The "Artificial Intelligence Global Market Report 2021: COVID-19 Growth and Change to 2030" report has been added to ResearchAndMarkets.com's offering. This report provides strategists, marketers and senior management with the critical information they need to assess the global artificial intelligence market. This report focuses on the artificial intelligence market which is experiencing strong growth. The report gives a guide to the artificial intelligence market which will be shaping and changing our lives over the next ten years and beyond, including the markets response to the challenge of the global pandemic. The global artificial intelligence market is expected to grow from $40.17 billion in 2020 to $51.56 billion in 2021 at a compound annual growth rate (CAGR) of 28.4%.

Lamb also said the modular method just makes more sense when scaling. Smart carts can cost retailers between $5,000 and $10,000 per unit and require a lot of maintenance compared to simple shopping carts, which tend to cost retailers less than $100 and will get beat up for years before being replaced. Amazon's walk-out tech is expected to cost retailers upwards of $1 million for installation and hardware, and that doesn't include maintenance over time. Currently, the full system that it's piloting is about $75,000 and includes 10 carts, an imaging station, a server station to run the system, full integration into a customer-facing store and Imagr support over the duration of the pilot. Imagr didn't share how much its current model of trolleys cost versus its modular system, but says it'll be a cheaper endeavor.

Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, we propose to detect and to show the use of such techniques as a way to offer interpretability. In particular, we define qualitatively descriptive features and we analyze their suitability for detecting deception techniques. We further show that our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.

In education, open-ended quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.

If the realization that Labor Day is just around the corner brings to mind all the fall essentials you've been meaning to get, you're in luck. Earning its nickname of being the Everything Store for the umpteenth time, Amazon is currently stacked with deals on trending items, devices, gadgets and more. The sales and deals team at Good Housekeeping did a deep dive into the best deals on Amazon ahead of the holiday weekend and found, to our delight, that a number of our readers' favorite brands and products loved by our Good Housekeeping Institute experts are already on sale.

Datasets are essential to AI models. They provide the truth by which we train AI models and measure a model's success. Engineers often look to the AI model as the key to delivering highly accurate results, but in reality it is often the data that determines an AI model success. Data flows through every step of the AI workflow, from model training to deployment, and the way it is prepared can be the main driver of accuracy when designing robust AI models. Engineers can use these five tips to improve their data preparation process and drive success when developing a complete AI system.

"Voice skins" have become a very popular feature for AI-based voice assistants, to help personalize some of the more anodyne aspects of helpful, yet also kind of bland and robotic, speaking voices you get on services like Alexa. Now a startup that is building voice skins for different companies to use across their services, and for third parties to create and apply as well, is raising some funding to fuel its growth. LOVO, the Berkeley, California-based artificial intelligence (AI) voice & synthetic speech tool developer, this week closed a $4.5 million pre-Series A round led by South Korean Kakao Entertainment along with Kakao Investment and LG CNS, an IT solution affiliate of LG Group. Its previous investor SkyDeck Fund and a private investor, vice president of finance at DoorDash, Michael Kim, also joined the funding. The proceeds will be used to propel its research and development in artificial intelligence and synthetic speech and grow the team.

It uses Amazon Textract, a machine learning service that automatically extracts text, handwriting, and data from scanned documents to provide highly …

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying k nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of eleven state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a Global-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.