Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn deep learning, most existing tools focus on simpler models. In this work, we present GAN Lab, the first interactive visualization tool designed for non-experts to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, users can interactively train generative models and visualize the dynamic training process's intermediate results. GAN Lab tightly integrates an model overview graph that summarizes GAN's structure, and a layered distributions view that helps users interpret the interplay between submodels. GAN Lab introduces new interactive experimentation features for learning complex deep learning models, such as step-by-step training at multiple levels of abstraction for understanding intricate training dynamics. Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web browsers, without the need for installation or specialized hardware, overcoming a major practical challenge in deploying interactive tools for deep learning.

We analyze the value-loading problem. This is the problem of encoding moral values into an AI agent interacting with a complex environment. Like many before, we argue that this is both a major concern and an extremely challenging problem. Solving it will likely require years, if not decades, of multidisciplinary work by teams of top scientists and experts. Given how uncertain the timeline of human-level AI research is, we thus argue that a pragmatic partial solution should be designed as soon as possible. To this end, we propose a preliminary research program. This roadmap identifies several key steps. We hope that this will allow scholars, engineers and decision-makers to better grasp the upcoming difficulties, and to foresee how they can best contribute to the global effort.

We aim to predict whether a primary school student will perform in the `below standard' band of a national standardized test. We exploit a data set containing test performance on the National Assessment Program - Literacy and Numeracy (NAPLAN); a test given annually to all Australian school students in grades 3, 5, 7, and 9. We separate the analysis into students in grade 5 and above, for which previous achievement may be used as a predictor; and students in grade 3, which must rely on family- and school-level predictors only. We train and compare a set of classifiers for reading and numeracy learning areas respectively. The classifiers achieve good predictive power in terms of area under the ROC curve, suggesting that it is feasible for schools to more accurately screen a large number of students for academic risk.

Tracing knowledge acquisition and linking learning events to interaction between peers is a major challenge of our times. We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge by Web interactions that we called ViewpointS. By exploiting the similarity with Edelman's Theory of Neuronal Group Selection (TNGS), we conjecture that it may be metaphorically considered a Collective Brain, especially effective in the case of trans-disciplinary representations. Far from being without doubts, in the paper we present the reasons (and the limits) of our proposal that aims to become a useful integrating tool for future quantitative explorations of individual as well as collective learning at different degrees of granu-larity. We are therefore challenging each of the current approaches: the logical one in the semantic Web, the statistical one in mining and deep learning, the social one in recommender systems based on authority and trust; not in each of their own preferred field of operation, rather in their integration weaknesses far from the holistic and dynamic behavior of the human brain.

Coherence of text is an important attribute to be measured for both manually and automatically generated discourse; but well-defined quantitative metrics for it are still elusive. In this paper, we present a metric for scoring topical coherence of an input paragraph on a real-valued scale by analyzing its underlying topical structure. We first extract all possible topics that the sentences of a paragraph of text are related to. Coherence of this text is then measured by computing: (a) the degree of uncertainty of the topics with respect to the paragraph, and (b) the relatedness between these topics. All components of our modular framework rely only on unlabeled data and WordNet, thus making it completely unsupervised, which is an important feature for general-purpose usage of any metric. Experiments are conducted on two datasets - a publicly available dataset for essay grading (representing human discourse), and a synthetic dataset constructed by mixing content from multiple paragraphs covering diverse topics. Our evaluation shows that the measured coherence scores are positively correlated with the ground truth for both the datasets. Further validation to our coherence scores is provided by conducting human evaluation on the synthetic data, showing a significant agreement of 79.3%.

One of India's biggest names in AI, Dr. Dakshinamurthy V Kolluru, took to the stage at Rakuten CTO Summit 2018 on March 14 in Bengaluru, also known as Bangalore. Speaking in front of a rapt audience of 51 CTOs and heads of engineering from Rakuten Group's businesses across the globe, Kolluru traced the fascinating history of machine learning, deep learning and artificial intelligence, elucidating on how AI can benefit businesses and improve customer experience. Rakuten, which promotes use of AI in all of its group companies, was keen to hear the thoughts of the man described by Analytics India Mag as "a visionary, an analytics expert and a passionate educator, who has been doing highly innovative work in the field of analytics -- be it consulting, product development, corporate training or educating -- since 1999, when analytics was not part of the common lingo that it has become today." The Founder and President of International School of Engineering (INSOFE) Hyderabad, Kolluru has helped set up many data science centers of excellence (COEs) and has conducted training for multinational corporations such as Johnson and Johnson in the US and Microsoft, HP, Broadridge Financial Services and others in India. Kolluru, whose expertise lies in simplifying complex ideas and communicating them clearly, drew on landmark studies to explain where AI and deep learning fit in the spectrum of technologies like machine learning and robotic process automation (RPA) and how they can help complex businesses like Rakuten solve problems across functions and verticals.

Digital imaging in all of its forms is becoming more powerful and more integral to medicine and healthcare. Deep learning can capitalize on all of the patterns that can be extracted from very large datasets and used for interpreting still and moving images, according to Naylor. "Deep learning and related machine-learning methods can also learn from massively greater numbers of images than any human expert, continue learning and adapting over time, mitigate interobserver variability, and facilitate better decision-making and more effective image-guided therapy," he wrote.

Demand for machine learning engineers has exploded in the past two years, as AI development and adoption continue to grow across industries, according to a report from Indeed. These professionals are among the most in-demand tech professionals, and among the highest paid, with average salaries of $134,449 in the US, according to another Indeed report. "Software is eating the world and machine learning is eating software," said Vitaly Gordon, vice president of data science and software engineering for Salesforce Einstein. "Machine learning engineering is a discipline that requires production grade coding, PhD level machine learning and a business acumen of a product manager. Finding such rare people can uplift a company from a follower into a leader in their space, and everyone is looking for them."

SINGAPORE: Singaporeans ranging from secondary students to working adults will get to pick up artificial intelligence (AI) basics for free as part of a programme called AI for Everyone (AI4E) unveiled on Thursday (Aug 30). The programme, which targets 10,000 participants, aims to familiarise them with AI and help them understand how it can be used in their daily lives, said Minister for Communications and Information S Iswaran at an event to commemorate the first-year milestone for AI Singapore (AISG). It will also help dispel fears that AI will replace jobs, according to a separate joint fact sheet by AISG, Info-communications Media Development Authority (IMDA) and the National University of Singapore (NUS). Materials for the three-hour workshop will be provided by tech giants Intel and Microsoft. These free workshops will start from the end of this month and will run for three years.

Internet-connected robots that can stream audio and video are increasingly helping housebound sick children and elderly people keep in touch with teachers, family and friends, combating the scourge of isolation and loneliness. Zoe Johnson, 16, hasn't been to school since she was 12. She went to the doctor in 2014 "with a bit of a sore throat", and "somehow that became A&E [accident and emergency]," says her mother, Rachel Johnson. The doctors diagnosed myalgic encephalomyelitis, ME for short, also known as Chronic Fatigue Syndrome - a debilitating illness affecting the nervous and immune systems. Zoe missed a lot of school but was able to continue with her studies with the help of an online tutor.