If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The showy science projects get all the attention in the constant quest to automate everything. That includes gigantic natural language processing models such as OpenAI's GPT-3, which can complete sentences, answer questions, and even write poetry. For those making commercial software, there is a more mundane but perhaps equally valuable task, which is to figure out what facts a machine should have access to and make that actually have value for humans. "We don't apologize for the fact that some of this requires brute force," says Dan Turchin, chief executive and co-founder of PeopleReign, a San Jose, California software startup that is automating the handling of support calls for things such as IT and benefits. His software has compiled, over a period of five years, a kind of encyclopedia of more than five million "domain concepts," structured information relating to things such as employee benefits, requests for computer support, and all manner of other things customers or employees might request, culled from a billion examples such as IT tickets, wikis, chat transcripts, etc.
The Digital technologies (digital employees, internet of things, artificial intelligence, virtual & augmented reality, block-chain, 3d printing., drones and robotics & automation) are electronic tools, systems, devices and resources that generate, store or process data. Well known examples include social media, online games, multimedia and mobile phones. Digital learning is any type of learning that uses technology. It can happen across all curriculum learning areas. The basic purpose of digital technology platform is that it should provide technology-enabled services to the business.
This story looks at some of the fundamental building blocks that work together to build a digital twin infrastructure for medicine. It explains how promising techniques like APIs, graph databases, ontologies, electronic health records are being combined to unlock digital transformation in healthcare. Digital twins could transform healthcare with a more integrated approach for capturing data, providing more timely feedback, and enabling more effective interventions. The information required to allow for better simulations lies scattered across medical records, wearables, mobile apps, and pervasive sensors. Medical digital twins can use raw digital ingredients like natural language processing (NLP), APIs, and graph databases to understand all the data and cut through the noise to summarize what is going on.
The use of Semantic Technologies - in particular the Semantic Web - has revealed to be a great tool for describing the cultural heritage domain and artistic practices. However, the panorama of ontologies for musicological applications seems to be limited and restricted to specific applications. In this research, we propose HaMSE, an ontology capable of describing musical features that can assist musicological research. More specifically, HaMSE proposes to address issues that have been affecting musicological research for decades: the representation of music and the relationship between quantitative and qualitative data. To do this, HaMSE allows the alignment between different music representation systems and describes a set of musicological features that can allow the music analysis at different granularity levels.
We present a system capable of automatically solving combinatorial logic puzzles given in (simplified) English. It uses an ontology to represent the puzzles in ASP which is applicable to a large set of logic puzzles. To translate the English descriptions of the puzzles into this ontology, we use a lambda-calculus based approach using Probabilistic Combinatorial Categorial Grammars (PCCG) where the meanings of words are associated with parameters to be able to distinguish between multiple meanings of the same word.
Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. The inexpressive Description Logic EL is of particular interest in this context since, on the one hand, several large biomedical ontologies are defined using EL. On the other hand, unification in EL has recently been shown to be NP-complete, and thus of significantly lower complexity than unification in other DLs of similarly restricted expressive power. However, the unification algorithms for EL developed so far cannot deal with general concept inclusion axioms (GCIs). This paper makes a considerable step towards addressing this problem, but the GCIs our new unification algorithm can deal with still need to satisfy a certain cycle restriction.
In ontology-based data access (OBDA), ontologies are used as an interface for querying instance data. Since in typical applications the size of the data is much larger than the size of the ontology and query, data complexity is the most important complexity measure. In this paper, we propose a new method for investigating data complexity in OBDA: instead of classifying whole logics according to their complexity, we aim at classifying each individual ontology within a given master language. Our results include a P/coNP-dichotomy theorem for ontologies of depth one in the description logic ALCFI, the equivalence of a P/coNP-dichotomy theorem for ALC/ALCI-ontologies of unrestricted depth to the famous dichotomy conjecture for CSPs by Feder and Vardi, and a non-P/coNP-dichotomy theorem for ALCF-ontologies.
We present a novel rewriting technique for conjunctive query answering over OWL 2 QL ontologies. In general, the obtained rewritings are not necessarily correct and can be of exponential size in the length of the query. We argue, however, that in most, if not all, practical cases the rewritings are correct and of polynomial size. Moreover, we prove some sufficient conditions, imposed on queries and ontologies, that guarantee correctness and succinctness. We also support our claim by experimental results.
Answering conjunctive queries (CQs) over a set of facts extended with existential rules is a key problem in knowledge representation and databases. This problem can be solved using the chase (aka materialisation) algorithm; however, CQ answering is undecidable for general existential rules, so the chase is not guaranteed to terminate. Several acyclicity conditions provide sufficient conditions for chase termination. In this paper, we present two novel such conditions--model-faithful acyclicity (MFA) and model-summarising acyclicity (MSA)--that generalise many of the acyclicity conditions known so far in the literature. Materialisation provides the basis for several widely-used OWL 2 DL reasoners.