This project explores the development of an AI-enhanced operator assistant for UNICOS, CERN's UNified Industrial Control System. While powerful, UNICOS presents a number of challenges, including the cognitive burden of decoding widgets, manual effort required for root cause analysis, and difficulties maintainers face in tracing datapoint elements (DPEs) across a complex codebase. In situations where timely responses are critical, these challenges can increase cognitive load and slow down diagnostics. To address these issues, a multi-agent system was designed and implemented. The solution is supported by a modular architecture comprising a UNICOS-side extension written in CTRL code, a Python-based multi-agent system deployed on a virtual machine, and a vector database storing both operator documentation and widget animation code. Preliminary evaluations suggest that the system is capable of decoding widgets, performing root cause analysis by leveraging live device data and documentation, and tracing DPEs across a complex codebase. Together, these capabilities reduce the manual workload of operators and maintainers, enhance situational awareness in operations, and accelerate responses to alarms and anomalies. Beyond these immediate gains, this work highlights the potential of introducing multi-modal reasoning and retrieval augmented generation (RAG) into the domain of industrial control. Ultimately, this work represents more than a proof of concept: it provides a basis for advancing intelligent operator interfaces at CERN. By combining modular design, extensibility, and practical AI integration, this project not only alleviates current operator pain points but also points toward broader opportunities for assistive AI in accelerator operations.

Our study of 20 knowledge workers revealed a common challenge: the difficulty of synthesizing unstructured information scattered across multiple platforms to make informed decisions. Drawing on their vision of an ideal knowledge synthesis tool, we developed Yodeai, an AI-enabled system, to explore both the opportunities and limitations of AI in knowledge work. Through a user study with 16 product managers, we identified three key requirements for Generative AI in knowledge work: adaptable user control, transparent collaboration mechanisms, and the ability to integrate background knowledge with external information. However, we also found significant limitations, including overreliance on AI, user isolation, and contextual factors outside the AI's reach. As AI tools become increasingly prevalent in professional settings, we propose design principles that emphasize adaptability to diverse workflows, accountability in personal and collaborative contexts, and context-aware interoperability to guide the development of human-centered AI systems for product managers and knowledge workers.

Judges in England and Wales will have approval for "careful use" of artificial intelligence (AI) to help produce rulings, but experts remain divided over how extensively judges or the wider law profession should seek to use the technology. "I would say AI is probably appropriate to cast a wide net to gather as much information as possible," William A. Jacobson, a Cornell University Law professor and founder of the Equal Protection Project, told Fox News Digital. "That might inform your decision, but I don't think it is at a place now – and I don't know if it ever will be – that it can actually do the sorting … and make the sort of decisions and determinations that you need to make, whether it's as a judge or a lawyer," Jacobson said. The Courts and Tribunals Judiciary, the body of various judges, magistrates, tribunal members and coroners in England and Wales, decided that judges may use AI to write opinions, and only opinions, with no leeway to use the technology for research or legal analyses due to the potential for AI to fabricate information and provide misleading, inaccurate and biased information. Caution over AI's use in the legal field partially stems from a few high-profile blunders that resulted from lawyers experimenting with the tech, which produced court filings that included references to fictional cases, known as "hallucinations."

As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.

Since its launch in November 2022, ChatGPT, the chatbot developed by OpenAI, has taken the business world by storm. Following this success, Microsoft has increased its investment in OpenAI and has launched a new version of its search engine Bing that provides users with generated answers in response to searches, as opposed to providing them with thousands of links to choose from. Not surprisingly, Google, as the incumbent in the search engine market, quickly reacted and is launching Bard, its own attempt to create an AI chatbot leveraging the power of large language models and integrate it into the search process. Moving beyond search, both Google and Microsoft are now making their chatbots available through an API (application programming interface, a form of a protocol), thereby enabling software developers from other firms to integrate their systems with these new chatbots. From finance to healthcare and from education to travel, industry observers expect an explosion of service innovations and new digital user experiences. Leveraging the capabilities of large language models, chatbots have developed amazing capabilities to generate human-like responses, and to speak in different languages and styles.

President of McorpCX and global CX influencer, helping companies radically improve how they connect with (and profit from) their customers. Generative artificial intelligence (AI) is having a moment. Essentially a broad category of AI algorithms that can create new content based on the data that's been used to train them, it includes text-driven algorithms like OpenAI's ChatGPT and Google's Bard chatbot, as well as image generators like DALL-E and Midjourney. This means you can ask one of these algorithms a question or to create something, and it does--something new and unique, based on the vast volumes of data they can learn from, interpret and respond to. At a cocktail party a few weekends ago, a friend jokingly asked ChatGPT to "write a country song in iambic pentameter about an accordion and a broken heart."

As businesses continue to look for ways to streamline their operations and reduce the risk of errors, many are turning to document AI to automate their accounts payable (AP) and accounts receivable (AR) processes. The use of payment AI and automation, and digitization software can greatly reduce errors and improve the efficiency of financial transactions. According to the Federal Reserve, 75% of bills are manually processed, which is slow, error prone, and leads to frustration from billers and payers. Businesses communicating with other businesses send a lot of PDF documents, however it is difficult to extract and organize important information from PDF documents, especially if they're not structured in a consistent way. This can lead to delays and errors, which can be costly for the business and frustrating for customers.

Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically, there has been a lack of applicable methodologies. Further, methodologies that have been applied, such as Agile, have several limitations including scientific unfalsifiability that reduce their applicability. Evident, a development methodology rooted in the philosophy of logical reasoning and EKB, a knowledge base topology, are proposed. Many pain points in data mining, machine learning and general knowledge management are alleviated conceptually. Evident can be extended potentially to accelerate philosophical exploration, science discovery, education as well as knowledge sharing & retention across the globe. EKB offers one solution of storing information as knowledge, a granular level above data. Related topics in computer history, software engineering, database, sensing hardware, philosophy, and project & organization & military managements are also discussed.

Artificial Intelligence has been making game-changing impacts in the business world. Nowadays, businesses, especially retailers, regardless of size, are increasingly enthusiastic to build and deploy AI solutions. In fact, retail businesses must implement AI-like trending digital solutions to remain competitive and improve customer experiences. A few weeks ago, when FuGenX decided to add more services to its AI service portfolio, it has conducted a series of deep research across industry verticals to find which industries have more pain points that can be solved by our cutting-edge AI solutions. As per the research reports, retail was expectedly at the top of the list.

For more than a year now, organizations have struggled to hold onto talent. According to the U.S. Bureau of Labor Statistics, 4.2 million people voluntarily quit their jobs in August 2022. At the same time, there were 10.1 million job openings. Between the Great Resignation and more recent trends like "quiet quitting," traditional approaches for winning talented workers haven't always cut it in this fiercely competitive market. An emerging wave of AI tools for talent management have the potential to help organizations find better job candidates faster, provide more impactful employee development, and promote retention through more effective employee engagement. But while AI might enable leaders to address talent management pain points by making processes faster and more efficient, AI implementation comes with a unique set of challenges that warrant significant attention.