To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm which endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, this study proposes a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method to generate explanations, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action.

In this independent report fAshIon after fashion, we examine the development of fAshIon (artificial intelligence (AI) in fashion) and explore its potentiality to become a major disruptor of the fashion industry in the near future. To do this, we investigate AI technologies used in the fashion industry through several lenses. We summarise fAshIon studies conducted over the past decade and categorise them into seven groups: Overview, Evaluation, Basic Tech, Selling, Styling, Design, and Buying. The datasets mentioned in fAshIon research have been consolidated on one GitHub page for ease of use. We analyse the authors' backgrounds and the geographic regions treated in these studies to determine the landscape of fAshIon research. The results of our analysis are presented with an aim to provide researchers with a holistic view of research in fAshIon. As part of our primary research, we also review a wide range of cases of applied fAshIon in the fashion industry and analyse their impact on the industry, markets and individuals. We also identify the challenges presented by fAshIon and suggest that these may form the basis for future research. We finally exhibit that many potential opportunities exist for the use of AI in fashion which can transform the fashion industry embedded with AI technologies and boost profits.

Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approximation functions lead to a trade-off between the semantic coherence and the ability to learn the joint data distribution. We propose a new, generalized ELBO formulation for multimodal data that overcomes these limitations. The new objective encompasses two previous methods as special cases and combines their benefits without compromises. In extensive experiments, we demonstrate the advantage of the proposed method compared to state-of-the-art models in self-supervised, generative learning tasks.

Artificial Intelligence (AI) is largely helping the oil & gas industry to shape its future. AI is predicted to highly impact the oil and gas industry over the coming years. Artificial intelligence has a number of potential applications in the oil and gas industry, from surveying to planning and forecasting, and facility management to safety. AI is being used for predicting equipment failure and scheduling maintenances in oilfields. A MarketsandMarkets report estimates, the global AI in Oil & Gas Market is expected to grow at a CAGR of 12.66%, from 2017 to 2022, to reach a projected market value of USD 2.58 Billion by 2022.

An algorithm designed by a research group from the Universities of Bath, Oxford and Twente may be able to help save African elephants from extinction. Coupled with high-resolution imagery, the algorithm enables a satellite to scan large areas of land in short periods of time and collect 5,000 km2 worth of photos, a good fit for the animals' grassland and forest habitats. The tech development is desperately needed as elephant numbers in Africa are estimated to be at just 415,000. The savanna elephant population has reduced by 60 per cent in the last 50 years and the number of forest elephants have fallen by 86 per cent in the previous three decades. The AI technology carries less risk of double counting, does not endanger humans in the data collection process and is less disturbing for the animals - an improvement on techniques used in the past. Earlier this year Dr Ben Okita, co-chair of the IUCN elephant specialist group, named poaching as one of the biggest threats to African elephants who are targeted by ivory traders.

AMP algorithms have two features that make them particularly attractive. First, they can easily be tailored to take advantage of prior information on the structure of the signal, such as sparsity or other constraints. Second, under suitable assumptions on a design or data matrix, AMP theory provides precise asymptotic guarantees for statistical procedures in the high-dimensional regime where the ratio of the number of observations n to dimensions p converges to a constant (Bayati and Montanari, 2012; Donoho et al., 2013; Sur et al., 2017). More generally, AMP has been also used to obtain lower bounds on the estimation error of first-order methods (Celentano et al., 2020), and in linear regression and low rank matrix estimation, it plays a fundamental role in understanding the performance gap between information-theoretically optimal and computationally feasible estimators (Reeves and Pfister, 2019; Barbier et al., 2019; Lelarge and Miolane, 2019). In these settings, it is conjectured that AMP achieves the optimal asymptotic estimation error among all polynomial-time algorithms (cf.

Image registration as an important basis in signal processing task often encounter the problem of stability and efficiency. Non-learning registration approaches rely on the optimization of the similarity metrics between the fix and moving images. Yet, those approaches are usually costly in both time and space complexity. The problem can be worse when the size of the image is large or the deformations between the images are severe. Recently, deep learning, or precisely saying, the convolutional neural network (CNN) based image registration methods have been widely investigated in the research community and show promising effectiveness to overcome the weakness of non-learning based methods. To explore the advanced learning approaches in image registration problem for solving practical issues, we present in this paper a method of introducing attention mechanism in deformable image registration problem. The proposed approach is based on learning the deformation field with a Transformer framework (AiR) that does not rely on the CNN but can be efficiently trained on GPGPU devices also. In a more vivid interpretation: we treat the image registration problem as the same as a language translation task and introducing a Transformer to tackle the problem. Our method learns an unsupervised generated deformation map and is tested on two benchmark datasets. The source code of the AiR will be released at Gitlab.

Sequential decision-making under cost-sensitive tasks is prohibitively daunting, especially for the problem that has a significant impact on people's daily lives, such as malaria control, treatment recommendation. The main challenge faced by policymakers is to learn a policy from scratch by interacting with a complex environment in a few trials. This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials. Specifically, the solution is a model-based reinforcement learning method. To avoid model bias, we apply Gaussian Process~(GP) regression to estimate the transitions explicitly. With the GP world model, we propose a variance-bonus reward to measure the uncertainty about the world. Adding the reward to the planning with MCTS can result in more efficient and effective exploration. Furthermore, the derived polynomial sample complexity indicates that VB-MCTS is sample efficient. Finally, outstanding performance on a competitive world-level RL competition and extensive experimental results verify its advantage over the state-of-the-art on the challenging malaria control task.

AI offers great potential to increase the adoption of renewable energy. Within two months, Omdena's AI community built an interactive map showing the top Nigerian regions for solar power installments. The solutions will provide helpful insights for the government and policy makers to take make decisions on where to allocate resources in the most effective way. Many communities are not connected to the national electricity grid altogether. Most of them work with environmentally devastating fossil fuel, which is expensive, unsustainable, noisy, and health-threatening.

COVID-19 has impacted the world in unprecedented ways, fast-tracking the use of digital tools and innovation to adapt. In a previous blog, we outlined six key technology trends driving social and behavioural changes in West Africa as a result of COVID-19. As we look towards life after the pandemic, we revisit some of these trends and detail key frontier technologies gaining traction in developing economies. The pandemic has driven the uptake of big data public-private partnerships for crisis response. Layering multiple types of data points (including mobile phone data, satellite imagery, ground weather measurements and open street maps) can be extremely effective when combined.