Data protection, June 2015 of people say their explicit approval should be required in all cases before their data is collected and processed As 2016 winds down, here's a quick summary of the top legal industry trends, new and old, to watch for in 2017. LEGAL TRENDS E-DISCOVERY CHANGES TO WATCH FOR IN 2017 Artificial intelligence (AI) gets smarter every day. Its potential to automate tasks like TAR, map judicial predispositions to predict rulings or analyze opposing counsel tactics to help prepare defenses continue to grow.

This tutorial introduces machine learning and data science concepts for developers. On the web, we already have many excellent resources for learning data science, however, the sheer amount of material can, in itself, be daunting. This is based on my insights from the Enterprise AI course and also the Data Science for IoT course which I teach at Oxford University. We explain concepts simply but in context. Many tutorials explain one specific aspect but do not show how it fits into the wider picture.

Harriet Green tells us she is very excited about the prospect of the cognitive era. And so she should be. The former Thomas Cook CEO, switched holidays for robots, when she joined IBM in 2015 to head up its Watson, Internet of Things, commerce and education department. "IoT is just an amazing force of the digitisation movement – connecting things to people," she tells Essential Retail. "It's really all about Watson's ability to take vast amounts of structured and unstructured data and process that data, whether its smell, sound, video or text."

Rogue One, the latest in the Star Wars franchise, has had mixed reviews but features one undisputed star: K-2SO, a gangly robot with the best lines. Movies of the distant future always tap into current anxieties, and the latest alarm is that the robots are coming. Droids may not conquer the world, but they will take over its work--white-collar as well as blue-collar. Could these filmmakers know something we don't? Previous scares, such as when TIME magazine reported on "the automation jobless" in the early 1960s, were just that.

IMAGE: This is the Architecture of the Adversarial Autoencoder (AAE). The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.

In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes and comment on their use and implementation. We then show, using several code examples, how the package can be used to replicate well-known results from the semi-supervised learning literature.

Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words. State-of-the-art algorithms including those by Mikolov et al. [5, 6] have been parallelized for multi-core CPU architectures, but are based on vector-vector operations with "Hogwild" updates that are memory-bandwidth intensive and do not efficiently use computational resources. In this paper, we propose "HogBatch" by improving reuse of various data structures in the algorithm through the use of minibatching and negative sample sharing, hence allowing us to express the problem using matrix multiply operations. We also explore different techniques to distribute word2vec computation across nodes in a compute cluster, and demonstrate good strong scalability up to 32 nodes. The new algorithm is particularly suitable for modern multi-core/many-core architectures, especially Intel's latest Knights Landing processors, and allows us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge.

Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the posterior distribution can be used to achieve a desired level of privacy and utility. To do so, we generalise differential privacy to arbitrary dataset metrics, outcome spaces and distribution families. This allows us to also deal with non-i.i.d or non-tabular datasets. We prove bounds on the sensitivity of the posterior to the data, which gives a measure of robustness. We also show how to use posterior sampling to provide differentially private responses to queries, within a decision-theoretic framework. Finally, we provide bounds on the utility and on the distinguishability of datasets. The latter are complemented by a novel use of Le Cam's method to obtain lower bounds. All our general results hold for arbitrary database metrics, including those for the common definition of differential privacy. For specific choices of the metric, we give a number of examples satisfying our assumptions.

In [3] we presented a prototype of a system that enables users to explore arguments for a given topic. This involves these steps: 1. Argument identification. In the first step, arguments concerning a given topic are identified in a given text and attacking and supporting relationships between the propositions are established. The result is an argumentation graph. In the future we hope to use argumentation mining techniques to automate this step. At this time, this is done manually by marking up some text.