In a world where teachers are often overworked, could artificial intelligence help teachers with their workload? According to the popular 1960's rock song, "In the year 2525", we start to notice drastic changes in our world; although, according to some education specialists, that may happen a great deal sooner – especially where education is concerned. A conference organised by the Westminster Education Forum held a debate on the future of England's exam system and heard that, in exam halls, the time for change might be a touch nearer than 2525. To be more precise, the consensus was that it would be the year 2025. According to one project put forward, 2025 will see the marking of the exam system taken over by artificial intelligence, eliminating the chance of human error.

Donald Trump's claim, in the aftermath of the Florida school shooting, that these events are the result of violent video games, resurrects old arguments about whether young people emulate the games they play. The World Health Organisation's (WHO) recent decision to consider video game addiction an official illness shows comparable concern. However, these responses demonstrate anxiety about the right things for the wrong reasons. Gaming cultures are connected to violence – but should be considered in terms of the rise of far right political discourse and the prominence of "alt-right" misogyny and racism. While Trump is firmly on the right and the WHO may embody normative centrism, there is an aspect of gaming that should worry the progressive left.

The recent revolution in digital technology has touched every sphere and facet of lives, and education sector has not been spared. Unlike any other sector, the link between digital technology and education is unique and complimentary. On one hand, digital technology has become the enabler by redefining the very basics of the sector and altering the rules of the game. On the other hand, today's young minds will decide the future direction of digital technology as they are going to be the innovators of tomorrow. So, equipping our students is key to success in the field.

There is new code to play around with for those interested in machine learning and space, a model that predicts hilarious search trends for sex site YouPorn, and another funny story about an ostensibly intelligent medical chatbot in New Zealand. Hunting exoplanets with ML – The machine learning code that a Google engineer and an astrophysicist used to detect exoplanets has been published online. Christopher Shallue, a senior software engineer at Google, and Andrew Vanderburg, a postdoctoral fellow studying astrophysics at the University of Texas, USA, discovered another planet lurking in the Kepler-90 system. It was a special find. Not only was it spotted using a convolutional neural network, but it meant that the Solar System was no longer the biggest planetary system found so far.

ABSTRACT A method to produce personalized classification models to automatically review online dating profiles on Tinder, based on the user's historical preference, is proposed. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings from a FaceNet model were used as the features to describe an individual's face. A user reviewed 8,545 online dating profiles. For each reviewed online dating profile, a feature set was constructed from the profile images which contained just one face. Two approaches are presented to go from the set of features for each face to a set of profile features. A simple logistic regression trained on the em-beddings from just 20 profiles could obtain a 65% validation accuracy. A point of diminishing marginal returns was identified to occur around 80 profiles, at which the model accuracy of 73% would only improve marginally after reviewing a significant number of additional profiles. Index Terms-- facial classification, facial attractiveness, online dating, classifying dating profiles 1. INTRODUCTION Online dating has become a commonplace in today's society.

Rectified linear units, or ReLUs, have become the preferred activation function for artificial neural networks. In this paper we consider two basic learning problems assuming that the underlying data follow a generative model based on a ReLU-network -- a neural network with ReLU activations. As a primarily theoretical study, we limit ourselves to a single-layer network. The first problem we study corresponds to dictionary-learning in the presence of nonlinearity (modeled by the ReLU functions). Given a set of observation vectors $\mathbf{y}^i \in \mathbb{R}^d, i =1, 2, \dots , n$, we aim to recover $d\times k$ matrix $A$ and the latent vectors $\{\mathbf{c}^i\} \subset \mathbb{R}^k$ under the model $\mathbf{y}^i = \mathrm{ReLU}(A\mathbf{c}^i +\mathbf{b})$, where $\mathbf{b}\in \mathbb{R}^d$ is a random bias. We show that it is possible to recover the column space of $A$ within an error of $O(d)$ (in Frobenius norm) under certain conditions on the probability distribution of $\mathbf{b}$. The second problem we consider is that of robust recovery of the signal in the presence of outliers, i.e., large but sparse noise. In this setting we are interested in recovering the latent vector $\mathbf{c}$ from its noisy nonlinear sketches of the form $\mathbf{v} = \mathrm{ReLU}(A\mathbf{c}) + \mathbf{e}+\mathbf{w}$, where $\mathbf{e} \in \mathbb{R}^d$ denotes the outliers with sparsity $s$ and $\mathbf{w} \in \mathbb{R}^d$ denote the dense but small noise. This line of work has recently been studied (Soltanolkotabi, 2017) without the presence of outliers. For this problem, we show that a generalized LASSO algorithm is able to recover the signal $\mathbf{c} \in \mathbb{R}^k$ within an $\ell_2$ error of $O(\sqrt{\frac{(k+s)\log d}{d}})$ when $A$ is a random Gaussian matrix.

The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions. We propose an approach called model extraction for interpreting complex, blackbox models. Our approach approximates the complex model using a much more interpretable model; as long as the approximation quality is good, then statistical properties of the complex model are reflected in the interpretable model. We show how model extraction can be used to understand and debug random forests and neural nets trained on several datasets from the UCI Machine Learning Repository, as well as control policies learned for several classical reinforcement learning problems.

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list. We decided to share this resource with you, and so here are Udemy's top selling courses.

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services.

CREATIVITY: IT'S AT THE HEART OF WHO WE HUMANS ARE… WE HUMANS ARE SPECIAL, RIGHT? Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece? OVER SOME 40,000 YEARS, HUMAN CREATIVITY HAS EXPLODED – FROM DRAWINGS ON CAVE WALLS THROUGH THE GREAT ART OF CENTURIES TO COME…. NOW, SCIENTISTS -- AND ARTISTS –ARE ASKING CAN A ROBOT TRULY IMAGINE AN ORIGINAL MASTERWORK? COMPUTATIONAL CREATIVITY IS LEADING US TO ASK NEW QUESTIONS ABOUT HUMAN CREATIVITY. IS THIS ESSENTIAL HUMAN TRAIT TRULY UNIQUE? WILL ARTIFICIAL INTELLIGENCE BE A COMPETITOR? OR CAN IT BE A COLLABORATOR, HELPING US TOWARD STILL UNIMAGINED CREATIONS? SCHAEFER: My first guest is a member of Google Brain's Magenta team. He is currently working on neural network models of sound and music and recently produced a synthesizer that designed its own sounds. SCHAEFER: Also with us, is an Assistant professor at the University of Illinois at Urbana Champaign in the Dept. of Electrical and Computer Engineering. He focuses on several surprising creative domains including the culinary arts and fashion and the theoretical foundations of creativity. SCHAEFER: Also with us is an Associate Professor of psychological and brain science at Dartmouth College. He's interested in the neural basis of imagination and in the evolution of human creativity. A former research fellow at MIT's Media lab and artist in residence at Google, please welcome Sougwen Chung.