As the world reopened cautiously in 2021, our correspondents seized the chance to venture out in search of stories that would astonish, delight, provoke and enlighten. We went from the heights of a Himalayan ski slope to the ocean depths off the Philippines where amiable giants dive, and from a rugged island where a whistling language is still used to an Italian atelier where robots carve the sculptures. If the pandemic often kept our reporters confined to urban settings in 2020, this year afforded them the chance to explore deep into the countryside. We observed a (bogus) diamond rush in rural South Africa and accompanied Indigenous hunters in Taiwan. We trekked to Canada's beaver dams, swam in a contested stream in northern Israel and returned home to a Tuscan village sliding back in time.

From the Mars Ingenuity helicopter's first powered flight on another world to the launch of the James Webb telescope that will peer into the earliest epoch of the Universe, 2021 was a huge year for humanity's space endeavors. Beyond the science milestones, billionaires battled to reach the final frontier first, an all-civilian crew went into orbit, and Star Trek's William Shatner waxed profound about what it meant to see the Earth from the cosmos, as space tourism finally came into its own. Star Trek's William Shatner waxed profound about what it meant to see the Earth from the cosmos, as space tourism finally came into its own Photo: AFP / Patrick T. FALLON NASA's Perseverance Rover survived its "seven minutes of terror," a time when the craft relies on its automated systems for descent and landing, to touch down flawlessly on Mars' Jezero Crater in February. Since then, the car-sized robot has been taking photos and drilling for samples for its mission: determining whether the Red Planet might have hosted ancient microbial life forms. A rock sample return mission is planned for sometime in the 2030s.

From the Mars Ingenuity helicopter's first powered flight on another world to the launch of the James Webb telescope that will peer into the earliest epoch of the Universe, 2021 was a huge year for humanity's space endeavors. Beyond the science milestones, billionaires battled to reach the final frontier first, an all-civilian crew went into orbit, and Star Trek's William Shatner waxed profound about what it meant to see the Earth from the cosmos, as space tourism finally came into its own. Star Trek's William Shatner waxed profound about what it meant to see the Earth from the cosmos, as space tourism finally came into its own Photo: AFP / Patrick T. FALLON NASA's Perseverance Rover survived its "seven minutes of terror," a time when the craft relies on its automated systems for descent and landing, to touch down flawlessly on Mars' Jezero Crater in February. Since then, the car-sized robot has been taking photos and drilling for samples for its mission: determining whether the Red Planet might have hosted ancient microbial life forms. A rock sample return mission is planned for sometime in the 2030s.

Information-theoretic measures have been widely adopted in the design of features for learning and decision problems. Inspired by this, we look at the relationship between i) a weak form of information loss in the Shannon sense and ii) the operation loss in the minimum probability of error (MPE) sense when considering a family of lossy continuous representations (features) of a continuous observation. We present several results that shed light on this interplay. Our first result offers a lower bound on a weak form of information loss as a function of its respective operation loss when adopting a discrete lossy representation (quantization) instead of the original raw observation. From this, our main result shows that a specific form of vanishing information loss (a weak notion of asymptotic informational sufficiency) implies a vanishing MPE loss (or asymptotic operational sufficiency) when considering a general family of lossy continuous representations. Our theoretical findings support the observation that the selection of feature representations that attempt to capture informational sufficiency is appropriate for learning, but this selection is a rather conservative design principle if the intended goal is achieving MPE in classification. Supporting this last point, and under some structural conditions, we show that it is possible to adopt an alternative notion of informational sufficiency (strictly weaker than pure sufficiency in the mutual information sense) to achieve operational sufficiency in learning.

Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video. Most existing approaches tackle this task by appearance similarity in continuous frames, while ignoring the abundant semantic features. In this paper, we explore to robustly track video text with contrastive learning of semantic and visual representations. Correspondingly, we present an end-to-end video text tracker with Semantic and Visual Representations(SVRep), which detects and tracks texts by exploiting the visual and semantic relationships between different texts in a video sequence. Besides, with a light-weight architecture, SVRep achieves state-of-the-art performance while maintaining competitive inference speed. Specifically, with a backbone of ResNet-18, SVRep achieves an ${\rm ID_{F1}}$ of $\textbf{65.9\%}$, running at $\textbf{16.7}$ FPS, on the ICDAR2015(video) dataset with $\textbf{8.6\%}$ improvement than the previous state-of-the-art methods.

Electrical utilities depend on short-term demand forecasting to proactively adjust production and distribution in anticipation of major variations. This systematic review analyzes 240 works published in scholarly journals between 2000 and 2019 that focus on applying Artificial Intelligence (AI), statistical, and hybrid models to short-term load forecasting (STLF). This work represents the most comprehensive review of works on this subject to date. A complete analysis of the literature is conducted to identify the most popular and accurate techniques as well as existing gaps. The findings show that although Artificial Neural Networks (ANN) continue to be the most commonly used standalone technique, researchers have been exceedingly opting for hybrid combinations of different techniques to leverage the combined advantages of individual methods. The review demonstrates that it is commonly possible with these hybrid combinations to achieve prediction accuracy exceeding 99%. The most successful duration for short-term forecasting has been identified as prediction for a duration of one day at an hourly interval. The review has identified a deficiency in access to datasets needed for training of the models. A significant gap has been identified in researching regions other than Asia, Europe, North America, and Australia.

Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR frameworks. In this work, we propose a novel approach to integrate LF-MMI criterion into E2E ASR frameworks in both training and decoding stages. The proposed approach shows its effectiveness on two of the most widely used E2E frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Experiments suggest that the introduction of the LF-MMI criterion consistently leads to significant performance improvements on various datasets and different E2E ASR frameworks. The best of our models achieves competitive CER of 4.1\% / 4.4\% on Aishell-1 dev/test set; we also achieve significant error reduction on Aishell-2 and Librispeech datasets over strong baselines.

Explainable artificial intelligence and interpretable machine learning are research fields growing in importance. Yet, the underlying concepts remain somewhat elusive and lack generally agreed definitions. While recent inspiration from social sciences has refocused the work on needs and expectations of human recipients, the field still misses a concrete conceptualisation. We take steps towards addressing this challenge by reviewing the philosophical and social foundations of human explainability, which we then translate into the technological realm. In particular, we scrutinise the notion of algorithmic black boxes and the spectrum of understanding determined by explanatory processes and explainees' background knowledge. This approach allows us to define explainability as (logical) reasoning applied to transparent insights (into black boxes) interpreted under certain background knowledge - a process that engenders understanding in explainees. We then employ this conceptualisation to revisit the much disputed trade-off between transparency and predictive power and its implications for ante-hoc and post-hoc explainers as well as fairness and accountability engendered by explainability. We furthermore discuss components of the machine learning workflow that may be in need of interpretability, building on a range of ideas from human-centred explainability, with a focus on explainees, contrastive statements and explanatory processes. Our discussion reconciles and complements current research to help better navigate open questions - rather than attempting to address any individual issue - thus laying a solid foundation for a grounded discussion and future progress of explainable artificial intelligence and interpretable machine learning. We conclude with a summary of our findings, revisiting the human-centred explanatory process needed to achieve the desired level of algorithmic transparency.

Amazon Alexa now is readily accessible in over 42 regions of the world and in a number of languages, making it more accessible than before. Alexa now can collaborate in much less prominent locations, such as the Cayman Islands and Cambodia, after initially being supported only in the United States, Canada, the United Kingdom, India, Japan, and Germany. However, it's not as simple as having to log into your Amazon account and order an Echo Dot or a full-fledged Amazon Echo smart speaker. We'll go over how to get Alexa if you live outside of the United States, which features you'll have access to, and some potential workarounds if you run into problems. If you really want Alexa, the very first thing you'll need is, well, an Alexa-enabled gadget.

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