Welcome to experience "Artificial Intelligence in Digital Marketing - Gold Edition 2022." Artificial Intelligence has transformed the virtual panorama, inclusive of Google's RankBrain personalising suggestions by Amazon. Artificial Intelligence (AI) is hastily turning into important in the daily happenings of the virtual global, with marketing and advertising and marketing being no exception. The idea of AI may also bring to thoughts 60's sci-fi with futuristic robots, however, it's definitely approximately so much greater. With the right understanding and evaluation of data and input, AI is playing an essential position in figuring out marketing trends. Brands and marketers are incorporating Machine Learning and Artificial Intelligence to save time and assets.

--Potentially, taking advantage of available side information boosts the performance of recommender systems; nevertheless, with the rise of big data, the side information has often several dimensions. Hence, it is imperative to develop decision-making algorithms that can cope with such a high-dimensional context in real-time. That is especially challenging when the decision-maker has a variety of items to recommend. In this paper, we build upon the linear contextual multi-armed bandit framework to address this problem. We develop a decision-making policy for a linear bandit problem with high-dimensional context vectors and several arms. Our policy employs Thompson sampling and feeds it with reduced context vectors, where the dimensionality reduction follows by random projection. Our proposed recommender system follows this policy to learn online the item preferences of users while keeping its runtime as low as possible. We prove a regret bound that scales as a factor of the reduced dimension instead of the original one. For numerical evaluation, we use our algorithm to build a recommender system and apply it to real-world datasets. The theoretical and numerical results demonstrate the effectiveness of our proposed algorithm compared to the state-of-the-art in terms of computational complexity and regret performance. Over the past decade, recommender systems have flourished to improve the economy by guiding decision-makers in different roles such as service providers, consumers, and producers, toward cost-effective and time-saving actions while retaining the constraints such as safety, privacy, and quality-of-service satisfaction.

In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that generally engage in discussions intended to reach one or several specific goals, often (but not always) within a specific domain. We also consider the concept of embodied conversational agents, briefly reviewing aspects such as character animation and speech processing. The many different approaches for representing dialogue in CAs are discussed in some detail, along with methods for evaluating such agents, emphasizing the important topics of accountability and interpretability. A brief historical overview is given, followed by an extensive overview of various applications, especially in the fields of health and education. We end the chapter by discussing benefits and potential risks regarding the societal impact of current and future CA technology.

Recent advance in deep learning has led to rapid adoption of machine learning based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backward compatibility is also an important aspect for industrial applications, yet it received little research attention. Backward compatibility requires that the new model does not regress on cases that were correctly handled by its predecessor. This work studies model update regression in structured prediction tasks. We choose syntactic dependency parsing and conversational semantic parsing as representative examples of structured prediction tasks in NLP. First, we measure and analyze model update regression in different model update settings. Next, we explore and benchmark existing techniques for reducing model update regression including model ensemble and knowledge distillation. We further propose a simple and effective method, Backward-Congruent Re-ranking (BCR), by taking into account the characteristics of structured output. Experiments show that BCR can better mitigate model update regression than model ensemble and knowledge distillation approaches.

The country singer invited Wyatt McKee, an avid fan, to sing a duet with him during his concert last month as he continues to wait for a new heart. A 6-year-old awaiting a heart transplant had his dreams fulfilled last month when Blake Shelton invited him on stage to sing a duet during his concert. The avid fan and Shelton's miniature duet partner, Wyatt McKee, and his mother Harley McKee, joined "Fox & Friends Weekend" to recount the emotional rendition of one of Shelton's top hits, "God's Country." "I can't even explain how it made me feel," Harley explained Sunday. "I don't think he quite grasped how big it was. He just had a blast, and that's what he wanted to do. He always tells us he wants Blake Shelton's phone number, so he would ask Siri all the time."

If you're looking to pick up a TV ahead of the Super Bowl, Samsung is currently discounting many of its QLED TVs, matching their lowest prices ever. Normally, the 75-inch model of the Samsung QN85A QLED TV costs $2,999.99 but is currently on sale at Samsung and Best Buy for $1,999.99. This massive, slim-bezel display features amazing visual fidelity and also includes a variety of other handy features. The Tizen OS grants access to most major streaming services and a number of helpful apps, and the TV features built-in support for Google Assistant and Amazon Alexa, allowing you to control playback or find your favorite show with ease. The already excellent picture quality is enhanced even further thanks to HDR10 support, a 120Hz refresh rate, and FreeSync compatibility for superior gaming performance.

Interactive recommender systems (RSs) allow users to express intent, preferences and contexts in a rich fashion, often using natural language. One challenge in using such feedback is inferring a user's semantic intent from the open-ended terms used to describe an item, and using it to refine recommendation results. Leveraging concept activation vectors (CAVs) [21], we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in RSs. A novel feature of our approach is its ability to distinguish objective and subjective attributes and associate different senses with different users. Using synthetic and real-world datasets, we show that our CAV representation accurately interprets users' subjective semantics, and can improve recommendations via interactive critiquing

Artificial intelligence has come a long way from being just a component of science function stories to reality. Currently, we have a host of intelligent machines like self-driving cars, smart virtual assistants, chatbots, and surgical robots, to name a few. Artificial intelligence has now become more mainstream with the ongoing industrial revolution. As our society is growing more dependent on technology, top technologies like AI and machine learning have been augmenting human capabilities and disrupting decades of old and traditional lifestyles. With the emergence of algorithm-driven artificial intelligence, the usefulness of AI continues to grow.

Under a new contract awarded by the Defense Advanced Research Projects Agency, Northrop Grumman will develop a prototype artificial intelligence assistant. The contract is part of DARPA's Perceptually-enabled Task Guidance program. The prototype will be embedded in an augmented reality (AR) headset to help rotary pilots perform expected and unexpected tasks. Northrop Grumman, in partnership with the University of Central Florida, will develop an Operator and Context Adaptive Reasoning Intuitive Assistant that will support UH-60 Blackhawk pilots, who fly with both visual and instrumented flight, which varies with weather, time of day, and other environmental factors. "The goal of this prototype is to broaden a pilot's skillset," said Erin Cherry, senior autonomy program manager, Northrop Grumman.

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.