Many modern technologies address the issues involved in developing such systematic automated information support solutions, but AI has been rarely applied for enhancing practices in employment management (Vrontis et al., 2022). Capabilities of AI are viewed in existing cases of studies for the construction of interventions in employment prospective, but various disruptive innovations to enhance the current frameworks of talent systems are not holistically studied in the past recent years. The advancement of general-purpose AI technology is of paramount task to revolutionizing workforce management (Agrawal et al., 2018). While creating new AI oriented applications for employment management, a number of obstacles such as dehumanization, biased algorithms and fairness in requirement have identified, so it is imperative to conduct precise design research (Tambe et al., 2019). A recent industry survey identified at least 300 HR technology start-ups developing AI tools for people management, with roughly 60 of these companies achieving traction in terms of clients and venture investment (Bailie & Butler, 2018). Furthermore, an AI-powered talent intelligence platform that aids in attracting, developing, and retaining outstanding employees, has just raised $220 million and is now valued at over $2 billion (Charlwood & Guenole, 2022).Many organizations have started with their massive investment in AI for workforce management.

Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform.

Humans have remarkable capacity to reason abductively and hypothesize about what lies beyond the literal content of an image. By identifying concrete visual clues scattered throughout a scene, we almost can't help but draw probable inferences beyond the literal scene based on our everyday experience and knowledge about the world. For example, if we see a "20 mph" sign alongside a road, we might assume the street sits in a residential area (rather than on a highway), even if no houses are pictured. Can machines perform similar visual reasoning? We present Sherlock, an annotated corpus of 103K images for testing machine capacity for abductive reasoning beyond literal image contents. We adopt a free-viewing paradigm: participants first observe and identify salient clues within images (e.g., objects, actions) and then provide a plausible inference about the scene, given the clue. In total, we collect 363K (clue, inference) pairs, which form a first-of-its-kind abductive visual reasoning dataset. Using our corpus, we test three complementary axes of abductive reasoning. We evaluate the capacity of models to: i) retrieve relevant inferences from a large candidate corpus; ii) localize evidence for inferences via bounding boxes, and iii) compare plausible inferences to match human judgments on a newly-collected diagnostic corpus of 19K Likert-scale judgments. While we find that fine-tuning CLIP-RN50x64 with a multitask objective outperforms strong baselines, significant headroom exists between model performance and human agreement. Data, models, and leaderboard available at http://visualabduction.com/

Dialog systems must be capable of incorporating new skills via updates over time in order to reflect new use cases or deployment scenarios. Similarly, developers of such ML-driven systems need to be able to add new training data to an already-existing dataset to support these new skills. In intent classification systems, problems can arise if training data for a new skill's intent overlaps semantically with an already-existing intent. We call such cases collisions. This paper introduces the task of intent collision detection between multiple datasets for the purposes of growing a system's skillset. We introduce several methods for detecting collisions, and evaluate our methods on real datasets that exhibit collisions. To highlight the need for intent collision detection, we show that model performance suffers if new data is added in such a way that does not arbitrate colliding intents. Finally, we use collision detection to construct and benchmark a new dataset, Redwood, which is composed of 451 ntent categories from 13 original intent classification datasets, making it the largest publicly available intent classification benchmark.

Artificial intelligence (AI) has the potential to unlock transformative economic, social and environmental opportunities for Australia. The potential for public benefit is significant, provided the development, adoption and use of AI is governed in a safe, responsible and sustainable manner. Governing AI in this way underpins community trust and stakeholder support and works to retain a social license. Importantly, good governance of AI also increases the likelihood that organisations will implement and scale up AI effectively and successfully. In other words, good governance creates a virtuous cycle whereby support for the widespread investment in and adoption of AI is maintained, and the transformative benefits of AI are more likely to be realised both at a business and societal level.

Leading Senior Constable Dr Janis Dalins is looking for 100,000 happy images of children – a toddler in a sandpit, a nine-year-old winning an award at school, a sullen teenager unwrapping a present at Christmas and pretending not to care. The search for these safe, happy pictures is the goal of a new campaign to crowdsource a database of ethically obtained images that Dalins hopes will help build better investigative tools to use in the fight against what some have called a "tsunami" of child sexual assault material online. Dalins is the co-director of AiLecs lab, a collaboration between Monash University and the Australian federal police, which builds artificial intelligence technologies for use by law enforcement. In its new My Pictures Matter campaign, people above 18 are being asked to share safe photos of themselves at different stages of their childhood. Once uploaded with information identifying the age and person in the image, these will go into a database of other safe images.

Up from just shy of $US300 million in 2020, this marked the biggest yearly jump since 2014, and put Australia ahead of South Korea, Hong Kong, Singapore, Spain and Portugal. However, Australia remained well behind France ($US1.55 billion), Canada ($US1.87 billion), Germany ($US1.98 billion), Israel ($US2.41 billion), the UK ($US4.65 billion), China ($US17.2 billion) and the US ($US52.8 billion). "The approach to AI within organisations remains very siloed, with considerations of AI ethics and governance seen as the province of tech and data teams. "If [consumer confidence in] AI is not a current priority it won't feature in corporate strategy considerations, and important future opportunities may be lost. "In the absence of better AI business and governance practices, AI adoption will continue to lag in Australian organisations, with long-term consequences for innovation, productivity and international competitiveness."

Artificial intelligence has come to stay in the healthcare industry. The term refers to a constellation of computational tools that can comb through vast troves of data at rates far surpassing human ability, in a way that can streamline providers' jobs. Regardless of the specific type of AI, these tools are generally capable of making a massive, complex industry run more efficiently. But several studies show it can also propagate racial biases, leading to misdiagnosis of medical conditions among people of colour, insufficient treatment of pain, under-prescription of life-affirming medications, and more. Many patients don't even know they've been enrolled in healthcare algorithms that are influencing their care and outcomes.

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted many GNN-based methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into meaningful low-dimensional representations used for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. To solve this problem, we propose a novel brain network representation framework, namely BN-GNN, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to train a meta-policy to automatically determine the optimal number of feature aggregations (reflected in the number of GNN layers) required for a given brain network. Extensive experiments on eight real-world brain network datasets demonstrate that our proposed BN-GNN improves the performance of traditional GNNs on different brain network analysis tasks.

A comprehensive understanding of human-object interaction (HOI) requires detecting not only a small portion of predefined HOI concepts (or categories) but also other reasonable HOI concepts, while current approaches usually fail to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable combinations of verbs and objects). In this paper, 1) we introduce a novel and challenging task for a comprehensive HOI understanding, which is termed as HOI Concept Discovery; and 2) we devise a self-compositional learning framework (or SCL) for HOI concept discovery. Specifically, we maintain an online updated concept confidence matrix during training: 1) we assign pseudo labels for all composite HOI instances according to the concept confidence matrix for self-training; and 2) we update the concept confidence matrix using the predictions of all composite HOI instances. Therefore, the proposed method enables the learning on both known and unknown HOI concepts. We perform extensive experiments on several popular HOI datasets to demonstrate the effectiveness of the proposed method for HOI concept discovery, object affordance recognition and HOI detection. For example, the proposed self-compositional learning framework significantly improves the performance of 1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO, respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively over 30% and 20%, respectively. Code is publicly available at https://github.com/zhihou7/HOI-CL.