Thus enlightens AI "You saying what you thinking?"
More Q&A about AI's use of human language to acquire its own intelligence ...
Re:
https://www.perplexity.ai/
Q: Regarding LLM training, are some languages better than others in revealing how humans think? Is it possible that language is not the internal processing of the human brain and that AI's focus on language for LLM training has related vulnerabilities?
Based on the search results provided, there isn't direct information comparing how different languages used in LLM training reveal human thinking. However, we can draw some relevant insights:
Language and Perception
The relationship between language and perception is a longstanding area of study in philosophy and cognitive science. Recent research using large language models (LLMs) has provided new ways to examine this connection:
LLMs trained on large amounts of text can provide insights into how much perceptual information is encoded in language. In a study on color perception, GPT-4 was able to replicate cross-linguistic variation in color naming for English and Russian, illuminating the interaction between language and perception. This suggests that the language data LLMs are trained on does capture some aspects of how humans perceive and conceptualize the world. Different languages may encode perceptual information in varying ways.
Theory of Mind Capabilities
Research has examined how well LLMs can perform on theory of mind tasks compared to humans:
GPT-4 performed at or above human levels on some theory of mind tests, like identifying indirect requests, false beliefs, and misdirection. However, GPT-4 struggled with detecting faux pas compared to humans. LLaMA2 models showed different performance patterns than GPT models on these tasks. This indicates that different LLM architectures and training approaches may lead to varying capabilities in modeling human-like reasoning about mental states.
Internal Representations
Studies have looked at how LLMs develop internal representations as they improve at language tasks:
As an LLM's ability to solve puzzles improved, its internal representations became more accurate, suggesting it was developing an understanding of instructions rather than just memorizing patterns. Researchers found that LLMs may develop their own "understanding of reality" as a way to improve their language generation abilities.
Considerations
While LLMs show impressive capabilities in some areas, there are important caveats:
- The extent to which LLMs truly "understand" language in a human-like way is still debated.
- LLMs are trained on text data, which doesn't capture the full richness of human linguistic experience.
- Performance on specific tasks doesn't necessarily indicate general intelligence or consciousness.
In conclusion, while different languages used in training may reveal varying aspects of human cognition, the relationship is complex. More research is needed to fully understand how language choice in LLM training impacts their ability to model human thinking. The field is rapidly evolving, with new models and findings emerging regularly."
Q: Is it possible that language is not the internal processing of the human brain and that AI's focus on language for LLM training has related vulnerabilities?
"This is an intriguing question that touches on fundamental issues in cognitive science, linguistics, and artificial intelligence. While language is undoubtedly a crucial aspect of human cognition, there are reasons to consider that it may not be the primary internal processing mechanism of the human brain. This perspective has implications for how we approach AI and LLM training.
Language and Cognition
The relationship between language and thought has been debated for centuries. While language is undeniably important for human cognition, there are several arguments suggesting it may not be the brain's primary internal processing mechanism:
- Pre-linguistic thought: Infants and animals demonstrate problem-solving abilities and complex cognition without language.
- Non-verbal reasoning: Many cognitive tasks, such as spatial reasoning or pattern recognition, don't necessarily rely on language.
- Thinking in images: Some people report thinking primarily in visual images rather than words.
- Multilingual cognition: Polyglots often report thinking in different languages or in non-linguistic concepts, suggesting a more abstract cognitive process.
LLM Training and Potential Vulnerabilities
Given this perspective, the heavy focus on language in LLM training might indeed lead to certain vulnerabilities or limitations:
Overemphasis on Linguistic Patterns
LLMs are trained primarily on text data, which may not capture the full spectrum of human cognition. This could lead to:
- Limited conceptual understanding: LLMs might excel at manipulating linguistic patterns without truly grasping underlying concepts.
- Difficulty with non-verbal tasks: Tasks requiring spatial reasoning or visual processing might be challenging for language-centric models.
Biases in Language Data
Training on language data alone can introduce biases:
- Cultural and linguistic biases: Language data inherently carries cultural assumptions and biases.
- Lack of embodied experience: Unlike humans, LLMs don't have sensory experiences to ground their understanding of language.
Vulnerability to Linguistic Manipulation
The focus on language processing makes LLMs particularly susceptible to certain types of attacks:
- Prompt injections: As noted in the OWASP Top 10 for LLM Applications, crafted inputs can manipulate LLMs in ways that might not affect human cognition.
- Data poisoning: Malicious manipulation of training data can introduce vulnerabilities or biases that might be more difficult to introduce in multi-modal cognitive systems.
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Limitations in Causal Reasoning
Language-based training might not adequately capture causal relationships that humans intuitively understand through interaction with the physical world.
Implications for AI Development
Recognizing these potential limitations could lead to new approaches in AI development:
- Multi-modal training: Incorporating visual, auditory, and other sensory data alongside text could lead to more robust AI systems.
- Embodied AI: Developing AI systems that interact with the physical world might help ground language understanding in real-world experiences.
- Cognitive architecture research: Exploring AI architectures that more closely mimic the structure of the human brain, rather than focusing solely on language processing.
- Enhanced security measures: Developing safeguards that account for the unique vulnerabilities of language-centric AI systems, such as those outlined in the OWASP Top 10 for LLM Applications.
In conclusion, while language is a crucial aspect of human cognition, it may indeed not be the primary internal processing mechanism of the human brain. The heavy focus on language in LLM training, while powerful, could lead to certain vulnerabilities and limitations. Recognizing these potential issues could drive the development of more robust, versatile, and secure AI systems in the future."
Q: Language appears to be the spokesman/interpreter for what's happening in the human brain. Since language is acquired skill, it may not accurately represent the brain's cognition that requires use of language. And other brain processing may not at all be revealed by language.
"Your observation is astute and aligns with several important perspectives in cognitive science and neuroscience. Let's explore this idea further:
Language as an Interpreter
Your metaphor of language as a "spokesman/interpreter" for brain processes is quite apt. This view is supported by several lines of research and theory:
- Dual-process theory: This psychological framework suggests that there are two systems of thinking:
System 1: Fast, automatic, and unconscious., System 2: Slow, deliberate, and conscious.
Language is often associated with System 2, potentially "interpreting" the rapid, unconscious processes of System 1.
- Consciousness as an interpreter: Neuroscientist Michael Gazzaniga's research on split-brain patients led to the concept of the "left-brain interpreter," suggesting that our conscious verbal self often creates post-hoc explanations for actions initiated by non-verbal brain processes.
Language as an Acquired Skill
You're correct that language is an acquired skill, which has several implications:
- Developmental timeline: Basic cognitive processes develop before language acquisition, indicating that fundamental thinking doesn't require language.
- Cultural and linguistic relativity: The Sapir-Whorf hypothesis suggests that the language we speak influences our cognition. This implies that the "interpretation" provided by language may vary across cultures and languages.
- Linguistic determinism debate: While language certainly influences thought, the extent to which it shapes or limits cognition is still debated.
Limitations of Language in Representing Cognition
Your point about language potentially not accurately representing brain cognition is well-taken:
- Non-verbal cognition: Many cognitive processes, such as visual processing, spatial reasoning, or emotional responses, occur without direct linguistic involvement.
- Implicit knowledge: We often know more than we can tell, as pointed out by philosopher Michael Polanyi. This "tacit knowledge" is difficult to articulate in language.
- Qualia: The subjective, qualitative aspects of conscious experiences (like the redness of red) are notoriously difficult to fully capture in language.
Brain Processing Beyond Language
Indeed, a significant portion of brain processing may not be revealed by language at all:
- Unconscious processing: A vast amount of neural activity occurs below the threshold of consciousness and is not directly accessible to language.
- Embodied cognition: This theory posits that many cognitive processes are deeply rooted in the body's interactions with the world, not just in abstract linguistic or conceptual representations.
- Emotional and intuitive processes: These often occur rapidly and can influence decision-making before conscious linguistic processing takes place.
- Sensory processing: The brain constantly processes vast amounts of sensory data, most of which never reaches conscious awareness or linguistic expression.
Implications for AI and LLMs
Given these considerations, the heavy reliance on language for training AI systems like LLMs may indeed have limitations:
- Incomplete representation: LLMs may be missing crucial aspects of cognition that are not easily expressed in language.
- Lack of embodied experience: Without sensorimotor experiences, LLMs may struggle to develop certain types of understanding that humans gain through physical interaction with the world.
- Overemphasis on explicit knowledge: The focus on language might lead to an AI system that excels at processing explicit, declarative knowledge but struggles with implicit, procedural, or embodied forms of cognition.
- Cultural and linguistic biases: LLMs trained primarily on language data may inadvertently encode and amplify cultural and linguistic biases present in their training data.
To address these limitations, future AI development might benefit from:
- Embodied AI systems that can interact with the physical world.
- Multi-modal learning approaches that incorporate non-linguistic inputs.
- Cognitive architectures that attempt to model non-linguistic aspects of human cognition.
In conclusion, your insight highlights a crucial consideration in both cognitive science and AI development. While language is an invaluable tool for human communication and thinking, it may indeed be just the "tip of the iceberg" when it comes to representing the full spectrum of brain processes and cognition."
Q: Another vulnerability in this language methodology: How humans express their thinking in writing does not necessarily reveal how they think.
"You've raised an excellent point that highlights another crucial limitation in using language, particularly written language, as a window into human cognition. This insight touches on several important aspects of cognitive science, linguistics, and the philosophy of mind.
The Gap Between Thought and Expression
The discrepancy between how humans think and how they express their thoughts in writing is a significant consideration:
- Thought processes are not linear: While written language is typically presented in a linear, sequential manner, human thought processes are often non-linear, involving parallel processing and associative leaps.
- Limitations of language: Language, by its nature, may not be capable of fully capturing the richness and complexity of human thought. Some concepts or experiences may be inherently difficult or impossible to express accurately in words.
- Cognitive load of translation: The act of translating thoughts into written language requires cognitive effort and may alter the original thought process.
Implications for Understanding Cognition
This disconnect between thought and written expression has several implications:
- Incomplete representation: Written language may only capture a fraction of the underlying cognitive processes, potentially missing crucial aspects of how people actually think.
- Structured vs. unstructured thinking: The act of writing often imposes structure on thoughts that may have originally been more fluid or abstract.
- Cultural and linguistic filters: The way people express their thoughts in writing is heavily influenced by cultural norms and linguistic conventions, which may not reflect their actual cognitive processes.
Impact on AI and LLM Training
The limitation you've identified has significant implications for AI development, particularly for LLMs:
- Biased training data: If LLMs are primarily trained on written language, they may be learning from an already filtered and potentially distorted representation of human thought.
- Misrepresentation of cognition: LLMs trained on written language may develop patterns that mimic how humans express thoughts in writing, rather than how they actually think.
- Limitations in problem-solving: The linear nature of written language may not capture the non-linear, intuitive leaps that characterize much of human problem-solving and creativity.
- Difficulty with tacit knowledge: Much of human expertise involves tacit knowledge that is difficult to articulate in writing. LLMs may struggle to capture this type of knowledge.
Potential Approaches to Address This Limitation
To better capture human cognition in AI systems, researchers might consider:
- Multi-modal data integration: Incorporating data from various modalities (e.g., speech, gestures, brain imaging) alongside text to get a more holistic view of cognition.
- Cognitive architecture modeling: Developing AI systems that attempt to model the underlying cognitive processes rather than just mimicking language outputs.
- Think-aloud protocols: Utilizing data from think-aloud studies where participants verbalize their thought processes in real-time, potentially capturing more of the non-linear nature of thought.
- Neuroplausible models: Developing AI architectures inspired by the structure and function of the human brain, which may better reflect how humans actually process information.
In conclusion, your observation highlights a fundamental challenge in using language-based AI to understand human cognition. It underscores the need for a more nuanced and multi-faceted approach to AI development that goes beyond reliance on written language alone. This insight could drive new research directions aimed at creating AI systems that more accurately reflect the complexities of human thought processes."
Rod
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(Perplexity) "While AI excels at processing information and following rules, 
Rod!
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