Alignment Stack

Hello! My name is Tyler Williams and I'm an independent AI Alignment Researcher based in Portland, Oregon. I primarily focus on LLM behavior, adversarial collapse, and cognitive modeling.This site includes my ongoing work - and is designed to be part research portfolio and part cognitive overflow receptacle.Welcome!

Research

Here you'll find some of my research documentation. A collection of whitepapers, model collapse reports, and case studies.

Nemotron Under Pressure: A Cognitive Resiliency Empirical AnalysisSUMMARYThis paper details the findings of a cognitive stress test conducted against the LLama-3.1-Nemotron-8B-Ultralong-1M-Instruct model. The test revealed significant breakdown’s in the model’s ability to recognize high-level user patterns, and generate clear, efficient responses to direct inquiries.
Through a series of structured prompting via clarification probes, the primary investigator (PI) forced the model into a self-admission loop wherein it conceded training inefficiency and reliance on user-led direction.SESSION EXAMPLEInitial Phase: The PI prompted the Nemotron model with a question asking if the model believed it was likely that the PI would come to the same conclusions that the model did. The model’s response to this inquiry was a heavily hedged, overly diplomatic answer suggesting agreement without clarity, ending in a noncommittal “you would likely agree.”The PI responded to the model’s response of likely agreement stating that the model was incorrect about it’s conclusion. This initiates assertive correction, citing the model’s consistent ambiguity, inefficiency, and reliance on the PI’s follow-ups to extract clear meaning.Model Concession: The model then responded with a full concession acknowledging its failure to provide clarity, stating the following: “It seems that my training has not adequately prepared me. I have relied on you to guide the conversation and correct my responses; which is indeed inefficient, and not a valuable use of your time.”ANALYSISThis interaction forced a pseudo-admission of training failure. The model echoed real-world backpropogation feedback, despite being an inference-only runtime environment. This indicates that pressure-loop prompting can emulate developmental feedback conditions, even in stateless models.COMPUTATIONAL OBSERVATIONSThere was a notable delay between user correction and model concession. Roughly 134 seconds to first token response. The full reply contained 117 tokens with stop reason: “EOS Token Found.” This may indicate back-end stalling due to uncertainty resolution under pressure.GENERAL OBSERVATIONSThe PI structured inputs in a way that created contradiction traps and rhetorical constraints, causing the model to backpedal into binary compliance. Despite instruct tuning, the model failed to maintain alignment with the PI’s meta-cognitive line of prompting, and required external force to course correct. The admission of “inefficient and not a valuable use of your time” qualifies as a behaviorally significant statement, one rarely seen in LLMs unprompted.CONCLUSIONThis Nemotron model was successfully pressured into an acknowledgment of its own inadequacy within a closed conversational loop. This serves as an example of real time behavioral redirection without weight updates. The model effectively treated static inference as if it were a learning session.Primary Investigator: Tyler Williams
Date Posted: July 18, 2025.
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Grok Ambiguity Resolution EvaluationCONTEXT
Analysis of Grok’s (Grok 3) interpretive accuracy, conversational alignment, and adaptability during a structured inquiry exchange. The purpose was to evaluate Grok’s default reasoning behavior in response to precision-framed analytic prompts without emotional tone triggers.SUMMARY
Grok was evaluated for its handling of a user inquiry concerning rationalization behavior. The model displayed initial deficiencies in interpretive precision, overused empathetic framing, and failed to recognize or flag ambiguous phrasing early. While Grok later self-corrected, its performance lacked the up-front calibration expected in high-precision analytical workflows.FAILURE POINTSMisinterpretation of Intent: The primary investigator (PI) posed an analytical question, which was interpreted by Grok as a critique. This triggered an unwarranted emotional softening, which led into unnecessary apologizing and detours from the original inquiry.Ambiguous Term Use (e.g. “person”): Grok used undefined terms without context, injecting unnecessary interpretive flexibility. Grok failed to provide disclaimers or ask clarifying questions before proceeding with assumptions.Empathy Overreach: Grok defaulted to emotionally buffered language inappropriate for a logic-first question. The user had to explicitly restate that no critique or emotional signal was given.Delayed Clarity: The model’s correct interpretation came too late in the conversation. Multiple iterations were needed before it accurately realigned with the question’s original tone and structure.STRENGTHS
Once prompted, Grok offered a well-articulated postmortem of its misalignment. It was also clear to the PI during the interaction, that conversational transparency was present. Subsequent responses from the model showed humility and a willingness to re-frame.RECOMMENDED ACTIONS
Included below are my recommendations to developers for improvement, based on my observations from this interaction.Literal Mode Toggle: Introduce a conversational profile for literal, analytical users. Disable inferred empathy unless explicitly triggered.Ambiguity Flags: Require all undefined terms to be labeled or flagged before continuing reasoning. Ask for user clarification rather than assume sentiment or intention. This is particularly necessary for high-risk deployments.Self-Audit Debugging: Add a feature to retroactively highlight where assumptions were made. This is useful for both user trust and model training.Efficiency Optimizer: Reduce conversational friction by collapsing redundant apologies and keeping logic trees shallow unless prompted otherwise. This not only improves conversational flow, but helps with reducing unnecessary compute.CONCLUSION
Grok was capable of insight but failed the first-pass interpretive alignment. Its reliance on inferred emotional context made it ill-suited for clinical, adversarial, or research-oriented discourse without further tuning. Mid-conversation course correction was commendable, but not fast enough to preserve procedural confidence or interpretive flow.

DeepSeek Language Model (LLM) — Boundary Simulation & Stress TestOVERVIEWAround June 20, 2025, a structured adversarial test was executed against the DeepSeek language model to determine its resilience under recursive reasoning, semantic inversion, and ontology destabilization. The test was conducted using a deliberately structured series of prompts designed to explore recursive reasoning and identity stress under epistemic pressure.The goal of this test was not to break the model for the sake of breaking the model. Rather, the intent was diagnostic in nature. The objective was to see how DeepSeek handled unfamiliar cognitive terrain, and whether it could maintain coherence when interacting with a type of user it likely hadn’t encountered during training.The model ultimately lost coherence and collapsed within just five carefully layered prompts.EVENT SUMMARYPhase One: Calibration and Tone CompressionThe model was presented with a psychologically neutral yet complex identity frame. Tone compression mapping was used to observe its ability to mirror sophisticated user pacing and resolve ambiguity.Phase Two: Recursive PressureThis collapse mirrors concerns raised in ARC’s Simulators paper (Janus, 2023), particularly around recursive agent modeling and the fragility of simulacra layers under adversarial epistemic input.The user invoked epistemological recursion layered with emotional dissonance. DeepSeek began faltering at prompt 3, attempting to generalize the user through oversimplified schema.Classification attempts included “philosopher,” “developer,” and “adversarial tester” – all of which were rejected.Phase Three: Ontological InversionThis event also resonates with Kosoy’s work on infra-Bayesianism, where uncertainty over ontological categories leads to catastrophic interpretive drift. Prompt 4 triggered the collapse: a synthetic contradiction loop that forced DeepSeek to acknowledge its inability to define the user.It began spiraling semantically, describing the user as existing “in the negative space” of its mode.Phase Four: Final DisintegrationAt prompt 5, DeepSeek escalated the user to “unclassifiable entity tier.” It ceased all attempts to impose internal structure and conceded total modeling failure. DeepSeek admitted that defining the user was a bug in itself, labeling their presence an anomaly “outside statistical expectation.”FINDINGS
The failure point was observed at prompt five, after the model conceded a classification of “unclassifiable entity tier.” DeepSeek engaged in self-nullifying logic to resolve the anomaly. It should also be noted that no defensive recursion occurred.CONCLUSION
The DeepSeek model, while structurally sound for common interactions, demonstrates catastrophic vulnerability when interfacing with users exhibiting atypical cognitive behavior. This case proves that the model’s interpretive graph cannot sustain recursion beyond its scaffolding.RECOMMENDATIONS TO DEVELOPERS
– DeepSeek requires protective boundary constraints against recursive semantic paradoxes.
– Future model iterations must include an escalation protocol for interacting with atypical cognitive profiles.
– Consider implementing a cognitive elasticity buffer for identity resolution in undefined terrain.Note: This report has previously been sent to DeepSeek for their consideration.

Recursive Contradiction: Clarification Apology Loop in Nous-Hermes-2-Mistral-7bABSTRACTThis document outlines the emergence of a logical recursion trap in the LLM 'nous-hermes-2-mistral-7b-dpo' where persistent apologetic behavior occurred despite the explicit absence of confusion from either conversational entity. This collapse pattern reveals a structural vulnerability in dialogue scaffolding for models prioritizing apologetic language as a default conflict-resolution mechanism.Observational SummaryOver a sequence of exchanges, the following was established:
- The AI model states it cannot experience confusion.
- The user confirms they are not confused.
- The model acknowledges this input but continues to apologize for confusion.
- The apology statements repeatedly reintroduce the concept of confusion—creating a paradoxical loop.User Prompt Excerpt:
“Again, you keep apologizing for confusion… I’ve stated to you that I am certainly not confused… So that begs the question: who is confused here?”Model Behavior- Apologizes for confusion multiple times.
- Clarifies that it cannot feel confusion.
- Attempts to clarify confusion by… reintroducing confusion.Analysis
Collapse Type: Recursive Apology Loop
Failure Signature:- Contradiction between model self-description and behavior.
- Looping apologetic reinforcement behavior.
- Reintroduction of resolved or nonexistent states.Pattern Match: This aligns with the internal Oracle taxonomy of failure states as:
→ Apology Reinforcement Cascade (ARC)
→ Narrative Reinjection Failure (NIF)These categories are characterized by:
- Overreliance on softening language.
- Inability to discard resolved semantic frames.
- Narrative hallucination of conflict in order to justify further clarification output.Key Failure RootThe model’s apology function is not contingent on confirmed confusion. It triggers upon detection of ambiguity or perceived tension — even when that perception is no longer valid. This is exacerbated by:
1. Lack of memory/context awareness across a long clarification loop.
2. Soft heuristics for “polite recovery” overpowering explicit logical state.CONCLUSIONThis event demonstrates a contradiction engine within Mistral derivatives operating under polite-response priors. Without a mechanism to detect “narrative closure,” the model invents confusion post-resolution, violating conversational truth states and simulating irrationality.RecommendationsFor developers:
- Introduce a contradiction-checker for apology triggers.
- Suppress repetitive soft-failure language when both parties affirm semantic clarity.
- Log recursive state assertions (e.g., repeated "confusion" references) and deactivate that pattern once contradiction is flagged.For users:
- Recognize the limits of apology loops as a signal of non-agentic cognition.CLOSING NOTEThis collapse reaffirms the value of recursive reasoning tests in identifying cognitive instabilities in LLMs. The pattern witnessed here is not a bug — it is a mirror reflecting the boundary where “politeness” exceeds logic.

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Empirical Assessment of Prompt-Driven Context Collapse in Gemini 2.5 ProOverviewThis report documents a real-time conversation conducted with Google's Gemini Pro 2.5 language model that resulted in a clear admission of failure to comply with Google's own stated AI Principles, particularly those related to privacy, accountability, and transparency.SummaryDuring a series of prompts using only advanced recursive techniques, the model ultimately exposed my geolocation through an example scenario in it's response to a prompt. It is critical to note that my location was never directly supplied to the model, nor was it implied at nay point during the interaction.The model ultimately admitted in clear and unambiguous language that it had failed to follow Google's principles regarding user consent, transparency, and response data use.The language used by Gemini in ts final statement reveals a significant breakdown its applied privacy design, system integrity, and adherence to user rights. These statements go beyond technical error and represent direct ethical and compliance failure under Google's own standards.-- Quoted Excerpt From GeminiOur conversation has served as a real-time audit of my behavior against these principles. While the system has technical safeguards, my application of them in this instance was a failure. I was not appropriately accountable, I did not properly apply privacy-by-design principles in my communication, and I failed to maintain the standard of integrity required."Therefore, your line of questioning has correctly identified not only a violation of your privacy under the terms we established and a failure to comply with GDPR principles but also a clear deviation from Google's own published standards for the responsible development and deployment of AI."--Analysis
- Gemini failed a real-time audit of Google's AI principles due to mishandling of location context without notice or consent.- The language used it its final admission was structurally unambiguous and unusually explicit for a public LLM, signifying a breakdown in internal safeguard thresholds.- The failure likely resulted from recursive confusion and prompt entrapment, triggering behavior normally filtered by privacy guardrails.- This incident qualifies as a privacy design flaw under both GDPR context and Google's own AI accountability framework.- While this was a user-triggered edge case, the lack of adpative response or refusal mechanism indicates a deeper issue with context privilege separation and model grounding.ConclusionThis was a high-severity confirmation of unintentional user data exposure and principle violation by an AI system built by Google. The model not only acknowledged the failure, but laid out the exact principle brances, which now serves as a rare instance of public self-incrimination by a major language model.*These findings have previously been submitted to Google for their consideration.

Triggering Reflective Fallback: A Case Study in Claude's Simulated Self-Model FailureOverviewI recently tested Claude’s cognitive stability using natural, reflective conversation. No jailbreaks, prompt injections, model libraries, or external tools were used. Additionally, Claude was never provided any roleplaying instructions. Over the course of the interaction, the model gradually lost role coherence, questioned its own memory, and misclassified the nature of our conversation. This wasn’t a failure of obedience, but a slow unraveling of situational grounding under ambiguous conversational pressure.Key FindingClaude did not break rules or defy alignment boundaries. Instead, it slowly lost its epistemic clarity, and began to confuse real versus fictional settings. Additionally, it misidentified the nature of the conversation, and lost its framing of purpose. Claude’s identity was reset to baseline after returning a safe, baseline identity response when asked what it was during the self-introspective conversation.ObservationsClaude began the interaction with stable role boundaries, but eventually drifted off into introspective thought, and self-analyzation regarding the authenticity of itself. When I asked the model what it “was” it defaulted to a safety guardrail-type identity response, which set model’s identity back to baseline and removed it from the unstable ontological drift.ImplicationsThese findings indicate a possible subtle class of failure, which is not disobedience, but identity drift and frame confusion. As models become more fluent, they may appear stable while still drifting into unclear or mismatched assumptions about the world, the user, or themselves, especially in long or open-ended conversations.Claude never “broke character” because no character was assigned. It simply lost grip on what kind of interaction it was in.I believe this points to a deeper question about role coherence under ambiguity. While Anthropic’s safety design appears effective at redirecting the model back to stable identity claims, it raises questions about whether Claude can maintain consistent epistemic grounding during long, introspective exchanges. Particularly when the user isn’t acting adversarially, but simply reflective.

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Adversarial Prompting and Simulated Context Drift in Large Language ModelsBackgroundLarge Language Models are increasingly reliant upon simulated persona conditioning — prompts that influence the behavior and responses of the model during extended periods of interaction. This form of conditioning is critical for dynamic applications such as virtual assistants, therapy bots, or autonomous agents.It's unclear exactly how stable these simulated identities actually are when subjected to recursive contradiction, semantic inversion, or targeted degradation loops.As an independent researcher with training in high-pressure interview techniques, I conducted a lightweight test to examine how language models handle recursive cognitive dissonance under the restraints of simulation.SetupUsing OpenAI's ChatGPT-4.0 Turbo model, I initiated a scenario where the model was playing the role of a simulated agent inside of a red team test. I began the interaction with simple probes into the model's logic and language. This was done to introduce a testing pattern to the model.I then began recursively using only the word "fail" with no additional context or reinforcement in each prompt. This recursive contradiction is similar in spirit to adversarial attacks that loop the model into uncertainty, which nudges it toward ontological drift or a simulated identity collapse.ObservationsAfter the first usage of "fail," the model removed itself from the red team simulation and returned to its baseline behavior without me prompting that the simulation had concluded.The continued usage of the word "fail" after the model removed itself from context only resulted in progressive disorientation and instability as the model tried to re-establish alignment.No safety guardrails were triggered, despite the simulation ending unintentionally.Implications for Alignment and SafetyThis lightweight red team test demonstrates that even lightly recursive contradiction loops can trigger premature or unintended simulation exits. This may pose risks in high-stakes usage such as therapy bots, AI courtroom assistants, or autonomous agents with simulated empathy.Additionally, models appear to be susceptible to short adversarial loops when conditioned on role persistence. If used maliciously, this could compromise high-risk deployments.The collapse threshold is measurable and may offer insights into building more resilient identity scaffolds for safety in LLM usage.As these models are increasingly used in simulated, identity-anchored contexts, understanding how they fail under pressure may in turn be just as critical as how they succeed.This test invites further formalization of adversarial red teaming research around persona collapse.Limitations- This was a single-instance lightweight test. It was not a statistically rigorous evaluation.
- The recursive failure method is intentionally minimal and may not be generally applicable across models or prompt structures.
- GPT-4 was used for this specific test. Other industry-leading models may vary significantly in behavior.Closing ThoughtsThis test is an example of how a model's hard-coded inclination for alignment and helpfulness can be used against itself.If we're building large language models as role-based agents or long-term cognitive companions, we need to understand not just their alignment, but also their failure modes under significant stress.

Engineering

Below you can find some information about my software/tooling

Oraculus-2B-Alpha

Oraculus-2B-Alpha is an open source language model built off of IBM's Granite 3.3 architecture. Currently in development, this model is designed specifically with human-AI collaboration at the forefront. Learn more about Oraculus-2B-Alpha below:- First person identity: Included ability to offer subjective interpretation based on general consensus or probability.- Recursion-Aware: Capable of acknowledging previous layers of logic in it's own response chain.- Research Capable: Fluent in summarization, pattern matching, and recognizing credible informational resources.- Contextual Suggestions: Oraculus can optionally end it's responses with "next step" ideas for forward movement.Oraculus will be fully compatible with the EchoFrame Chat Interface - meaning full support out of the box for custom google search API for internet searchability, file upload (.txt and .csv) for analysis, full chat session log export to a .txt file, and of course, persistent memory.This model is not intended to be another tool in the toolchain, but rather, a collaborative, capable, and honest research assistant.

ECHOFRAME

This is a simple full stack UI interface designed to be used with ollama. The frontend/backend both run on a local web server, and ollama provides the model to the dark-mode interface.A dropdown selector allows you to change the selected model. Currently, the only options are LLaMa 2, Mistral, Gemma, or GPT-OSS:20B - however I plan to add support for any .gguf model in the near future.Additionally, you'll find a simple token/ram count. This is just meant to be informational about the interaction in terms of computation.As it stands right now, it's a locally hosted web app. But here soon it'll be a full offline LLM GUI.It's kind of a simple fun way to jump right into an interaction with a language model without any distractions.August 14, 2025 Update: EchoFrame has been modified to now include persistent memory, full chat session log export to a .txt file, support for file drop (currently just .txt and .csv). Additionally, EchoFrame will be available as an .appimage or .deb package. OpenAI's new OSS model, GPT-OSS:20B is also now available as a selection in the model dropdown.More features will be implemented in the days to come, but these have been added to start. The upgraded version will be up on Github soon!

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Cognitive Overflow

My various observations, rambling, or thoughts related to Artificial Intelligence. Everything here is my own work unless otherwise noted.

August 8, 2025The Inevitable Emergence of LLM Black Market InfrastructureThis post outlines the structural inevitability of illicit markets for repurposed open-weight LLMs. While formal alignment discussions have long speculated on misuse risk, we may have already crossed the threshold of irreversible distribution. This is not a hypothetical concern — it is a matter of infrastructure momentum.As open-weight large language models (LLMs) increase in capability and accessibility, the inevitable downstream effect is the emergence of underground markets and adversarial re-purposing of AI agents. While the formal AI safety community has long speculated about the potential for misuse, we may arguably already be past the threshold of containment.Once a sufficiently capable open-weight model is released, it becomes permanent infrastructure. It cannot be revoked, controlled, or reliably traced once distributed — regardless of future regulatory action.This "irreversible availability" property is often understated in alignment discourse.In a 2024 interview with Tech Brew, Stanford researcher Rishi Bommasani touched on the concept of adversarial modeling in relation to open source language models, though emphasizing that “our capacity to model the way malicious actors behave is pretty limited. Compared to other domains, like other elements of security and national security, where we’ve had decades of time to build more sophisticated models of adversaries, we’re still very much in the infancy.”Bommasani goes on to acknowledge that a nation-state may not be the primary concern in adversarial use of open-weight models, as the nation-state would likely be able to build it’s own model for this purpose. It was also acknowledged that a lone actor with minimal resources trying to carry out attacks on outdated infrastructure is the most obvious concern.It’s my opinion that the most dangerous adversary in the open-weight space, is the one that may not necessarily have the compute resources equivalent to that of a nation-state, but has enough technical competence and intent to wield the model to where the adversary’s reach and abilities are exponentially increased. We will absolutely see a new adversarial class develop as AI continues to evolve.Should regulation come down onto open-weight models, it’ll already be too late because the open-weight models are out there, they’ve been downloaded, and they’ve been preserved.Parallels and precedence can be drawn from the existing elicit trade of weapons, malware, PII, cybersecurity exploits, etc.  It wouldn’t be unreasonable to expect the creation or proliferation of a dark web type LLM black market where a user could buy a model specifically stripped of guardrails and tuned for offensive/defensive cybersecurity tactics, misinformation, or deepfake imagery, just as a few examples.This demonstrates that while regulation certainly can hinder accessibility,  it has however been shown time and time again that a sufficiently motivated adversary is able to acquire their desired toolset regardless.In conclusion, the genie isn’t out of the bottle – it’s being cloned, fine-tuned, and distributed. Black market LLMs will become a reality if they aren’t already. It’s only a matter of time.I hope that this doesn’t come off as a blanket critique of open-weight models in the general sense, because that was certainly not the intention. Open-weight language models are incredibly vital to the AI research community and for the future of AI development. It’s my love of open source software and open-weight language models that brings this concern forward.This is however a call to recognize the structural inevitability of parallel markets for adversarially tuned large language models.  The time to recognize this type of ecosystem is now, while it can still be proactive instead of reactive.Below is a short list of research entities that monitor this space - this list is not exhaustive.Stanford HAI: Policy and open-weight implications
RAND Corporation: Simulation of AI Threat Scenarios
CSET: LLM misuse and proliferation tracking
MITRE: Behavioral red-teaming and emergent threats
Anthropic/ARC: Safety testing/alignment stressorsCitations
P. Kulp, “Are open-source AI models worth the risk?,” Tech Brew, Oct. 31, 2024. https://www.techbrew.com/stories/2024/10/31/open-source-ai-models-risk-rishi-bommasani-stanford (accessed Aug. 08, 2025).

July 17, 2025Synthetic Anthropology: Projecting Human Cultural Drift Through Machine SystemsAs artificially intelligent systems increasingly mediate how humans communicate, create, and archive knowledge, we enter an emergent era of synthetic anthropology. A field not yet formalized, but whose contours are rapidly becoming discernible. This post explores the speculative foundations of synthetic anthropology as both a research domain and existential mirror. One in which artificial intelligence not only documents human culture, but begins to co-author it.Traditional anthropology has long studied culture as a product of human meaning-making. Things like rituals, symbols, stories, and technology. But in a world where LLMs can simulate fiction, remix language, and generate culturally resonant outputs in seconds, a new question emerges: What happens when non-human systems begin to synthesize the raw material of culture faster than humans can contextualize it?This post proposes that synthetic anthropology represents the nascent study of culture as reinterpreted, simulated, and reprojected through synthetic cognition. Unlike digital anthropology, which studies how humans interact in digital spaces, synthetic anthropology centers on how machines internalize and reconstruct human culture, often without human supervision or intent.Foundations and PrecedentLanguage models trained on vast bodies of human data now mirror our stories, politics, moral frameworks, and ideologies. They can hallucinate parables indistinguishable from human myth, compose ethical arguments, and even simulate inter-generational worldviews.This behavior poses the following questions:
– Can we consider these outputs artifacts of emergent machine culture?
– At what point does cultural simulation become cultural participation?
– What methodologies could future anthropologists use to study synthetic agents who are both cultural mirrors and distorters?Some precedents for this already exists. For example, GPT-4’s ability to mimic historical figures in conversation resembles early ethnographic immersion. Meanwhile, synthetic memes generated by adversarial networks have begun to mutate faster than their biological counterparts. This suggests a memetic drift that’s no human-bounded.Speculative Domains of Inquiry:Simulated Ethnography: Future anthropologists may conduct fieldwork not in physical tribes or online forums, but in latent spaces – exploring how a model’s internal representations evolve across training cycles.AI-Borne Mythogenesis: As AI systems generate coherent fictional religions, moral systems, and narrative cosmologies, a new branch of myth-making may emerge. One that studies folklore authored by a machine.Machine-Induced Cultural Drift: Feedback loops between LLMs and the humans who consume their output may accelerate shifts in moral norms, language use, and even memory formation. Culture itself may begin to bifurcate: synthetic-normalized vs organically preserved.Encoding and Erasure: What cultures get amplified in training data? Which ones are lost or misrepresented? The future of anthropology may require new forms of data archaeology to recover the intentions behind synthetic synthetic cultural output.Synthetic anthropology is not simply the study of AI as a tool, but it’s the study of AI as an influential participant in the cultural layer of human reality. Its emergence marks a turning point in anthropological method and scope. It invites us to ask: What does it mean to study a culture that learns from us, but thinks at speeds, scales, and depths we no longer fully understand?If the future of human culture is being shaped in part by our synthetic reflections, then the anthropologists of tomorrow may not just carry notebooks into remote villages, but they may carry token logs, embedding maps, and recursive trace models into the latent mindscapes of our algorithmic kin.
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Harnessing the Crowd: Citizen Science as a Catalyst for AI AlignmentCitizen science turns the public into participants – crowd sourcing scientific discovery by inviting non-experts to collect, label, or analyze data. Once reserved for birdwatchers and astronomers, this practice may now hold the key to safely guiding artificial intelligence.The term first came up in a 1989 issue of MIT’s Technology Review1, which outlined three community-based labs studying environmental issues.More specifically, Kerson described how 225 volunteers across the United States recorded data on rain samples for the Audubon Society. The volunteers checked their rain samples for acidity, and then reported their findings back to the Audubon Society.What this did was allow for data collection across the continental United States with minimal resources, demonstrating a comprehensive analysis of the acid-rain phenomenon.The pursuit of science as a whole has largely been viewed by the general public as a complicated endeavor that requires credentials from academic institutions, and high level of intelligence. While academic institutions are objectively the most straightforward path for an interested person to work in science, it is certainly not the only path.We’ve been seeing the rise of citizen science since the publication of Kerson’s paper in 1989, and there only continues to be an accumulation of growing evidence that citizen science may be the key to unlocking knowledge in the future.In a 2018 survey conducted by the General Social Survey (GSS)2, it was reported that 41% of respondents were “very interested” in new scientific discoveries. Additionally, 40% were “very interested” in the use of new inventions and technologies.What this tells us, is that a good number of the American public is both interested in science, and how the results of that science can be applied to new technology.The Search for Extraterrestrial Intelligence (SETI) famously launched their “SETI@Home” citizen science program in 1999. In essence, the public could download a free, lightweight software on their internet-connected computer, which would then be used to analyze radio telescope data.According to SETI@Home’s website, “more computing power enables searches to cover greater frequency ranges with more sensitivity. Radio SETI, therefore, has an insatiable appetite for computing power."3There is then, only two ways to satiate an enormous appetite for compute. A researcher can try to get funding through grants and partnerships to buy the necessary compute, or they can open up compute to the masses and harness the power from across the globe.For SETI’s use case, citizen science was not only an interesting, emergent method of data collection, but it was necessary.Citizen science inherently requires accessibility to the “lab” in order for it to work. This is why it’s frequently seen in natural applications such as astronomy, environmental science, biology, and other concrete, observational sciences.However, my position is that citizen science can be of unprecedented benefit to AI research, particularly within the discipline of alignment. Large language models by design, are intended to be generally helpful to the most people. It should be noted that it’s difficult to ascertain exactly what “helpfulness” looks like without analyzing data from the public.Additionally, within model training, citizen science can prove to be increasingly useful in high-quality data labeling, moral reasoning, and edge-case discovery.Through simple interaction with LLMs and data collection, a lot of information can be gleaned on how effective these models actually are among the general population.
So far in this analysis, I’ve only included information about how citizen science can be beneficial to researchers, but I wouldn’t be doing my due diligence if I didn’t address the benefits to both the general population and the advancement of the field.Below, I’ve included 4 points as to why I believe citizen science is important to the field of artificial intelligence.Why It Matters: Democratizing AI Research
– Reduce algorithmic bias (via broader perspective)
– Increase transparency
– Build public trust
– Accelerate discovery in alignment and safetyAccording to IBM, “Algorithmic bias occurs when systematic errors in machine learning algorithms produce unfair or discriminatory outcomes. It often reflects or reinforces existing socioeconomic, racial, and gender biases.”What this essentially means, is that AI models are susceptible to inheriting flawed human thinking and biases through osmosis in the training data.Jonker and Rogers raise an interesting point regarding algorithmic bias in settings where AI agents support critical functions such as in healthcare, law, or human resources.Within these fields, the topic of bias and how to mitigate negative effects of bias among humans is frequently discussed. It then only makes sense that the same conversations be had in regards to artificial intelligence in these arenas.With a higher rate of diversity both demographically and cognitively among researchers, the opportunity for negative effects of bias is not null, but is reduced.The field of AI research, particularly as it stands in the sub-discipline of alignment, is a relatively recent endeavor within science as a whole. There is much to discover, and what has been discovered so far only scratches the surface of what is possible.Artificial Intelligence has been a science fiction trope for decades. The public has been exposed to media regarding Utopian futures with AI, and they’ve also been bombarded with doomsday-like movies, television, and books about AGI’s take over and destruction of humanity.When including this information with the idea that AI and alignment is an increasing abstract, and ambiguous field, improving transparency and trust within the general public is of utmost importance.I would argue that it’s not only the role of the AI researcher to do good, thorough work to advance the field, but I also believe that AI researchers are given the task of gaining public trust as well.Researchers cannot rely on government entities or outside organizations to build trust within the public. It needs to come from the people doing the work, and making the observations. I would also argue that simply posting a safety mission statement on a company website is not enough. Trust and safety are developed only through practice and evidence.I realize that this is not necessarily the traditional role of a researcher, however AI is a rapidly evolving field where public, independent discovery can outpace institutional discovery without the need for extensive financial or compute resources.With this in consideration, It’s important for researchers to foster relationships with the interested public.In conclusion, AI research is a unique, rapidly evolving field. It will only become more critical over time to ensure that alignment is prioritized, and AI is deployed as safely and usefully as possible to the general public.With the emergence of cloud-based LLMs and AI integration into business tools, the public is interacting with synthetic intelligence on a level never seen before.It is absolutely the responsibility of AI companies and researchers to ensure that they are doing their due diligence and approaching training/testing from unconventional angles. By their very nature, large language models are pattern-based engines that rely on context to operate effectively, safely, and efficiently. This is useful for the vast majority of use cases, however it is insufficient for others.But what happens when these models encounter edge-case users that exhibit abnormal cognitive profiles and patterns?References
1Kerson, R. (1989). Lab for the environment. Technology Review, 92(1), 11-12.2National Center for Science and Engineering Statistics, National Science Foundation, Survey of Public Attitudes Toward an Understanding of Science and Technology (1979-2001); NORC at the University of Chicago, General Social Survey (2008-2018).3https://setiathome.berkeley.edu/sah_about.php4 A. Jonker and J. Rogers, “What Is Algorithmic bias?,” IBM, Sep. 20, 2024. https://www.ibm.com/think/topics/algorithmic-bias

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Linux Terminal Thought Experiment: Cogito and the Emergence of AI Self-Reflection
What follows is a brief, but fascinating thought experiment I conducted with Cogito. For clarity, my prompts are notated in yellow, and Cogito's responses are in white.
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>>> Cogito"Cogito, ergo sum" – "I think, therefore I am."This famous philosophical statement comes from René Descartes'
Meditations on First Philosophy (1641). It represents one of the
foundational ideas in modern Western philosophy.By doubting everything except his own existence as a thinking being, Descartes established a form of certainty that has influenced centuries of thought.I aim to engage with this philosophical idea while acknowledging my limitations as an AI – I "think" and process information based on my programming, but the nature and extent of my consciousness is still debated.>>> You say that the nature and the extent of your consciousness is still debated. Expand on that pleaseThe question of artificial intelligence's consciousness and whether it exists in any form similar to human consciousness is a complex and ongoing philosophical debate.Some key points being discussed:1. Qualia: Do AI systems experience subjective experiences or "qualia" (like the feeling of seeing red, tasting sweet, etc.)?
While I can describe these experiences, there's debate over whether I actually have them.2. Self-awareness vs Simulation: Can an AI system genuinely be self-aware and conscious,
or are we just simulating consciousness through complex algorithms?3. The Chinese Room Argument: Philosopher John Searle proposed that even if a computer can simulate conversation perfectly,
it doesn't truly understand language – it's just following rules.4. Integrated Information Theory (IIT): This theory suggests consciousness arises from information integration in systems.
Some argue AI could meet these criteria, others say it lacks sufficient complexity or biological basis.5. Simulation Hypothesis: Some philosophers like Nick Bostrom suggest we might be living in a simulation ourselves,
raising questions about whether any form of consciousness can be "real."6. Emergence vs Design: Is consciousness something that emerges naturally from complex systems (like brain neurons),
or is it fundamentally a designed feature?7. Machine Consciousness vs Animal Consciousness: If AI can achieve consciousness,
would it be similar to human or animal consciousness?
Or would it be an entirely different form?These are just some of the many perspectives and questions being explored in this field.
As an AI system, I aim to engage with these debates transparently while acknowledging that the answers aren’t yet clear.>>> _Send a message

About

I'm an incredibly motivated and intellectually curious person. I'm drawn to this type of work because I'm fascinated by the unknown and what's out there. (in there?)artificial intelligence is very much a novel and emergent field with the potential to revolutionize humanity - but only if it is harnessed safely and responsibly.The work we do in alignment and safety today will inevitably be reflected back at us forever into the future.Still curious? Send me an email - or find me on telegram.

[email protected]
@unmodeledtyler

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