Streetlib is an online self-publishing solution that is geared towards independent authors. When you register for an account you can upload your e-book use a free ISBN that they give you. There are a ton of distribution options, but Amazon, Apple, Kobo, Google Play and Tolino generate the most sales. There are over 180,000 titles from 75,000 authors and the average indie is earning $35,000 per year. The markets that Streetlib finds that are most successful are mainly outside the United States, such as Italy, France, Germany, Spain, UK, Mexico, Canada and India.

This is an edited and updated version of a two part blog written by me which was published as a two part series on the LexisNexis Enterprise Solutions portal on 6th & 13th June 2016. The blog posts (combined here) reflect an element of the content of my keynote speech I shared with delegates at the Lexis InterActionShare conference in April 2016. It is reproduced with kind permission. At the recent LexisNexis Enterprise Solutions' InterAction Share event in London, I shared with delegates my insight and advice in relation to the rise of smart technologies, artificial intelligence (AI), robots and machine learning in the legal ecosystem and how these technologies are being, and will be, deployed in the industry. By addressing all of the above, it naturally led to my tackling the challenging question "what is the next legal frontier?" It's important to realise and understand that it is inevitable that the roles of lawyers, general counsel, marketers, business development, social media and CRM specialists etc. are going to change in light of such overwhelming technological advances.

In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions. Attribute interactions in classification tasks mean that two or more attributes together provide stronger evidence for a particular class label. Knowledge of such interactions makes models more interpretable by revealing associations between attributes. This has applications, e.g., in pharmacovigilance to identify interactions between drugs or in bioinformatics to investigate associations between single nucleotide polymorphisms. We also show how the found attribute partitioning is related to a factorisation of the data generating distribution and empirically demonstrate the utility of the proposed method.

We consider the problem of minimizing the sum of an average function of a large number of smooth convex components and a general, possibly non-differentiable, convex function. Although many methods have been proposed to solve this problem with the assumption that the sum is strongly convex, few methods support the non-strongly convex cases. Adding a small quadratic regularization is a common trick used to tackle non-strongly convex problems; however, it may worsen the quality of solutions or weaken the performance of the algorithms. Avoiding this trick, we propose a new accelerated stochastic mirror descent method for solving the problem without the strongly convex assumption. Our method extends the deterministic accelerated proximal gradient methods of Paul Tseng and can be applied even when proximal points are computed inexactly. Our direct algorithms can be proven to achieve the optimal convergence rate $O(\frac{1}{k^2})$ under a suitable choice of the errors in calculating the proximal points. We also propose a scheme for solving the problem when the component functions are non-smooth and finally apply the new algorithms to a class of composite convex concave optimization problems.

In recent years it has become popular to study machine learning problems in a setting of ordinal distance information rather than numerical distance measurements. By ordinal distance information we refer to binary answers to distance comparisons such as $d(A,B)

Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.

An "art" landscape photograph from Interlaken, Switzerland, produced from a Google Earth image by Creatism -- Google's new experimental "deep-learning system for artistic content creation." There are many professions where human workers are being replaced by intelligent machines. Cashiers at stores and restaurants, factory workers, even farm laborers are all being swapped out for robots at a dizzying pace. Until now, however, those in the artistic professions felt pretty safe from the threat. After all, how could an algorithm ever replicate the inenarrable process of human creativity?

IBM's Watson supercomputer is one of the world's best-known artificial intelligence systems. But fame, it turns out, doesn't mean fortune. A scathing report from investment bank Jefferies claims that from an earnings per share perspective "it seems unlikely to us under almost any scenario that Watson will generate meaningful earnings results over the next few years". IBM Watson made its debut as a research project in 2006 and later gained fame after beating two human champions on classic US quiz show Jeopardy!. IBM has since spent a lot of time and money promoting its flagship product, posting more than 200 press releases on Watson, according to Jefferies.

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After decades of false starts, artificial intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize. AI's latest breakthrough is being propelled by machine learning--a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.