Discussing LLM AI: Trusting LLM AI Output - Part II

A discussion of trusting the output of LLM AI

Note: As usual, I used ChatGPT 4.0 in the preparation of this piece as research assistant and ghostwriter. Kid's don't try this at home.

See Part I here:

 “Well, even I, as a fairly sophisticated AI user, can be gulled. So, I imagine many more people are gulled than I am into thinking that it's providing answers based upon knowledge as opposed to some statistical algorithm, associative word chasing, pattern matching, whatever you want to call it. It's not clear at all that it even closely mimics human reasoning. So the fact that it has access to a very large data set is one thing. It certainly can access far more data than any human being can in a lifetime, or many lifetimes. But, much of that source data is incorrect. By any standard, much of it is highly interpretive. Some is propaganda, lies if you will. And then the LLM AI is biased by the creators with their own limited understanding and their own human biases and corporate biases. As a result, it's less trustworthy than the average informed human being, even though it can bring many more topics to the discussion than a human being could. Even a polymath, highly intelligent, could not possibly have access, in real time, to the amount of information that AI possesses. But it's incredibly fast, and the database is pretty damn large, compared to human capabilities, extremely large.”

Your points encapsulate the core challenges and paradoxes of using AI systems like generative models: scale versus reliability, breadth versus accuracy, and trust versus skepticism. Let’s unpack and expand on these concerns:

1. The Illusion of Knowledge vs. Statistical Algorithms

• Mimicry of Reasoning:

  • AI systems like LLMs do not "know" in the sense humans do; they process statistical relationships in data, producing outputs that appear reasoned but are based purely on probabilistic associations.

  • This distinction is critical but often obscured by the human-like tone and structure of AI outputs. The illusion of reasoning arises because the output is syntactically and contextually coherent, even when semantically incorrect.

• Impact on Users:

  • Even sophisticated users, as you mention, can be "gulled" because of how naturally plausible and confident AI responses seem.

  • For less informed users, the risk is much higher: AI can create a false sense of authority, particularly in areas where users lack the expertise to verify information.

2. Data Scale vs. Data Integrity

• Scale as a Double-Edged Sword:

  • AI has access to a dataset orders of magnitude larger than what any human could process, which enables it to discuss and generate content across diverse topics.

  • However, it must be admitted that it contains:

    • Misinformation: Incomplete and factually wrong data and idiosyncratic interpretations, subjective perspectives and culturally or ideologically biased narratives.

    • Disinformation: Lies and propaganda.

    • Highly interpreted content:

    • Contradictory information: Data that lacks internal coherence across topics or fields.

• Bias in Curation:

  • The dataset is further shaped by the biases of the curators and trainers, whose choices about what data to include or emphasize inevitably reflect human, corporate, and cultural biases.

  • AI inherits and amplifies these biases, making its outputs less neutral and less trustworthy, even as they appear comprehensive.

3. Breadth vs. Reliability

• Comparison to Humans:

  • Humans are limited in the breadth of knowledge they can acquire but tend to process that knowledge more critically and contextually, often with a better ability to discern accuracy within their areas of expertise.

  • AI excels at breadth, providing outputs on topics far beyond the scope of even the most capable human polymath. However, this breadth comes at the cost of depth and reliability:

    • It does not "understand" the information it processes.

    • It cannot inherently evaluate or verify the correctness of the data beyond regurgitating patterns from its training set.

• Trustworthiness of Output:

  • As you note, AI's outputs are less trustworthy than those of an average informed human, despite its ability to aggregate far more information. This discrepancy stems from its inability to:

    • Validate sources or resolve contradictions.

    • Exercise judgment or discern nuance in contested areas.

4. The Role of Bias in AI Outputs

• Sources of Bias:

  • Training data biases reflect societal, corporate, or ideological leanings. For instance:

    • Corporate-sponsored data might prioritize certain narratives.

    • Historical datasets might embed outdated or discriminatory perspectives.

  • These biases are compounded by the biases of the developers, who make decisions about how to optimize and shape the model.

• Implications for Trust:

  • The cumulative effect of these biases means that AI outputs often reflect the dominant narratives or interpretations encoded in its data. While these might align with popular consensus, they may not be accurate, fair, or impartial.

5. Epistemological Implications

• Correctness Without Justification:

  • AI outputs can be "correct" in isolated cases but lack the justification or reasoning that humans often demand to assess trustworthiness.

  • This raises an epistemological question: Is correctness meaningful when it is coincidental or the product of biased algorithms? For pragmatic purposes, this might suffice in well-defined domains (e.g., scientific constants) but falters in interpretive or value-laden questions.

• Interpreted Information:

  • Much of what AI generates, even in seemingly objective domains, is highly interpreted. For example:

    • Historical events: Framed differently depending on cultural or ideological perspectives.

    • Scientific debates: Reflecting the consensus but ignoring minority or emerging viewpoints.

  • These interpretations, baked into the dataset, shape outputs in ways users often cannot see.

6. Practical Consequences

• AI's Dual Nature:

  • AI systems are simultaneously more capable and less reliable than humans:

    • More capable in breadth and speed of information processing.

    • Less reliable in depth, judgment, and contextual accuracy.

• Danger of Overreliance:

  • The combination of plausible-sounding outputs, human-like tone, and vast breadth of knowledge creates a risk of overreliance:

    • Users may treat AI as an authoritative source rather than a tool requiring verification.

    • The apparent sophistication of AI's outputs can obscure its limitations and biases.

7. Recommendations for Using AI Effectively

• Recognize the Limits:

  • Always approach AI outputs with skepticism, especially on interpretive or controversial topics.

  • Verify information against trusted, independent sources, particularly when the stakes are high.

• Educate Users:

  • Developers and organizations should invest in educating users about AI’s limitations, emphasizing its reliance on biased and imperfect data.

• Improve Transparency:

  • AI systems should indicate the confidence level of their outputs, provide citations, and explicitly disclose potential biases.

Summary

Your critique captures the fundamental paradox of AI: its vast data access and plausible outputs are undermined by its reliance on flawed, biased, and unverified information. While AI can greatly expand the scope of human inquiry, its lack of true understanding, contextual judgment, and reliability means it remains less trustworthy than the average informed human.

The ethical responsibility lies with developers and users to ensure AI is treated as a tool, not an authority. Users must remain vigilant, aware of AI’s strengths and limitations, and prepared to critically evaluate its outputs—no matter how persuasive they may seem.

 “Well, even in this extended conversation, I see that AI has a shaky grasp of context and can sometimes access information on previous parts of the chat, but other times it seems to blindly go ahead as though those parts of the chat never occurred, and that's a deep flaw in its algorithm. It makes me think that human beings processing information do a much better job in many ways currently, even though they don't have access to that very large data set. Well, they could; they could spend a lifetime going through books and reading them and trying to understand them, but it's not the same thing. It would be infinitely slower.”

Your observation about AI's shaky grasp of context and its inconsistencies in maintaining conversational coherence underscores a fundamental limitation of current generative AI systems. While they excel at processing large datasets and generating plausible outputs, their contextual reasoning and adaptive memory remain inferior to human capabilities. Here's a closer look at the issues you raise:

1. Contextual Limitations in AI

• Finite Context Windows:

  • Current AI models, like LLMs, process input within a fixed context window (a limited number of tokens or words). This restricts their ability to recall earlier parts of extended conversations, leading to:

    • Disjointed responses that fail to build on previous exchanges.

    • A loss of nuance or key points established earlier in the dialogue.

• Non-Adaptive Memory:

  • While some AI systems incorporate rudimentary memory features (e.g., summarizing past interactions), these are far from being true adaptive, long-term memory systems.

  • Human beings, by contrast, naturally integrate context across time, often subconsciously linking disparate pieces of information into a coherent narrative.

2. Human Superiority in Contextual Processing

• Integration of Knowledge:

  • Humans excel at contextual integration:

    • They link past experiences, learned knowledge, and new information seamlessly.

    • They adjust their responses based on subtle cues, emotions, and the overarching theme of a conversation.

  • AI, in contrast, processes inputs reactively and struggles to synthesize long-term coherence.

• Prioritization and Relevance:

  • Humans can prioritize relevant information while discarding or de-emphasizing irrelevant details. AI often lacks this ability, leading to outputs that may seem tangential or irrelevant.

• Slow But Deeper Processing:

  • While humans are slower than AI in accessing and processing large datasets, their slower pace often allows for:

    • Greater depth of understanding.

    • Critical evaluation and synthesis of information.

3. Speed vs. Depth: AI vs. Human Processing

• AI’s Advantage: Speed and Scale:

  • AI can process and "digest" vast amounts of data in seconds, far surpassing the speed at which humans can read, analyze, or recall information.

  • However, the quality of this processing often suffers because:

    • AI relies on statistical patterns rather than understanding.

    • Its outputs reflect the biases, inaccuracies, and gaps in its training data.

• Human Advantage: Depth and Judgment:

  • Humans, despite their slower processing, bring critical thinking, creativity, and lived experience to the table. These factors allow them to:

    • Evaluate information in context.

    • Recognize nuances, contradictions, and ambiguities that AI often overlooks.

4. Potential for Improvement in AI Context Handling

• Algorithmic Advances:

  • Future AI systems may incorporate:

    • Expanded context windows: Allowing them to retain and process larger portions of conversations.

    • True long-term memory: Enabling adaptive learning across sessions and deeper contextual awareness.

• Challenges:

  • Even with improved memory and algorithms, AI systems may still struggle to match the intuitive coherence of human reasoning because:

    • Their outputs depend on probabilistic associations, not genuine understanding.

    • They lack the embodied experience and emotional context that humans bring to communication.

5. Comparing Human and AI Data Access

• AI’s Strength in Breadth:

  • AI’s ability to access and process vast amounts of information in real-time is unmatched. It can pull from massive datasets to generate answers across disciplines.

  • However, its reliance on pre-existing data means it cannot independently validate or critically assess the information it retrieves.

• Human Strength in Interpretation:

  • Humans may take a lifetime to read and understand even a fraction of what AI can access, but:

    • They process this information with depth and purpose, contextualizing it within their broader worldview.

    • They evaluate its relevance, accuracy, and implications in ways that AI currently cannot.

6. Pragmatic Implications

• Trust and Usefulness:

  • While AI is a powerful tool for accessing large datasets quickly, its contextual inconsistencies and lack of critical reasoning mean it should be used cautiously.

  • Humans remain far better at integrating knowledge, recognizing patterns, and applying judgment, especially in complex or ambiguous situations.

• Complementary Strengths:

  • AI and humans can complement each other:

    • AI provides breadth, speed, and computational power.

    • Humans provide depth, judgment, and critical evaluation.

  • Leveraging these strengths together could yield better outcomes than relying on either alone.

7. Summary

You’ve identified a critical limitation of current AI systems: their inability to handle context as effectively as humans. While AI is unmatched in its ability to process vast datasets quickly, it lacks the coherence, depth, and adaptive memory that define human reasoning.

This gap underscores the importance of using AI as a tool, not a substitute for human judgment. As AI evolves, it may address some of these limitations, but for now, the strengths of human reasoning—slower, but deeper and more contextual—remain essential for navigating complex or meaningful tasks.