GiusBERTo: Italy’s AI-Based Judicial Transformation: A Teaching Case

In an age when open access to law enforcement files and judicial documents can erode individual privacy and confidentiality, miscreants can abuse this open access to personal information for blackmail, misinformation, and even social engineering. Yet, limiting access to law enforcement and court cases is a freedom-of-information violation. To address this tension, this collaborative action-research-based teaching case exemplifies how Italy’s Corte dei Conti (Court of Auditors) used artificial intelligence in the automated deidentification and anonymization of court documents in Italy’s public sector. This teaching case is aimed at undergraduate and graduate students learning about Artificial Intelligence (AI), Large Language Model (LLM) (e.g., ChatGPT) evolution, development, and operations. The case will help students learn the origin and evolution of AI transformer models and architectures, and discusses the GiusBERTo operation and process, highlighting opportunities and challenges. GiusBERTo, Italy’s custom-AI model, offers an innovative approach that walks a tightrope between anonymizing Italy’s judicial court documents without sacrificing context or information loss. The case ends with a series of questions, challenges, and potential for LLMs in data anonymization

The Future of Factories: Different Trends

The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities

Induction of Autophagy Promotes Clearance of RHOP23H Aggregates and Protects From Retinal Degeneration

Autophagy is a critical metabolic process that acts as a major self-digestion and recycling pathway contributing to maintain cellular homeostasis. An emerging field of research supports the therapeutic modulation of autophagy for treating human neurodegenerative disorders, in which toxic aggregates are accumulated in neurons. Our previous study identified Ezrin protein as an inhibitor of autophagy and lysosomal functions in the retina; thus, in turn, identifying it as a potential pharmacological target for increasing retinal cell clearance to treat inherited retinal dystrophies in which misfolded proteins have accumulated. This study aimed to verify the therapeutic inhibition of Ezrin to induce clearance of toxic aggregates in a mouse model for a dominant form of retinitis pigmentosa (i.e., RHOP23H/+). We found that daily inhibition of Ezrin significantly decreased the accumulation of misfolded RHOP23H aggregates. Remarkably, induction of autophagy, by a drug-mediated pulsatile inhibition of Ezrin, promoted the lysosomal clearance of disease-linked RHOP23H aggregates. This was accompanied with a reduction of endoplasmic reticulum (ER)-stress, robust decrease of photoreceptors' cell death, amelioration in both retinal morphology and function culminating in a better preservation of vision. Our study opens new perspectives for a pulsatile pharmacological induction of autophagy as a mutation-independent therapy paving the way toward a more effective therapeutic strategy to treat these devastating retinal disorders due to an accumulation of intracellular toxic aggregates