Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intelligent automation, personalized experiences, and efficient information retrieval, driving operational efficiency and strategic growth. However, developing and evaluating such systems is challenging due to the inherent complexity of enterprise environments, where data is fragmented across multiple sources and governed by sophisticated access controls. We present EnterpriseBench, a comprehensive benchmark that simulates enterprise settings, featuring 500 diverse tasks across software engineering, HR, finance, and administrative domains. Our benchmark uniquely captures key enterprise characteristics including data source fragmentation, access control hierarchies, and cross-functional workflows. Additionally, we provide a novel data generation pipeline that creates internally consistent enterprise tasks from organizational metadata. Experiments with state-of-the-art LLM agents demonstrate that even the most capable models achieve only 41.8% task completion, highlighting significant opportunities for improvement in enterprise-focused AI systems.

In this paper we develop a method to coordinate the deployment of a multi-robot team to reach some locations of interest, so-called primary goals, and to transmit the information from these positions to a static Base Station (BS), under connectivity constraints. The relay positions have to be established for some robots to maintain the connectivity at the moment in which the other robots visit the primary goals. Once every robot reaches its assigned goal, they are again available to cover new goals, dynamically re-distributing the robots to the new tasks. The contribution of this work is a two stage method to deploy the team. Firstly, clusters of relay and primary positions are computed, obtaining a tree formed by chains of positions that have to be visited. Secondly, the order for optimally assigning and visiting the goals in the clusters is computed. We analyze di ff erent heuristics for sequential and parallel deployment in the clusters, obtaining sub-optimal solutions in short time for di ff erent number of robots and for a large amount of goals.

We present an ethical decision-making framework that refines a pre-trained reinforcement learning (RL) model using a task-agnostic ethical layer. Following initial training, the RL model undergoes ethical fine-tuning, where human feedback is replaced by feedback generated from a large language model (LLM). The LLM embodies consequentialist, deontological, virtue, social justice, and care ethics as moral principles to assign belief values to recommended actions during ethical decision-making. An ethical layer aggregates belief scores from multiple LLM-derived moral perspectives using Belief Jensen-Shannon Divergence and Dempster-Shafer Theory into probability scores that also serve as the shaping reward, steering the agent toward choices that align with a balanced ethical framework. This integrated learning framework helps the RL agent navigate moral uncertainty in complex environments and enables it to make morally sound decisions across diverse tasks. Our approach, tested across different LLM variants and compared with other belief aggregation techniques, demonstrates improved consistency, adaptability, and reduced reliance on handcrafted ethical rewards. This method is especially effective in dynamic scenarios where ethical challenges arise unexpectedly, making it well-suited for real-world applications.

The public has rapidly become fascinated with the power of a new artificial intelligence technology -- ChatGPT -- a chatbot developed by the research and deployment company OpenAI and launched late last year. Already it's demonstrated the ability to serve up detailed answers to complex questions while using the information it processes and feedback from users to improve its ability to respond. ChatGPT has proven to be versatile, with users using the technology to compose music, debug computer code, write restaurant reviews, generate advertising copy and answer test questions. It's able to deliver its responses in a conversational way, and has sparked excitement about its potential, along with some concerns with how it might be used. But what exactly is ChatGPT and what does it say about the state of AI now, and in the future?

In this article, we will discuss some notable data science use cases in marketing. As far as the primary goal of data science is to extract actionable insights from data, the marketing sphere cannot exclude the application of these insights for its advantage. Big data in marketing offers a chance to understand the target audience a lot better. Data science is mostly employed in marketing areas of search engine optimization (SEO), profiling, customer engagement, responsiveness, and real-time marketing campaigns. Furthermore, new ways to employ data science and analytics for marketing emerge every day.

The primary goal of this course is to provide you a comprehensive learning framework to use Python for data science. Data Science with Python involves not only using Python language to clean, analyze and visualize data, but also applying Python programming skills to predict and identify trends useful for decision-making. Since data revolution has made data as the new oil for organizations, today's decisions are driven by multidisciplinary approach of using data, mathematical models, statistics, graphs, databases for various business needs such as forecasting weather, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. The modern data-powered technology systems are driven by identifying, integrating, storing and analyzing data for useful business decisions. Scientific logic backed with data provides solid understanding of the business and its analysis.

For the past few years, headlines have abounded about how robots are going to take over -- specifically people's jobs. It's true robots have been taking on more mundane tasks, but there's more to the story. A recent McKinsey report found that more companies were pursuing automation in 2020. However, many of the organizations surveyed said they were automating with an eye toward their personnel, complementing existing talent to allow for growth. For modern finance teams interested in automating tasks, below is a look at two types of automation technology.

The cellist Jan Vogler famously claimed that art is what makes us human. But what if machines start making art too? Here's an example of a piece of art made by an artificial intelligence (AI): On the right side of the picture is a computer running an AI that has been trained with images of graffiti. It controls a plotting head that sprays water onto concrete blocks, on the left. The resulting patterns are a form of computer-generated art. Is this fine art in the true sense?

I have a theory that much recent tech development and inno-vation over the last decade or so has an unspoken overarching agenda. It has been about creating the possibility of a world with less human interaction. This tendency is, I suspect, not a bug--it's a feature. We might think Amazon was about making books available to us that we couldn't find locally--and it was, and what a brilliant idea--but maybe it was also just as much about eliminating human contact. The consumer technology I am talking about doesn't claim or acknowledge that eliminating the need to deal with humans directly is its primary goal, but it is the outcome in a surprising number of cases.

A goal for which a value is sought during an expert system consultation. Primary goals are terminal objectives for the consultation: once their value are found, or a determination is made that they cannot be found, the consultation ends.