The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence

Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access to large training data sets and clear performance evaluation criteria, ranging from pattern recognition and classification to generative models. Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Generative AI, in general, and Large Language Models in particular, may represent an opportunity to augment and accelerate the scientific discovery of fundamental deep science with quantitative models. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery, including self-driven hypothesis generation and open-ended autonomous exploration of the hypothesis space. Integrating AI-driven automation into the practice of science would mitigate current problems, including the replication of findings, systematic production of data, and ultimately democratisation of the scientific process. Realising these possibilities requires a vision for augmented AI coupled with a diversity of AI approaches able to deal with fundamental aspects of causality analysis and model discovery while enabling unbiased search across the space of putative explanations. These advances hold the promise to unleash AI's potential for searching and discovering the fundamental structure of our world beyond what human scientists have been able to achieve. Such a vision would push the boundaries of new fundamental science rather than automatize current workflows and instead open doors for technological innovation to tackle some of the greatest challenges facing humanity today.Comment: 35 pages, first draft of the final report from the Alan Turing Institute on AI for Scientific Discover

The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version

ABSTRACTS

AbstractThe purpose of this department is to give sufficient information about the subject matter of each publication to enable users to decide whether to read it. It is our intention to cover all books, articles, and other materials in the field.Books for abstracting and eventual review should be sent to this department. Materials should be sent to Prof. David E. Zitarelli, Department of Mathematics, Temple University, Philadelphia, PA 19122, U.S.A. (e-mail: [email protected])Readers are invited to send reprints, autoabstracts, corrections, additions, and notices of publications that have been overlooked. Be sure to include complete bibliographic information, as well as transliteration and translation for non-European languages. We need volunteers willing to cover one or more journals for this department.In order to facilitate reference and indexing, entries are given abstract numbers which appear at the end following the symbol #. A triple numbering system is used: the first number indicates the volume, the second the issue number, and the third the sequential number within that issue. For example, the abstracts for Volume 20, Number 1, are numbered: 20.1.1, 20.1.2, 20.1.3, etc.For reviews and abstracts published in Volumes 1 through 13 there are anauthor indexin Volume 13, Number 4, and asubject indexin Volume 14, Number 1.The initials in parentheses at the end of an entry indicate the abstractor. In this issue there are abstracts by Vı́ctor Albis (Bogotá), Irving Anellis (Ames, IA), Thomas L. Bartlow (Villanova, PA), David Bressoud (St. Paul, MN), Catherine Goldstein (Paris), Herbert Kasube (Peoria, IL), Albert C. Lewis (Hamilton), Laura Nurzia (Reading, GB), James V. Rauff (Decatur, IL), Paul Wolfson (West Chester), and David E. Zitarelli

Probabilistic models for music

This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate music given arbitrary contexts in a genre similar to a training corpus, using a minimal amount of data. For instance, a carefully designed generative model could guess what would be a good accompaniment for a given melody. Conversely, we propose generative models in this thesis that can be sampled to generate realistic melodies given harmonic context. Most computer music research has been devoted so far to the direct modeling of audio data. However, most of the music models today do not consider the musical structure at all. We argue that reliable symbolic music models such a the ones presented in this thesis could dramatically improve the performance of audio algorithms applied in more general contexts. Hence, our main contributions in this thesis are three-fold: We have shown empirically that long term dependencies are present in music data and we provide quantitative measures of such dependencies; We have shown empirically that using domain knowledge allows to capture long term dependencies in music signal better than with standard statistical models for temporal data. We describe many probabilistic models aimed to capture various aspects of symbolic polyphonic music. Such models can be used for music prediction. Moreover, these models can be sampled to generate realistic music sequences; We designed various representations for music that could be used as observations by the proposed probabilistic models

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Tag based Bayesian latent class models for movies : economic theory reaches out to big data science

For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data