Blaming violence on video games over-simplifies a deeply complicated issue. If you're wondering whether the video game your child is playing is appropriate, there's a long-standing rating system in place to guide you. But that system, established in 1994 after Senate hearings into violence in video games, is "good but not perfect," says Jeff Haynes, senior editor for video games at Common Sense Media, a non-profit group that curates its own library of ratings and reviews of games, movies apps, TV shows and other content. What that means is parents need to be especially vigilant when it comes to assessing whether the games their children play are appropriate. "The biggest thing we constantly try to push is know your kids and know the content your kids are playing and get involved with what your kids are interested in," Haynes says.

Chatbots assist people daily with everything from ordering pizza to dealing with customer service issues. So, it's no surprise that higher education institutions are embracing them to interact with their No. 1 customer: students. Whether it's navigating the admissions process or scheduling classes, universities have embraced artificial intelligence to streamline student interactions and offer timely support. SIGN UP: Get more news from the EdTech newsletter in your inbox every two weeks! Rather than blindly searching the internet for information on colleges, students could be asking chatbots their questions.

This PhD studentship is funded for three years by the Big C Charity. Funding comprises Home/EU fees, an annual stipend (£14,553 for 2017 entry – this increases each year in line with the GDP deflator) and £1000 per annum to support research training. Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue.

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.