The adoption of artificial intelligence (AI) offers transformative potential for small and medium-sized enterprises (SMEs), particularly in enhancing financial decision-making processes. However, SMEs often face significant barriers to implementing AI technologies, including limited resources, technical expertise, and data management capabilities. This paper presents a conceptual model for the adoption of AI in financial decision-making for SMEs. The proposed model addresses key challenges faced by SMEs, including limited resources, technical expertise, and data management capabilities. The model is structured into layers: data sources, data processing and integration, AI model deployment, decision support and automation, and validation and risk management. By implementing AI incrementally, SMEs can optimize financial forecasting, budgeting, investment strategies, and risk management. This paper highlights the importance of data quality and continuous model validation, providing a practical roadmap for SMEs to integrate AI into their financial operations. The study concludes with implications for SMEs adopting AI-driven financial processes and suggests areas for future research in AI applications for SME finance.

Social media platforms today strive to improve user experience through AI recommendations, yet the value of such recommendations vanishes as users do not understand the reasons behind them. This issue arises becaus e explainability in social media is general and lacks alignment with user - specific needs. In this vision paper, we outline a user - segmented and context - aware explanation layer by proposing a visual explanation system with diverse explanation methods. The p roposed system is framed by the variety of user needs and contexts, showing explanations in different visualized forms, including a technically detailed version for AI experts and a simplified one for lay users. Our framework is the first to jointly adapt explanation style (visual vs. numeric) and granularity (expert vs. lay) inside a single pipeline.

The EU-funded Climaborough project supports European cities to achieve carbon neutrality by 2030. Eleven cities in nine countries will deploy in real conditions products and services fostering climate transition in their local environment. The Climaborough City Platform is being developed to monitor the cities' overall progress towards their climate goals by aggregating historic and real-time data and displaying the results in user-friendly dashboards that will be used by non-technical experts to evaluate the effectiveness of local experimental initiatives, identify those that yield significant impact, and assess the potential consequences of scaling them up to a broader level. In this paper, we explain how we have put in place a low-code/no-code strategy in Climaborough in response to the project's aim to quickly deploy climate dashboards. A low-code strategy is used to accelerate the development of the dashboards. The dashboards embed a no-code philosophy that enables all types of citizen profiles to configure and adapt the dashboard to their specific needs.

Artificial intelligence (AI) systems are increasingly supplementing or taking over tasks previously performed by humans. On the one hand, this relates to low-risk tasks, such as recommending books or movies, or recommending purchases based on previous buying behavior. But it also includes crucial decision making by highly autonomous systems. Many current systems are opaque in the sense that their internal principles of operation are unknown, leading to severe safety and regulation problems. Once trained, deep-learning systems perform well, but they are subject to surprising vulnerabilities when confronted with adversarial images.9 The decisions may be explicated after the fact, but these systems carry the risk of wrong decisions affecting the well being of people.

Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models and training methodologies, and technical expertise to deploy these models. This could present a significant obstacle for language community members and linguists to use NLP tools. This paper introduces the CMU Linguistic Annotation Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages, even with limited training data. We describe various tools and APIs that are currently available and how developers can easily add new models/functionality to the framework. Code is available at https://github.com/neulab/cmulab along with a live demo at https://cmulab.dev

At Johnson Controls, we transform the environments where people live, work, learn and play. From optimizing building performance to improving safety and enhancing comfort, we drive the outcomes that matter most. Dedicated to protecting the environment, we deliver our promise in industries such as healthcare, education, data centers, and manufacturing. We are searching for a highly motivated professional that will provide resourcefulness and technical expertise in the HVAC and building controls space. You will provide technical expertise in a variety of areas, including fault detection and diagnostics (FDD), Artificial Intelligence (AI), and Machine Learning (ML), to continue to establish Johnson Controls as the leader in delivering outcomes for our customers.

Stable Diffusion is an AI model that can generate images from text prompts. The tool is similar to MidJourney or DALL-E 2. You can access the Stable Diffusion model online or deploy it on your local machine. In this article, we will review both approaches as well as share some practical tools. DreamStudio is an online tool created by Stability AI, the team responsible for Stable Diffusion. It provides access to the latest version of Stable Diffusion models (for example, below, you can see that I generated the image using Stable Diffusion ver 2.1–768).

We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable. Wood Mackenzie is looking for a dynamic Data Engineering Manager with demonstrated leadership capability to actively lead the way in modern software development practices and standards. This role is integral to a high functioning and innovative team, providing a unique blend of business and technical savvy to perceive the big-picture vision with the know-how to make that vision a reality. The person that fills this role must be a self-starter with a strong work ethic, energized by a challenge, passionate about bringing great products to market and love the thrill of creating a new standard for what's possible.

This designation recognizes that vFunction has demonstrated technical proficiency and proven customer success automating and accelerating customer application migration and modernization journeys. AWS launched the AWS Migration and Modernization Competency to allow customers to easily and confidently engage highly specialized AWS Partners that help AWS customers modernize their applications, either before or after they are moved to AWS. The AWS Migration and Modernization Competency takes on the heavy lifting of identifying and validating industry leaders with proven customer success and technical proficiency in migration and application modernization tooling. This lets enterprises breathe new life into their legacy applications, and refactor old code for new cloud environments. The vFunction patented and award winning platform first analyzes monolithic applications and allows architects to automate and accelerate the re-architecting and rewriting of their legacy Java and .NET applications into microservices.

At a time when every business wants to be on social media, it's easy to overlook the value of knowing how to write well. However, this is something that will never stop being important in terms of marketing and creating good content for your business. Before this time in history, it was extremely difficult to write human-like content for humans. But when machines can write their own marketing copy these days - often much better than writers like you and I could ever dream of doing ourselves - what do we have left? How does AI Copywriting work? AI Copywriting is a new way of leveraging the growing popularity of AI technology in marketing. This means that companies are using algorithms to write automated content on social media and other channels, instead of hiring freelance writers who may or may not be able to deliver results at such an affordable price tag! Not only does this save them money, but they get high-quality content without compromising their reputation. So if you're interested in how AI can change the game for your company's social media efforts down the road, keep reading our tutorial below.