Ethiopia's 13-month war has seen yet another dramatic turn as the federal government's counteroffensive against fighters from the northern Tigray region has made substantial advances, reversing the spectacular gains made recently by the Tigrayan forces in their push southwards. State media said this week the country's "joint gallant security forces" had retaken the strategic towns of Dessie and Kombolcha, the latest in a series of battleground victories since Prime Minister Abiy Ahmed said last month he would head to the front line and urged Ethiopians to join the fight. As fighting drags on, the government, with its tiny air force of 22 combat-capable aircraft, seems to have also realised that air power and timely intelligence can make all the difference in a conflict – especially one fought over vast and often mountainous areas like in Ethiopia's north. Although there has been no official confirmation, analysts have pointed to credible reports saying Ahmed's government has reached out in recent months to manufacturers of cheap and efficient armed drones hoping that air power will turn the tide in its way. Photographic evidence has pointed to the presence of Chinese Wing Loong 2 Unarmed Aerial Vehicles or UAVs at Ethiopian military bases, while a Bellingcat investigation in August found strong indications that Iranian armed drones, along with their ground control stations, had been spotted at Semera Airport.

Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules. To address this limitation, Linformer and Informer are proposed to reduce the quadratic complexity to linear (modulo logarithmic factors) via low-dimensional projection and row selection respectively. These two models are intrinsically connected, and to understand their connection, we introduce a theoretical framework of matrix sketching. Based on the theoretical analysis, we propose Skeinformer to accelerate self-attention and further improve the accuracy of matrix approximation to self-attention with three carefully designed components: column sampling, adaptive row normalization and pilot sampling reutilization. Experiments on the Long Range Arena (LRA) benchmark demonstrate that our methods outperform alternatives with a consistently smaller time/space footprint.

Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. Initially consisting of unsupervised models with independence assumptions, more recently, weak supervision and correlated features have been explored, but without a causal view of the generative process. In contrast, we work under the regime of a causal generative process where generative factors are either independent or can be potentially confounded by a set of observed or unobserved confounders. We present an analysis of disentangled representations through the notion of disentangled causal process. We motivate the need for new metrics and datasets to study causal disentanglement and propose two evaluation metrics and a dataset. We show that our metrics capture the desiderata of disentangled causal process. Finally, we perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective.

Data-driven societal event forecasting methods exploit relevant historical information to predict future events. These methods rely on historical labeled data and cannot accurately predict events when data are limited or of poor quality. Studying causal effects between events goes beyond correlation analysis and can contribute to a more robust prediction of events. However, incorporating causality analysis in data-driven event forecasting is challenging due to several factors: (i) Events occur in a complex and dynamic social environment. Many unobserved variables, i.e., hidden confounders, affect both potential causes and outcomes. (ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial. In this work, we introduce a deep learning framework that integrates causal effect estimation into event forecasting. We first study the problem of Individual Treatment Effect (ITE) estimation from observational event data with spatiotemporal attributes and present a novel causal inference model to estimate ITEs. We then incorporate the learned event-related causal information into event prediction as prior knowledge. Two robust learning modules, including a feature reweighting module and an approximate constraint loss, are introduced to enable prior knowledge injection. We evaluate the proposed causal inference model on real-world event datasets and validate the effectiveness of proposed robust learning modules in event prediction by feeding learned causal information into different deep learning methods. Experimental results demonstrate the strengths of the proposed causal inference model for ITE estimation in societal events and showcase the beneficial properties of robust learning modules in societal event forecasting.

Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems. However, prior research has highlighted gaps between the intended design of these tools and practices and their use within particular contexts, including gaps caused by the role that organizational factors play in shaping fairness work. In this paper, we investigate these gaps for one such practice: disaggregated evaluations of AI systems, intended to uncover performance disparities between demographic groups. By conducting semi-structured interviews and structured workshops with thirty-three AI practitioners from ten teams at three technology companies, we identify practitioners' processes, challenges, and needs for support when designing disaggregated evaluations. We find that practitioners face challenges when choosing performance metrics, identifying the most relevant direct stakeholders and demographic groups on which to focus, and collecting datasets with which to conduct disaggregated evaluations. More generally, we identify impacts on fairness work stemming from a lack of engagement with direct stakeholders, business imperatives that prioritize customers over marginalized groups, and the drive to deploy AI systems at scale.

Creating what-if stories requires reasoning about prior statements and possible outcomes of the changed conditions. One can easily generate coherent endings under new conditions, but it would be challenging for current systems to do it with minimal changes to the original story. Therefore, one major challenge is the trade-off between generating a logical story and rewriting with minimal-edits. In this paper, we propose EDUCAT, an editing-based unsupervised approach for counterfactual story rewriting. EDUCAT includes a target position detection strategy based on estimating causal effects of the what-if conditions, which keeps the causal invariant parts of the story. EDUCAT then generates the stories under fluency, coherence and minimal-edits constraints. We also propose a new metric to alleviate the shortcomings of current automatic metrics and better evaluate the trade-off. We evaluate EDUCAT on a public counterfactual story rewriting benchmark. Experiments show that EDUCAT achieves the best trade-off over unsupervised SOTA methods according to both automatic and human evaluation. The resources of EDUCAT are available at: https://github.com/jiangjiechen/EDUCAT.

This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. Rather than compressing sequential information at every timestep as in conventional recurrent neural network-based methods, the proposed architecture generates a latent variable in each data block with a length of multiple timesteps and passes the most relevant information to the next block for policy optimization. The proposed blockwise sequential model is implemented based on self-attention, making the model capable of detailed sequential learning in partial observable settings. The proposed model builds an additional learning network to efficiently implement gradient estimation by using self-normalized importance sampling, which does not require the complex blockwise input data reconstruction in the model learning. Numerical results show that the proposed method significantly outperforms previous methods in various partially observable environments.

If you thought judgment, ethics, and even creativity were the unique purview of humans, think again. The latest industry analyst predictions about artificial intelligence (AI) are out, and they're certain to oust a ton of assumptions we've made to date. Read on to find out just how smart, creative, and sincere AI will become during the next few years. Organizations are just starting to tap the incredible computational powers of AI for creativity, human productivity, and business results. Noting that South Africa granted the first patent to a creative AI system in 2021, Forrester researchers predicted creative AI systems will win dozens of patents in 2022.

GNW The Global Edge Artificial Intelligence Market size was estimated at USD 572.00 million in 2020 and expected to reach USD 701.73 million in 2021, at a CAGR 23.35% to reach USD 2,014.99 million by 2026. Market Statistics: The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period. Market Segmentation & Coverage: This research report categorizes the Edge Artificial Intelligence to forecast the revenues and analyze the trends in each of the following sub-markets: Based on Processor, the market was studied across ASIC, CPU, and GPU.

When you think about agriculture, what comes to mind? Tractors? Fields of corn? Big red barns? Often we don't think of computers. But computers and technology are playing a huge role in making our food system more sustainable and reliable. In the past few decades, high-tech machinery and robotics have changed the agroindustry. High-tech farming is making our crops more resilient against pathogens, harvest times more precise, and food yields more robust. Next-generation sequencing and machine learning now make high-tech advances possible at the genome level, particularly when untangling plant-microbe interactions. As technology advances, we can leverage these tools to promote sustainable agricultural practices.