This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the human-in-the-loop control of semi-autonomous UAVs. The goal of the system is to facilitate the operator intervention in critical scenarios (e.g. avoid safety issues or financial risks). Explainability is supported via user-friendly abstractions on Belief-Desire-Intention agents. To evaluate the effectiveness of the system, a human-computer interaction study is proposed.

This "Artificial Intelligence in Marketing Market" research report provides a comprehensive overview of the markets between 2019-2024 and offers an …

Game Theory, quite unlike its name, is a serious affair to deal with when it comes to the configuration and planning of an AI model. In essence, while linear machine learning deals largely with single-dimensional elements in their very nature, the true power of AI is actually unleashed with game theory application, and it's various facets. To understand game theory power in AI, however, it is essential to understand the basics of what actually constitutes game theory and its applications. So here's the promised primer on what game theory actually comprises. In its textbook definition, "Game Theory is the study of strategic interaction".

In today's so-called smart era when everything is getting virtual, the implementation of Artificial Intelligence in e-Commerce is a remarkable movement towards progress. By adopting the AI technology, e-commerce businesses are creating a boom in the market. Artificial Intelligence is somewhat a challenge to the creative power of a human being. It aims to achieve something which at times becomes tedious for human employees. With the right coding, it learns faster and better than compared to humans. The absence of emotional issues and health-related restrictions allow it to think logically and perform better. It has the power to detect any fraudulent activity which might be overlooked by humans. For a strong understanding of the behavior of human users and to provide the customers with a satisfactory experience, the e-commerce companies are reportedly adopting the new AI technology. Artificial Intelligence in eCommerce has now emerged as an individual business solution and is dominating the market. Now the question lies in the part that how are the business companies improvising this unique and innovative technology? Many a time, the sales team fails to keep track of the leading products and services in the market and hence could not impress potential buyers who might have an interest in the item.

The professional world has changed a lot in the last decades. Now, many workers are remote or freelance contractors that get hired and paid on a per-project basis. Thanks to the nomadic nature of the digital workforce, the technology that these employees use is entirely different from the last generation's -- and the evolution of this technology does not seem to be slowing down any time soon. Below, 15 members of Forbes Technology Council explore some of the cutting-edge technology trends that already are or will soon be transforming the workplace, and how companies can adapt to make the most of these changes. Embracing a "remote-first" culture at workplaces will be a key factor in companies' success in the coming decade.

AI could improve operational consistencies and enhance equipment utilization, Navis survey shows. Global container terminals are expected to embrace automated decision making powered by artificial intelligence (AI) as they pursue ways to improve operational consistencies and enhance equipment utilization, a new survey shows. The findings indicate that container terminals, regardless of their AI maturity, are increasingly aware of the possibilities of automated decision-making, according to supply chain technology provider Navis LLC. The Oakland, California-based firm said its TechValidate customer survey included responses from nearly 60 Navis customers, representing a cross-section of container terminals around the world using various degrees of automation. In addition to the 86% who cited operational consistency and equipment utilization as the most important benefits of automated decision-making, port operators also named other goals.

Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated using imitation learning, where the agent learns to imitate human demonstrated decisions. However, human guidance is not limited to the demonstrations. Other types of guidance could be more suitable for certain tasks and require less human effort. This survey provides a high-level overview of five recent learning frameworks that primarily rely on human guidance other than conventional, step-by-step action demonstrations. We review the motivation, assumption, and implementation of each framework. We then discuss possible future research directions.

This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Traditionally, researchers have relied on Elo ratings for this purpose, with recent works also using methods based on Nash equilibria. Unfortunately, Elo is unable to handle intransitive agent interactions, and other techniques are restricted to zero-sum, two-player settings or are limited by the fact that the Nash equilibrium is intractable to compute. Recently, a ranking method called {\alpha}-Rank, relying on a new graph-based game-theoretic solution concept, was shown to tractably apply to general games. However, evaluations based on Elo or {\alpha}-Rank typically assume noise-free game outcomes, despite the data often being collected from noisy simulations, making this assumption unrealistic in practice. This paper investigates multiagent evaluation in the incomplete information regime, involving general-sum many-player games with noisy outcomes. We derive sample complexity guarantees required to confidently rank agents in this setting. We propose adaptive algorithms for accurate ranking, provide correctness and sample complexity guarantees, then introduce a means of connecting uncertainties in noisy match outcomes to uncertainties in rankings. We evaluate the performance of these approaches in several domains, including Bernoulli games, a soccer meta-game, and Kuhn poker.

Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis. In this paper, we focus on the {\em generation} of hypotheses from premises in a multimodal setting, to generate a sentence (hypothesis) given an image and/or its description (premise) as the input. The main goals of this paper are (a) to investigate whether it is reasonable to frame NLI as a generation task; and (b) to consider the degree to which grounding textual premises in visual information is beneficial to generation. We compare different neural architectures, showing through automatic and human evaluation that entailments can indeed be generated successfully. We also show that multimodal models outperform unimodal models in this task, albeit marginally.

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.