The sixth generation (6G) systems are generally recognized to be established on ubiquitous Artificial Intelligence (AI) and distributed ledger such as blockchain. However, the AI training demands tremendous computing resource, which is limited in most 6G devices. Meanwhile, miners in Proof-of-Work (PoW) based blockchains devote massive computing power to block mining, and are widely criticized for the waste of computation. To address this dilemma, we propose an Evolved-Proof-of-Work (E-PoW) consensus that can integrate the matrix computations, which are widely existed in AI training, into the process of brute-force searches in the block mining. Consequently, E-PoW can connect AI learning and block mining via the multiply used common computing resource. Experimental results show that E-PoW can salvage by up to 80 percent computing power from pure block mining for parallel AI training in 6G systems.

But in her new book, Atlas of AI, leading AI scholar Kate Crawford flips this moral on its head. The problem, she writes, was with the way people defined Hans's achievements: "Hans was already performing remarkable feats of interspecies communication, public performance, and considerable patience, yet these were not recognized as intelligence." So begins Crawford's exploration into the history of artificial intelligence and its impact on our physical world. Each chapter seeks to stretch our understanding of the technology by unveiling how narrowly we've viewed and defined it. Crawford does this by bringing us on a global journey, from the mines where the rare earth elements used in computer manufacturing are extracted to the Amazon fulfillment centers where human bodies have been mechanized in the company's relentless pursuit of growth and profit.

Aveva, a global leader in industrial software, driving digital transformation and sustainability, has stressed how the role of the'Connected Worker' will be instrumental in enabling digital solutions to optimise business returns in a post-pandemic world. Now more than ever, keeping frontline industrial workers safe, while at the same time ensuring business continuity and operational resilience, is vital. For example, Connected Worker technology is helping many Aveva customers maintain their critical operations and keep workers safe, while in parallel saving businesses time and money. Those that haven't digitised their operations will struggle as they face the demand for social distancing and remote work brought on by the pandemic. According to Aveva's Head of Asset Performance Management, Kim Custeau, the business drivers for digital transformation have evolved considerably since the onset of the pandemic a year ago.

Please read the other post Red Wine Quality prediction using AzureML, AKS. This was done using machine learning techniques and not using deep learning. The same thing is accomplished here but using the deep learning framework Keras. Most of the things remain the same compared to the machine learning method, but a few steps change. I am going to highlight the changed aspects here only so that it is easy to follow.

At the turn of the 20th century, a German horse took Europe by storm. Clever Hans, as he was known, could seemingly perform all sorts of tricks previously limited to humans. He could add and subtract numbers, tell time and read a calendar, even spell out words and sentences--all by stamping out the answer with a hoof. "A" was one tap; "B" was two; 2 3 was five. He was an international sensation--and proof, many believed, that animals could be taught to reason as well as humans.

Elon Musk's Neuralink touts its brain chip as a way to help people suffering with mobility issues regain control of their lives, but has also proposed using the technology to merge humans with computer. The move would provide the average person with super-human intelligence that hooks their brain up to the cloud where memories can be stored, thoughts can be exchanged and experiences can be had. Although the abilities of an implanted chip may sound limitless, such wonders come with great responsibilities that Musk, scientists and other companies need to address – specifically privacy. 'If the widespread use becomes hooking us to the cloud, not as therapies, and merge humans with AI the economic model will be to sell our data,' Dr. Susan Schneider, the founding director of the new Center for the Future Mind, told Daily Mail. 'Our inner most thoughts would be sold to the highest bidder.

For e-commerce companies with large product selections, the organization and grouping of products in meaningful ways is important for creating great customer shopping experiences and cultivating an authoritative brand image. One important way of grouping products is to identify a family of product variants, where the variants are mostly the same with slight and yet distinct differences (e.g. color or pack size). In this paper, we introduce a novel approach to identifying product variants. It combines both constrained clustering and tailored NLP techniques (e.g. extraction of product family name from unstructured product title and identification of products with similar model numbers) to achieve superior performance compared with an existing baseline using a vanilla classification approach. In addition, we design the algorithm to meet certain business criteria, including meeting high accuracy requirements on a wide range of categories (e.g. appliances, decor, tools, and building materials, etc.) as well as prioritizing the interpretability of the model to make it accessible and understandable to all business partners.

In recent years, the use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Although these models can often bring substantial improvements in the accuracy and efficiency of organizations, many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Hence, these models are often regarded as black-boxes, in the sense that their internal mechanisms can be opaque to human audit. In real-world applications, particularly in domains where decisions can have a sensitive impact--e.g., criminal justice, estimating credit scores, insurance risk, health risks, etc.--model interpretability is desired. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models. This survey reviews the most relevant and novel methods that form the state-of-the-art for addressing the particular problem of explaining individual instances in machine learning. It seeks to provide a succinct review that can guide data science and machine learning practitioners in the search for appropriate methods to their problem domain.

Adversarial training (AT) has become the de-facto standard to obtain models robust against adversarial examples. However, AT exhibits severe robust overfitting: cross-entropy loss on adversarial examples, so-called robust loss, decreases continuously on training examples, while eventually increasing on test examples. In practice, this leads to poor robust generalization, i.e., adversarial robustness does not generalize well to new examples. In this paper, we study the relationship between robust generalization and flatness of the robust loss landscape in weight space, i.e., whether robust loss changes significantly when perturbing weights. To this end, we propose average- and worst-case metrics to measure flatness in the robust loss landscape and show a correlation between good robust generalization and flatness. For example, throughout training, flatness reduces significantly during overfitting such that early stopping effectively finds flatter minima in the robust loss landscape. Similarly, AT variants achieving higher adversarial robustness also correspond to flatter minima. This holds for many popular choices, e.g., AT-AWP, TRADES, MART, AT with self-supervision or additional unlabeled examples, as well as simple regularization techniques, e.g., AutoAugment, weight decay or label noise. For fair comparison across these approaches, our flatness measures are specifically designed to be scale-invariant and we conduct extensive experiments to validate our findings.

Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are shaping the future of wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing; we motivate the corresponding use-cases, and we present few future research directions in the field.