Modern AI systems are man-made objects that leverage machine learning to support our lives across a myriad of contexts and applications. Despite extensive epistemological and ethical debates, their metaphysical foundations remain relatively under explored. The orthodox view simply suggests that AI systems, as artifacts, lack well-posed identity and persistence conditions -- their metaphysical kinds are no real kinds. In this work, we challenge this perspective by introducing a theory of metaphysical identity of AI systems. We do so by characterizing their kinds and introducing identity criteria -- formal rules that answer the questions "When are two AI systems the same?" and "When does an AI system persist, despite change?" Building on Carrara and Vermaas' account of fine-grained artifact kinds, we argue that AI trustworthiness provides a lens to understand AI system kinds and formalize the identity of these artifacts by relating their functional requirements to their physical make-ups. The identity criteria of AI systems are determined by their trustworthiness profiles -- the collection of capabilities that the systems must uphold over time throughout their artifact histories, and their effectiveness in maintaining these capabilities. Our approach suggests that the identity and persistence of AI systems is sensitive to the socio-technical context of their design and utilization via their trustworthiness, providing a solid metaphysical foundation to the epistemological, ethical, and legal discussions about these artifacts.

DOLCE, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. DOLCE is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. DOLCE clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, DOLCE is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years DOLCE has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases.

Scientists at the University of Michigan have developed an open-source, user-friendly, artificial intelligence driven software called LabGym that automatizes animal behavior analysis in various model systems and could be a boon to life scientists across the spectrum of basic science and drug development. The findings were published in the article"LabGym: quantification of user-defined animal behaviors using learning-based holistic assessment," in the journal Cell Reports Methods on February 24. Measuring animal behavior is instrumental in understanding fundamental neural processes as well as assessing therapeutic and adverse effects of drugs. Bing Ye, PhD, professor of life sciences at the University of Michigan, and his team analyze movements and behaviors in the model organism Drosophila melanogaster (fruit flies) to understand mechanisms involved in the development and function of the nervous system in humans. "Behavior is a function of the brain. So, analyzing animal behavior provides essential information about how the brain works and how it changes in response to disease," said Yujia Hu, a neuroscientist in Ye's lab and lead author of the study.

A team from the University of Michigan has developed a new software tool to help researchers across the life sciences more efficiently analyze animal behaviors. The open-source software, LabGym, capitalizes on artificial intelligence to identify, categorize and count defined behaviors across various animal model systems. Scientists need to measure animal behaviors for a variety of reasons, from understanding all the ways a particular drug may affect an organism to mapping how circuits in the brain communicate to produce a particular behavior. Researchers in the lab of U-M faculty member Bing Ye, for example, analyze movements and behaviors in Drosophila melanogaster-;or fruit flies-;as a model to study the development and functions of the nervous system. Because fruit flies and humans share many genes, these studies of fruit flies often offer insights into human health and disease.

Facial recognition tools have significant error rates that differ by race. An AI hiring tool from Amazon "learned" gender bias against women and favored male candidates. We know diversity bias is rampant in artificial intelligence. But decisions made based on prejudiced AI systems aren't just an ethical dilemma; they're a financial one. The more unbiased a system, the more likely it is to maximize profits, make better hiring or selling recommendations and provide accurate risk predictions.

As robo-advisory platforms become more common around the world, the focus of these service providers has shifted to creating a robust investment framework rather than just providing a convenient and affordable way to invest, says Michele Ferrario, co-founder and CEO of Singapore-based robo-advisor StashAway. After all, robo-advisory platforms are already commonplace in some countries, with the bigger players in the US -- such as Wealthfront and Betterment -- managing billions of dollars. The first robo-advisor was established a decade ago. "Globally, robo-advisory models are maturing. The basic value proposition of a very nice user interface and low-cost investing is not enough anymore and the importance of the investment framework and asset allocation is gaining prominence. You will see that the new players in Europe have a very strong focus on risk management and asset allocation and you will see more established US players talk about how they manage money," says By comparison, robo-advisors are not as common in Southeast Asia.