Popular approaches for quantifying predictive uncertainty in deep neural networks often involve a set of weights or models, for instance via ensembling or Monte Carlo Dropout. These techniques usually produce overhead by having to train multiple model instances or do not produce very diverse predictions. This survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they admit "what they don't know" and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting. Finally, we survey the application of the same paradigm to regression problems. We also provide a reflection on the strengths and weaknesses of the mentioned approaches compared to existing ones and provide the most central theoretical results in order to inform future research.

The next generation of wireless technology could affect a wide range of industries, from healthcare to financial services to retail. The technology enables faster data transfer speeds -- up to 10x faster than the speeds achievable with older standards -- lower latency, and greater network capacity. As a result, 5G creates a tremendous opportunity for numerous industries, but also sets the stage for large-scale disruption. Download the free report to understand what 5G is, the industries it's disrupting, and the drivers paving the way for its implementation. As of June 2021, commercial 5G services have already been deployed across more than 1,500 cities in 60 countries worldwide, according to Viavi Solutions. The number of IoT devices -- which will rely on 5G to transmit vast amounts of data in real time -- is projected to grow from 12B in 2020 to 30B in 2025, per IoT Analytics, more than 4 devices for every person on Earth. Executives across industries are already jostling to take advantage of 5G tech -- and avoid being disrupted by it. Earnings call mentions of 5G have soared in recent years. From enabling remote robotic surgery and autonomous cars to improving crop management, 5G is poised to transform many of the world's biggest industries. The impact of 5G on manufacturing could be huge. It's estimated that improved connectivity through 5G will create $13T in global economic value across industries by 2035, according to IHS Markit. A third of that total is projected to come from the manufacturing sector alone. This would enable manufacturers to build "smart factories" that rely on automation, augmented reality, and IoT. And with 5G powering large amounts of IoT devices and sensors around the factory, artificial intelligence can be integrated more deeply with operations. On fast-paced assembly lines, even microseconds of latency can cause costly disruptions for the manufacturer.

An exploratory survey of the key coordinates of the Enterprise Artificial Intelligence (AI) market strategically describes multiple aspects of the industry through a systematically organised data representation followed by extracting deepest information from various reliable sources. It compiles a series of statistically significant data explaining the Enterprise Artificial Intelligence (AI) market size and volume ratios coupled with the market infrastructure specifications delivering the market estimation and metrics along with industry valuation. The study intends to deliver an all-inclusive market analysis offering optimum client satisfaction. It delivers a highly informative and relevant market study offering valuable insights into the Enterprise Artificial Intelligence (AI) market growth and development. The forecast represented in the market study helps picture the growth predictions realistically determined based on current growth determinants.

For the majority of organizations, continuously delivering and integrating AI solutions within enterprise applications and business workflows is a complex afterthought. On average, it takes about eight months to get an AI-based model integrated within a business workflow and for it to deliver tangible value. However, to reduce AI project failures, organizations must efficiently operationalize their AI architectures. Gartner expects that by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives. Organizations should consider model operationalization (ModelOps) for operationalizing AI solutions.

We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train. Unlike standard ARMs, they do not require causal masking of model representations, and can be trained using an efficient objective similar to modern probabilistic diffusion models that scales favourably to highly-dimensional data. At test time, ARDMs support parallel generation which can be adapted to fit any given generation budget. We find that ARDMs require significantly fewer steps than discrete diffusion models to attain the same performance. Finally, we apply ARDMs to lossless compression, and show that they are uniquely suited to this task. Contrary to existing approaches based on bits-back coding, ARDMs obtain compelling results not only on complete datasets, but also on compressing single data points. Moreover, this can be done using a modest number of network calls for (de)compression due to the model's adaptable parallel generation. Deep generative models have made great progress in modelling different sources of data, such as images, text and audio. These models have a wide variety of applications, such as denoising, inpainting, translating and representation learning. A popular type of likelihood-based models are Autoregressive Models (ARMs). Although very effective, ARMs require a pre-specified order in which to generate data, which may not be an obvious choice for some data modalities, for example images. Further, although the likelihood of ARMs can be retrieved with a single neural network call, sampling from a model requires the same number of network calls as the dimensionality of the data. Recently, modern probabilistic diffusion models have introduced a new training paradigm: Instead of optimizing the entire likelihood of a datapoint, a component of the likelihood bound can be sampled and optimized instead. Works on diffusion on discrete spaces (Sohl-Dickstein et al., 2015; Hoogeboom et al., 2021; Austin et al., 2021) describe a discrete destruction process for which the Work done during as research intern at Google Brain.

Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining highdimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. In this review, we present a unified treatment of the theoretical foundations of HD computing with a focus on the suitability of representations for learning.

Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and help people make healthy and thoughtful decisions around food and diet. In many typical (Recommender System) RS domains, smart nudges have been proven effective in shaping users' consumption patterns. In recent years, knowledgeable nudging and incentifying choices started getting attention in the food domain as well. To develop smart nudging for promoting healthier food choices, we combined Machine Learning and RS technology with food-healthiness guidelines from recognized health organizations, such as the World Health Organization, Food Standards Agency, and the National Health Service United Kingdom. In this paper, we discuss our research on, persuasive visualization for making users aware of the healthiness of the recommended recipes. Here, we propose three novel nudging technology, the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage users to choose healthier recipes. We also propose a Topic Modeling based portion-size recommendation algorithm. To evaluate our proposed smart-nudges, we conducted an online user study with 96 participants and 92250 recipes. Results showed that, during the food decision-making process, appropriate healthiness cues make users more likely to click, browse, and choose healthier recipes over less healthy ones.

Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various types of sub-tasks, requiring different kinds of reasoning. Certain types of reasoning have proven to be more difficult to learn in a monolingual context, and in the crosslingual context, similar observations may shed light on zero-shot transfer efficiency and few-shot sample selection. Hence, to investigate the effects of types of reasoning on transfer performance, we propose a category-annotated multilingual NLI dataset and discuss the challenges to scale monolingual annotations to multiple languages. We statistically observe interesting effects that the confluence of reasoning types and language similarities have on transfer performance.

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first discovered in late December 2019, in Wuhan, China, and then spread worldwide causing a lot of panic and death. Users of social networking sites such as Facebook and Twitter have been focused on reading, publishing, and sharing novelties, tweets, and articles regarding the newly emerging pandemic. A lot of these users often employ sarcasm to convey their intended meaning in a humorous, funny, and indirect way making it hard for computer-based applications to automatically understand and identify their goal and the harm level that they can inflect. Motivated by the emerging need for annotated datasets that tackle these kinds of problems in the context of COVID-19, this paper builds and releases AraCOVID19-SSD a manually annotated Arabic COVID-19 sarcasm and sentiment detection dataset containing 5,162 tweets. To confirm the practical utility of the built dataset, it has been carefully analyzed and tested using several classification models.

Artificial intelligence (AI) will create new job opportunities, not destroy them. AI will displace some jobs but will create new ones. The main aim of this article is intended to focus the minds of our political and business leaders as they consider what strategies to pursue to grow the economy (GDP), business activity and stimulate job creation whilst also taking into account the growing challenges of the environment with climate change mitigation increasingly on the agenda. Let's start by reviewing the types of AI and where we are now. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task.