This monograph presents a modular cognitive architecture for artificial intelligence grounded in the formal modeling of belief as structured semantic state. Belief states are defined as dynamic ensembles of linguistic expressions embedded within a navigable manifold, where operators enable assimilation, abstraction, nullification, memory, and introspection. Drawing from philosophy, cognitive science, and neuroscience, we develop a layered framework that enables self-regulating epistemic agents capable of reflective, goal-directed thought. At the core of this framework is the epistemic vacuum: a class of semantically inert cognitive states that serves as the conceptual origin of belief space. From this foundation, the Null Tower arises as a generative structure recursively built through internal representational capacities. The theoretical constructs are designed to be implementable in both symbolic and neural systems, including large language models, hybrid agents, and adaptive memory architectures. This work offers a foundational substrate for constructing agents that reason, remember, and regulate their beliefs in structured, interpretable ways.

This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understand and interact with the explanations provided by these algorithms. To achieve this, we employed a multi-disciplinary approach that included state-of-the-art research methods from social sciences to measure the comprehensibility of explanations generated by a state-of-the-art lachine learning model, specifically the Gradient Boosting Classifier (XGBClassifier). We conducted an extensive empirical user study involving interviews with 39 participants from three different groups, each with varying expertise in data science, data visualization, and domain-specific knowledge related to the dataset used for training the machine learning model. Participants were asked a series of questions to assess their understanding of the model's explanations. To ensure replicability, we built the model using a publicly available dataset from the UC Irvine Machine Learning Repository, focusing on edible and non-edible mushrooms. Our findings reveal limitations in existing XAI methods and confirm the need for new design principles and evaluation techniques that address the specific information needs and user perspectives of different classes of AI stakeholders. We believe that the results of our research and the cross-disciplinary methodology we developed can be successfully adapted to various data types and user profiles, thus promoting dialogue and address opportunities in HCAI research. To support this, we are making the data resulting from our study publicly available.

Automated Decision-Making Systems (ADS) have become pervasive across various fields, activities, and occupations, to enhance performance. However, this widespread adoption introduces potential risks, including the misuse of ADS. Such misuse may manifest when ADS is employed in situations where it is unnecessary or when essential requirements, conditions, and terms are overlooked, leading to unintended consequences. This research paper presents a thorough examination of the implications, distinctions, and ethical considerations associated with digitalization, digital transformation, and the utilization of ADS in contemporary society and future contexts. Emphasis is placed on the imperative need for regulation, transparency, and ethical conduct in the deployment of ADS.

This guide to'colliding opposite disciplines with your research' is intended to help students and researchers, or indeed anyone who might otherwise be looking for some ideas on how to approach research or methods for designing concepts and solutions, to broaden their thinking and approach to research. This guide is mainly focused on the disciplines of science and engineering with the idea of collaborating with other distinct disciplines. However, the overall principles remain for any multidisciplinary research. With the assistance of this guide, it will help to open new ways of thinking about research, highlight the'unseen' benefits of multidisciplinary approaches to research and how they can be extremely advantageous and can lend for an optimal delivery. It will help you to contemplate how, when, and why you should open up your research to other disciplines.

The advancements in AI technology have left no field untouched. With Artificial Intelligence tools taking over mundane tasks, (in addition to seemingly creative tasks), it has become a question of when, not if, AI will replace human workers in various industries. While some may argue that AI will replace human workers in all industries, in this article, I'm about to give you the real tea on why managers are more likely to be replaced'first'. According to Gartner, by 2030, 80% of today's project management's work will be automated, eliminating the discipline and replacing PM traditional functions with AI. In a global survey by Pega, 78% of the executives surveyed believe that increasing the use of AI and robots will dramatically reduce the middle management ranks.

Abishek Gupta is the founder and principal researcher at the Montreal AI Ethics Institute and senior Responsible AI leader and expert at Boston Consulting Group; Steven Mills is the Global GAMMA Chief AI Ethics Officer at Boston Consulting Group. The Responsible AI (RAI) domain is at an inflection point: We are moving decidedly from principles to practice. As organizations mature their understanding, they are feeling the pressure to act from customer demands and impending regulatory requirements. RAI means developing and operating artificial intelligence systems that align with organizational values and widely accepted standards of right and wrong while achieving transformative business impact. But successfully operationalizing RAI requires a leader with the right mix of knowledge, skills, abilities and experience, and RAI remains a nascent field.

I try to be updated about A.I. even if the topic has deflagrated in the latest years and is now a rapidly growing bunch of niches. So, recently I used a public domain website to generate the content for a post starting from the title "Using Artificial Intelligence in Your Trading Strategy to Maximize Returns". After the intro and under the subtitle "Know your data", this paragraph: A key concept in AI is that the more data you have, the smarter your algorithm becomes. This means that you need to input as much data as possible before starting trading. You can use any information that can help with your predictions, like financial news or social media updates.

Excellent written and verbal communication and presentation skills.

Coupang is reimagining the shopping experience with the goal of wowing each customer from the instant they open the Coupang app to the moment an order is delivered to their door. Powered by an outstanding end-to-end e-commerce and logistics network and a fanatical culture of customer centricity, Coupang has broken tradeoffs around speed, selection and price. Today, we provide exceedingly fast shipping speeds on millions of items including fresh groceries, delivered within hours nationwide, 365 days a year. We are doing this for millions of consumers in Korea. Korea is home to one of the largest and fastest growing e-commerce opportunities anywhere in the world.

Eyes are important, don't get me wrong. So are ears, noses, tongues, fingers, balance calibration organs and everything else that feeds that massive brain of yours.1 Salinity detectors in narwhals, electrical sensors in freshwater bottom feeders, echolocation in bats all provide sensory input that humans couldn't adequately process. Every beast has its own senses relevant to its own living conditions. Even your smartphone has cameras, microphones, gyroscopes, an accelerometer, a magnetometer, interfaces for phone/GPS/Bluetooth/WiFi, and some have a barometer, proximity sensors, and ambient light sensors. Biometric sensing equipment in today's phones can include optical, capacitive or ultrasonic fingerprint readers and an infrared map sensor for faces.