Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.

Competition amongst universities is set to increase with institutions closely differentiating themselves to attract and retain the best quality students, academics and staff. Key to this differentiation will be an extensive technology adoption and innovation strategy, enhancing the student experience, delivery of learning content, community engagement and campus management. The education technology (Edutech) market in Australia is expected to grow significantly amidst increasing student demand for education services and technology innovation, competition amongst institutions and decreasing acquisition costs. Frost & Sullivan anticipates that as the learning experience becomes increasingly digitised, technologies and solutions incorporating big data and analytics, collaboration, Augmented / Virtual Reality technology, Artificial Intelligence and learning management systems will play a key role within universities in the coming years. Frost & Sullivan's most recent analysis, Australian Edutech Market: Key Trends, Technologies and Opportunities 2016-2022 finds that the Australian Edutech Market is expected to grow to AUD 1.7 Billion by 2022.

The 2017 New Rules for the Digital Age report from Deloitte found that only 5 percent of the companies surveyed said they have strong digital leadership development programs and a clear majority (65 percent) said they have no significant program to drive digital leadership skills. Josh Bersin, a principal at the Bersin by Deloitte research group, says the challenge is that companies don't realize how much more complicated digital transformation is than simply acquiring new technology. "Digital technology is easy to buy, but once you turn it on it changes the way you work and how you deliver products and services," Bersin told CIO.com. "From the CIO's perspective, it may seem relatively easy to implement artificial intelligence (AI), social media and other new technology, but these things have a disruptive impact on the workplace." For example, the study found that companies feel 31 percent "less ready" to redesign their organization around digital business models than they did last year.

We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, such as commonly encountered in mobile robotics applications. An evaluation of different strategies for acquisition, updating, and final training on the CIFAR-10 dataset shows that incremental network updates with final training on the accumulated acquisition set are essential for best performance, while limiting the amount of required human labeling labor.

Robots are where it's at lately with funding, especially so in China. Vertex Ventures China has led a $14 million financing of a Chinese warehouse and logistics robot, Geek . Prior investors Banyan Capital and Shanghai Volcanic Stone Capital participated in the funding. Meanwhile, a Shenzhen-based education and robotics startup, Makeblock, raised $30 million in a Series B round from Shenzhen Capital Group and Evolution Media China. Makeblock, essentially a robot-building kit for kids, has drawn $36 million in funding and is gearing up for expansion into new markets and education hardware.

The launch of the Samsung Galaxy S8 March 29 will represent a comeback for the company's mobile unit, which is recovering by the Galaxy Note 7 disaster. What's unclear is how Samsung Galaxy S8 flagship phone will fare given the multiple unknowns the company is facing. Samsung's Galaxy S8 lands amid a backdrop of the company's January report on what went wrong with the Note 7 and how it is fixing its processes to prevent battery problems in the future. That report went a long way to allaying fears, but brand recoveries take time. The device will have new artificial intelligence tools via a service called Bixby.

The New Zealand branch of leading Asia-Pacific law firm MinterEllison, known as MinterEllisonRuddWatts, has launched a joint venture with tech investment platform Goat Ventures to develop a new legal AI company. The JV will provide NZ$2m as co-investment to develop a completely new legal AI business. The firm said it wanted to focus on AI because legal services are well suited to this technology. The MinterEllison project in New Zealand is different in that it is specifically looking at building a legal AI application, most other law firms that have invested in or supported new legal tech have had a more general outlook. Also, this JV is focused on creating just one standalone legal AI business and is not seeking to support start-ups in general or seeking to be an incubator or accelerator to other legal tech businesses.

TRAVELLERS sometimes have to show their travel documents five times when catching a flight: at check-in, at security, then occasionally at outbound immigration, before another check when boarding. Finally there is passport control at the destination. Each is a potential queue. So regular flyers will be interested in anything that might speed up the process. One answer could be facial-recognition technology.

This paper presents a novel approach for localising a GPS (Global Positioning System)-denied Unmanned Aerial Vehicle (UAV) with the aid of a GPS-equipped UAV in three-dimensional space. The GPS-equipped UAV makes discrete-time broadcasts of its global coordinates. The GPS-denied UAV simultaneously receives the broadcast and takes direction of arrival (DOA) measurements towards the origin of the broadcast in its local coordinate frame (obtained via an inertial navigation system (INS)). The aim is to determine the difference between the local and global frames, described by a rotation and a translation. In the noiseless case, global coordinates were recovered exactly by solving a system of linear equations. When DOA measurements are contaminated with noise, rank relaxed semidefinite programming (SDP) and the Orthogonal Procrustes algorithm are employed. Simulations are provided and factors affecting accuracy, such as noise levels and number of measurements, are explored.

The future is already here – it's just not evenly distributed.