A survey by research firm Gartner found that the use of AI in digital commerce companies is usually considered a success, with 70% of the organisations claiming very, or extremely successful, implementation of the technology. A total of 307 digital commerce organisations were surveyed for the study. These companies are currently using or piloting the technology to understand the adoption, value, success and challenges of AI in digital commerce. Organisations participated in this study were from the US, Canada, Brazil, France, Germany, the UK, Australia, New Zealand, India and China. Among the respondents, three-quarters said that they are seeing double-digit improvements in the outcomes they measure.

Classification of description logic (DL) ontologies is a key computational problem in modern data management applications, so considerable effort has been devoted to the development and optimisation of practical reasoning calculi. Consequence-based calculi combine ideas from hypertableau and resolution in a way that has proved very effective in practice. However, existing consequence-based calculi can handle either Horn DLs (which do not support disjunction) or DLs without number restrictions. In this paper, we overcome this important limitation and present the first consequence-based calculus for deciding concept subsumption in the DL ALCHIQ+. Our calculus runs in exponential time assuming unary coding of numbers, and on ELH ontologies it runs in polynomial time. The extension to disjunctions and number restrictions is technically involved: we capture the relevant consequences using first-order clauses, and our inference rules adapt paramodulation techniques from first-order theorem proving. By using a well-known preprocessing step, the calculus can also decide concept subsumptions in SRIQ---a rich DL that covers all features of OWL 2 DL apart from nominals and datatypes. We have implemented our calculus in a new reasoner called Sequoia. We present the architecture of our reasoner and discuss several novel and important implementation techniques such as clause indexing and redundancy elimination. Finally, we present the results of an extensive performance evaluation, which revealed Sequoia to be competitive with existing reasoners. Thus, the calculus and the techniques we present in this paper provide an important addition to the repertoire of practical implementation techniques for description logic reasoning.

Level-set optimization formulations with data-driven constraints minimize a regularization functional subject to matching observations to a given error level. These formulations are widely used, particularly for matrix completion and sparsity promotion in data interpolation and denoising. The misfit level is typically measured in the l2 norm, or other smooth metrics. In this paper, we present a new flexible algorithmic framework that targets nonsmooth level-set constraints, including L1, Linf, and even L0 norms. These constraints give greater flexibility for modeling deviations in observation and denoising, and have significant impact on the solution. Measuring error in the L1 and L0 norms makes the result more robust to large outliers, while matching many observations exactly. We demonstrate the approach for basis pursuit denoise (BPDN) problems as well as for extensions of BPDN to matrix factorization, with applications to interpolation and denoising of 5D seismic data. The new methods are particularly promising for seismic applications, where the amplitude in the data varies significantly, and measurement noise in low-amplitude regions can wreak havoc for standard Gaussian error models.

Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems. However, recent research on adversarial examples poses enormous challenges on the robustness of SER systems by showing the susceptibility of deep neural networks to adversarial examples as they rely only on small and imperceptible perturbations. In this study, we evaluate how adversarial examples can be used to attack SER systems and propose the first black-box adversarial attack on SER systems. We also explore potential defenses including adversarial training and generative adversarial network (GAN) to enhance robustness. Experimental evaluations suggest various interesting aspects of the effective utilization of adversarial examples useful for achieving robustness for SER systems opening up opportunities for researchers to further innovate in this space.

Being able to navigate to a target with minimal supervision and prior knowledge is critical to creating human-like assistive agents. Prior work on map-based and map-less approaches have limited generalizability. In this paper, we present a novel approach, Hybrid Asynchronous Universal Successor Representations (HAUSR), which overcomes the problem of generalizability to new goals by adapting recent work on Universal Successor Representations with Asynchronous Actor-Critic Agents. We show that the agent was able to successfully reach novel goals and we were able to quickly fine-tune the network for adapting to new scenes. This opens up novel application scenarios where intelligent agents could learn from and adapt to a wide range of environments with minimal human input.

The typical pipeline for a supervised machine learning project involves firstly the collection of a significant sample of labelled examples typically referred to as training data. Depending on whether the labels are continuous or categorical, the supervised learning task is known as regression or classification respectively. Next, once the training data is sufficiently clean and complete, it is used to directly build a predictive model using the machine learning algorithm of choice. The predictive model is then used to label new unlabelled examples, and if the labels of the new examples are known a priori by the user (but not used by the learning algorithm) then the predictive accuracy of the model can be evaluated. Different models can therefore be directly compared. In the usual case, the training data is "real", i.e. the model is learned directly from labelled examples that were collected specifically for that purpose. However, quite frequently, modifications are made to the training data after it is collected. For example, it is standard practice to remove outlier examples and normalise numeric values. Moreover, the machine learning algorithm itself may specify modifications to the training data.

Technology is fighting death with data – including machine learning and artificial intelligence. Last week marked the annual Health Informatics New Zealand conference, in which local and global innovators spoke about how their work is changing local healthcare models. Amongst the New Zealand companies leading the way are Umbrellar and Auckland's Mercy Radiology and Clinics. "If we want healthcare service delivery to be consistent across the country, and we want to provide the best value to patients, we need to embrace digital technology," comments Mercy Radiology and Clinics CEO Lloyd McCann. "We need to address cultural barriers to change in the sector. Data, information and intelligence will help us drive this cultural transformation."

The control room falls silent as multiple video streams from live bodycams fill its display monitors. The windowless building, its meter-thick walls baking in the late-afternoon heat, carries no signage, no identifying markers. As the men enter, the control room displays adjust from sunlight to show a near pitch-black corridor with doorways visible to the left. The audio is silent, save for the tread of rubber boots through pooled condensation and the hum of a generator somewhere inside. The four men had seen images of the hostage before they reached the building.

Scientists have created a strange chest-mounted robot that could feed the greediest of people when they're too full to move. The peculiar'Arm-A-Dine' robot arm attaches to the middle of someone's chest and takes food from their plate to their mouth. The robot, which is still just a prototype, is designed to augment the social experience of eating, researchers say. The robotic arm was created by Exertion Games Lab at RMIT University in Australia and the Indian Institute of Information Technology Design. 'Arm-A-Dine is our design exploration of a novel two-person playful eating system that focuses on a shared feeding experience', researchers told Spectrum.

Scientists have found a tiny new region of the brain that only humans have - and they believe it could be what makes our species unique. Researchers say the incredible find could help find a treatment for Parkinson's and motor neurone disease. For thirty years scientists - who have likened the discovery to finding a new star - suspected this region existed but were unable to see it. Scientists have found a new region of the brain that only humans have - and they believe it could be what makes our species unique. Professor George Paxinos from Neuroscience Research Australia (NeuRA) found the hidden region. He was able to find the region, called the Endorestiform Nucleus, thanks to better staining and imaging techniques.