We present DreamLLM-3D, a composite multimodal AI system behind an immersive art installation for dream re-experiencing. It enables automated dream content analysis for immersive dream-reliving, by integrating a Large Language Model (LLM) with text-to-3D Generative AI. The LLM processes voiced dream reports to identify key dream entities (characters and objects), social interaction, and dream sentiment. The extracted entities are visualized as dynamic 3D point clouds, with emotional data influencing the color and soundscapes of the virtual dream environment. Additionally, we propose an experiential AI-Dreamworker Hybrid paradigm. Our system and paradigm could potentially facilitate a more emotionally engaging dream-reliving experience, enhancing personal insights and creativity.

Dreaming is a fundamental but not fully understood part of human experience that can shed light on our thought patterns. Traditional dream analysis practices, while popular and aided by over 130 unique scales and rating systems, have limitations. Mostly based on retrospective surveys or lab studies, they struggle to be applied on a large scale or to show the importance and connections between different dream themes. To overcome these issues, we developed a new, data-driven mixed-method approach for identifying topics in free-form dream reports through natural language processing. We tested this method on 44,213 dream reports from Reddit's r/Dreams subreddit, where we found 217 topics, grouped into 22 larger themes: the most extensive collection of dream topics to date. We validated our topics by comparing it to the widely-used Hall and van de Castle scale. Going beyond traditional scales, our method can find unique patterns in different dream types (like nightmares or recurring dreams), understand topic importance and connections, and observe changes in collective dream experiences over time and around major events, like the COVID-19 pandemic and the recent Russo-Ukrainian war. We envision that the applications of our method will provide valuable insights into the intricate nature of dreaming.

A consistent body of evidence suggests that dream reports significantly vary from other types of textual transcripts with respect to semantic content. Furthermore, it appears to be a widespread belief in the dream/sleep research community that dream reports constitute rather ``unique'' strings of text. This might be a notable issue for the growing amount of approaches using natural language processing (NLP) tools to automatically analyse dream reports, as they largely rely on neural models trained on non-dream corpora scraped from the web. In this work, I will adopt state-of-the-art (SotA) large language models (LLMs), to study if and how dream reports deviate from other human-generated text strings, such as Wikipedia. Results show that, taken as a whole, DreamBank does not deviate from Wikipedia. Moreover, on average, single dream reports are significantly more predictable than Wikipedia articles. Preliminary evidence suggests that word count, gender, and visual impairment can significantly shape how predictable a dream report can appear to the model.

In the field of dream research, the study of dream content typically relies on the analysis of verbal reports provided by dreamers upon awakening from their sleep. This task is classically performed through manual scoring provided by trained annotators, at a great time expense. While a consistent body of work suggests that natural language processing (NLP) tools can support the automatic analysis of dream reports, proposed methods lacked the ability to reason over a report's full context and required extensive data pre-processing. Furthermore, in most cases, these methods were not validated against standard manual scoring approaches. In this work, we address these limitations by adopting large language models (LLMs) to study and replicate the manual annotation of dream reports, using a mixture of off-the-shelf and bespoke approaches, with a focus on references to reports' emotions. Our results show that the off-the-shelf method achieves a low performance probably in light of inherent linguistic differences between reports collected in different (groups of) individuals. On the other hand, the proposed bespoke text classification method achieves a high performance, which is robust against potential biases. Overall, these observations indicate that our approach could find application in the analysis of large dream datasets and may favour reproducibility and comparability of results across studies.

Scientists are developing AI tools to help analyze dreams, in the hopes of better understanding where dreams come from and helping people address real-life problems, especially around mental health. Scientists in the UK and Italy have created an AI tool to analyze dream reports, which are text reports written by the dreamer when they wake up. The analysis of dream reports previously demanded a time-consuming manual annotation of text, which is why dream reports have recently been mined with algorithms focused on identifying emotions, according to a recent account in the Royal Society Open Science journal. The goal is to mine important aspects of dream reports, such as characters and interactions, in a principled way grounded in academic literature. The team designed a tool that automatically scores dream reports based on a widely-used dream analysis scale.

Google search queries and social media posts provide a means of peering into the ideas, concerns, and expectations of millions of people around the world. Using the right web-scraping bots and big data analytics, everyone from marketers to social scientists can analyze this information and use it to draw conclusions about what's on the mind of massive populations of users. Could A.I. analysis of our dreams help do the same thing? That's a bold, albeit intriguing, concept -- and it's one that researchers from Nokia Bell Labs in Cambridge, U.K., have been busy exploring. They've created a tool called "Dreamcatcher" that can, so they claim, use the latest Natural Language Processing (NLP) algorithms to identify themes from thousands of written dream reports.

Dreams and nightmares are a natural occurrence to humans. Some say that dreams reflect the mentality and thoughts of a person, while some others think it is a desire that dreams project. They portray the deep fear thorough a vision. As everyone stays curious about what dreams represent, Artificial Intelligence (AI) took its way to find an answer. Earlier, technology has accelerated and made artificial intelligence dream.

Aiello and his collaborators applied their AI tool to dream reports collected in the DreamBank, a massive database put together by Adam Schneider and UC Santa Cruz professor emeritus G. William Domhoff. The dream reports are more thorough than my brief dream journal entries. One, from a blind person, reads: "I was at a religious retreat. We were sitting in a dining room, eating dinner. There were roses on the table, I smelled their fragrance. We had a Thanksgiving-type dinner with my favorite things (turkey, stuffing, cranberries) and my favorite kind of dessert, pumpkin pie. And it was in the middle of spring, which was most ironic."

Have you ever woken up from a dream that made absolutely no sense and wondered what it was all about? A team of researchers claim the dream you've experienced is just a continuation of what is happening in your every day life - with no deeper or hidden meaning. Experts from the Nokia Bell Labs in Cambridge created a Natural Language Processing technique that can automatically analyse dreams and quantify them. According to what sleep scientists call the'continuity hypothesis,' our dreams reflect what we experience in our real lives and the new tool proves the theory. Because our dreams reflect every day life, the authors say it could be possible to build a tool that could help in mental health diagnosis and treatment.

In a companion paper, Murtagh (2012), we discussed how Matte Blanco's work linked the unrepressed unconscious (in the human) to symmetric logic and thought processes. We showed how ultrametric topology provides a most useful representational and computational framework for this. Now we look at the extent to which we can find ultrametricity in text. We use coherent and meaningful collections of nearly 1000 texts to show how we can measure inherent ultrametricity. On the basis of our findings we hypothesize that inherent ultrametricty is a basis for further exploring unconscious thought processes.