A new artificial intelligence system could assess tipping points in the world's ecosystems, and act as an early warning system to help stop "runaway climate change", researchers have said. Climate tipping points are a particular threat to life on Earth, as when they are reached, they can set off chain reactions of climate-altering processes, supercharging global heating and rapidly exacerbating the existing climate crisis. Examples include the melting of the Arctic permafrost, which could release massive amounts of the potent greenhouse gas methane, which would generate further rapid heating; the breakdown of ocean current systems, which would cause almost immediate major changes to global weather patterns; and ice sheet disintegration, which could lead to rapid sea-level rises. Using a "deep-learning" algorithm, the researchers examined thresholds beyond which rapid or irreversible change happens in a system. Chris Bauch, professor of applied mathematics at the University of Waterloo ...

Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. Artificial Intelligence (AI) has been getting creative for some time now and inventing new things, including everything from new kinds of batteries, computer chips, furniture, and rocket engines, all the way through to new kinds of vehicles and sports apparel, for companies as diverse as Airbus, Amazon, GM, NASA, and Under Armour. But despite this quantum leap recently the US Patent Office declined to credit AI for its inventions. Now that's changed, and in what seems to be a world first Intellectual property (IP) officials in South Africa have made history in a landmark decision to award a patent that names an AI as the inventor. The patent – which was filed by an international team of lawyers and researchers led by the University of Surrey's, Professor of Law and Health Sciences, Ryan Abbott – is for a food container based on fractal geometry.

Line of sight satellite systems, unmanned aerial vehicles, high-altitude platforms, and microwave links that operate on frequency bands such as Ka-band or higher are extremely susceptible to rain. Thus, rain fade forecasting for these systems is critical because it allows the system to switch between ground gateways proactively before a rain fade event to maintain seamless service. Although empirical, statistical, and fade slope models can predict rain fade to some extent, they typically require statistical measurements of rain characteristics in a given area and cannot be generalized to a large scale system. Furthermore, such models typically predict near-future rain fade events but are incapable of forecasting far into the future, making proactive resource management more difficult. In this paper, a deep learning (DL)-based architecture is proposed that forecasts future rain fade using satellite and radar imagery data as well as link power measurements. Furthermore, the data preprocessing and architectural design have been thoroughly explained and multiple experiments have been conducted. Experiments show that the proposed DL architecture outperforms current state-of-the-art machine learning-based algorithms in rain fade forecasting in the near and long term. Moreover, the results indicate that radar data with weather condition information is more effective for short-term prediction, while satellite data with cloud movement information is more effective for long-term predictions.

Much of the world's population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against people with disabilities, i.e., prejudice that favors those with typical abilities. We report on various analyses based on word predictions of a large-scale BERT language model. Statistically significant results demonstrate that people with disabilities can be disadvantaged. Findings also explore overlapping forms of discrimination related to interconnected gender and race identities.

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies. The approach extracts key features from signals representing known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system were green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.

Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation. However, there is a more natural and intuitive relevancy among entities being always ignored, which is that how one entity is close to another semantically, without the consideration of any explicit relation. We name such semantic phenomenon in knowledge graph as proximity pattern. In this work, we explore the problem of how to define and represent proximity pattern, and how it can be utilized to help knowledge graph embedding. Firstly, we define the proximity of any two entities according to their statistically shared queries, then we construct a derived graph structure and represent the proximity pattern from global view. Moreover, with the original knowledge graph, we design a Chained couPle-GNN (CP-GNN) architecture to deeply merge the two patterns (graphs) together, which can encode a more comprehensive knowledge embedding. Being evaluated on FB15k-237 and WN18RR datasets, CP-GNN achieves state-of-the-art results for Knowledge Graph Completion task, and can especially boost the modeling capacity for complex queries that contain multiple answer entities, proving the effectiveness of introduced proximity pattern.

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in the presence of limited data. We present a novel variational message passing algorithm as applied to Latent Dirichlet Allocation (LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In situations where marginalisation leads to non-conjugate messages, we use ideas from sampling to derive approximate update equations. In cases where conjugacy holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is used. Our algorithm, ALBU (approximate LBU), has strong similarities with Variational Message Passing (VMP) (which is the message passing variant of VB). To compare the performance of the algorithms in the presence of limited data, we use data sets consisting of tweets and news groups. Additionally, to perform more fine grained evaluations and comparisons, we use simulations that enable comparisons with the ground truth via Kullback-Leibler divergence (KLD). Using coherence measures for the text corpora and KLD with the simulations we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.

The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification. Generative models have been used as adversarially robust classifiers on simple datasets such as MNIST, but this robustness has not been observed on more complex datasets like CIFAR-10. Additionally, on natural image datasets, previous results have suggested a trade-off between the likelihood of the data and classification accuracy. In this work, we investigate score-based generative models as classifiers for natural images. We show that these models not only obtain competitive likelihood values but simultaneously achieve state-of-the-art classification accuracy for generative classifiers on CIFAR-10. Nevertheless, we find that these models are only slightly, if at all, more robust than discriminative baseline models on out-of-distribution tasks based on common image corruptions. Similarly and contrary to prior results, we find that score-based are prone to worst-case distribution shifts in the form of adversarial perturbations. Our work highlights that score-based generative models are closing the gap in classification accuracy compared to standard discriminative models. While they do not yet deliver on the promise of adversarial and out-of-domain robustness, they provide a different approach to classification that warrants further research.

Today AWS Step Functions expands the number of supported AWS services from 17 to over 200 and AWS API Actions from 46 to over 9,000 with its new capability AWS SDK Service Integrations. When developers build distributed architectures, one of the patterns they use is the workflow-based orchestration pattern. This pattern is helpful for workflow automation inside a service to perform distributed transactions. An example of a distributed transaction is all the tasks required to handle an order and keep track of the transaction status at all times. Step Functions is a low-code visual workflow service used for workflow automation, to orchestrate services, and help you to apply this pattern.

Afrofuturism, if you're unfamiliar, is a movement in literature, music, art, video games, movies, etc., featuring futuristic or science fiction themes which incorporate elements of global Black history and culture, or better yet, making them central themes. We've seen some games that take the concept to heart, like Usoni, but few go beyond including Black or African characters to actually include their stories or experiences. We Are The Caretakers is an unapologetically Afrofuturist sci-fi squad-management RPG about protecting endangered animals--and your planet--from extinction. In the game, you recruit, train, manage and build squads of arcane protectors called the Caretakers. Set in the land of Shadra, a fictional nation in Africa, the story revolves around defending Raun, rhino-like creatures, from human and alien poachers.