Some of the excesses of 2021 have shown us how digital technologies can undermine what philosophers call future "human flourishing." A lot has been written on this topic in the first few days of the new year, but take two examples -- MIT Technology Review's list of the worst excesses of technology and Fast Company's 5 best and worst tech moments of 2021 -- and it's evident how little power people affected by technologies have when things go wrong under current systems. What's also clear as we enter 2022 is that global tolerance for technology's unchecked disruption of societal institutions, conventions, and values is waning. This is the year governments will pass legislation to control the effects of digital technologies on societies, across many jurisdictions and in relation to numerous existing and emergent technologies. The EU AI and Digital Services Acts, the UK Online Safety Bill, and the US SAFE TECH Act are just a few of the efforts underway. Legislation is a marker of societal concern, but it's also clear that non-specialist, "ordinary" people have an increasingly sophisticated understanding of the relationship between technology and society.

We are living in a hyperconnected world, where every device is connected, and are generating data at an unprecedented rate. If we look at a smartwatch or a smartphone, smart cars, smart factories, smart homes, or smart cities, the enormous data generated is collected at the source, processed and smart decisions are required to be executed instantly. This is possible when two powerful technologies come together such as Edge Computing and Artificial Intelligence (AI). Therefore, looking into poetic justification by the acclaimed music group Aerosmith, in these interesting times, we are "living on the edge". 'Edge AI' is the amalgamation of two incredible technologies: Edge, which is all about bringing computation and data closer to end users to improve efficiency, and AI, which comprises of data-driven intelligence.

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!

Google unveiled Wednesday at CES 2022 a range of new ways to keep its Android devices connected -- and that includes cars. As more vehicles go electric and automakers evolve into software developers, expect to see more plays directed at turning cars into connected devices. Take Volvo Cars, for instance. The automaker and Google announced at CES 2022 new content and services that will be coming to future Volvo vehicles, including the ability to download and use the YouTube app via Google Play Store and the ability to communicate with the Google Home ecosystem. New Volvo car models are equipped with an Android Automotive operating system and have embedded voice-controlled Google Assistant, Google Play Store, Google Maps and other Google services into its infotainment system.

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the $\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator. The new regularization and the corresponding operator can be generally used in other problems that require column-wise sparsity. Moreover, we impose the spatial-spectral total variation regularization in the log-based nonconvex RPCA model, which enhances the global piece-wise smoothness and spectral consistency from the spatial and spectral views in the recovered HSI. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs.

Humans ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials' recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive and systematic review for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions.

This paper quantifies the quality of heatmap-based eXplainable AI methods w.r.t image classification problem. Here, a heatmap is considered desirable if it improves the probability of predicting the correct classes. Different XAI heatmap-based methods are empirically shown to improve classification confidence to different extents depending on the datasets, e.g. Saliency works best on ImageNet and Deconvolution on ChestX-Ray Pneumonia dataset. The novelty includes a new gap distribution that shows a stark difference between correct and wrong predictions. Finally, the generative augmentative explanation is introduced, a method to generate heatmaps maps capable of improving predictive confidence to a high level.

Artificial Intelligence (AI) in Fintech Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

Google unveiled at CES on Wednesday a range of new ways to keep its Android devices connected, and that includes cars. As more vehicles go electric and automakers evolve into software developers, we can only expect to see more plays directed at turning cars into connected devices. One exemplar of this phenomenon is Volvo Cars, which will launch a direct integration with the Google Home ecosystem in the coming months, both Volvo and Google announced on Wednesday. The integration should allow car owners to turn their car on and off, control the temperature and get car information like battery life by issuing voice commands to Google Assistant-enabled home and mobile devices. Once customers pair their Volvo car to their Google account, they also can talk directly to Google while in their car.

Deep neural networks (DNNs) have achieved the state of the art performance in numerous fields. However, DNNs need high computation times, and people always expect better performance in a lower computation. Therefore, we study the human somatosensory system and design a neural network (SpinalNet) to achieve higher accuracy with fewer computations. Hidden layers in traditional NNs receive inputs in the previous layer, apply activation function, and then transfer the outcomes to the next layer. In the proposed SpinalNet, each layer is split into three splits: 1) input split, 2) intermediate split, and 3) output split. Input split of each layer receives a part of the inputs. The intermediate split of each layer receives outputs of the intermediate split of the previous layer and outputs of the input split of the current layer. The number of incoming weights becomes significantly lower than traditional DNNs. The SpinalNet can also be used as the fully connected or classification layer of DNN and supports both traditional learning and transfer learning. We observe significant error reductions with lower computational costs in most of the DNNs. Traditional learning on the VGG-5 network with SpinalNet classification layers provided the state-of-the-art (SOTA) performance on QMNIST, Kuzushiji-MNIST, EMNIST (Letters, Digits, and Balanced) datasets. Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets. The scripts of the proposed SpinalNet are available at the following link: https://github.com/dipuk0506/SpinalNet