Zohran Mamdani gave Hinge an unofficial boost last month when the New York mayoral candidate revealed that he met his wife, Rama Duwaji, through swiping. "There is still hope on those dating apps," he said on the Bulwark podcast a week before his stunning victory in the Democratic primary. The tidbit spread over social media, cementing the 33-year-old democratic socialist's status as a millennial everyman. A subsequent Cosmopolitan headline read: "Zohran Mamdani could make history (as the first NYC mayor to meet his wife on Hinge)." Representatives for Hinge would not comment, but plenty of eligible New Yorkers did, claiming they would redownload the app due to Mamdani's success, in spite of their dating fatigue.

This paper explores hallucination phenomena in large language models (LLMs) through the lens of language philosophy and psychoanalysis. By incorporating Lacan's concepts of the "chain of signifiers" and "suture points," we propose the Anchor-RAG framework as a novel approach to mitigate hallucinations. In contrast to the predominant reliance on trial-and-error experiments, constant adjustments of mathematical formulas, or resource-intensive methods that emphasize quantity over quality, our approach returns to the fundamental principles of linguistics to analyze the root causes of hallucinations in LLMs. Drawing from robust theoretical foundations, we derive algorithms and models that are not only effective in reducing hallucinations but also enhance LLM performance and improve output quality. This paper seeks to establish a comprehensive theoretical framework for understanding hallucinations in LLMs and aims to challenge the prevalent "guess-and-test" approach and rat race mentality in the field. We aspire to pave the way for a new era of interpretable LLMs, offering deeper insights into the inner workings of language-based AI systems.

This paper proposes a novel framework for understanding large language models (LLMs) by reconceptualizing them as semiotic machines rather than as imitations of human cognition. Drawing from structuralist and post-structuralist theories of language-specifically the works of Ferdinand de Saussure and Jacques Derrida-I argue that LLMs should be understood as models of language itself, aligning with Derrida's concept of 'writing' (l'ecriture). The paper is structured into three parts. First, I lay the theoretical groundwork by explaining how the word2vec embedding algorithm operates within Saussure's framework of language as a relational system of signs. Second, I apply Derrida's critique of Saussure to position 'writing' as the object modeled by LLMs, offering a view of the machine's 'mind' as a statistical approximation of sign behavior. Finally, the third section addresses how modern LLMs reflect post-structuralist notions of unfixed meaning, arguing that the "next token generation" mechanism effectively captures the dynamic nature of meaning. By reconceptualizing LLMs as semiotic machines rather than cognitive models, this framework provides an alternative lens through which to assess the strengths and limitations of LLMs, offering new avenues for future research.

While the term `art' defies any concrete definition, this paper aims to examine how digital images produced by generative AI systems, such as Midjourney, have come to be so regularly referred to as such. The discourse around the classification of AI-generated imagery as art is currently somewhat homogeneous, lacking the more nuanced aspects that would apply to more traditional modes of artistic media production. This paper aims to bring important philosophical considerations to the surface of the discussion around AI-generated imagery in the context of art. We employ existing philosophical frameworks and theories of language to suggest that some AI-generated imagery, by virtue of its visual properties within these frameworks, can be presented as `readymades' for consideration as art.

Hypermedia APIs enable the design of reusable hypermedia clients that discover and exploit affordances on the Web. However, the reusability of such clients remains limited since they cannot plan and reason about interaction. This paper provides a conceptual bridge between hypermedia-driven affordance exploitation on the Web and methods for representing and reasoning about actions that have been extensively explored for Multi-Agent Systems (MAS) and, more broadly, Artificial Intelligence. We build on concepts and methods from Affordance Theory and Human-Computer Interaction that support interaction efficiency in open and evolvable environments to introduce signifiers as a first-class abstraction in Web-based MAS: Signifiers are designed with respect to the agent-environment context of their usage and enable agents with heterogeneous abilities to act and to reason about action. We define a formal model for the contextual exposure of signifiers in hypermedia environments that aims to drive affordance exploitation. We demonstrate our approach with a prototypical Web-based MAS where two agents with different reasoning abilities proactively discover how to interact with their environment by perceiving only the signifiers that fit their abilities. We show that signifier exposure can be inherently managed based on the dynamic agent-environment context towards facilitating effective and efficient interactions on the Web.

You've got them written up on the wall, on your mousepad, emblazoned across your screen or maybe even in your reception. They are, of course, your brand values, and they provide the basis for your marketing efforts – hence their importance. Brand values and personality define what your brand stands for and the response you want it to evoke. Communications from your brand might not always convey the intended personality or be consistent across even a small selection of brand touchpoints. We're often asked to test communications and assess brand perceptions, addressing things like their relevance, distinctiveness, alignment and consistency across different touchpoints like websites, promotions, ads, products and services.

Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions. Central to this predictive approach is the assertion that it is possible to construct ontologies in terms of predictions about sensation, behaviour, and time---to categorize the world into entities which express all aspects of the world using only predictions. This construction of ontologies is integral to predictive approaches to machine knowledge where objects are described exclusively in terms of how they are perceived. In this paper, we ground the Pericean model of semiotics in terms of Reinforcement Learning Methods, describing Peirce's Three Categories in the notation of General Value Functions. Using the Peircean model of semiotics, we demonstrate that predictions alone are insufficient to construct an ontology; however, we identify predictions as being integral to the meaning-making process. Moreover, we discuss how predictive knowledge provides a particularly stable foundation for semiosis\textemdash the process of making meaning\textemdash and suggest a possible avenue of research to design algorithmic methods which construct semantics and meaning using predictions.

We start with a description of Lacan's work that we then take into our analytics methodology. In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the "letting the data speak" Correspondence Analysis analytics platform of Jean-Paul Benz\'ecri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu.

The HoMT workshop at the University of Pennsylvania is a place for presenting work in progress, and this is such work. In the text below, I have omitted references, and mention of "the handout" doesn't mean anything here, except that I have linked to things on the handout that exist on the Web. If you'd like to correspond about the topic and correct or inform me about the use of print-based interfaces, please contact me: nickm at this domain. Update, 1 March 2004: I made several changes, thanks to comments from Tom Van Vleck, whose work I cite in my talk. Update, 20 August 2004: Further work on this topic has resulted in "Continuous Paper: Print Interfaces and Early Computer Writing," a talk given at ISEA. My topic today is what some call "electronic writing," although "computer writing" is also a reasonable term for it. "Electronic writing" makes this sound a bit like a quadraphonic hi-fi, while "computer writing" is something you might expect to find in PC Magazine -- the helpful advice column about defragmenting your hard disk and such.

In both semantic model standards Topic Maps and RDF/OWL and in many other NoSQL approaches to solve efficiently the problem of how to represent relations and relationships one major stumbling block is raised beyond all efforts: the namespace. It is a language problem, the babel we have in our civilized world is transferred into our IT systems. But machines do not have to understand our language, we do. The problem here is that from a semantic point of view, similar diagrams are in need from users that want to express business processes but when we reach the implementation stage software engineers have to marry business requirements with the technical constrains of the database system hence the ER diagram you see. Generally speaking this is known as "The Model", a conceptual view of the user on data. The ER version of the model has several limitations, due to the architecture of RDBMS.