We are not claiming the Universe is conscious. We are building a disciplined system that tests whether public cosmic data contains patterns too large, subtle, or multidimensional for humans to inspect manually.
FloLabs Group / TARRL
CosmosIntelligence
AI Search for Universal-Scale Information Networks
An open, remote research and product initiative using AI to study cosmic-scale structure, signals, anomalies, and information-flow patterns across public space data.
Open to the world
No experience necessary
Research + product
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The big question
Can AI help us detect hidden structure, signal behavior, and information-like patterns in the observable Universe?
The inspiring idea: galaxies, filaments, light, spectra, plasma, gravity, and time-varying signals may form a vast network. The scientific task is to measure what is real and reject what is illusion.
Our standard: every candidate pattern must survive data provenance checks, known-physics controls, statistical testing, and independent review.
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What we are building
One initiative, three connected outputs.
Cosmic research engine
Ingests public astronomy data, learns normal astrophysics, creates cosmic graphs, and ranks anomalies for review.
AI Space View App
A public-facing app for exploring space images, maps, signals, and AI-discovered anomalies through an educational visual interface.
FloBrain Space Buddy
A conversational AI guide inside FloBrain that helps users learn astronomy, review datasets, organize research tasks, and explain discoveries.
The research team and product team work in parallel: science creates credibility, the app creates public engagement, and FloBrain Space Buddy turns the work into a scalable AI learning experience.
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Open to anyone, anywhere
Remote. Global. Beginner-friendly. Serious about contribution.
No experience necessary to begin. Curiosity, reliability, persistence, and willingness to learn matter more than credentials at the entry point.
Join as an intern if you want structured learning and hands-on contribution. Join as a research associate if you already have stronger skills and want to help lead methods, review, or product work.
AI / ML
models, embeddings, anomaly detection
Astronomy
data realism, sky catalogs, known phenomena
Data Engineering
pipelines, metadata, reproducibility
Design / Product
AI Space View App, UX, visualizations
Writing / Education
explainers, curriculum, research pages
Project Ops
tasks, documentation, coordination
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Role pathways
Start where you are. Grow into deeper responsibility.
Intern
Entry path for learners. Help with research summaries, data labeling, app research, content, testing, documentation, and guided technical tasks.
Research Associate
Contribution path for stronger builders and specialists. Help with model development, graph methods, signal analysis, review systems, and product architecture.
Team Lead
Coordination path for reliable contributors. Help organize tasks, mentor others, maintain standards, and connect research work to product output.
Everyone contributes to a real initiative: research outputs, AI systems, product features, education materials, and public demonstrations.
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Build path: from open data to public product
No fixed timeline. The team advances through milestones.
Map
Identify public archives, datasets, sky regions, formats, metadata, and research questions.
Ingest
Create repeatable pipelines for images, catalogs, spectra, light curves, and graph-ready structures.
Model
Train baseline models to understand normal data and rank anomalies for human review.
Validate
Apply evidence standards, control tests, review workflows, and reproducibility checks.
Productize
Turn research workflows into AI Space View App features and FloBrain Space Buddy experiences.
Milestone discipline matters more than deadlines: document progress, ship small outputs, improve the model, and keep connecting research to product.
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What contributors will produce
Visible outputs keep the project real, useful, and accountable.
Research briefs
methods, literature summaries, benchmark datasets, anomaly findings, and negative results
Open data maps
curated archive lists, metadata guides, sample regions, and reproducible notebooks
Candidate dashboard
ranked anomalies, evidence-ladder status, raw-data links, and review notes
App features
AI Space View exploration tools, visual explainers, search, annotation, and guided learning
FloBrain workflows
Space Buddy memory, task systems, dataset summaries, and research operations
Public education
lectures, explainers, website pages, short videos, and student-friendly guides
Even if no extraordinary signal is found, the work still advances AI astronomy, anomaly detection, science education, and the Space Buddy product ecosystem.
Join the search.
Help build the AI layer that studies the Universe as a dynamic network of data, signals, structure, and possibility.
Open to interns and research associates around the world.
Remote participation. No experience necessary to begin.
Contribute to both open research and the AI Space View App / FloBrain Space Buddy commercial product track.
FloLabs Group / TARRL
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Why this is possible now
Open sky archives plus modern AI create a new way to study the Universe.
Open space archives
NASA, ESA, SDSS, DESI, ALMA and others expose enormous public astronomy datasets.
Foundation models
Self-supervised AI can learn structure from images, spectra, light curves, catalogs, and graphs.
Graph intelligence
The cosmic web can be modeled as nodes, edges, clusters, paths, voids, and recurring motifs.
Signal search maturity
SETI and radio astronomy methods provide useful controls for noise, interference, and false positives.
Reference archives and methods: NASA MAST, NASA HEASARC, ESA Gaia, ESA Euclid, SDSS, DESI Legacy Surveys, Berkeley SETI / Breakthrough Listen.
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Scientific anchor: networks can rhyme across scales
The cosmic-web / brain analogy is a mathematical starting point, not a conclusion.
Published research has compared structural, morphological, network, and information-capacity properties of neuronal networks and the cosmic web. That does not prove cosmic consciousness. It does justify careful, quantitative network comparison.
Our method begins with measurable network structure, time-varying signals, anomaly detection, and evidence standards.
Network
topology, clustering, centrality, motifs
Signal
light curves, spectra, radio pulses, bursts
Evidence
replication, prediction, falsification
Scientific basis includes Vazza & Feletti, “The Quantitative Comparison Between the Neuronal Network and the Cosmic Web,” Frontiers in Physics.
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The data universe: not just space pictures
Beautiful images inspire people. Raw and calibrated data trains the AI.
Images
FITS, sky surveys, morphology
Spectra
chemical signatures, redshift, lines
Light curves
brightness over time, pulses, flares
Radio
narrowband, broadband, drift patterns
High energy
EUV, X-ray, gamma-ray, CMB
Graphs
cosmic web, clusters, filaments
Core data sources to integrate: NASA MAST, HEASARC, Exoplanet Archive, ESA Gaia, ESA Euclid, SDSS, DESI Legacy Surveys, ALMA, and open SETI datasets.
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Research pipeline: learn normal before searching weird
Most “patterns” are ordinary physics or instrument effects. The model must learn that first.
Normal sky
stars, galaxies, quasars, nebulae, survey artifacts
Normal variability
pulsars, variable stars, transits, flares, supernovae
Normal instruments
noise, saturation, radio interference, compression, calibration effects
Normal physics
gravity, lensing, plasma jets, redshift, dust, known spectra
Only after learning “normal” can the system rank what remains anomalous.
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What counts as evidence?
The project succeeds by rejecting weak patterns quickly.
- 1Visible in raw data
- 2Replicated across instruments
- 3Survives known astrophysics
- 4Statistically unlikely under controls
- 5Information-rich or compressible
- 6Predicts future observations
No prediction = interesting pattern.
Prediction + replication = research candidate.
This is how we protect the work from hype and protect contributors from weak science.
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AI architecture: the cosmic foundation model stack
A multimodal AI stack that converts open archives into testable candidates.
Open science data lake
Multimodal embeddings
Cosmic graph
Anomaly + information engine
Validation pipeline
Candidate dashboard
Vision model
galaxy morphology and survey images
Spectral model
emission/absorption lines and redshift
Time-series model
light curves, bursts, pulses
Graph model
cosmic web connectivity and motifs
Information engine
entropy, compression, symbolic structure
Control filters
instrument artifacts, radio interference, false positives
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Two tracks move in parallel
The initiative is both a research program and a product launch path.
Research Track
Build data pipelines, train models, map the cosmic graph, document methods, rank anomalies, and create review workflows.
Product Track
Turn the work into the AI Space View App and FloBrain Space Buddy: a visual, conversational, educational, and commercial space-intelligence experience.
The commercial project matters because it creates a public interface for learning, discovery review, recruitment, and long-term sustainability.
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AI Space View App
A commercial product that lets anyone explore the Universe with AI.
Explore
Search and browse open space images, sky maps, spectra, light curves, and cosmic regions.
Explain
Ask AI what a galaxy, nebula, signal, light curve, or anomaly might mean in plain English.
Compare
View cosmic structures alongside brain-network, graph, and information-theory visualizations.
Contribute
Help classify patterns, annotate data, review candidates, and participate in public science tasks.
Product vision: the easiest way for students, researchers, lifelong learners, and space enthusiasts to see what AI is discovering in open cosmic data.
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FloBrain Space Buddy
A conversational AI research and learning companion inside FloBrain.
Learn
Guided explanations of astronomy, AI, network science, SETI methods, and evidence standards.
Research
Dataset summaries, literature review support, task tracking, meeting notes, and project memory.
Analyze
Prompt-driven review of candidate anomalies, plots, metadata, and control-test status.
Build
Support for interns and research associates working on app features, datasets, workflows, and demos.
FloBrain Space Buddy turns a complex research initiative into a guided, persistent, AI-powered learning and productivity environment.
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Selected sources and evidence base
The initiative is grounded in open public archives, peer-reviewed research, and reproducible methods.
Vazza & Feletti, Frontiers in Physics
Quantitative comparison of neuronal networks and the cosmic web, including methodological caveats.
NASA MAST / STScI
Public archive for optical, ultraviolet, and near-infrared astronomy data from major missions.
NASA HEASARC
Public archive for high-energy astrophysics including EUV, X-ray, gamma-ray, and related data.
ESA Gaia Archive
Large-scale public star-position, motion, and photometric data for mapping the Milky Way.
ESA Euclid
Large-scale mapping of cosmic structure, dark matter, and galaxy evolution through public science releases.
SDSS + DESI Legacy Surveys
Large public sky imaging, spectroscopy, and extragalactic survey catalogs.
Berkeley SETI / Breakthrough Listen
Machine-learning and signal-processing methods relevant to technosignature search and false-positive control.