The majority of UK accountants think that automation will play a significant role in the accountancy profession within the next five years, according to a new report from FreeAgent. FreeAgent's Future of Accountancy report has revealed that 96% of accountants believe that either all or some accountancy work will be automated by 2022, while just 3% said they do not think automation will affect any accountancy work by that time. In addition, 42% of respondents said they were intending to retrain in order to safeguard their role in the future, while 23% said they might consider doing so. In contrast, just one in five accountants said they were not considering any retraining to future-proof their job. Ed Molyneux, CEO and co-founder of FreeAgent, said: "We are seeing a continuing shift in the landscape of the accounting profession. Technology is driving new ways of working with clients, while forthcoming legislative changes such as Making Tax Digital and PSD2 will significantly accelerate the pace of automation. "However, this should not be cause for alarm.

Leading US hospital Mayo Clinic has unveiled a new startup company that uses artificial intelligence to discover novel treatments. Launched in partnership with American tech company nference, Qrativ (pronounced'curative') combines Mayo Clinic's medical expertise and clinical data with nference's AI platform nferX. The deep learning-driven AI sifts through masses of medical literature and clinical data to uncover insights into disease and will form the basis of Qrativ's Darwin.ai These insights can then be used to guide the development of new drugs. Initially, Qrativ will focus on drug repurposing for rare diseases and highly targeted patient populations.

Trying to handle and hide our true emotions is a challenge we all share as humans, and trying to discern what other people are hiding from us is something that fascinates us even more. Yet latest technological developments seem to signal that there is no place to hide anymore: Scientists at the University of Oulu in Finland have developed facial recognition software that can read human microexpressions at a success rate that beats humans at the same task. But what exactly are microexpressions? According to the Paul Ekman Group, founded by renowned psychologist Paul Ekman, who conducted groundbreaking research in the correlations between emotions and facial expressions – and has been dubbed "the best human lie detector in the world" in the process – microexpressions are "facial expressions that occur within 1/25th of a second and expose a person's true emotions". We make them involuntarily, even when we are trying to conceal our true emotional response. Perhaps more importantly, microexpressions are the same across all ages and genders, regardless of the person's unique cultural background, and they appear even when the subject is trying to hide their emotions without realizing they are doing so – in essence, when they are lying to themselves, too.

The AI era is officially here. Or so goes the word around the marketing bonfire. According to a recent Forrester Research report, "The Promise and Potential Peril of AI," 57% of marketers identified improving customer experience as being the biggest benefit of AI to their business. But how has AI been able to achieve this improved customer experience? In years, past pattern recognition was performed manually, which, Fine says, led to what can be amounted to an "educated guess" about customer demographics.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This comprehensive course by Jose Portilla will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

This paper describes the Quantitative Criticism Lab, a collaborative initiative between classicists, quantitative biologists, and computer scientists to apply ideas and methods drawn from the sciences to the study of literature. A core goal of the project is the use of computational biology, natural language processing, and machine learning techniques to investigate authorial style, intertextuality, and related phenomena of literary significance. As a case study in our approach, here we review the use of sequence alignment, a common technique in genomics and computational linguistics, to detect intertextuality in Latin literature. Sequence alignment is distinguished by its ability to find inexact verbal similarities, which makes it ideal for identifying phonetic echoes in large corpora of Latin texts. Although especially suited to Latin, sequence alignment in principle can be extended to many other languages.

Over the past half decade, sensors capable of precisely measuring distances have dropped in price dramatically. RGB-D (RGB Distance) cameras such as the Microsoft Kinect are able to assign distances to individual pixels, and LIDAR (Light Detection and Ranging) scanners are more effective and affordable. A combination of these advances in hardware and research into SLAM (Simultaneous Localization and Mapping) have allowed robots and self driving cars to stitch together individual images into maps of their environment. Whereas 2D image based object detection and segmentation has seen plenty of advancement, the processing of point cloud data is still slightly lagging. This can be attributed partly to the ubiquity of 2D images and relative scarcity of point cloud data, but also partly to the convenient nature of RGB images, as spatial relationships between pixels are encoded in the structure of the image itself by the indices of pixels in the matrix. CNNs exploit this efficiently, as individual pixels can be matched with individual weights, resulting in a computationally cheap operation. In a point cloud however, individual points can exist in any location in the array, and spatial information is encoded explicitly alongside other information. A map generated from an RGB-D camera would consist of points that would each be structured as such: [X,Y,Z,R,G,B].

The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. These models represent each data view via shared and individual components, and have been successfully applied for exploratory dimension reduction, association analysis between the views, and further learning tasks such as consensus clustering. Despite these advances, there remain significant challenges in modeling partially-shared components, and identifying the number of components of each type (shared/partially-shared/individual). In this work, we formulate a novel linked component model that directly incorporates partially-shared structures. We call this model SLIDE for Structural Learning and Integrative DEcomposition of multi-view data. We prove the existence of SLIDE decomposition and explicitly characterize the identifiability conditions. The proposed model fitting and selection techniques allow for joint identification of the number of components of each type, in contrast to existing sequential approaches. In our empirical studies, SLIDE demonstrates excellent performance in both signal estimation and component selection. We further illustrate the methodology on the breast cancer data from The Cancer Genome Atlas repository.