In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments.

Across the world, governments are investing in machines that they hope will run their social security systems and other services more cheaply and effectively than humans. The Guardian's Automating Poverty series includes reports from the US, Australia and India as well as the UK. The roles played by technology in these countries are all different. But taken together, the articles reveal how automation, machine learning and artificial intelligence are extending their reach into people's lives through the delivery of public services. As with all automation processes, speed and efficiency provide the rationale.

Employees have more trust in robots than they do their human managers, a global study has revealed. A survey across 10 countries have found that 64 percent prefer to seek advice or guidance from artificial intelligence over their boss and 82 percent feels it does a better job. The majority of workers are also optimistic, excited and grateful about having robot co-workers and nearly a quarter reported having a loving and gratifying relationship with the intelligent-style software. The study was conducted by the US technology company Oracle and research firm Future Workplace. The team surveyed 8,370 employees, managers and HR leaders and'found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent.'

Workers in India (89%) and China (88%) are more trusting of robots over their managers, followed by Singapore (83%), Brazil (78%), Japan (76%), UAE (74%), Australia/New Zealand (58%), the U.S. (57%), the U.K. (54%), and France (56%). More men (56%) than women (44%) have turned to AI over their managers.

AI has continued to rapidly advance. With intelligent tools now available to businesses, how can enterprises transform their processes and insights using the latest AI techniques? If your business doesn't have an AI roadmap, it will suffer significant losses and damage your ability to compete. This is the stark message that many industry watchers are defining, as we enter a new era of AI. The headlines in the technology press that shout that AI is the next seismic shift in how your business will operate, need to be tempered with a reality check.

Conferencing solution company Zoom announced a slew of new features this week at its Zoomtopia 2019 conference in San Jose, California -- 300 in total, to be exact. Among the highlights are AI-powered transcripts and meeting notes in Zoom Meetings, in addition to a Zoom Rooms people counter informed by facial recognition. On the Zoom Meetings side, live transcripts tap startup Otter.ai's Now, attendees can take notes directly in the Zoom interface or use live transcription for voice note taking, the latter of which is parsed by algorithms to derive action items automatically in the Meeting Timelines interface. Furthermore, meeting hosts can now bring their own interpreter with a mutli-channel audio experience that mixes the original and interpreter audio, enabling listeners to understand the interpreter while hearing the original speaker's tone.

Fledgling gal-bots are the latest hires in the virtual assistant landscape. Meet Mia and Marge: two virtual assistants in the banking world – each brought into existence by women, both of whom carry deep institutional knowledge, subject matter expertise and long-standing credibility. UBank's Lee Hatton (Mia) and The Royal Bank of Scotland's (RBS) MaryAnn Fleming (Marge) are among 40 women who have been recognized as 2019's women leaders in A.I. by IBM. These leaders have succeeded in garnering acceptance of A.I. in the workplace, elevating their customers' experience and their companies' brands. It seems mortgage consumer complaints consistently surface around the loan application process according to UBank CEO, Lee Hatton.

Australian IBM cybersecurity engineers have developed an artificial intelligence (AI) system to analyse network connections and employee communications at an enterprise scale. The model detects changes in users' behaviour and can automatically triggers investigations even if the changes occur across multiple platforms. IBM research found the root cause for 52 per cent of data breaches in Australia was malicious or criminal attacks which often use methods like phishing and social engineering. The new IBM solution, developed in the company's Gold Coast cybersecurity lab as part of a hackathon, uses AI to monitor changes in employee behaviour and flags indicators of compromise. It was debuted to the industry at last week's Australian Cyber Conference in Melbourne as a way of showing what can be done but the solution is not something that can be bought directly from IBM. Currently known as "QRadar Insider Threat Detector with Watson" it uses IBM's AI model, Watson, to analyse user generated content – like emails, Word documents, and Slack messages – to detect both the tone of content and employees' typical behaviour or "personalities".

'Divorced from reality,' says critical law professor Are "virtuous sex robots" the way of the future? University researchers suggest that robots created for human pleasure should be designed so that they can grant or withhold consent, as well as teach sex education. Anco Peeters, a doctoral student at Australia's University of Wollongong, and Pim Haselager, associate professor at The Netherlands' Radboud University, published "Designing Virtuous Sex Robots" in the International Journal of Social Robotics last month. The paper examined four areas: "virtue ethics and social robotics," "Contra instrumentalist accounts," "Consent practice through sex robots" and "Implications of virtuous sex robots." The authors do not focus on child sex robots or sex robots that play into rape fantasies, but "the potential positive aspects of intimate human–robot interactions through the cultivation of virtues."

Effective and efficient recommendation is crucial for modern e-commerce platforms. It consists of two indispensable components named Click-Through Rate (CTR) prediction and Conversion Rate (CVR) prediction, where the latter is an essential factor contributing to the final purchasing volume. Existing methods specifically predict CVR using the clicked and purchased samples, which has limited performance affected by the well-known sample selection bias and data sparsity issues. To address these issues, we propose a novel deep CVR prediction method by considering the post-click behaviors. After grouping deterministic actions together, we construct a novel sequential path, which elaborately depicts the post-click behaviors of users. Based on the path, we define the CVR and several related probabilities including CTR, etc., and devise a deep neural network with multiple targets involved accordingly. It takes advantage of the abundant samples with deterministic labels derived from the post-click actions, leading to a significant improvement of CVR prediction. Extensive experiments on both offline and online settings demonstrate its superiority over representative state-of-the-art methods.