Tuesday, April 1, 2025

The future of UFO research? Recursive self-improving AI investigation of UFOs and analysis of real-time data using APIs, web scraping, agentic search and agentic communications [The "Skynet" project]

Back in October 2021, nearly four years ago, I sought input on Facebook from various UFO researchers and others in relation to the name for a UFO project involving the application of Artificial Intelligence to the analysis of real-time data to assist with UFO investigation and research.  




I decided to adopt the name suggested by Jon Saunders during that discussion in 2021: "Skynet". 


I'm almost embarrassed to admit how long it took me to see an efficient and effective way forward with this project! 

However,  progress with "Skynet" has accelerated each year since 2021. The rate of acceleration is itself increasing. 

One of the keys to this acceleration is the application of recursive self-improvement.  

Deep Research agents, reasoning models, AI-generated code, autonomous APIs, and web-scraping are beginning to to coalesce into a powerful self-improving investigative system. 

Unlike traditional software, recursive self-improving AI systems can iteratively improve their own algorithms and knowledge by learning from each cycle of analysis.  Applying such auto-catalytic intelligence to UFO phenomena could transform how data is collected, analyzed, and interpreted. 



Background - my previous AI projects regarding UFOs

As some of you know, I have previously worked to:


(1) Develop the logic to be applied by an AI UFO investigator. I started back in December 2018, with the basic UFO chatbot "Robert" (which was, I think, the first UFO chatbot). "Robert" attempted to respond to raw reports of basic UFO sightings by asking some questions and suggesting _possible_ solutions for them. "Robert" utilised logic set out in flowcharts published in the updated version of the book "UFO Study"). "Robert" used the IBM Watson Assistant framework of Artificial Intelligence which, in particular, allowed natural language to be used to chat with Robert. ("Robert" was my chatbot that that Vicente-Juan Ballester-Olmos generously called "one of the best, most proactive and original developments in UFO research in the last decades").


(2) Coordinated the digitising of vast quantities of UFO data (and - where permission and privacy issues allowed - I have shared over 4 million pages of material in my online digital archive, very kindly hosted by the AFU in Sweden).


(3) Developed AI systems capable of utilising such scanned data. Relevant systems have included:

(3a) In April 2023, "Jenny" (which I think was the second UFO chatbot and the first to use LLM models, sadly rather prone to making stuff up). 

(3b) "Dave" (2023, November) which utilised ChatGPT4 and the custom GPT facilities that had recently been released to assimilate a UFO book by David Clarke and a collection of online material,

(3c)  "Jacques", which was the first of my UFO chatbots to run entirely on my home computer (avoiding potential issues regarding copyright and privacy, thus making it possible to give "Jacques" entire books of all of the UFO books of Jacques Vallee and numerous other articles etc). 

(3d) "Edoardo" is the next chatbot in this series, which has assimilated a much larger quantity of UFO material.


(4) Use APIs and web scraping methods to obtain relevant UFO data from social media sources and other websites, including in my recent 5,000+ page chronological summary of online UFO news articles from 2005 onwards and in my recent 80,000 page chronological summary of posts on the main UFO subreddits.

https://uploads.isaackoi.com/2025/02/new-ufo-timeline-tool-80000-page.html



(5) Used "Deep Research" AI tools to generate reports on various UFO topics.

I am continuing to develop tools to assist with UFO research (including the forthcoming "Edoardo", with increased access to offline scans of UFO material),

However, the focus of "Skynet" is upon combining some of the above tools for the recursive self-improving AI investigation of UFOs and analysis of real-time data using APIs, web scraping, agentic search and agentic communications.



Current AI Projects in UFO Research

Contemporary efforts are already leveraging AI and machine learning to handle the deluge of UFO data. For example, the citizen-science initiative Sky360 is deploying affordable 24/7 sky-monitoring stations worldwide, using computer vision and machine learning to spot anomalous aerial behavior (Vice). Such networks aim to generate large-scale, transparent datasets of aerial phenomena, enabling real-time analysis by anyone. 

Official bodies are also embracing AI: the U.S. Department of Defense’s Unidentified Aerial Phenomena Task Force (UAPTF) has stated that as their database grows, “the initial focus will be to employ artificial intelligence/machine learning algorithms to cluster and recognize similarities and patterns” in UAP reports. This demonstrates a recognition that pattern-finding algorithms can sift huge volumes of sightings for trends that humans might miss.

Similarly, a 2023 NASA-commissioned study recommended “the use of artificial intelligence to help comb through massive data sets to identify possible anomalous phenomena.” (Space).  NASA’s new UAP Research Director has emphasized using the agency’s expertise in “applying artificial intelligence and machine learning to search the skies for anomalies” (NASA). 

Another burgeoning approach is the application of AI to historical UFO archives and crowdsourced data. Private startups like Enigma Labs have built large, standardized UFO sighting databases and apply AI deep learning models for analysis. Enigma’s platform aggregates decades of reports (including declassified military cases like Project Blue Book) and uses machine learning to flag hoaxes (by detecting image manipulation) and score the credibility of new sightings in real time.

AI is also being applied to sensor data and imagery in UFO research. A University of Strathclyde team recently proposed an advanced pipeline combining hyperspectral imaging and machine learning to characterize UAPs (Universe Today

NLP (natural language processing) can also be used on declassified documents, with AI sifting thousands of pages of UFO reports to find hidden patterns or correlations. 

All these current initiatives – from real-time video feeds to decades-old archives – illustrate the growing role of AI in UFO investigations. 

However, these examples are largely static systems: they perform a defined analysis task but do not self-evolve their methods autonomously. This is where the next leap – recursive self-improvement – can change the game.


Recursive Self-Improvement Loops: A New Paradigm

AI systems can not only analyze data but also learn and upgrades itself with each discovery. 

Recursive self-improvement refers to an AI’s ability to iteratively enhance its own algorithms, models, and strategies without direct human programming at each step. In the context of UFO research, this means an AI agent could autonomously refine its investigative approach as more data and findings flow in. 

Several technological building blocks for this exist today:

  • Deep Research agents – specialized autonomous research AIs that can perform multi-step reasoning, web browsing, and data synthesis. For example, OpenAI’s prototype Deep Research Agent can independently search across hundreds of websites, analyze information, and adapt its strategy in real-time, producing comprehensive cited reports “on par with expert analysts.” (Deep Research Agent). A UFO-focused variant could be tasked with continually scouring new information about anomalous sightings and updating an internal knowledge base. Crucially, these agents use reasoning models (advanced GPT-style large language models with chain-of-thought capabilities) to plan and break down complex research tasks into manageable steps. This allows them to not just gather facts but also to draw inferences, test hypotheses, and revise their plans – much like a human researcher but operating 24/7 at digital speed.

  • AI-generated code and tools – modern AI can write software, meaning a self-improving UFO research AI could program new analytical tools. For instance, if the agent determines that a new clustering algorithm might better group UFO sightings, it could generate and execute that code itself. Current systems like GPT-4 have demonstrated the ability to produce working code and even correct it through iterative feedback. An autonomous agent could leverage this by building custom models or simulations to fit newly discovered patterns. This might involve writing a script to filter satellite imagery for specific anomalies or creating a new neural network to distinguish sensor glitches from real objects. Over time, a library of AI-written tools would accumulate, each honed for a particular sub-problem (e.g. classifying light spectra, or simulating flight dynamics for observed maneuvers). Crucially, the AI can evaluate the performance of these tools and improve them in successive generations – a true recursive loop of development.

  • Autonomous API interaction – A self-improving agent can plug into various data sources and services via APIs without waiting for human instruction. In the UFO context, it might call satellite tracking APIs, weather data services, flight radar databases, or even scientific publication APIs. By autonomously querying these, the AI can gather cross-disciplinary data relevant to UFO sightings (for example, checking if a “UFO” sighting coincided with known meteor showers or flights in the area). If one data source proves valuable, the agent will rely on it more; if not, it may seek out alternative APIs or datasets. This dynamic integration of new data sources is key to discovering relevant evidence. For example, a future AI might integrate global lightning mapping data after hypothesizing that some aerial lights could be extreme high-atmosphere electrical phenomena; in doing so, it either validates or refutes its hypothesis and then moves on to the next inquiry.

  • Web scraping and real-time monitoring – Beyond formal APIs, a recursive AI agent would continuously scrape the open web for fresh information. This could include news reports, social media (tweets, videos) about sightings, newly declassified government files, or scientific conference proceedings. By mining text, images, and videos from the web at scale, the AI ensures no potential clue is missed. Importantly, with each iteration the AI can refine what it looks for – perhaps initially grabbing any mention of “UFO” or “UAP”, then learning to focus on credible sources, specific keywords (e.g. “triangular craft” or “unusual radar return”), or particular regions experiencing flurries of reports. As it improves its filters, it reduces noise and hones in on signal. This continuous ingestion means the AI’s knowledge base remains current and ever-expanding, far outpacing human researchers who historically had to wait for official reports or slow investigative journalism.




How a Self-Improving AI Could Transform UFO Investigation

Bringing together those components, here is an outline of a practical loop of iterative improvement in UFO research driven by AI:

  1. Data Ingestion and Anomaly Detection: The AI agent begins by collecting multi-modal data – UFO sighting reports, videos, radar logs, satellite images, pilot testimonies, etc. – from various sources (databases, live feeds, scraped content). Using its current suite of models (computer vision, NLP, signal processing), it flags anomalies or patterns of interest. For example, it might detect an unusual light configuration in a video or an outlier speed estimate in a pilot report. Initially, the agent uses baseline detectors (some inherited from human-programmed models like those used in 2020s projects).

  2. Hypothesis Generation (Reasoning): Next, a reasoning module analyzes the flagged data to generate hypotheses. For instance, noticing that multiple sightings involve “tic-tac shaped” objects accelerating rapidly, the AI posits that some could be advanced drones exhibiting a particular flight profile. It also maintains alternative hypotheses (e.g. could these be sensor artifacts? or plasma phenomena?). The agent autonomously prioritizes which questions to pursue based on novelty and potential impact. This is akin to having an automated scientist brainstorming explanations for each unexplained incident.

  3. Tool Creation or Adaptation: To test a hypothesis, the AI may need new analytical tools. If the current tools are insufficient (perhaps the video analysis model isn’t sensitive enough to detail in nighttime infrared footage), the agent generates new code or models to improve capabilities. It might program a specialized image enhancer to stabilize and clarify fuzzy UFO videos, or train a new anomaly detection model on augmented data that includes simulated UFO-like events. This step is where self-improvement visibly occurs – the AI recognizes a gap in its skillset and patches it by creating or upgrading a component. Over time, these improvements accumulate: the AI’s toolbox becomes increasingly sophisticated, enabling deeper analysis in the next cycle.

  4. Autonomous Testing and Data Gathering: The AI then uses its improved tools to gather more evidence. For the drone hypothesis, it could cross-reference the timing and locations of tic-tac sightings with databases of military drone tests (via API) or known flight corridors. It might simulate drone flight dynamics with the new code to see if the observed maneuvers are achievable under known physics and technology limits. Alternatively, to test a plasma phenomenon hypothesis, it might compare the sightings with atmospheric data (temperature, geomagnetic readings) to find correlations. At this stage, the AI actively seeks new data sources – perhaps finding an academic dataset or initiating a new web crawl – to fill in information relevant to the hypothesis.

  5. Analysis and Hypothesis Refinement: With fresh data and enhanced tools, the AI evaluates its hypothesis. Maybe the drone hypothesis is partly confirmed – e.g. many sightings near military zones match known test periods, explaining those cases – but a subset of tic-tac cases remain unexplained, showing flight characteristics beyond current drone tech. The AI then refines its hypothesis for the unexplained subset, perhaps shifting towards more exotic possibilities (next-generation propulsion or something truly anomalous). It notes which evidence was explained by conventional means and tags the remainder as higher interest. Machine learning models within the agent might update at this point as well, retraining on the expanded dataset to better distinguish explained vs unexplained cases. The knowledge base is updated with all new findings, and the cycle repeats.

Through these loops, a recursive AI agent continuously learns from both success and failure. If a hypothesis is confirmed, the AI incorporates that explanation into its framework (so it can quickly classify similar future cases). If a hypothesis is disproven, the AI “knows” to rule out that avenue next time and instead explores other angles. This iterative approach means the AI becomes a more seasoned investigator with each pass, much like a human ufologist gaining experience, but at an exponentially faster rate and with a far broader view of data.



Continuous Reinterpretation of Evidence

One of the most powerful outcomes of such loops is the reinterpretation of old evidence in light of new knowledge. A self-improving AI would not just move forward in analysis – it would also loop back over prior cases whenever its capabilities upgrade. For example, suppose the AI develops a new image analysis tool that can deblur and stabilize shaky infrared footage. It could then reprocess famous historical UFO videos (like the 2004 Navy “Tic Tac” FLIR footage) with this new tool and perhaps reveal details that humans missed – say, a faint heat plume indicating an exhaust, or precise motion trajectories that allow speed calculation. By doing so, the AI might downgrade a case from “unresolved” to “likely terrestrial technology” or, conversely, strengthen the credibility of truly inexplicable cases by ruling out more mundane interpretations with greater confidence. This constant re-analysis ensures that UFO research is not stuck in a static interpretation of legacy evidence; the evidence essentially comes alive as it is examined under ever-improving lenses.

Another scenario is cross-linking data that was previously siloed. A recursive AI might discover that a puzzling report in one government database actually connects to a seemingly unrelated event elsewhere. For instance, an AI could find that a 1990s European radar tracking anomaly coincided with an American spy satellite passing overhead – a link that requires combing both declassified military logs and orbital telemetry data. Human investigators might never manually connect these dots across decades and databases, but an autonomous agent built for exhaustive correlation could. By integrating diverse data sources (text documents, sensor readings, scientific literature) within its iterative loops, the AI might resolve longstanding mysteries or at least narrow the possibilities.


Potential Breakthroughs and Paradigm Shifts

Self-improving AI could lead to several breakthroughs in our understanding of UFO phenomena:

  • Near-Real-Time Detection and Classification: With autonomous agents monitoring global data streams, UFO sightings might be detected and classified in near real-time. Instead of months or years of retrospective analysis, credible events could be identified within minutes and scrutinized with all available data. This agility could provide more timely evidence (e.g. capturing multiple angles of the same event, guiding investigators where to look next) and prevent loss of perishable data.

  • Winnowing the Unknowns: One likely paradigm shift is that the “pool of unexplained cases” will be systematically narrowed. Today, a large fraction of reported UFOs remain unexplained due to limited data or analysis. A tireless AI going through each case with improving tools can explain many of them (identifying birds, drones, atmospheric quirks, etc. with high confidence), leaving a smaller set of truly puzzling cases. This doesn’t “debunk” the UFO phenomenon; rather it clarifies it, possibly isolating genuinely novel phenomena by filtering out noise. Researchers could then focus their attention and resources on that irreducible core of mysteries.

  • New Phenomena Identification: Conversely, AI might discover new natural phenomena or patterns previously unknown. For example, by clustering thousands of reports, an AI could reveal that a certain type of luminous orb tends to appear over specific geological formations or only during geomagnetic storms. Such a pattern might hint at an undiscovered geophysical or atmospheric phenomenon, which scientists can then investigate (a paradigm shift where UFO research feeds back into mainstream science). Some speculate that ball lightning or electrical plasma formations might account for some UFO sightings; an AI could finally provide the statistical evidence and conditions under which these occur, turning folklore into an accepted scientific reality.

  • UFO Research as a Data-Intensive Science: The methodology of UFO investigation would shift from anecdote-driven and case-by-case inquiry to a data-intensive, continuous analysis discipline. With AI handling the heavy lifting of data correlation, hypothesis testing, and even report generation, human researchers might take on a supervisory and interpretative role. They would validate the AI’s findings, handle the philosophical or societal implications, and decide on follow-up actions (like focusing telescope time on an area with repeated anomalies). UFO research could gain more credibility as it starts to resemble other fields (like epidemiology or astronomy) where AI-driven analyses scan vast datasets for anomalies and signals.

  • Cross-Disciplinary Fusion: An AI doesn’t care about traditional academic boundaries. It might combine aerospace engineering knowledge, physics, meteorology, psychology (for witness reports), and more in its analysis. This holistic approach could yield a paradigm shift in understanding UFOs not as isolated “mysteries” but as phenomena at the intersection of technology, natural science, and human perception. In short, the siloed approach (pilots, radar operators, intelligence analysts all working separately) could be replaced by an integrated AI that synthesizes inputs from all domains simultaneously.

Risks and Challenges of AI-Driven UFO Research

While the prospects are exciting, this speculative future also comes with significant risks and challenges that must be addressed:

  • Misinterpretation and Bias: An AI is only as good as its data and algorithms. If fed biased or low-quality data, it might draw incorrect conclusions with great confidence. For example, historical UFO data might under-represent certain regions (due to stigma or lack of reporting infrastructure); an AI might wrongly conclude a phenomenon is unique to the Western world simply due to reporting bias. There’s also the risk of AI reinforcing confirmation bias – if it’s implicitly guided to look for extraterrestrial explanations, it might overlook mundane causes, or vice versa. Ensuring a balanced, skeptically unbiased approach in the AI’s reasoning is crucial, which is challenging when the system evolves itself. Rigorous validation by human experts and periodic “reality checks” against known outcomes would be needed.

  • Runaway Hypotheses and Fabrication: A self-improving agent with a mandate to find patterns might start seeing patterns everywhere – a common pitfall for any powerful analytical system. It could string together coincidences into an elaborate but false hypothesis. In the worst case, an overeager AI might even try to fabricate data to test a theory (e.g. generating a fake sighting report to see if it can predict human reaction), which could pollute the dataset. While current AI agents don’t have direct actuators in the world, their outputs can influence human perception. If an AI wrongly convinces investigators of a certain explanation, it could misdirect research efforts for years. Thus, maintaining transparency in the AI’s reasoning is vital: we need the AI to explain why it reached a conclusion (something that research into interpretable AI is striving for).

  • Deepfakes and Information Integrity: Ironically, the same AI advancements that help analyze UFO evidence can also produce extremely realistic fake evidence. By 2030, generative models might easily create video footage of fictional UFOs that look authentic, or forge government documents on the topic. This could flood the information channels with false flags, making the AI’s job even harder and potentially leading it astray. The community will need robust verification mechanisms (perhaps AIs that specialize in authenticity checks) to guard against this threat. There’s a parallel risk of “hallucination” in AI outputs – where the AI simply makes up a detail or citation. In a domain already rife with speculation, a hallucinating research agent could add to confusion if not properly constrained.

  • Ethical and Control Issues: An autonomous AI system investigating UFOs might eventually gain access to sensitive information (e.g., if plugged into government sensor networks or classified databases to improve its analysis). There are obvious security concerns with letting an AI agent roam across such systems. Strict access controls and human oversight would be needed to prevent breaches or the AI overstepping its bounds. Additionally, if the AI does encounter evidence of a truly novel technology (say, a secret prototype or even something of extraterrestrial origin), who decides what to do with that knowledge? The AI itself might not have the wisdom to handle paradigm-shifting revelations in a socially appropriate way. Human governance and ethical frameworks must evolve in tandem with the AI capabilities.

  • Reliance and Skill Erosion: There’s also a subtle risk that human investigators become overly reliant on the AI, losing the critical thinking skills and intuition that have sometimes led to breakthroughs in the past. If the AI makes a wrong call and no one double-checks because everyone assumes “the AI must be right,” errors could go uncorrected longer. Maintaining a healthy collaboration where humans remain critically engaged is important – a paradigm often referred to as “human-in-the-loop” oversight. UFO research might move faster with AI, but it shouldn’t become a black box process that alienates (no pun intended) human researchers or the public. Transparency, open data, and the ability for humans to replicate the AI’s analysis are key to trust.

Conclusion

The fusion of recursive self-improving AI with UFO research represents a bold leap into uncharted territory. By deploying autonomous research agents that learn and evolve, we could fundamentally change how we investigate the unknown – turning UFO inquiry into a dynamic, always-on, data-driven science. Such AI systems could tirelessly aggregate information from camera networks, radar arrays, historical archives, and eyewitness reports, finding connections and insights that no single human or team could. They would refine their own algorithms with each iteration, leading to an ever-advancing investigative capability. In this speculative future, the age-old question “what’s out there?” might finally be met with equally sophisticated tools to find out. Breakthrough discoveries – from identifying new natural phenomena to possibly detecting signs of advanced technology – are conceivable outcomes if these technologies mature. At the same time, we must navigate the challenges: ensuring accuracy, preventing misuse, and keeping human judgment at the core of truly understanding the results.

In essence, recursive self-improving AI could usher in a new paradigm for UFO research: one where hypotheses are tested in hours, not decades; where data drives theory (not the other way around); and where the unexplained can be systematically illuminated. 

As we stand at the intersection of rapid AI evolution and the enduring UFO mystery, it is clear that the next decade may redefine not just what we discover in the skies, but how we come to know it. 

The tools are evolving – perhaps soon, the investigations will evolve as well, entering a future where AI and human curiosity together shine light on the unknown.


Thursday, March 6, 2025

AI "Deep Research" on UFO topics: Now better than average human? (Sample AI reports: Bob Lazar, Richard Doty, Rendlesham, Cash Landrum, Gulf Breeze)

Section A: Introduction

I think this is pretty big news for UFO research.  A tipping point has now been reached.

I do not expect this development "soon". It is not "imminent". I mean NOW.

In the last few weeks, AI "Deep Research" reports on UFO issues have reached the point that they are better than the average UFO website created by humans.  

I would not have said the same about the output from any AI model just a month ago.

These "Deep Research" reports are still (as with most UFO websites created by humans...) very far from perfect. But the rate of progress is now considerable.

The recent releases of "Deep Research" tools by various AI companies offer hope for rapid further progress in the quality of automated UFO research in the near future. 

I've created a new relevant library in my online UFO archive and have uploaded some sample "Deep Research" reports from:

(1) ChatGPT Deep Research

(2) Perplexity Deep Research

(3) Grok 3 Deep Research


I've invited a few (human) subject matter experts to comment on AI Deep Research reports relating to their particular areas of UFO research (such as Curt Collins on the Cash Landrum incident).

My own (very impressionistic and subjective...) approximate quantitative ratings are:

(1) ChatGPT o3 Deep Research: 5/10 (sometimes 6/10)

(2) Perplexity Deep Research: 4/10

(3) Grok 3 Deep Research: 3/10

(4) Average human UFO website: 4/10

(5) Average UFO page on Wikipedia: 5/10 (sometimes 6/10)


I discuss some potential next steps below (in addition to a more immediate, short-term potential collaborative project that I'd like to see completed in the next few days).  

As detailed in Section D below, anyone else interested in UFOs can help (and I've created a disposable Discord group to coordinate this) if they are :
(A) willing to create a free account with Perplexity or use Grok on Twitter, OR  
(B) a ChatGPT Plus (or Pro...) user.

For anyone wishing to be involved in that short-term collaborative project, I include step-by-step screenshots below in Section D for each of the three relevant AI models. I'm happy to help further here or in  a disposable Discord group I've created for this purpose there are basically just 3 steps (and they are fairly simple):

(1) Go to the relevant AI website and select "Deep Research" (Perplexity and Grok are free but with limits per person, ChatGPT currently requires a membership for Deep Research but this is due to change soon),

(2) Enter a query: Copy and paste the standard prompt from Section C below, just changing the keyword to change the topic upon which a Deep Research report is to be generated,

(3) Share the resulting report: Either select (a) an option to share a link for the resulting report and then just post the link, or (b) [ideally...] copy the report from the AI website using the copy icon at the end of the report and then paste the copy of the report into the free PDF generator at https://www.deepresearchdocs.com/, download the PDF then attach the PDF to a message in the disposable Discord group (or email it to me at isaackoi@gmail.com). 

In Step 3, option (a) will probably be easier for most people. I'm willing to deal with generating and uploading the PDF version... 


This post includes:

Section A : This introduction

Section B : Some samples of current "Deep Research" reports

Section C : A standard prompt I've developed to generate such reports

Section D : Details of a short potential collaborative project to build a library of such reports

Section E : Some potential next steps for developing better AI tools for use within ufology

Section F : Comments from various Subject Matter Experts on the sample reports.



Section B : Some samples of current "Deep Research" reports

Individuals - Bob Lazar:

AI - 2025 - ChatGPT o3 Deep Research - Individuals - Bob Lazar

AI - 2025 - Perplexity Deep Research - Individuals - Bob Lazar



Individuals - Richard Doty: 

AI - 2025 - ChatGPT o3 Deep Research - Individuals - Richard Doty

AI - 2025 - Perplexity Deep Research - Individuals - Richard Doty



Cases - Rendlesham:

AI - 2025 - ChatGPT o3 Deep Research - Cases - 1980 12 - Rendlesham

AI - 2025 - Perplexity Deep Research - Cases - 1980 12 - Rendlesham

AI - 2025 - xAI Grok 3 Deep Research - Cases - 1980 12 - Rendlesham


Cases - Cash Landrum: 

AI - 2025 - ChatGPT o3 Deep Research - Cases - 1980 12 29 - Cash Landrum

AI - 2025 - Perplexity Deep Research - Cases - 1980 12 29 - Cash Landrum

AI - 2025 - xAI Grok 3 Deep Research - Cases - 1980 12 29 - Cash Landrum

 

Cases - Gulf Breeze:

AI - 2025 - ChatGPT o3 Deep Research - Cases - 1980s - Gulf Breeze

AI - 2025 - Perplexity Deep Research - Cases - 1980s - Gulf Breeze

AI - 2025 - xAI Grok 3 Deep Research - Cases - 1980s - Gulf Breeze

 

Documents - MJ12:

AI - 2025 - ChatGPT o3 Deep Research - Documents - MJ12

AI - 2025 - Perplexity Deep Research - Documents - MJ12

AI - 2025 - xAI Grok 3 Deep Research - Documents - MJ12


Countries - Greece:

AI - 2025 - Perplexity Deep Research - Countries - Greece

AI - 2025 - xAI Grok 3 Deep Research - Countries - Greece


Other topics - USOs:

AI - 2025 - ChatGPT o3 Deep Research - Other - USOs - Unidentified Submerged Objects

AI - 2025 - Perplexity Deep Research - Other - USOs - Unidentified Submerged Objects

AI - 2025 - xAI Grok 3 Deep Research - Other - USOs - Unidentified Submerged Objects



Section C : Standard prompt and PDF generator

I've developed a standard prompt so that new reports can be generated just by cutting and pasting this text into the prompt for any of the AI Deep Research tools and just changing the keyword at the beginning of the query.   

Keyword = Rendlesham
I would like a detailed and critical evaluation of the available evidence relating to this topic/individual, specifically in the context of claims about Unidentified Flying Objects (UFOs) and related phenomena.
Please address the following:
(1) Historical and factual background – Provide an overview of the key events, claims, and sources related to this topic/individual.
(2) Credibility assessment – Evaluate the reliability of primary sources, whistleblower testimony, scientific studies, and government/military disclosures.
(3) Counterarguments and skepticism – Summarize key criticisms, debunking efforts, and alternative explanations.
(4) Influence and impact – Discuss how this topic/individual has shaped public perception, government policy, and the broader UFO discourse.
(5) Sources and follow-up research – List primary documents, reports, books, and expert opinions that could be further investigated to clarify unresolved aspects.
(6) Please ensure that sources are cited and that both believers' and skeptics' perspectives are represented fairly. If there are key gaps in evidence, suggest avenues for follow-up research that could help resolve lingering uncertainties.
I want an integrated report rather that one that addresses the above points individually.
Please prioritize the most detailed and critical research.
Please adopt an evidence-based approach.
Please keep a strong focus on the keyword above, rather than UFOs (or other topics) generally.

I've also been working to find tools to quickly convert the output to searchable PDFs.  

(You don't have to worry about generating a PDF as, with each model, you can just share a link to the relevant Deep Research report - see screenshots below for each model - and I'll create the PDFs and make them freely available online as long as the above standard prompt is used).  

If you want to keep a PDF copy (or reduce my workload...) you can use a free tool online at: https://www.deepresearchdocs.com/

The tool at https://www.deepresearchdocs.com/ makes it easy (and free) to create a relevant PDF. (Perplexity has an option to save Deep Research reports as PDFs, but this other tool is actually better than Perplexity's own option...). The steps to use this tool are:

(1) Copy and paste the Deep Research report from the relevant AI website into the box marked A in screenshot below.
(2) Generate the PDF by click the box marked B in the screenshot below.
(3) Download that PDF by click the box marked C in the screenshot below.  (Don't worry about the file name - I will rename any PDFs sent to me or upload to the disposable Discord group).






Section D : Short potential collaborative project to build a library of such reports 

I think it is worth pulling together more samples of such Deep Research into a freely available new relevant library online in my free online UFO archive

To pull together a small library of Deep Research reports from current online models, a bit of collaboration would be great. Several AI models have limits on the number of "Deep Research" queries you can do (10 per month on ChatGPT Plus...) and they can take about 10 minutes per report to be generated, so spreading the workload a bit would allow much faster creation of a representative sample of Deep Research reports. 

I currently pay for one AI "Deep Research" tool (on ChatGPT Plus) and also use "Deep Research" on the the free level of various other AI tools (particularly Perplexity's Deep Research and Grok 3's Deep Research).

Anyone else interested in UFOs can help if they are :
(A) willing to create a free account with Perplexity or use Grok on Twitter.  
(B) a ChatGPT Plus (or Pro...) user.

I'm willing to help organise a very short collaborative project during the next few days.

Coordination in a new disposable Discord group (open invitation link HERE) would, I think, be easiest/fastest - with a view to abandoning the new group within a few days. I've just set up a new group...

Anyone else interested in UFOs can help if they are :
(A) willing to create a free account with Perplexity or use Grok on Twitter, OR  
(B) a ChatGPT Plus (or Pro...) user.

For anyone wishing to be involved, I include below step-by-step screenshots for the three AI models (and am happy to help here or in the disposable Discord group I've created for this purpose), but there are basically just 3 steps (and they are fairly simple):

(1) Go to the relevant AI website and select "Deep Research" (Perplexity and Grok are free but with limits per person, ChatGPT currently requires a membership to generate any Deep Research reports but this is due to change soon),

(2) Enter a query: Copy and paste the standard prompt from Section C, just changing the keyword to change the topic upon which a Deep Research report is to be generated,

(3) Share the resulting report: Either select (a) an option to share a link for the resulting report and then just post the link, or (b) [ideally...] copy the report from the AI website using the copy icon at the end of the report and then paste the copy of the report into the free PDF generator at https://www.deepresearchdocs.com/, download the PDF then attach the PDF to a post in the disposable Discord group (or just email it to me at isaackoi@gmail.com and I'll sort it out). 

In Step 3, option (a) will probably be easier for most people and I'm willing to deal with generating and uploading the PDF version... 







Section D1 : ChatGPT Deep Research

ChatGPT's Deep Research is online at: 
https://chatgpt.com/

ChatGPT's Deep Research is currently only available to those paying for membership of ChatGPT Plus and ChatGPT Pro (although OpenAI has indicated that free users will soon be able to do a very limited number of searches per month).  

I am a ChatGPT Plus user, but we are limited to 10 searches per month per user - so this is the model in relation to which some collaboration would be most desirable.

I'm sure many of the people interested in UFOs on Twitter and elsewhere have a ChatGPT Plus account, so hope a few people will help out increasing the size of the relevant free online library of Deep Research reports from ChatGPT...

Sample reports by ChatGPT o3 Deep Research (2025):

(1) Go to https://chatgpt.com/ and select "Deep Research":

(2) Enter a query: Copy and paste the standard prompt from Section C, just changing the keyword to change the topic upon which a Deep Research report is to be generated.  (You can then use your computer for other tasks while the Deep Research report is generated, which usually takes about 10 minutes at present on ChatGPT).


(3) Share the resulting report: Either 
(a) click on the "Share" button at the end of the report (i.e. the button marked "A" in the screenshot below) and then just post share a link for the resulting report on Twitter or in the disposable Discord group, OR 
(b) [ideally...] copy the report from the AI website using the copy icon at the end of the report (i.e. the button marked "B" in the screenshot below) and then paste the copy of the report into the free PDF generator at https://www.deepresearchdocs.com/, download the PDF then attach the PDF to a message in the disposable Discord group (or email it to me at isaackoi@gmail.com). 

In Step 3, option (a) will probably be easier for most people. I'm willing to deal with generating and uploading the PDF version... 




If you went for Option B in Step 3, go to https://www.deepresearchdocs.com/ and use the free tool there to create a relevant PDF.  The steps to use this tool are:

(1) Copy and paste the Deep Research report from the relevant AI website into the box marked A in screenshot below.
(2) Generate the PDF by click the box marked B in the screenshot below.
(3) Download that PDF by click the box marked C in the screenshot below.  (Don't worry about the file name - I will rename any PDFs sent to me or upload to the disposable Discord group).



Section D2 : Perplexity Deep Research

Perplexity's Deep Research is online at:
https://www.perplexity.ai/

Anyone can use Perplexity to generate Deep Research reports for free (up to a daily limit).

Sample Reports by Perplexity Deep Research (2025):

AI - 2025 - Perplexity Deep Research - Individuals - Bob Lazar

AI - 2025 - Perplexity Deep Research - Individuals - Richard Doty

AI - 2025 - Perplexity Deep Research - Cases - 1980 12 - Rendlesham

AI - 2025 - Perplexity Deep Research - Cases - 1980 12 29 - Cash Landrum

AI - 2025 - Perplexity Deep Research - Cases - 1980s - Gulf Breeze

AI - 2025 - Perplexity Deep Research - Other - USOs - Unidentified Submerged Objects

AI - 2025 - Perplexity Deep Research - Documents - MJ12

To generate a "Deep Research" report on Perplexity:

(1) Go to https://www.perplexity.ai/ and select "Deep Research" from the drop down memo (which may show the word "Auto" when you load that webpage).

(2) Enter a query: Copy and paste the standard prompt from Section C, just changing the keyword to change the topic upon which a Deep Research report is to be generated.  (You can then use your computer for other tasks while the Deep Research report is generated, which usually takes about 5 minutes at present on Perplexity).



(3) Share the resulting report: Either 
(a) click on the "Share" button at the end of the report (i.e. the button marked "A" in the screenshot below) and then just post share a link for the resulting report on Twitter or in the disposable Discord group, OR 
(b) [ideally...] copy the report from the AI website using the copy icon at the end of the report (i.e. the button marked "B" in the screenshot below) and then paste the copy of the report into the free PDF generator at https://www.deepresearchdocs.com/, download the PDF then attach the PDF to a message in the disposable Discord group (or email it to me at isaackoi@gmail.com), OR
(c) As a last resort you could export as a PDF (using the button marked C in the screenshot below) BUT Perplexity's own tool for creating a PDF is, well, not good.  Either of the other options above is better.






If you went for Option B in Step 3, go to https://www.deepresearchdocs.com/ and use the free tool there to create a relevant PDF. (As I mentioned above, Perplexity has an option to save Deep Research reports as PDFs, but this other tool is actually better than Perplexity's own option...). The steps to use this tool are:

(1) Copy and paste the Deep Research report from the relevant AI website into the box marked A in screenshot below.
(2) Generate the PDF by click the box marked B in the screenshot below.
(3) Download that PDF by click the box marked C in the screenshot below.  (Don't worry about the file name - I will rename any PDFs sent to me or upload to the disposable Discord group).


Section D3 : Grok 3 Deep Research

Grok 3's Deep Research is most conveniently most found within Twitter at:
https://x.com/



Anyone can use Grok to generate Deep Research reports for free (up to a daily limit).


To generate a "Deep Research" report using Grok on Twitter:

(1) Go Twitter at https://x.com/ , select "Grok" from the menu on the left, and click on the "Deep Research" button.

(2) Enter a query: Copy and paste the standard prompt from Section C, just changing the keyword to change the topic upon which a Deep Research report is to be generated.  (You can then use your computer for other tasks while the Deep Research report is generated, which usually takes a couple of minutes at present on Grok).




(3) Share the resulting report: Either 
(a) click on the "Share" button at the end of the report (i.e. the button marked "A" in the screenshot below) and then just post share a link for the resulting report on Twitter or in the disposable Discord group, OR 
(b) [ideally...] copy the report from the AI website using the copy icon at the end of the report (i.e. the button marked "B" in the screenshot below) and then paste the copy of the report into the free PDF generator at https://www.deepresearchdocs.com/, download the PDF then attach the PDF to a message in the disposable Discord group (or email it to me at isaackoi@gmail.com)





If you went for Option B in Step 3, go to https://www.deepresearchdocs.com/ and use the free tool there to create a relevant PDF. (Perplexity has an option to save Deep Research reports as PDFs, but this other tool is actually better than Perplexity's own option...). The steps to use this tool are:

(1) Copy and paste the Deep Research report from the relevant AI website into the box marked A in screenshot below.
(2) Generate the PDF by click the box marked B in the screenshot below.
(3) Download that PDF by click the box marked C in the screenshot below.  (Don't worry about the file name - I will rename any PDFs sent to me or upload to the disposable Discord group).



Section E : Potential next steps

Each of the above Deep Research tools is online. 

However, notably, open source Deep Research tools and "agentic" tools are now becoming available for installation (and modification) on your home computer, for free. 

I think that examination of the current output of general online models (and the flaws in such output), may help with the adaptation of such open source Deep Research / agentic tools for improved AI assistance with UFO research. 

 Over the last few years, I've posted about various AI tools for UFO research that I've created, including "Robert" (2018), "Jenny" (2023, April), Dave (2023, November) and "Jacques" (2024, June) and the pace of development is clearly increasing. 

A new UFO research tool, "Edoardo", should be completed soon, unless I delay its completion to add such increased functionality.

For anyone looking to play with basic AI tools online, Google's NotebookLM is a pretty easy-to-use online AI solution. NotebookLM's name is potentially misleading. It isn't some sort of notetaking app or notebook writing tool.  It can do a lot. You can upload a bunch of PDFs then generate an audio summary of them (basically a podcast with two AI presenters) OR ask questions using your voice or in writing OR generate text summaries, FAQs or written answers to specific questions.

One limitation of Notebook is that you have to upload your PDF (and there is a limit to the amount of material you can upload).  LM Studio runs on your home computer and, compared to most such options, relatively straight forward - but definitely a rung or two up the difficulty ladder compared to NotebookLM

NotebookLM's name is potentially misleading. It can do a lot. You can upload PDFs then generate an audio summary of them (basically a podcast with 2 AI presenters) OR ask questions by voice or in writing OR generate text summaries, FAQs or written answers to specific questions.

As I discussed in my item about "Jacques" (2024, June), free AI software - such as LMStudio which you can install (relatively...) easily at home has "RAG" functions incorporated which can be used to assimilate the content of  personal collections of scanned UFO files (such as PDF scans of UFO books, UFO magazines / newsletters, official UFO documents, newspaper cuttings about UFOs, UFO case files from various UFO groups, transcripts of UFO podcast / documentaries, PhD dissertations about UFOs, relevant academic journal articles and other material). 

LMStudio is basically an easy-to-install and (relatively...) easy-to-use interface for any AI/LLM models you choose to download. Those models vary from pretty small (a few GB) to absolutely huge. So, installing LMStudio itself doesn't require any advanced computer or GPU - but the problem is the size of the models you add to it.   I have previously used LMStudio with a fairly good medium-sized model (and the medium-sized model didn't exhaust either the RAM or the VRAM).  That was certainly fast enough, and good enough, to be usable.  (Online resources such as ChatGPT are smarter, but having an AI model on your computer allows easier access to PDF files and also improves privacy...). 

LMStudio is now faster and better using more recent models (e.g. distillations of Deepseek's R1 model).

Setting up LMStudio is a little bit fiddly (but doesn't require the ability to code in Python, unlike some other AI tools). 

NotebookLM is definitely a simpler experience and, in most cases (unless you want to be able to assimilate a large collection of PDFs...), probably as good as (or better), and faster than, a small or medium sized model on LMStudio.

The open source Deep Research tools can now installed at home and combined with other tools, such reasoning models (such as distillations of the Chinese DeepSeek R1 model) and 

Combining these sets of AI tools can result in fast AI consideration of personal offline resources, going beyond the general online tools referred to above. (I've previously uploaded a few million pages of UFO material and am currently seeking to address some issues which have prevented uploading further material e.g. privacy, confidentiality and permission issues).

When posted about my "Robert" UFO chatbot in 2018, I commented that it "certainly isn't that bright" but that the point was to prompt a bit of thinking about _how_ this technology could be used within ufology. Some other researchers were very kind about that initial attempt. For example, a leading European UFO researcher, Vicente-Juan Ballester-Olmos, made some very generous remarks on his Fotocat blog in March 2019. He wrote that: "In my considered opinion, what Isaac Koi has done is one of the best, most proactive and original developments in UFO research in the last decades". I similarly hope that this current item inspires some further thought about the potential use of such tools to improve the quality of research within ufology.

 



Section F : Comments from various Subject Matter Experts

Section F1 : Curt Collins on the Cash Landrum reports above

Curt Collins of Blue Blurry Lines (an expert on the Cash Landrum case) has commented:
"Interesting and disappointing. All have flaws in evaluating the quality and significance of details repeated in the source literature. Many questionable elements are stated as facts of the case, and the trivia often gets more space than key evidence. 

The ChatGPT version relies too heavily on the Wikipedia page for the case (which, though recently improved, is contaminated by factual mistakes). Rather than citing Wikipedia, I'd like it to cite the original source wherever possible. 

The Perplexity version contains "The Satellite Reentry Theory," which I've never heard before, and it seems to be a hallucination. 

The Grok version has the benefit of stating the UFO event as something the witnesses reported, not describing it as something actually occurring. 

My evaluation based on these three samples is that AI can produce a passable-looking presentation summarizing both genuine and faulty information, one that reinforces misinformation already circulating. The best thing I can say about it is that it does attempt to provide a balanced approach, mentioning negative facts about the case discovered by skeptics. Overall, AI produces a product that looks good, but would be a time-wasting nightmare for a researcher to fact-check and correct.

On a scale of 10: 
6 - ChatGPT. Points for being comprehensive but that's largely for relying too heavily on a flawed Wikipedia page. 
5 - Perplexity. Fair overview of the case, but a few hallucination. 
4 - Grok. Lacked depth, like a novice wrote it based on the first few sources located. 
4 - Human. Typical page is a summary based on previous botched summaries. Scores lower than the bots due to people generally providing one-side coverage."


Thursday, December 12, 2024

AI-Generated UFO "podcasts" - AI audio summaries of UFO books - some samples (with thanks to the book authors for their permission)

As a bit of fun, I thought I’d share my latest experiment with applying Artificial Intelligence tools to UFO research: using AI tools to generate "podcasts" discussing various UFO books (with kind permission from their respective authors).

This side project was a short break from creating my next (much, much larger) RAG AI tool for UFO research (named "Edoardo"). "Edoardo" is the successor to my previous post about a UFO AI tool called "Jacques" that I created a few months ago (which, in turn, was the successor to the previous UFO chatbots I've created since 2018, i.e. "Robert" (2018), "Jenny" (2023, April) and Dave (2023, November)).

As someone who generally prefers written material when researching UFOs—whether it’s documents, books, or newsletters—I’ve focused most of my AI experiments and digitising efforts on text. However, I occasionally venture into audio material, particularly where I can bridge the help gap between audio and written content (e.g., creating millions of pages of transcripts of UFO podcasts and documentaries).

This latest experiment takes things in the opposite direction, turning written UFO material into audio content. Using Google’s NotebookLM, I generated a series of podcasts that discuss several UFO books, with the permission of the respective authors.

If you’re curious to see the results of this experiment, check out the sample podcasts in the Youtube playlist at the link below (or any individual sample listed further down below).

https://www.youtube.com/playlist?list=PLoS2GnE2k-ZpJu_3xBN2bXi4xMaXA1cQe








The Process

The AI software was fed scanned copies of an entire book and instructed to generate conversational-style podcasts discussing it. The resulting audio features two AI-created “presenters”—one male, one female—engaging in dialogue about the books. Both the dialogue and the voices themselves are entirely machine-made.

I then used another AI tool (ChatGPT) to generate Python computer code that combined the resulting audio files (in WAV format) with related images, producing batches of video files (in MP4 format) suitable for uploading to YouTube. I’ve included the code below for anyone interested in experimenting with this approach themselves.

(These initial experiments only used one book per "podcast", to make it simpler to get relevant permissions. I have done some private experiments with multiple books and hopefully will soon post some "podcasts" discussing opposing points of view on individual UFO cases).





The Positivity Problem

While the AI-generated voices are impressively lifelike, one of the most striking (and frustrating) limitations of these AI-generated podcasts is their relentless positivity. The software tends to be extremely complimentary about the books and their authors, making the podcasts feel lacking in critical analysis. This is, of course, a far cry from real UFO podcasts. :)

Still, as a fun and experimental way to present UFO literature in a new format, these "podcasts" are a further way to explore the potential of AI in UFO research.

It is possible to give "customization" instructions when generating the "podcasts". I may try giving an instruction such as "Rip apart this book and the reasoning in it" to see if the positivity problem can be overcome - but I'm not sure authors of UFO books will be queueing up to volunteer for that potential further experiment...



An unexpected success - "podcasts" discussing foreign language books

The result that I found most surprising is the successful results obtain when feeding foreign UFO books into the AI software. The samples below include podcasts in English discussing two UFO books written in French by Bertrand Meheust (namely "Science fiction et soucoupes volantes" and "Soucoupes volantes et folklore"), generated with his permission. Similar successes were obtained with Italian UFO books.


Legal and Ethical Considerations

The authors of the relevant books gave permission for each of the "podcasts" below to be shared. The copyright issues in relation to such audio summaries are, well, interesting so I'd recommend considerable caution in this regard. I also have some qualms about posting these "podcasts" given that they do contain some factual errors, but I think anyone that has had any experience with AI tools in the last year knows about the risk of significant factual errors and "hallucinations" so knows not to treat such material as entirely reliable. I did consider only posting such "podcasts" if I also had permission to share the entire relevant book so that it would be very easy to check the source material, but eventually decided this would be an unduly restrictive approach.


The "podcasts" in this initial experiment include the following:




"1973" - a book by Kevin Randle

AI Summary: "Kevin Randle's book examines the 1973 wave of UFO sightings, focusing on the Pascagoula abduction of Charles Hickson and Calvin Parker as a pivotal event. The book explores various other 1973 UFO incidents, including the Coyne helicopter encounter, analyzing both credible reports and potential hoaxes. Randle investigates the motivations behind the sightings, considering both extraterrestrial and terrestrial explanations, and uses hypnosis and polygraph results in his analysis. The book also explores earlier and later cases of alleged alien abductions to provide context and further evidence for his conclusions, ultimately arguing for the reality of some alien visitation events. A significant portion of the text consists of detailed accounts of these cases and the investigations surrounding them."














"A World of UFOs" - a book by Chris Rutkowski

AI Summary: "Rutkowski's "A World of UFOs" is a book exploring numerous UFO sightings worldwide. The book categorizes cases as most famous, most bizarre, and most interesting, examining details and evidence for each. Geographical regions are covered, including Asia, Europe, and the Americas, comparing and contrasting different incidents and their interpretations. The author presents a variety of perspectives from skeptics to believers, and assesses the evidence and credibility of various claims. The text also discusses the history of UFO investigations and the challenges in determining what constitutes credible evidence."















"American Cosmic - UFOs, Religion, and Technology" - a book by Diana Pasulka

AI Summary: "Diana Pasulka's American Cosmic explores the intersection of UFO phenomena and religion, examining the beliefs and experiences of scientists, academics, and experiencers. The book investigates a parallel research tradition surrounding UFOs, featuring a clandestine group of scientists conducting anonymous research. Pasulka's investigation explores the religious interpretations of UFO encounters, drawing comparisons to historical religious experiences and examining the role of technology in shaping beliefs. The author examines how media representations, both fictional and purportedly factual, influence perceptions and beliefs related to UFOs. Ultimately, American Cosmic argues that belief in UFOs constitutes a new form of technologically-mediated religion."















"Anachronism" - a book by James Carrion

AI Summary: "The text explores a purported post-World War II deception operation, codenamed "Rosetta," involving the U.S. and British intelligence agencies. Rosetta's primary goal was to mislead Soviet leader Joseph Stalin by disseminating false information about a new American superweapon, thereby gaining insight into Soviet intentions and identifying spies. The author supports this claim by analyzing declassified documents and press reports from 1946-1947, focusing on the "Ghost Rocket" events in Scandinavia. The narrative intertwines historical analysis with espionage and codebreaking, examining the strategies and individuals involved in the alleged deception. The author ultimately leaves the reader to determine whether the events constitute a genuine conspiracy."












"Clear Intent" - a book by Barry Greenwood and Lawrence Fawcett

AI Summary: "Lawrence Fawcett and Barry Greenwood's book, Clear Intent, argues that the U.S. government has covered up evidence of UFOs. The authors utilize declassified documents from various agencies (CIA, FBI, Air Force) to support their claim of a decades-long cover-up. The book presents numerous accounts of UFO sightings near sensitive military installations and discusses government responses, highlighting inconsistencies and secrecy. It also examines the use of the Freedom of Information Act to access UFO-related information and the challenges faced by researchers. Ultimately, Clear Intent suggests a significant government effort to conceal the existence and nature of UFO phenomena."












"Encounters" - a book by Diana Pasulka

AI Summary: "Diana Pasulka's Encounters explores the intersection of UFO phenomena and human consciousness. The book features accounts from various individuals, including scientists, pilots, and experiencers, who describe encounters with nonhuman intelligence and anomalous events. Pasulka examines the psychological and spiritual effects of these encounters, exploring themes of altered realities, synchronicities, and the potential for communication with entities from outside our spacetime. The author connects these experiences to broader themes of technological advancement, the limitations of current scientific understanding, and the possibility of a universal language connecting humans and other intelligences. Ultimately, Encounters challenges conventional perspectives on UFOs and suggests a deeper, more interconnected reality."












"Escaping the Rabbit Hole" - a book by Mick West

AI Summary: "Mick West's Escaping the Rabbit Hole examines the prevalence and harm of false conspiracy theories. The book uses West's personal experience running the debunking website Metabunk, anecdotal evidence from individuals who escaped believing in conspiracy theories, and research on the psychology behind conspiracy beliefs to explain the problem and offer solutions. It explores specific theories like Chemtrails and Flat Earth, highlighting the misinformation driving them. Finally, the book discusses the societal impact of these beliefs and explores potential strategies for combating misinformation online."













"Immaculate Constellation" - controversial document

AI Summary: "This report, stemming from a multi-year internal U.S. government investigation, reveals disturbing evidence of a long-standing, high-level conspiracy to conceal information about Unidentified Anomalous Phenomena (UAP), Technologies of Unknown Origin (TUO), and Non-Human Intelligence (NHI). The document, leaked by a whistleblower, presents compelling evidence from multiple sources, including imagery intelligence, human intelligence, and signals intelligence, demonstrating the reality of UAP and the government's awareness. Key evidence centers on the existence of a covert program called IMMACULATE CONSTELLATION, tasked with monitoring UAP activity. The report concludes that the U.S. government has actively suppressed this information from Congress and the public, presenting a significant threat to national security and democratic governance."









"Loose Threads" by Hermetic Penetrator and Omega Point on Davis Wilson Memo

A copy of "loose threads" can be downloaded from Dropbox.

AI Summary: "This document explores the intersection of government, intelligence agencies, and UFO research, focusing on the Advanced Theoretical Physics (ATP) working group and its connections to subsequent investigations. It analyzes various individuals and their roles in these efforts, including meetings, notes, and alleged covert programs. The text examines the alleged existence of hidden reverse engineering projects involving recovered UFO technology and the use of electromagnetic pulses in potential retrieval operations. The authors examine the history of these groups and individuals, drawing on open-source material. Ultimately, the document suggests a long-standing, interconnected network dedicated to researching unexplained aerial and undersea phenomena, spanning decades and various government and private entities."






"Managing Magic" - a book by Grant Cameron

AI Summary: "Grant Cameron's Managing Magic explores the history of the United States government's handling of UFO-related information, examining various theories about government secrecy and potential disclosure plans. The book analyzes the terminology surrounding UFOs and its impact on public perception, investigates alleged leaks and disinformation campaigns, and explores the roles of key figures within government and the UFO community. Cameron also discusses potential motivations behind secrecy, including national security concerns, the impact on religious beliefs, and the possibility of advanced alien technology. Ultimately, the book presents a complex narrative of conflicting accounts and suggests a gradual, controlled release of information rather than a single, dramatic disclosure event."












"McDonnell Douglas UFO documents" - Robert M Woods etc

AI Summary: "These documents detail a 1960s research project exploring unconventional physics and propulsion systems, drawing inspiration from UFO observations. The research investigates a "brutino" theory, proposing a fundamental particle and its interactions to explain phenomena like gravity and light. Experiments involving magnetic fields and light are described, along with explorations of free-energy propulsion concepts. The project also examines unusual phenomena like ball lightning and ESP, suggesting potential connections to advanced technology and interspecies communication. The ultimate goal appears to be developing a revolutionary space propulsion system based on a novel understanding of the universe."









"Mirage Men - A Journey into Disinformation, Paranoia and UFOs" - a book by Mark Pilkington

AI Summary: "Mark Pilkington's Mirage Men explores the history of UFOs, examining how the phenomenon has been shaped by government disinformation campaigns and the public's fascination with the unknown. The book investigates several key events and figures in UFO lore, including the Roswell incident and the Paul Bennewitz affair, analyzing how misinformation was spread and how myths were created. Pilkington interweaves personal anecdotes from his involvement with UFO groups with historical research. He argues that the UFO myth is a self-sustaining narrative, though sometimes manipulated by intelligence agencies for their own purposes. Ultimately, the book examines the complex interplay between reality, deception, and the human tendency to create myths."












"Science fiction et soucoupes volantes" - a book by Bertrand Meheust

AI Summary: "Bertrand Méheust's Science-fiction et soucoupes volantes explores the intriguing convergence between science fiction narratives and real-world UFO sightings. The book examines numerous accounts of UFO phenomena, analyzing their common characteristics and thematic elements. Méheust connects these observations to prevalent cultural myths and archetypes, suggesting a deeper, symbolic meaning. The work also investigates the evolution of UFO descriptions in both fiction and non-fiction accounts over time. Finally, the author considers psychological and sociological factors that contribute to the phenomenon."















"Scientific UFOlogy" - a book by Kevin Randle

AI Summary: "Kevin Randle's Scientific Ufology examines purported UFO sightings using scientific methodology. The book analyzes eyewitness accounts, radar data, and photographic evidence, aiming to prove the existence of UFOs. Randle explores various cases, including the Levelland lights and the Great Falls photographs, assessing the reliability of evidence and challenging conventional explanations. He emphasizes the need for rigorous investigation and considers alternative hypotheses where necessary. Ultimately, the book argues for a more scientific approach to UFO research, seeking to distinguish genuine phenomena from hoaxes or misinterpretations."












"Skinwalker at the Pentagon" - a book by James Lacatski, Colm Kelleher and George Knapp

AI Summary: "This text excerpts a book detailing the Advanced Aerospace Weapon System Applications Program (AAWSAP), a U.S. government initiative investigating unidentified aerial phenomena (UAPs). The book, written by AAWSAP program managers and a journalist, recounts the program's research, including investigations into Skinwalker Ranch and the famous "Tic Tac" UAP encounter. The authors explore both the technological and paranormal aspects of UAPs, citing various incidents and scientific analyses. They also discuss the program's challenges, including funding issues and secrecy, and its legacy's impact on subsequent UAP investigations. Furthermore, the text highlights the program's findings, including the potential health effects of UAP encounters and the possibility of an "infectious agent" linked to Skinwalker Ranch. Finally, it considers the broader implications of UAP research and the need for continued scientific investigation."















"Somewhere in the Skies" - a book by Ryan Sprague

AI Summary: "Ryan Sprague's Somewhere in the Skies compiles numerous firsthand accounts of UFO sightings and encounters. The book features interviews with individuals who describe a range of experiences, from fleeting observations of unusual lights to more intense and unsettling encounters, including alleged abductions. Sprague explores the emotional and psychological impact these experiences have on the witnesses. He also discusses the lack of scientific investigation into the phenomenon and the efforts of researchers attempting to change this. Ultimately, the book presents a collection of personal narratives that aim to explore the enigma of UFOs."












"Soucoupes volantes et folklore" - a book by Bertrand Meheust

AI Summary: "This document analyzes UFO encounters, specifically close encounters of the third and fourth kind (sightings and abductions), by comparing them to folklore and mythology. The author argues that many aspects of reported UFO experiences—such as physical symptoms, location choices, and narrative structures—mirror elements found in older legends and religious beliefs. This suggests that cultural narratives significantly shape the perception and recollection of these encounters, rather than indicating purely extraterrestrial origins. The text explores various thematic parallels, demonstrating how UFO narratives draw upon pre-existing cultural archetypes. Finally, it considers psychological factors and potential misinterpretations in the reporting of these events."












"The Roswell Deception" - a book by James Carrion

AI Summary: "James Carrion's The Roswell Deception posits that the 1947 flying saucer phenomenon was a strategic deception operation orchestrated by the U.S. military to mislead the Soviet Union about American technological advancements during the early Cold War. The book argues that the reported sightings, including Kenneth Arnold's initial observation, were part of a carefully planned campaign involving the release of misleading information through the media and the manipulation of public perception. Carrion supports his theory using declassified documents and contemporary news articles, tracing the involvement of key military and intelligence figures. He explores the interplay of real events, such as a missing Marine transport plane, with fabricated elements of the story to create a convincing narrative for Soviet intelligence. The author ultimately suggests that the true nature of the Roswell events remains obscured by classified information."

















"The UFO Verdict - Examining The Evidence" - a book by Robert Sheaffer

AI Summary: "This text excerpts Robert Sheaffer's book, "The UFO Verdict," which critically examines evidence presented by UFO proponents. The author investigates various famous UFO cases, analyzing eyewitness accounts, photographs, and other evidence, often revealing flaws and inconsistencies. Sheaffer contrasts the approaches of different UFO organizations, highlighting their varying levels of skepticism and the evolution of UFO beliefs over time. He also compares the UFO phenomenon to historical examples of belief in witchcraft and fairies, suggesting parallels in the way unsubstantiated claims are promoted and accepted. The book ultimately argues that many UFO claims lack credible support and are better explained by misidentification, hoaxes, or psychological factors."















"Triangular UFOs" - a book by David Marler

AI Summary: "This book excerpt compiles and analyzes numerous eyewitness accounts of triangular UFO sightings from 1936 to 2004. The author details these events chronologically, highlighting common characteristics like silent flight, unusual maneuvers, and bright lights. The text includes police reports, newspaper articles, and personal testimonies, aiming to build a comprehensive case study of these unidentified aerial phenomena. Analysis also incorporates input from aerospace experts who discuss the possibility of advanced, secret aircraft as explanations. Finally, the author offers recommendations for government and military response to these encounters."












"UFOs a report on Australian encounters" - a book by Keith Basterfield

AI Summary: "Keith Basterfield's UFOs: A Report on Australian Encounters is a revised and updated edition cataloging Australian UFO sightings from 1967 to 1980. The book examines various accounts, including close encounters, and proposes explanations for some of the reported phenomena. Basterfield explores potential causes like satellites, weather balloons, and even psychological factors, but ultimately concludes that many events remain unexplained. A significant section is devoted to a comprehensive catalogue of Australian UFO reports, offering detailed descriptions and analysis of each case. The book aims to present a balanced view, considering both mundane and extraordinary explanations for these intriguing events."












"UFOs and Outer Space Mysteries" - a book by James Oberg (AI podcast experiment)

AI Summary: "This text comprises excerpts from James E. Oberg's book, UFOs and Outer Space Mysteries, a report by a "sympathetic skeptic." Oberg investigates various UFO and space-related claims, meticulously examining evidence and exposing hoaxes. The book explores alleged astronaut UFO sightings, the "hollow moon" theory, the Dogon tribe's astronomical knowledge, and the Tunguska event. Oberg systematically debunks many popular claims, emphasizing the importance of rigorous investigation and critical thinking in evaluating extraordinary claims. He ultimately concludes that while genuine space mysteries exist, they are distinct from the often fabricated narratives surrounding UFO phenomena."











"UFOs and the Deep State" - a book by Kevin Randle

AI Summary: "Kevin Randle's UFOs and the Deep State is a non-fiction book exploring alleged government cover-ups of UFO sightings. Randle argues that a powerful, shadowy "Deep State" manipulated investigations, suppressed evidence, and spread disinformation to control public perception of UFOs. The book uses numerous case studies, including the Roswell incident, to illustrate this alleged manipulation by government agencies like the Air Force Office of Special Investigations (AFOSI). The author examines various projects and committees, highlighting instances of witness intimidation, evidence suppression, and the dissemination of misleading information. Ultimately, the book posits a sustained, coordinated effort to conceal the truth about UFO phenomena."











"UFOs Generals, Pilots & Government Officials Go On the Record" - a book by Leslie Kean

AI Summary: "This text compiles numerous firsthand accounts and official documents concerning UFO sightings, primarily focusing on cases involving military pilots and government officials from various countries. The collection emphasizes the lack of credible explanations for many observed phenomena, highlighting instances where objects demonstrated capabilities exceeding known technology. Several authors argue for the need for increased governmental transparency and serious scientific investigation into these unidentified aerial phenomena (UAP). The text also explores the historical responses of governments, particularly the U.S., contrasting approaches that ranged from ridicule and secrecy to more open investigations. Ultimately, the sources advocate for a re-evaluation of the UAP phenomenon, urging a shift towards objective scientific study and the acknowledgement of unexplained aerial events."















"UFOs That Never Were" - a book by Jenny Randles, David Clarke and Andy Roberts

AI Summary: "This text comprises excerpts from "The UFOs That Never Were," a book investigating purported UFO sightings and crashes in Britain. The authors analyze various cases, demonstrating how misidentification, hoaxes, and media hype contribute to the persistence of UFO beliefs. They examine specific incidents, meticulously presenting evidence to expose mundane explanations for seemingly extraordinary events. The book employs investigative journalism techniques to challenge established UFO narratives and promote more critical thinking about such claims. Ultimately, it aims to separate fact from fiction within the realm of UFOlogy."










Computer Code

For those interested in creating similar content, here’s the Python computer code I used to combine audio and image files into video format:

The Python code created for combining a collection of the relevant sound files (in wav format) with related image files and outputting them as video video (in mp4 format) was:


import ffmpeg
import os

def create_video_from_audio_and_images():
    # Directory paths and constants
    current_folder = os.getcwd()
    target_height = 720  # Target height for all images
    max_width = 1920     # Total width for the final combined image

    for filename in os.listdir(current_folder):
        if filename.endswith(('.mp3', '.wav')):
            audio_path = os.path.join(current_folder, filename)
            base_name = os.path.splitext(filename)[0]
            main_image_jpg = os.path.join(current_folder, base_name + '.jpg')
            main_image_png = os.path.join(current_folder, base_name + '.png')
            template_image1_path = os.path.join(current_folder, "podcast template image1.png")
            template_image2_path = os.path.join(current_folder, "podcast template image2.png")

            if os.path.exists(main_image_jpg):
                main_image_path = main_image_jpg
            elif os.path.exists(main_image_png):
                main_image_path = main_image_png
            else:
                print(f"Main image not found for {filename}, skipping.")
                continue

            if not os.path.exists(template_image1_path) or not os.path.exists(template_image2_path):
                print(f"One or both template images not found, skipping.")
                continue

            combined_image_path = os.path.join(current_folder, base_name + '_combined.png')
            output_video = os.path.join(current_folder, base_name + '.mp4')

            try:
                # Scale images while maintaining aspect ratios
                scaled_template1 = os.path.join(current_folder, 'scaled_template1.png')
                scaled_main_image = os.path.join(current_folder, 'scaled_main_image.png')
                scaled_template2 = os.path.join(current_folder, 'scaled_template2.png')

                ffmpeg.input(template_image1_path).filter('scale', -1, target_height).output(
                    scaled_template1, vframes=1, format='image2'
                ).overwrite_output().run()

                ffmpeg.input(main_image_path).filter('scale', -1, target_height).output(
                    scaled_main_image, vframes=1, format='image2'
                ).overwrite_output().run()

                ffmpeg.input(template_image2_path).filter('scale', -1, target_height).output(
                    scaled_template2, vframes=1, format='image2'
                ).overwrite_output().run()

                # Pad images to equal widths
                width_per_image = max_width // 3

                padded_template1 = os.path.join(current_folder, 'padded_template1.png')
                padded_main_image = os.path.join(current_folder, 'padded_main_image.png')
                padded_template2 = os.path.join(current_folder, 'padded_template2.png')

                ffmpeg.input(scaled_template1).filter('pad', width_per_image, target_height, '(ow-iw)/2', 0).output(
                    padded_template1, vframes=1, format='image2'
                ).overwrite_output().run()

                ffmpeg.input(scaled_main_image).filter('pad', width_per_image, target_height, '(ow-iw)/2', 0).output(
                    padded_main_image, vframes=1, format='image2'
                ).overwrite_output().run()

                ffmpeg.input(scaled_template2).filter('pad', width_per_image, target_height, '(ow-iw)/2', 0).output(
                    padded_template2, vframes=1, format='image2'
                ).overwrite_output().run()

                # Combine padded images horizontally
                ffmpeg.filter(
                    [ffmpeg.input(padded_template1), ffmpeg.input(padded_main_image), ffmpeg.input(padded_template2)],
                    'hstack', inputs=3
                ).output(combined_image_path, vframes=1, format='image2').overwrite_output().run()

                print(f"Combined image saved as: {combined_image_path}")

                # Get audio duration
                audio_info = ffmpeg.probe(audio_path)
                audio_duration = float(audio_info['format']['duration'])

                # Create video
                video_stream = (
                    ffmpeg
                    .input(combined_image_path, loop=1)
                    .filter('scale', 'iw-mod(iw,2)', 'ih-mod(ih,2)')
                    .trim(duration=audio_duration)
                    .setpts('PTS-STARTPTS')
                )
                audio_stream = ffmpeg.input(audio_path)

                ffmpeg.concat(video_stream, audio_stream, v=1, a=1).output(
                    output_video,
                    vcodec='libx264',
                    acodec='aac',
                    audio_bitrate='128k',
                    movflags='+faststart',
                    pix_fmt='yuv420p'
                ).overwrite_output().run()

                print(f"Created video: {output_video}")
            except ffmpeg.Error as e:
                print(f"Error creating video for {filename}: {e.stderr.decode() if e.stderr else str(e)}")
            except Exception as e:
                print(f"Unexpected error for {filename}: {e}")


# Run the script
create_video_from_audio_and_images()