With the rapid integration of Machine Learning (ML) in business applications and processes, it is crucial to ensure the quality, reliability and reproducibility of such systems. We suggest a methodical approach towards ML system quality assessment and introduce a structured Maturity framework for governance of ML. We emphasize the importance of quality in ML and the need for rigorous assessment, driven by issues in ML governance and gaps in existing frameworks. Our primary contribution is a comprehensive open-sourced quality assessment method, validated with empirical evidence, accompanied by a systematic maturity framework tailored to ML systems. Drawing from applied experience at Booking.com, we discuss challenges and lessons learned during large-scale adoption within organizations. The study presents empirical findings, highlighting quality improvement trends and showcasing business outcomes. The maturity framework for ML systems, aims to become a valuable resource to reshape industry standards and enable a structural approach to improve ML maturity in any organization.

Mobile applications (Apps) could expose children to inappropriate themes such as sexual content, violence, and drug use. Maturity rating offers a quick and effective method for potential users, particularly guardians, to assess the maturity levels of apps. Determining accurate maturity ratings for mobile apps is essential to protect children's health in today's saturated digital marketplace. Existing approaches to maturity rating are either inaccurate (e.g., self-reported rating by developers) or costly (e.g., manual examination). In the literature, there are few text-mining-based approaches to maturity rating. However, each app typically involves multiple modalities, namely app description in the text, and screenshots in the image. In this paper, we present a framework for determining app maturity levels that utilize multimodal large language models (MLLMs), specifically ChatGPT-4 Vision. Powered by Chain-of-Thought (CoT) reasoning, our framework systematically leverages ChatGPT-4 to process multimodal app data (i.e., textual descriptions and screenshots) and guide the MLLM model through a step-by-step reasoning pathway from initial content analysis to final maturity rating determination. As a result, through explicitly incorporating CoT reasoning, our framework enables ChatGPT to understand better and apply maturity policies to facilitate maturity rating. Experimental results indicate that the proposed method outperforms all baseline models and other fusion strategies.

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that combines the strengths of convolutional neural networks (CNNs) and transformers. Additionally, this study introduces a new tomato dataset named KUTomaData, explicitly designed to train deep-learning models for tomato segmentation and classification. KUTomaData is a compilation of images sourced from a greenhouse in the UAE, with approximately 700 images available for training and testing. The dataset is prepared under various lighting conditions and viewing perspectives and employs different mobile camera sensors, distinguishing it from existing datasets. The contributions of this paper are threefold:Firstly, the authors propose a novel method for tomato maturity classification using a modular convolutional transformer. Secondly, the authors introduce a new tomato image dataset that contains images of tomatoes at different maturity levels. Lastly, the authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification. The effectiveness of the proposed framework in handling cluttered and occluded tomato instances was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno Annotated Tomato, as benchmarks. The evaluation results across these three datasets demonstrate the exceptional performance of our proposed framework, surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated Tomato, respectively.

Assessing the maturity of security practices during the development of Machine Learning (ML) based software components has not gotten as much attention as traditional software development. In this Blue Sky idea paper, we propose an initial Machine Learning Security Maturity Model (MLSMM) which organizes security practices along the ML-development lifecycle and, for each, establishes three levels of maturity. We envision MLSMM as a step towards closer collaboration between industry and academia.

Abstract-- This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies through the use of centrality measures, clustering coefficients, and degree of fusion measures. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape and the maturity level of the domain. The results of this study have significant implications for future development and advancements in artificial intelligence and provide a clear understanding of key technology areas of fusion. Furthermore, this paper contributes to AI public policy research by offering a data-driven perspective on the current state and future direction of the field. However, it is important to acknowledge the limitations of this research and call for further studies to build on these results. With these findings, we hope to inform and guide future research in the field of AI, contributing to its continued growth and success. AI has the potential to revolutionize a wide range of industries from healthcare and finance to transportation and agriculture [1] last but not least environmental hard and societal changes [2]. With the ability to analyze vast amounts of data and automate tasks that were once exclusively performed by humans, AI is reshaping the way we live and work. Given the potential of AI, it is essential to study and understand its applications, fusion of technologies, changes over the years in the domain as well as societal impacts. This understanding is crucial for policymakers, as they must develop effective policies that keep pace with the rapid advancement of AI technology. Moreover, the study of AI is also relevant for individuals, businesses, and organizations, as they must be prepared to adapt to the changes brought about by AI. The study of AI is crucial in today's era to unlock the full potential of this groundbreaking technology and to address the challenges and opportunities it presents.

We'll talk about practical steps to level up your organisation when it comes to AI applications. The strategy here is mostly adapted for large enterprises with enough capital and structure to carry out a plan. On the other hand, taking into account that large enterprises are often slower to adapt, more decision-makers need to be convinced, and thus a plan to power your organisation with AI might be harder to implement. Let's now go back to enterprises. They are organized into departments by their core function, ranging from sales/marketing to customer service, product development, etc.

Women are the key piece to the puzzle of realizing the highest maturity levels of digital enterprises, but unless we realize this, our progress in AI and technology will remain stagnant. To close the gender gap in science, technology, engineering, and math (STEM) and to accelerate advances in artificial intelligence and the sciences, we must encourage and support women on all levels, from the government to enterprise and establish equal employment opportunities for all. Women make up a fraction of the artificial intelligence workforce, whether in the form of research and development or as employees at technology inclined firms. According to the World Economic Forum, "Non-homogeneous teams are more capable than homogenous teams of recognizing their biases and solving issues when interpreting data, testing solutions or making decisions." In other words, diverse teams and especially those that emphasize women at their epicenter, are a necessary provision for enterprises to adopt, build, realize and accelerate enterprise AI maturity levels.

Like many ML practitioners, I started my ML journey with Kaggle competitions. But the comfortable setup of Kaggle, where you are handed largely clean data along with features and labels, could not be further from the reality of today's ML practitioner. In fact, the model building process itself is merely a small fraction of the work that needs to be done when developing an ML solution and deploying and maintaining it in production. It is useful to speak about ML production systems in terms of various degrees of maturity, where the least mature systems are one-off models, and the most mature systems run on autopilot, updating themselves with minimal human intervention. Here, I make a broad categorization of ML systems into four levels of increasing maturity, and discuss some of the challenges involved at each level. Disclaimer: given the choice of medium (a blog post, not a book chapter), this list will certainly be incomplete, and I didn't intend it to be.

Like many ML practitioners, I started my ML journey with Kaggle competitions. But the comfortable setup of Kaggle, where you are handed largely clean data along with features and labels, could not be further from the reality of today's ML practitioner. In fact, the model building process itself is merely a small fraction of the work that needs to be done when developing an ML solution and deploying and maintaining it in production. It is useful to speak about ML production systems in terms of various degrees of maturity, where the least mature systems are one-off models, and the most mature systems run on autopilot, updating themselves with minimal human intervention. Here, I make a broad categorization of ML systems into four levels of increasing maturity, and discuss some of the challenges involved at each level.

The project management field has the imperative to increase the project probability of success. Experts have developed several project management maturity models to assets and improve the project outcome. However, the current literature lacks of models allowing correlating the measured maturity and the expected probability of success. This paper uses the characteristics of Bayesian networks to formalize experts' knowledge and to extract knowledge from a project overcost database. It develops a method to estimate the impact of project management maturity on the risk of project overcost. A general framework is presented. An industrial case is used to illustrate the application of the method.