The banking industry is becoming a digital rather than a physical system. So what sort of leaders should be running a modern bank? Should they be accountants or engineers -- or both? When I was completing a PhD in artificial intelligence (AI) at the University of Cambridge 20 years ago, many of my engineering classmates went to work for banks, and some of them run large Wall Street financial companies. The fourth industrial revolution is shrinking the world of work at a rapid rate.

In my undergraduate studies, I majored in philosophy with a focus on ethics, spending countless hours grappling with the notion of fairness: both how to define it and how to effect it in society. Little did I know then how critical these studies would be to my current work on the machine learning education team where I support efforts related to the responsible development and use of AI. As ML practitioners build, evaluate, and deploy machine learning models, they should keep fairness considerations (such as how different demographics of people will be affected by a model's predictions) in the forefront of their minds. Additionally, they should proactively develop strategies to identify and ameliorate the effects of algorithmic bias. To help practitioners achieve these goals, Google's engineering education and ML fairness teams developed a 60-minute self-study training module on fairness, which is now available publicly as part of our popular Machine Learning Crash Course (MLCC).

We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation. Furthermore, we present a novel system called Spark Serving that allows users to run any Apache Spark program as a distributed, sub-millisecond latency web service backed by their existing Spark Cluster. All MMLSpark contributions have the same API to enable simple composition across frameworks and usage across batch, streaming, and RESTful web serving scenarios on static, elastic, or serverless clusters. We showcase MMLSpark by creating a method for deep object detection capable of learning without human labeled data and demonstrate its effectiveness for Snow Leopard conservation.

Abstract-- Conventional deep learning classifiers are static in the sense that they are trained on a predefined set of classes and learning to classify a novel class typically requires retraining. In this work, we address the problem of Low-Shot networkexpansion learning. We introduce a learning framework which enables expanding a pre-trained (base) deep network to classify novel classes when the number of examples for the novel classes is particularly small. We present a simple yet powerful hard distillation method where the base network is augmented with additional weights to classify the novel classes, while keeping the weights of the base network unchanged. We show that since only a small number of weights needs to be trained, the hard distillation excels in low-shot training scenarios. Furthermore, hard distillation avoids detriment to classification performance on the base classes. Finally, we show that low-shot network expansion can be done with a very small memory footprint by using a compact generative model of the base classes training data with only a negligible degradation relative to learning with the full training set. I. INTRODUCTION In many real-life scenarios, a fast and simple classifier expansion is required to extend the set of classes that a deep network can classify.

Current state-of-the-art reading comprehension models rely heavily on recurrent neural networks. We explored an entirely different approach to question answering: a convolutional model. By their nature, these convolutional models are fast to train and capture local dependencies well, though they can struggle with longer-range dependencies and thus require augmentation to achieve comparable performance to RNN-based models. We conducted over two dozen controlled experiments with convolutional models and various kernel/attention/regularization schemes to determine the precise performance gains of each strategy, while maintaining a focus on speed. We ultimately ensembled three models: crossconv (0.5398 dev F1), attnconv (0.5665), and maybeconv (0.5285). The ensembled model was able to achieve a 0.6238 F1 score using the official SQuAD evaluation script. Our individual convolutional model crossconv was able to exceed the performance of the RNN-plus-attention baseline by 25% while training 6 times faster.

This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomalous input. Detecting anomalies is a critical component for multiple machine learning problems including detecting adversarial noise. More broadly, this work is a step towards giving neural networks the ability to recognize an out-of-distribution sample. This is the first work to introduce "Subset Scanning" methods from the anomalous pattern detection domain to the task of detecting anomalous input of neural networks. Subset scanning treats the detection problem as a search for the most anomalous subset of node activations (i.e., highest scoring subset according to non-parametric scan statistics). Mathematical properties of these scoring functions allow the search to be completed in log-linear rather than exponential time while still guaranteeing the most anomalous subset of nodes in the network is identified for a given input. Quantitative results for detecting and characterizing adversarial noise are provided for CIFAR-10 images on a simple convolutional neural network. We observe an "interference" pattern where anomalous activations in shallow layers suppress the activation structure of the original image in deeper layers.

Retail to cloud-computing giant Amazon plans to hire over 1,000 new staff across three sites in the UK, and will open a new office in Manchester next year. "These are Silicon Valley jobs in Britain, and further cement our long-term commitment to the UK." said Doug Gurr, Amazon's UK country manager. A new corporate office in Manchester, due to open next year, will be located in the Hanover Building in the Northern Quarter. The company said the six-storey, 90,000 square-foot site will house at least 600 new staff working on software development, machine learning and R&D. Amazon said it will also expand its development centre in Edinburgh, adding 250 new staff where it already has hundreds of software engineers, machine learning scientists and user experience designers.

At 25, Ms Annabelle Kwok has already made a name for herself. Two years ago, she made waves when she co-founded SmartCow, an artificial intelligence (AI) company that came up with an electronic board that could run various AI software. Last year, Ms Kwok left SmartCow and started NeuralBay, a company that focuses on detection and recognition software related to humans, objects and text, and offers AI-driven solutions for multinational corporations. Her clients include an aviation corporation, an automation industry company and chocolate company Ferrero. Ms Kwok traces her interest in tech to a box of Lego bricks with electric cables called Lego Mindstorms, which her parents, who work in banking, bought for her when she was in primary school.

Sir Anthony Seldon (pictured) warned that the technology could manipulate young people into doing'unspeakable' things, by impersonating teachers or celebrities Online predators could use artificial intelligence to manipulate children into'unspeakable' actions by posing as teachers or celebrities, a professor has warned. Sir Anthony Seldon said people should'wake up and smell the silicone' about the risk AI poses to children. Speaking at the launch of the UK's first Institute for Ethical AI in Education (IEAIED), warned schools were not teaching'critical thinking' to help pupils fend off dangerous technology. Sir Anthony, vice-chancellor of the University of Buckingham, said: 'These machines can impersonate teachers, they can either impersonate people who they don't know but look plausible, they can impersonate public figures. 'Or they can impersonate teachers – the headteacher of the school – and insinuate and manipulate children into thinking that this is the real teacher telling them to do things.

The relationships are predicted from local polynomial regressions. Shaded areas indicate 95% confidence intervals. Preferences concerning time, risk, and social interactions systematically shape human behavior and contribute to differential economic and social outcomes between women and men. We present a global investigation of gender differences in six fundamental preferences. Our data consist of measures of willingness to take risks, patience, altruism, positive and negative reciprocity, and trust for 80,000 individuals in 76 representative country samples. Gender differences in preferences were positively related to economic development and gender equality. This finding suggests that greater availability of and gender-equal access to material and social resources favor the manifestation of gender-differentiated preferences across countries. Fundamental preferences such as altruism, risk-taking, reciprocity, patience, or trust constitute the foundation of choice theories and govern human behavior.