I swear by the Hypatia, by lovelace, by Turing, by Fisher (and/or Bayes), and by all the statisticians and data scientists, making them my witness, that i will carry out, according to my ability and judgement, this oath and this indenture. Could this be the first line of a "Hippocratic Oath" for mathematicians and data scientists? Hannah Fry, Associate Professor in the mathematics of cities at University College London, argues that mathematicians and data scientists need such an oath, just like medical doctors who swear to act only in their patients' best interests. "In medicine, you learn about ethics from day one. It has to be there from day one and at the forefront of your mind in every step you take," Fry argued. But is a tech version of the Hippocratic Oath really required?

Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

Consumers International has released a new study on consumer experiences of artificial intelligence (AI) and the ways in which AI enabled services shape their consumer journeys and experiences, and consumer outcomes. The report'Artificial Intelligence: consumer experiences in new technology' contains a summary of new findings about the consumer experience of AI from IPSOS Global's participatory research with families and individuals in India, Australia and Japan, and insights from interviews with expert stakeholders from the region. It also incorporates the results of the multi-stakeholder roundtable held in Singapore in March 2019, where consumer organisations, businesses, academics and regulators discussed how AI enabled technology can deliver the best possible outcomes for consumers, whilst recognising and mitigating against potential challenges and risks.

Although GAN-based methods have received many achievements in the last few years, they have not been such successful in generating discrete data. The most important challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. Despite several attempts done to alleviate this problem, none of the existing GAN-based methods has improved the performance of text generation (using measures that evaluate both the quality and the diversity of generated samples) compared to a generative RNN that is simply trained by the maximum likelihood approach. In this paper, we propose a new framework for generating discrete data by an adversarial approach in which we do not need to pass the gradient to the generator. In the proposed method, the update of either the generator or the discriminator can be accomplished straightforwardly. Moreover, we leverage the discreteness of data to explicitly model the data distribution and ensure the normalization of the generated distribution and consequently the convergence properties of the proposed method. Experimental results generally show the superiority of the proposed DGSAN method compared to the other GAN-based approaches for generating discrete sequential data.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington

Voice and the rise of home devices and smart speakers are opening up new possibilities for researchers, enabling respondents to engage beyond simply typing a response and creating opportunities for ongoing dialogue. In an ESOMAR paper, What market research can learn from Alexa & Siri, a trio of authors – Young Ham (Kantar Australia), Jason Dodge (Kantar US) and Rebecca Southern (Kantar Australia) – extol the benefits of chatbots and AI. "These can help bridge the gap between quantitative and qualitative, offering more in-depth ways to better understand today's consumers," they write. "These give the chance to participate in a more interactive, flowing manner that is more conversational than a typed response." And for marketing and insights teams, they add, "AI can deliver smarter, more impactful consumer engagement... at scale".

Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of tall buildings in megacities. To fully understand the interference effects of buildings, it often requires a substantial amount of wind tunnel tests. Limited wind tunnel tests that only cover part of interference scenarios are unable to fully reveal the interference effects. This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming. Four machine learning models including decision tree, random forest, XGBoost, generative adversarial networks (GANs), were trained based on 30% of a dataset to predict both mean and fluctuating pressure coefficients on the principal building. The GANs model exhibited the best performance in predicting these pressure coefficients. A number of GANs models were then trained based on different portions of the dataset ranging from 10% to 90%. It was found that the GANs model based on 30% of the dataset is capable of predicting both mean and fluctuating pressure coefficients under unseen interference conditions accurately. By using this GANs model, 70% of the wind tunnel test cases can be saved, largely alleviating the cost of this kind of wind tunnel testing study.

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification.

Organizations today are focused on identifying avenues to introduce AI into daily tasks and deliverables. While the common perception is that it creates a sense of insecurity among employees, contrary to this belief, employees are in fact more receptive and ready to deploy AI into their work, a study by Dale Carnegie reveals. During a roundtable discussion on "Preparing people for the Human Machine Partnerships of the future," conducted by Dale Carnegie in New Delhi, experts explored ways in which industry leaders can incorporate AI technology into their HR Tech, performance feedback systems, upskilling initiatives, etc. The panel discussion was led by Dale Carnegie representatives including Pallavi Jha, MD & Chairperson, Dale Carnegie of India; Mark Marone, Director - Research & Thought Leadership, Dale Carnegie and Associates; Juliette Dennett, Managing Director, Dale Carnegie Northern England; and Jordan Wang, Managing Director New South Wales, Dale Carnegie Australia. The survey that saw participation from 3,846 respondents across 13 countries, aimed to assess the readiness of the global workforce to accept AI in their work, feedback systems, skilling needs, etc., highlighted that 42 percent of the organizations globally are already using AI in one form or the other.

Abstract--Mining causality from text is a complex and crucial natural language understanding task. Most of the early attempts at its solution can group into two categories: 1) utilizing co-occurrence frequency and world knowledge for causality detection; 2) extracting cause-effect pairs by using connectives and syntax patterns directly. However, because causality has various linguistic expressions, the noisy data and ignoring implicit expressions problems induced by these methods cannot be avoided. In this paper, we present a neural causality detection model, namely Multilevel Causality Detection Network (MCDN), to address this problem. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and integrate a novel Relation Network to infer causality at segment level. To the best of our knowledge, in touch with the causality tasks, this is the first time that the Relation Network is applied. The experimental results on the AltLex dataset, demonstrate that: a) MCDN is highly effective for the ambiguous and implicit causality inference; b) comparing with the regular text classification task, causality detection requires stronger inference capability; c) the proposed approach achieved state-of- the-art performance. I. Introduction Automatic text causality mining is a critical but difficult task because causality is thought to play an essential role in human cognition when making decisions [1]. Thus, automatic text causality has been studied extensively in a wide range of areas, such as industry [2], physics [3] and healthcare [4], etc. A tool to automatically scour the plethora of textual content on the web and extract meaningful causal relations could help us construct causal chains to unveil previously unknown relationships between events [5] and accelerates the discovery of the intrinsic logic of the events [6]. Many research efforts have been made to mine causality from text corpus with complex sentence structures in the books or newspapers. In Causal-TimeBank [7] authors introduced "CLINK" and "C-SIGNAL" tag to mark events causal relation and causal signals respectively based on specific templates (e.g., "A happened because of B").