From targeted online advertising to more precise recommendation engines, consumer markets are bursting with innovation around machine learning and advanced analytics. While there's less buzz around business-to-business markets, these innovations are changing the game in B2B as well, even in old-line industries selling what might be considered commodity products. A growing number of B2B companies are using data and analytics to add services that bring new elements of value to customers, and in some cases new sources of revenue. These elements are fundamental attributes of a company's offering in their most essential and discrete forms – things like product quality, flexibility, and associated expertise; they lift value propositions above commodity status and benefit customers in particular ways. Consider recent moves by Australia-based Orica, which provides packaged explosives materials to mining companies worldwide.

How do we train workers for jobs that don't exist?

As artificial intelligence continues to creep into everyday life, the Australian government has pledged $29.9 million over four years to enhance local AI capabilities. Treasurer Scott Morrison announced in Tuesday night's budget that "research in artificial intelligence" was to be included as part of the Government's $2.4 billion investment into Australia's science and technology capacity. The Department of Industry, Innovation and Science will receive the bulk of the funding ($26 million), alongside the CSIRO ($2.3 million) and the Department of Education and Training ($1.5 million). "This measure will support Cooperative Research Centre projects, PhD scholarships and school-related learning to increase knowledge and develop the skills needed for AI and machine learning," the budget papers state. Professor of Artificial Intelligence at the University of New South Wales and ACS AI Ethics Committee Member, Professor Toby Walsh, welcomed the funding, but questioned whether it was enough to poise Australia as a global leader in the field.

Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA). Motivated by the observation that questions can relate to both object instances and their parts, we propose a novel attention mechanism that jointly considers reciprocal relationships between the two levels of visual details. The bottom-up attention thus generated is further coalesced with the top-down information to only focus on the scene elements that are most relevant to a given question. Our design hierarchically fuses multi-modal information i.e., language, object- and gird-level features, through an efficient tensor decomposition scheme. The proposed model improves the state-of-the-art single model performances from 67.9% to 68.2% on VQAv1 and from 65.3% to 67.4% on VQAv2, demonstrating a significant boost.

Australia and other countries around the world are seeing an increased use of artificial intelligence (AI). This week, Google unveiled a new feature called Duplex that uses artificial technology to generate a realistic-sounding voice assistant. The Australian government also announced in Tuesday's federal budget it would invest more than $2.4 billion in technology and science, including leading research in artificial intelligence. RMIT Senior Lecturer Dr Andy Song believes the definition has been debated for quite a long time. "There is no premise definition," Dr Song said.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

Oracle has announced that it is leveraging its AI capabilities to deliver a better consumer experience to attendees of Heineken Urban Polo. Heineken Urban Polo is a series of events in New Zealand cities that combines polo, fine cuisine and music. The Oracle Intelligent Bot will answer questions about the event through a Facebook interface. It enables attendees to ask questions about how to get there, where the bar is, what times the matches start, when the pony parade is on, rules of the game, details of players, which DJs are playing and when. As more people interact with the bot it will apply machine learning to improve its answers about the various subjects.

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.

Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore,it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.