Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. However, the generated levels are often unplayable without additional editing. We propose a generate-then-repair framework for automatic generation of playable levels adhering to specific styles. The framework constructs levels using a generative adversarial network (GAN) trained with human-authored examples and repairs them using a mixed-integer linear program (MIP) with playability constraints. A key component of the framework is computing minimum cost edits between the GAN generated level and the solution of the MIP solver, which we cast as a minimum cost network flow problem. Results show that the proposed framework generates a diverse range of playable levels, that capture the spatial relationships between objects exhibited in the human-authored levels.

NLU models often exploit biases to achieve high dataset-specific performance without properly learning the intended task. Recently proposed debiasing methods are shown to be effective in mitigating this tendency. However, these methods rely on a major assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. In this work, we present the first step to bridge this gap by introducing a self-debiasing framework that prevents models from mainly utilizing biases without knowing them in advance. The proposed framework is general and complementary to the existing debiasing methods. We show that it allows these existing methods to retain the improvement on the challenge datasets (i.e., sets of examples designed to expose models' reliance on biases) without specifically targeting certain biases. Furthermore, the evaluation suggests that applying the framework results in improved overall robustness.

Artificial intelligence promises to expertly diagnose disease in medical images and scans. However, a close look at the data used to train algorithms for diagnosing eye conditions suggests these powerful new tools may perpetuate health inequalities. A team of researchers in the UK analyzed 94 data sets--with more than 500,000 images--commonly used to train AI algorithms to spot eye diseases. They found that almost all of the data came from patients in North America, Europe, and China. Just four data sets came from South Asia, two from South America, and one from Africa; none came from Oceania.

Hitachi Construction Machinery (HCM) and its consolidated subsidiary, Wenco International Mining Systems, have jointly developed "ConSite Mine", a new technology platform that helps resolve problems at mine sites by remotely monitoring mining machines on a 24/7 basis through the use of IoT and AI based analysis of equipment operations data. According to Hitachi, it has developed this technology to help customers and HCM dealers predict costly maintenance issues before they occur, such as the occurrence of cracks in excavator booms or arms, by utilising machine learning and applied analysis technologies. Detailed information from these predictive alerts are provided on the web-based ConSite Mine dashboard and other items. Currently, Hitachi is piloting the technology in Australia, Zambia and Indonesia. "ConSite Mine" will be further modified based on customer feedback before wider commercial release in 2021.

Artificial Intelligence is the defining technology that has the power to boost a nation to new heights. This holds true even for the Australia-Pacific region too. In New Zealand, companies are now better positioned to roll out AI applications to boost the economy and bring higher revenues. Various research reports have pointed out that Kiwi organizations view AI to challenge the competitive landscape over the coming years. Compared to Australia, New Zealand displays a more favorable market for AI in the future.

By now, you've likely heard a thing or two about machine learning. But what exactly does that mean? The key question is: what problem is machine learning meant to solve? What does it do well that other branches of artificial intelligence can't? Machine learning handles big data much more efficiently than either human brains or other approaches to artificial intelligence.

Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. Existing AHC methods, which are based on a distance measure, have one key issue: it has difficulty in identifying adjacent clusters with varied densities, regardless of the cluster extraction methods applied on the resultant dendrogram. In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it. We analyse the condition under which existing AHC methods fail to extract clusters effectively; and the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms such as existing traditional AHC algorithms, HDBSCAN, GDL and PHA. In each of these algorithms, our empirical evaluation shows that a recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel.

The gradient boosting machine is one of the powerful tools for solving regression problems. In order to cope with its shortcomings, an approach for constructing ensembles of gradient boosting models is proposed. The main idea behind the approach is to use the stacking algorithm in order to learn a second-level meta-model which can be regarded as a model for implementing various ensembles of gradient boosting models. First, the linear regression of the gradient boosting models is considered as a simplest realization of the meta-model under condition that the linear model is differentiable with respect to its coefficients (weights). Then it is shown that the proposed approach can be simply extended on arbitrary differentiable combination models, for example, on neural networks which are differentiable and can implement arbitrary functions of gradient boosting models. Various numerical examples illustrate the proposed approach.

Recent work on the lottery ticket hypothesis has produced highly sparse Transformers for NMT while maintaining BLEU. However, it is unclear how such pruning techniques affect a model's learned representations. By probing Transformers with more and more low-magnitude weights pruned away, we find that complex semantic information is first to be degraded. Analysis of internal activations reveals that higher layers diverge most over the course of pruning, gradually becoming less complex than their dense counterparts. Meanwhile, early layers of sparse models begin to perform more encoding. Attention mechanisms remain remarkably consistent as sparsity increases.

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation measures. Such measures could be aided by the development of suitable machine learning models predicting, for example, the evolution of the collision risk in time. In an attempt to study this opportunity, the European Space Agency released, in October 2019, a large curated dataset containing information about close approach events, in the form of Conjunction Data Messages (CDMs), collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, a machine learning competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying machine learning methods to this problem domain.