AI is becoming a part of our daily lives, from approving loans to diagnosing diseases. AI model outputs are used to make increasingly important decisions, based on smart algorithms and data. But if we can't understand these decisions, how can we trust them? One approach to making AI decisions more understandable is to use models that are inherently interpretable. These are models that are designed in such a way that consumers of the model outputs can infer the model's behaviour by reading the parameters of the model. Popular inherently interpretable models include Decision Trees and Linear Regression.

This paper reviews our previous trials of Nudge-XAI, an approach that introduces automatic biases into explanations from explainable AIs (XAIs) with the aim of leading users to better decisions, and it discusses the benefits and challenges. Nudge-XAI uses a user model that predicts the influence of providing an explanation or emphasizing it and attempts to guide users toward AI-suggested decisions without coercion. The nudge design is expected to enhance the autonomy of users, reduce the risk associated with an AI making decisions without users' full agreement, and enable users to avoid AI failures. To discuss the potential of Nudge-XAI, this paper reports a post-hoc investigation of previous experimental results using cluster analysis. The results demonstrate the diversity of user behavior in response to Nudge-XAI, which supports our aim of enhancing user autonomy. However, it also highlights the challenge of users who distrust AI and falsely make decisions contrary to AI suggestions, suggesting the need for personalized adjustment of the strength of nudges to make this approach work more generally.

Communication robots have the potential to contribute to effective human-XAI interaction as an interface that goes beyond textual or graphical explanations. One of their strengths is that they can use physical and vocal expressions to add detailed nuances to explanations. However, it is not clear how a robot can apply such expressions, or in particular, how we can develop a strategy to adaptively use such expressions depending on the task and user in dynamic interactions. To address this question, this paper proposes DynEmph, a method for a communication robot to decide where to emphasize XAI-generated explanations with physical expressions. It predicts the effect of emphasizing certain points on a user and aims to minimize the expected difference between predicted user decisions and AI-suggested ones. DynEmph features a strategy for deciding where to emphasize in a data-driven manner, relieving engineers from the need to manually design a strategy. We further conducted experiments to investigate how emphasis selection strategies affect the performance of user decisions. The results suggest that, while a naive strategy (emphasizing explanations for an AI's most probable class) does not necessarily work better, DynEmph effectively guides users to better decisions under the condition that the performance of the AI suggestion is high.

"Produce better-quality products but at less operational cost and with efficiency" is a timeless goal for the manufacturing industry. The role and future of AI and ML in the manufacturing industry are promising. AI and ML can enable the manufacturing industry to scale their businesses and help them grow. The "Smart Manufacturing" revolution is already making it easier for businesses to attain this objective than ever before. According to many experts, artificial intelligence and machine learning are expected to affect factories and the manufacturing sector in the future significantly.

Recently Walt Mayo, CEO of expert.ai, The hype surrounding responsible AI has existed for over a generation but AI for the sake of AI is just keeping up with the Joneses. In implementing any AI solution, it's necessary to make sure your technology and business teams have goals and committed partners that are mutually accountable to deliver on its promise. Natural Language is a distinct area within AI. Even the natural language (NL) arena is highly fractured, and there are many different NL functional areas.

Artificial Intelligence will have a long and lasting impact on our lives. It is already at work in many of the devices we use and it will only become more indispensable as time goes on. And it's not just about machines doing things that we would otherwise do ourselves, like driving cars or organizing our lives. AI will also make all sorts of things cheaper, more convenient, and safer–from medical diagnoses to movie recommendations to home security. In the past few years, artificial intelligence (AI) has made incredible strides. We've seen AI beat humans in complex games like Go and chess, and it is now being used in a variety of applications such as autonomous vehicles, facial recognition, and fraud detection.

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. The goal is to create algorithms that can automatically learn and improve from experience. Machine learning algorithms can be divided into two categories: supervised and unsupervised. Supervised algorithms learn from a set of training data that has been labeled with the correct answers. Unsupervised algorithms learn from data that has not been labeled, and must learn to recognize patterns on their own.

Ayanna howard, roboticist, ACM Athena Lecturer, and dean of The Ohio State University College of Engineering, is optimistic about the ability of robots to help people. She understands the challenges that must be addressed for that to happen, and has worked throughout her career not just to advance the technical state of the art, but to quantify and overcome issues including trust and bias in artificial intelligence (AI). Here, she talks about self-driving cars, accessible coding, and how to incorporate different perspectives into hardware and software design. The pandemic heightened public interest in robots--suddenly, we all want robot cleaners and robot grocery deliverers and so on. How is that impacting the robotics community?

Conferences, media, vendors, automation industry consultants, business consultants, and even politicians are discussing and making presentations about how the Internet of Things (IoT) and Industry 4.0 are creating a revolution in manufacturing. I am convinced we are at a juncture of major industrial automation changes driven by technology advancements. The digital revolution of business functions, including accounting, supply chain, human resources, procurement, customer services, business intelligence, and distribution management, has been refined over multiple generations. In contrast, the industrial and process automation industries have not transformed at the same rate. They must be digitized now for manufacturers to compete. At the end of this article I have the results of a small survey of readers that may be interesting.

In the world of data mining and analyzing data for business growth, data science is a hot topic of discussion among professionals and organizations. Data analytics courses are in huge demand among the courses for data professionals. Students and working professionals are highly interested to have a strong understanding of different aspects and elements of data science. Students can access multiple virtual data science courses on multiple educational platforms having collaborations with reputed educational institutes. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates.