There are those who debate whether the new decade of the 2020s commenced on 1 Jan 2020 or 1 Jan 2021. Either way, one suspects that many around the world will hope that at some point during the course of 2021 the current year will mark a shift away from the events of 2020 and allow for a new start. For a definition of AI, Machine Learning and Deep Learning see the Article an Intro to AI. A new administration is in place in the US and the talk is about a major push for Green Technology and the need to stimulate next generation infrastructure including AI and 5G to generate economic recovery with David Knight forecasting that 5G has the potential - the potential - to drive GDP growth of 40% or more by 2030. The Biden administration has stated that it will boost spending in emerging technologies that includes AI and 5G to $300Bn over a four year period. On the other side of the Atlantic Ocean, the EU have announced a Green Deal and also need to consider the European AI policy to develop next generation companies that will drive economic growth and employment.

Advanced artificial intelligence has identified thousands of possible "gravitational lenses" -- warps in space-time predicted by Albert Einstein -- promising to enhance our understanding of dark matter and the evolution of galaxies. Einstein realized that mass warps space, and massive galaxies and galaxy clusters can distort space around them to such a degree that they form a cosmic lens, bending and magnifying the path of light from more distant galaxies through that warped space. Gravitational lenses are important tools for cosmologists. They can magnify the light of distant galaxies that are too faint to be otherwise seen in detail, or reveal where invisible dark matter is warping space. However, astronomers had only about a hundred good gravitational lenses to use.

Journalists are automating processes and using systems that mimic human behavior to design tasks related to news gathering, content creation, distribution, marketing, and subscriptions in Latin America. FREMONT, CA: Artificial intelligence (AI) has stopped merely being a science fiction component and has become a reality in recent years. Automation of processes and the creation of systems that mimic human behavior have reached journalism and are being used to design tasks of news gathering, content creation, distribution, marketing, and subscriptions. Automating processes and creating systems that mimic human behavior have reached journalism. The use of AI is currently somewhat limited, despite its enormous potential, and the region is ravenous for information regarding the subject.

Technological advancements have spurred the usage of machine learning based applications in sports science. Physiotherapists, sports coaches and athletes actively look to incorporate the latest technologies in order to further improve performance and avoid injuries. While wearable sensors are very popular, their use is hindered by constraints on battery power and sensor calibration, especially for use cases which require multiple sensors to be placed on the body. Hence, there is renewed interest in video-based data capture and analysis for sports science. In this paper, we present the application of classifying S\&C exercises using video. We focus on the popular Military Press exercise, where the execution is captured with a video-camera using a mobile device, such as a mobile phone, and the goal is to classify the execution into different types. Since video recordings need a lot of storage and computation, this use case requires data reduction, while preserving the classification accuracy and enabling fast prediction. To this end, we propose an approach named BodyMTS to turn video into time series by employing body pose tracking, followed by training and prediction using multivariate time series classifiers. We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors. We compare BodyMTS to state-of-the-art deep learning methods which classify human activity directly from videos and show that BodyMTS achieves similar accuracy, but with reduced running time and model engineering effort. Finally, we discuss some of the practical aspects of employing BodyMTS in this application in terms of accuracy and robustness under reduced data quality and size. We show that BodyMTS achieves an average accuracy of 87\%, which is significantly higher than the accuracy of human domain experts.

The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently developed deep learning approaches such as graph neural networks (GNNs) open new opportunity to significantly extend scope for modelling novel high-performance catalysts. Nevertheless, the graph representation of particular crystal structure is not a straightforward task due to the ambiguous connectivity schemes and numerous embeddings of nodes and edges. Here we present embedding improvement for GNN that has been modified by Voronoi tesselation and is able to predict the energy of catalytic systems within Open Catalyst Project dataset. Enrichment of the graph was calculated via Voronoi tessellation and the corresponding contact solid angles and types (direct or indirect) were considered as features of edges and Voronoi volumes were used as node characteristics. The auxiliary approach was enriching node representation by intrinsic atomic properties (electronegativity, period and group position). Proposed modifications allowed us to improve the mean absolute error of the original model and the final error equals to 651 meV per atom on the Open Catalyst Project dataset and 6 meV per atom on the intermetallics dataset. Also, by consideration of additional dataset, we show that a sensible choice of data can decrease the error to values above physically-based 20 meV per atom threshold.

Approximate computing Approximate computing (AxC) has been long accepted as a design refers to techniques that exploit the inherent error resilience alternative for efficient system implementation at the cost of relaxed of several applications to achieve improvements in efficiency (e.g., accuracy requirements. Despite the AxC research activities energy and performance) at all layers of the computing stack [60]. in various application domains, AxC thrived the past decade when For example, prior analysis on a benchmark suite of 12 recognition, it was applied in Machine Learning (ML). The by definition approximate mining and search applications showed that 83% of the runtime is notion of ML models but also the increased computational spent in tasks that are amenable to approximation [15, 60]. The origins overheads associated with ML applications-that were effectively of approximate computing (AxC) can be traced back to various mitigated by corresponding approximations-led to a perfect matching fields including computer arithmetic (floating point representation) and a fruitful synergy. AxC for AI/ML has transcended beyond [63], arithmetic units (adders [54] and multipliers [80]), digital academic prototypes. In this work, we enlighten the synergistic signal processing (filter design) [27], algorithms (approximation nature of AxC and ML and elucidate the impact of AxC in designing algorithms) [62], and networking (best-effort packet delivery) [9].

Given the wide and ever growing range of different efficient Transformer attention mechanisms, it is important to identify which attention is most effective when given a task. In this work, we are also interested in combining different attention types to build heterogeneous Transformers. We first propose a DARTS-like Neural Architecture Search (NAS) method to find the best attention for a given task, in this setup, all heads use the same attention (homogeneous models). Our results suggest that NAS is highly effective on this task, and it identifies the best attention mechanisms for IMDb byte level text classification and Listops. We then extend our framework to search for and build Transformers with multiple different attention types, and call them heterogeneous Transformers. We show that whilst these heterogeneous Transformers are better than the average homogeneous models, they cannot outperform the best. We explore the reasons why heterogeneous attention makes sense, and why it ultimately fails.

As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).

Pokémon are unique little monsters, but often inspired by real-life animals or even some objects. And thinking about this freedom of diversity, a new model of Artificial Intelligence manages to transform any known character into a unique and unique Pokémon version. A website reveals a new Artificial Intelligence software that is capable of transforming a phrase or a character into a Pokémon in a matter of seconds, with beautiful and well-made images, which have been catching the public's attention. The application in question was developed by programmer Justin Pinkney, and called text-to-Pokemon. It allows you to put the name of any celebrity or character on the site to have access to a version of him in the world of skillful little monsters.

Phoneme recognition is a very important part of speech recognition that requires the ability to extract phonetic features from multiple frames. In this paper, we compare and analyze CNN, RNN, Transformer, and Conformer models using phoneme recognition. For CNN, the ContextNet model is used for the experiments. First, we compare the accuracy of various architectures under different constraints, such as the receptive field length, parameter size, and layer depth. Second, we interpret the performance difference of these models, especially when the observable sequence length varies. Our analyses show that Transformer and Conformer models benefit from the long-range accessibility of self-attention through input frames.