A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on density-based clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

Imagine a network of mine sites operated remotely--drilling, analysing core samples, collecting and interpreting data wirelessly from machine to machine, and transmitting real-time information into the cloud, absolutely without physical, human touch. In fact, it is fast becoming the reality in an industry that's increasingly powered by artificial intelligence a.k.a When we think of AI, we think of robots and machines capable of independent thought or autonomous movement. These are possibilities, and even realities, in today's world where practically anything can be automated. AI, however, goes beyond hardware, and its applications are farther-reaching than we can perhaps imagine.

Every year, the OECD Forum brings together experts, academics and thought leaders from the private and public sector to discuss key economic and social challenges on the international agenda. The theme of this year's Forum was "World in EMotion" – a theme that reflects the profound changes brought about by globalisation, shifting politics and digitalisation, and the challenges and opportunities that they present. Nowhere are these changes more rapid – and perhaps far-reaching – than in the field of artificial intelligence (AI), and its implications for values and ethics. I attended a very interesting panel on this subject, alongside Peter Gluckman, Chair of the International Network for Government Science Advice in New Zealand; Geoff Mulgan, Chief Executive of NESTA in the UK; Eric Salobir head of Optic; Pallaw Sharma, Senior Vice President at Johnson & Johnson; and Jess Whittlestone, Research Associate at the Centre for the Future of Intelligence at Cambridge University. As Pallaw explained, technology and AI are not magic powers; they are just extraordinary amplifiers and accelerators that add speed and accuracy.

LOS ANGELES, June 26, 2019 /PRNewswire-PRWeb/ -- An Ovation Fertility abstract about the use of artificial intelligence in IVF, presented at the 35th European Society of Human Reproduction and Embryology (ESHRE) Annual Meeting on June 24, 2019, was pre-selected for consideration for two prestigious awards at the event: The Basic Science Award for Oral Presentation, awarded to the presenting author of the best oral presentation on a basic science topic, and the Fertility Society of Australia Exchange Award, which provides an educational travel grant for the presenting author to present the data of his/her oral presentation at the annual meeting of the Fertility Society of Australia (FSA). Only five presentations at ESHRE 2019 were selected to receive awards and be rewarded with a prize of EURO 2,000, and one additional presentation was selected for the FSA Exchange Award. Special award committees, composed of senior scientists and clinicians, made the selections for each award. Ovation's oral presentation about the use of artificial intelligence in IVF, "Artificial intelligence (AI) technology can predict human embryo viability across multiple laboratories with varying demographics with high accuracy and reproducibility," detailed a study that sought to find out if artificial intelligence (AI) and computer vision can improve embryo viability prediction using static 2D images of Day 5 embryos from multiple laboratories. In most IVF labs, embryologists select which embryos to transfer based on morphological assessment, using high-powered microscopes to examine each embryo and assign it a subjective "grade."

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i.e., patches) and the task is to predict a single class label to the WSI. However, in many reallife applications such as computational pathology, discovering the key instances that trigger the bag label is of great interest because it provides reasons for the decision made by the system. In this paper, we propose a deep convolutional neural network (CNN) model that addresses the primary task of a bag classification on a histopathology image and also learns to identify the response of each instance to provide interpretable results to the final prediction. We incorporate the attention mechanism into the proposed model to operate the transformation of instances and learn attention weights to allow us to find key patches. To perform a balanced training, we introduce adaptive weighing in each training bag to explicitly adjust the weight distribution in order to concentrate more on the contribution of hard samples. Based on the learned attention weights, we further develop a solution to boost the classification performance by generating the bags with hard negative instances. We conduct extensive experiments on colon and breast cancer histopathology data and show that our framework achieves state-of-the-art performance.

Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decision to be made, and to give an informative explanation. We explore a new method of generating counterfactual explanations, which instead of explaining why a particular classification was made explain how a different outcome can be achieved. This gives the recipients of the explanation a better way to understand the outcome, and provides an actionable suggestion. We show that the introduced method of Constrained Adversarial Examples (CADEX) can be used in real world applications, and yields explanations which incorporate business or domain constraints such as handling categorical attributes and range constraints.

Recent progress in Generative Adversarial Networks (GANs) has shown promising signs of improving GAN training via architectural change. Despite some early success, at present the design of GAN architectures requires human expertise, laborious trial-and-error testings, and often draws inspiration from its image classification counterpart. In the current paper, we present the first neural architecture search algorithm, automated neural architecture search for deep generative models, or AGAN for abbreviation, that is specifically suited for GAN training. For unsupervised image generation tasks on CIFAR-10, our algorithm finds architecture that outperforms state-of-the-art models under same regularization techniques. For supervised tasks, the automatically searched architectures also achieve highly competitive performance, outperforming best human-invented architectures at resolution $32\times32$. Moreover, we empirically demonstrate that the modules learned by AGAN are transferable to other image generation tasks such as STL-10.

The portal uses an artificial intelligence chatbot to screen users for mental health issues such as depression and anxiety. It recommends mental health resources, allows users to search for and book professional help and includes a Wellbeing Portal to monitor their progress and goals.

Decision Point AI is a business unit of Veriluma Limited (ASX: VRI) one of Australia's leading Artificial Intelligence companies providing prescriptive analytics software solutions, serving Europe and the USA. The world is experiencing Data Overload and Insights are highly interpretive and time sensitive. Critical decisions cannot wait for manual analysis to create contextual meaning from insights, they need to be automated to deliver decision guidance and Augmented Outcomes for Companies, Executives and Shareholders. The platform can deal with what is known and can also consider what is not known. It extends the results of big data tools, descriptive and predictive analytics as well as business intelligence solutions to deliver assessments with actionable outcomes. Assessments highlight what has contributed to the likelihood, indicate potential risks, opportunities and conflicts.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.