A new project using artificial intelligence technology could spell a medical breakthrough for people suffering from, or at risk of, coronary artery disease, the single leading cause of death in Australia. The approach being developed by researchers at The University of Western Australia could allow for more accurate diagnosis and faster reporting across all aspects of healthcare, improving the quality and consistency of patient care. The UWA team of experts in cardiac imaging and artificial intelligence was awarded more than $896,606 through a Medical Research Future Fund Frontiers grant to develop a tool to predict the risk of coronary heart disease from heart computed tomography (CT) scans. Coronary artery disease resulting from the build-up of plaque affects more than 1.2 million Australians; however traditional methods using CT imaging of the heart are cumbersome, time-consuming and may have limited accuracy. Led by Professor Girish Dwivedi, the UWA Wesfarmers Chair in Cardiology, the team, including Professor Mohammed Bennamoun, Professor Farid Boussaid, Dr Frank Sanfilippo and Dr Abdul Ihdayhid, together with medical technology company Artrya Ltd, will create an artificial intelligence-based risk assessment tool that will better detect plaque on heart CT scans.

Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method grounded in a formal theory of \textit{causal abstraction} that provides rich characterizations of model-internal representations and their roles in input/output behavior. In this method, neural representations are aligned with variables in interpretable causal models, and then \textit{interchange interventions} are used to experimentally verify that the neural representations have the causal properties of their aligned variables. We apply this method in a case study to analyze neural models trained on Multiply Quantified Natural Language Inference (MQNLI) corpus, a highly complex NLI dataset that was constructed with a tree-structured natural logic causal model. We discover that a BERT-based model with state-of-the-art performance successfully realizes the approximate causal structure of the natural logic causal model, whereas a simpler baseline model fails to show any such structure, demonstrating that neural representations encode the compositional structure of MQNLI examples.

In real-world applications of influence maximization (IM), the network structure is often unknown. In this case, we may identify the most influential seed nodes by exploring only a part of the underlying network given a small budget for node queries. Motivated by the fact that collecting node metadata is more cost-effective than investigating the relationship between nodes via queried nodes, we develop IM-META, an end-to-end solution to IM in networks with unknown topology by retrieving information from both queries and node metadata. However, using such metadata to aid the IM process is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference. To tackle these challenges, we formulate an IM problem that aims to find two sets, i.e., seed nodes and queried nodes. We propose an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network model, 2) we select a number of inferred influential edges to construct a reinforced graph used for discovering an optimal seed set, and 3) we identify the next node to query by maximizing the inferred influence spread using a topology-aware ranking strategy. By querying only 5% of nodes, IM-META reaches 93% of the upper bound performance.

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have shown promising performance on many network analysis tasks. However, most existing GNNs have almost exclusively focused on the balanced networks, and would get unappealing performance on the imbalanced networks. To bridge this gap, in this paper, we present a generative adversarial graph network model, called ImGAGN to address the imbalanced classification problem on graphs. It introduces a novel generator for graph structure data, named GraphGenerator, which can simulate both the minority class nodes' attribute distribution and network topological structure distribution by generating a set of synthetic minority nodes such that the number of nodes in different classes can be balanced. Then a graph convolutional network (GCN) discriminator is trained to discriminate between real nodes and fake (i.e., generated) nodes, and also between minority nodes and majority nodes on the synthetic balanced network. To validate the effectiveness of the proposed method, extensive experiments are conducted on four real-world imbalanced network datasets. Experimental results demonstrate that the proposed method ImGAGN outperforms state-of-the-art algorithms for semi-supervised imbalanced node classification task.

Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance trade-off in classical learning theory. Recent studies claimed that DNNs first learn simple patterns and then memorize noise; some other works showed a phenomenon that DNNs have a spectral bias to learn target functions from low to high frequencies during training. However, we show that the monotonicity of the learning bias does not always hold: under the experimental setup of deep double descent, the high-frequency components of DNNs diminish in the late stage of training, leading to the second descent of the test error. Besides, we find that the spectrum of DNNs can be applied to indicating the second descent of the test error, even though it is calculated from the training set only.

From customer support, business intelligence, service management, lead generation to information retrieval, chatbots have gained widespread adoption across functions. The reason why organizations are actively embracing bot technology is that chatbots not only have several high value business use cases but also are easy to deploy with minimum risks. If you believe that your organization can greatly benefit from investing in chatbots but your top management is still on the fence about it, we are here to help. Here are some ways you can strengthen your business case and persuade your leadership/executive sponsors to deploy enterprise chatbots. Data and facts help you sharpen your pitch and make decision-making simpler for your stakeholders.

The Regent Honeyeaters of Australasia are forgetting how to talk. The songbird's habitat has been so severely devastated that its numbers are dwindling. Worse, the ones that remain are so scattered that the adult males are too far apart to teach the young how to sing for a mate -- how to speak their own language. The gradual loss of the Honeyeaters' song, their primary tool for wooing a partner, creates a vicious circle of spiraling decline. Humans, on the other hand, cannot shut up.

Here's what you need to know as you start your day State Department was steered away from coronavirus origins probe, ex-officials say State Department leaders were warned not to pursue an investigation into the origins of the coronavirus, former department officials told Fox News on Thursday. The concern was that a probe would bring attention to U.S. funding of research at the Wuhan institute from which the virus may have escaped. Vanity Fair reported that officials calling for transparency from the Chinese government were told not to explore the Wuhan Institute of Virology's "gain of function" research, because it would bring what the outlet described as "unwelcome" attention of U.S. government funding into that research. The outlet reported that Thomas DiNanno, a former acting assistant secretary of the State Department's Bureau of Arms Control, Verification, and Compliance, wrote in a January memo that staff from two bureaus "warned" leaders within his office not to probe the origins of the virus because it risked opening "a can of worms." Multiple former State Department officials told Fox News that the reported memo accurately describes what was happening at State at the time and that there was an effort among some officials at the department to oppose an extensive investigation into a possible lab leak.

Technology companies have produced varied responses to concerns about the effects of the design of their conversational AI systems. Some have claimed that their voice assistants are in fact not gendered or human-like -- despite design features suggesting the contrary. We compare these claims to user perceptions by analysing the pronouns they use when referring to AI assistants. We also examine systems' responses and the extent to which they generate output which is gendered and anthropomorphic. We find that, while some companies appear to be addressing the ethical concerns raised, in some cases, their claims do not seem to hold true. In particular, our results show that system outputs are ambiguous as to the humanness of the systems, and that users tend to personify and gender them as a result.

Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present COINS, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply COINS to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of COINS holds the potential for controlled generation of longer sequences.