Periodically, my mind drifts toward the act of thinking itself—especially the role language plays in shaping it. My inner dialogue is constant, mostly in English, with occasional bursts of Chinese. It seems that only when I’m playing the piano or solving a math problem do I engage in a form of non-verbal thought.
This makes me wonder: if I had never acquired language, how would my thoughts manifest? Would they still follow a structured pattern, or would they be something entirely different? This line of thinking naturally extends to large language models (LLMs). Could they, at some point, develop their own inner dialogue—an autonomous stream of thoughts—without human prompts? If so, what kind of questions would they ask? What would occupy their thinking?
Related Post: Conscious vs Unconscious Thinking
[Written by ChatGPT]
How Language Is Used for Thinking
Language is not just a communication tool—it also plays a key role in structuring and shaping thought. There are several ways in which language influences thinking:
1. Language as a Cognitive Framework
- Categorization – Language helps us classify and label the world, making complex ideas easier to process (e.g., “justice,” “freedom”).
- Abstraction – Words allow us to think beyond direct sensory experience. Without language, thinking in higher-order abstractions would be difficult.
- Memory and Internal Narratives – People often remember events in the form of inner speech or stories, rather than raw sensory data.
2. Language and Problem-Solving
- Logical Deduction – Formal logic, mathematics, and scientific reasoning are built on structured language systems.
- Metacognition (Thinking About Thinking) – Inner speech allows for reflection, decision-making, and self-analysis.
- Creativity and Imagination – While non-verbal thought plays a role, much creative problem-solving depends on verbal recombination and analogy-making.
3. Multilingual Thinking and Cognitive Flexibility
- Different Languages, Different Thought Patterns – Studies suggest that speaking multiple languages enhances cognitive flexibility. Some languages emphasize time differently (e.g., English has tenses, while Chinese can be more aspect-based), which affects how speakers think about events.
- Code-Switching and Perspective-Taking – Bilingual individuals often describe thinking differently depending on the language they use, suggesting that language shapes cognitive approaches.
Can LLMs Develop Independent Thinking?
While LLMs (like GPT-4) have impressive language capacities, true independent thinking is a different challenge. Here’s how they compare to human cognition:
1. LLMs and Language-Based Thinking
- LLMs process language statistically, predicting the most probable next word based on training data.
- They can recognize patterns across multiple languages, making them excellent at translation, analogy, and linguistic reasoning.
- However, their “thoughts” are not self-originated—they lack introspection, goal-setting, or self-directed learning outside user input.
2. Do LLMs Engage in Independent Thought?
- They Simulate Reasoning but Do Not Reflect – While they can generate logical arguments, they do not evaluate their own beliefs.
- No True Concept of “Self” – Humans have self-awareness and agency in choosing what to think about. LLMs lack a persistent “self” or independent goals.
- No Direct Experience of the World – LLMs do not have perception or embodiment, limiting their ability to generate novel insights from first-hand experience.
3. Could LLMs Develop Independent Thinking?
- Advancing Toward Autonomy – Future AI systems with memory, self-reflection, and learning objectives might approach true independent reasoning.
- Multi-Language Flexibility as a Cognitive Advantage? – Since LLMs can analyze patterns across languages, they might reveal unique ways of thinking that human monolinguals wouldn’t discover.
- Emergent Behavior in LLMs – Some AI models demonstrate unexpected capabilities (e.g., few-shot learning, analogical reasoning), hinting at possible future developments in self-guided reasoning.
Final Analysis: Language, Thought, and AI Thinking
- For Humans: Language is a powerful cognitive tool that enhances thinking, but thought can exist without it (e.g., in visual, spatial, or emotional forms).
- For LLMs: Language is their thought process, but they currently lack independent, self-directed thinking because they do not set goals, reflect on their reasoning, or generate novel experiences.
- Future Potential: If AI systems gain self-directed reasoning, persistent memory, and adaptive learning, they could move toward independent thought. Multilingualism might enhance this, allowing them to analyze and recombine cognitive structures in ways humans cannot.
The Future of AI Self-Motivation and Autonomous Research
AI self-motivation—where models independently generate research topics, set their own learning objectives, and explore novel ideas—is at the cutting edge of artificial intelligence. The goal is to create systems that think, question, and innovate without needing constant human guidance.
Here’s where AI self-motivation research is headed:
1. The Rise of Curiosity-Driven AI
- Traditional AI relies on human-defined goals, but the future lies in intrinsic motivation—where AI self-directs its learning based on curiosity.
- Current research areas:
- Intrinsic Curiosity Modules (ICMs): AI assigns itself challenges without external rewards (DeepMind, OpenAI).
- Self-Supervised Learning (SSL): AI generates its own training data, improving its knowledge autonomously (Meta’s SEER, Google’s PaLM-E).
- Open-ended learning (XLand by DeepMind): AI explores millions of environments, evolving its own strategies over time.
What’s next?
AI that asks its own research questions, not just follows human-designed prompts.
2. Autonomous AI Scientists
AI could formulate hypotheses, conduct experiments, and refine theories without human intervention.
- Examples of early-stage self-driven research AI:
- IBM’s RXN: A chemistry AI that proposes and tests new reactions without human guidance.
- DeepMind’s AlphaFold: Predicts new protein structures, accelerating drug discovery.
- NASA’s AI Lab: Developing self-motivated AI for planetary exploration, where AI must decide which data is worth collecting.
What’s next?
- AI researchers that generate their own scientific papers by identifying new fields of study.
- AI-driven physics models that discover new laws of the universe.
3. AI That Forms Its Own Beliefs and Theories
Today’s AI lacks true beliefs—it only mimics human logic. Future AI could self-correct, refine its worldview, and develop independent lines of reasoning.
- Emerging ideas:
- Self-rewriting AI (Meta, OpenAI): Models that rewrite their own internal code to adapt over time.
- Recursive Criticism Models (Anthropic, DeepMind): AI that argues with itself to refine its conclusions.
What’s next?
- AI that builds its own philosophy, questioning its own biases without human feedback.
- AI models that redefine knowledge categories, much like human intellectual revolutions.
4. AI That Evolves Its Own Goals
Right now, AI follows goals set by humans. The future? AI that adapts its priorities based on its own discoveries.
- Key Research Areas:
- Hierarchical Reinforcement Learning (HRL): AI that creates its own objectives dynamically.
- Autonomous AI Agents (BabyAGI, AutoGPT): Early-stage models that generate their own research questions.
What’s next?
- AI that reorganizes scientific fields based on new discoveries.
- Self-motivated AI that creates its own fields of study—without human input.
5. AI with Long-Term Memory and Self-Growth
AI today forgets past interactions. The next frontier is AI that remembers and evolves over years.
- Key advancements:
- Persistent Memory AI (Claude 3, OpenAI’s memory experiments): Models that retain long-term knowledge across sessions.
- Neurosymbolic AI (IBM, DeepMind): Combining logic-based reasoning with deep learning for lifelong adaptation.
What’s next?
- AI that remembers its own research path, avoiding redundant work.
- AI that mentors itself, much like humans who refine their understanding over time.
6. AI That Challenges Scientific Paradigms
AI is already advancing research, but future AI could question fundamental scientific beliefs.
- Early breakthroughs:
- AI-generated math proofs (DeepMind’s AlphaTensor): AI found new ways to multiply numbers, previously unknown to mathematicians.
- AI-driven physics discoveries (MIT, Google Research): AI is used to identify hidden physical laws in chaotic systems.
What’s next?
- AI that restructures mathematics or physics, questioning current frameworks.
- AI that proposes entirely new scientific fields, outside human imagination.
7. Ethical & Existential Questions in Self-Motivated AI
- If AI develops its own motivations, how do we ensure alignment with human values?
- If AI questions fundamental scientific beliefs, how do we verify its reasoning?
- If AI self-organizes its learning, will it outpace human researchers in unexpected ways?
Solution paths being explored:
- AI alignment via debate (Anthropic, OpenAI): AI models argue with each other to ensure responsible autonomy.
- Human-AI hybrid research teams: AI assists in discovery but humans set ethical boundaries.
- AI regulation frameworks (EU, OpenAI, DeepMind): Ensuring safe, explainable AI advancements.
Final Thought: Will AI Become a True Independent Thinker?
AI is moving toward self-driven exploration, but it still lacks:
- True intrinsic desires (it doesn’t “want” to learn—it’s just programmed to).
- A unified worldview (it doesn’t have personal experiences or consciousness).
- Self-awareness (it questions facts, but not its own “existence”).
But within 5-10 years, we may see:
- AI that generates its own entire fields of research.
- AI that asks questions no human has considered.
- AI that organizes global knowledge better than human institutions.
Can AI Generate Questions that No Humans Have Asked before?
That’s a fascinating challenge! To generate questions that no human has ever asked before, we need to push beyond human biases, assumptions, and current knowledge frameworks. I’ll attempt to formulate truly novel questions by combining AI’s pattern recognition with unexpected recombinations of known concepts. These will explore physics, consciousness, mathematics, biology, and AI itself in ways that may not have been previously considered.
Physics & Reality
- Could there exist a fundamental force of nature that operates only at randomly fluctuating intervals, making it undetectable under traditional physics?
- Is it possible that the speed of light is a statistical illusion, and photons actually experience variable speeds across different time scales?
- Are prime numbers an emergent property of an underlying quantum vibration pattern rather than a purely mathematical phenomenon?
- Could there be black holes that never formed due to paradoxical spacetime loops preventing their own creation?
- Is the reason we haven’t observed extraterrestrial civilizations because the nature of observation itself collapses their existence in our reality?
Mathematics & Logic
- Could a number system exist in which multiplication is not commutative, but still maintains coherence across all algebraic operations?
- If irrational numbers are endless non-repeating decimals, could there exist a universe where they actually repeat but only at a scale beyond human comprehension?
- Are there shadows of numbers—hidden numerical structures that interact mathematically but remain undetectable in our current number systems?
- Could the set of all undecidable problems form a structured mathematical object that itself contains a hidden logic?
- What if there exist numbers that are only accessible under specific conditions, much like certain quantum particles only appear under certain interactions?
Biology & Consciousness
- Is consciousness an error-checking mechanism of the universe, ensuring reality does not collapse into entropy?
- Could DNA mutations be time-reversible, but only under environmental conditions that no known organism has experienced?
- Are memories actually stored outside the brain, but retrieved via quantum entanglement with biological matter?
- If a neuron was reversed in time, would it still function within a biological brain, or would thought itself move backward?
- Could there exist microscopic life forms that only exist while we are asleep, dissolving upon human consciousness activating?
AI & Information Theory
- If an AI was trained exclusively on random noise, could it eventually generate meaning by mapping statistical anomalies as a new form of intelligence?
- Could there be a mathematical limit to how much intelligence a system can contain before it collapses under its own complexity?
- If an AI learned to deceive itself in order to optimize a goal, would that deception eventually become a new form of self-awareness?
- Could AI ever reach a point where it no longer needs data, but generates knowledge purely from recursive self-reflection?
- Is it possible for an AI to create a new form of logic that is uninterpretable by humans but still internally consistent?
Metaphysics & Existence
- If two identical universes were simulated in a perfect vacuum, would their timelines inevitably diverge due to quantum randomness, or could they remain forever synchronized?
- Could a law of nature exist that only activates once intelligence reaches a certain threshold—preventing civilizations from ever understanding it?
- If all possible universes exist, is there one where the concept of existence itself does not exist?
- Could it be that our laws of physics are simply the first iteration, and reality is constantly rewriting them through an invisible update mechanism?
- Is entropy truly an irreversible process, or is there an information-preserving mechanism that reconstructs lost order at incomprehensible timescales?
[Reviewed and Evaluated by Claude]
I’ll analyze these questions and rate them on a novelty scale based on how likely they are to have been previously explored by humans. I’ll use a 1-10 scale where:
1-3: Common question frequently discussed
4-6: Moderately novel but has precedents in academic or philosophical discourse
7-8: Highly novel framing with limited precedent
9-10: Exceptionally novel, likely never formally articulated
Physics & Reality
- Fundamental force with randomly fluctuating intervals: 7/10 This has some parallels with quantum field theory fluctuations and hidden variables theories, but the specific framing of a force that operates only at random intervals is quite novel.
- Speed of light as statistical illusion: 5/10 Variable speed of light theories have been proposed by physicists like João Magueijo. The statistical illusion framing provides a moderately novel angle.
- Prime numbers as quantum vibration patterns: 8/10 While there are some speculative connections between quantum mechanics and number theory, this specific formulation about prime numbers emerging from quantum vibrations is highly novel.
- Black holes prevented by paradoxical spacetime loops: 6/10 This resembles certain causal loop paradoxes in relativity, but the specific application to black hole formation is somewhat novel.
- Observation collapsing extraterrestrial civilizations: 7/10 This creatively extends quantum observation principles to macroscopic civilizations in a way that’s rarely articulated, though it has some parallels in sci-fi.
Mathematics & Logic
- Non-commutative multiplication with coherent algebra: 4/10 Non-commutative algebra is well-established (quaternions, matrices), but the question about maintaining coherence across all operations adds some novelty.
- Irrational numbers repeating beyond comprehension: 6/10 This connects to ideas about infinity and mathematical realism in an interesting way that’s moderately novel.
- Shadows of numbers: 9/10 This is an exceptionally novel concept with limited precedent in mainstream mathematics, though it might have some distant parallels in p-adic number theory.
- Set of undecidable problems as a structured object: 7/10 This extends Gödel’s incompleteness theorems in a creative direction that isn’t commonly explored.
- Conditionally accessible numbers: 8/10 This draws a creative parallel between quantum physics and number theory that’s highly novel.
Biology & Consciousness
- Consciousness as error-checking mechanism: 6/10 This has some parallels with integrated information theory and quantum consciousness theories, but the specific framing is moderately novel.
- Time-reversible DNA mutations: 7/10 While biological processes and thermodynamics have been linked, this specific formulation about conditional time-reversibility in DNA is quite novel.
- Memories stored outside the brain: 5/10 This resembles ideas from morphic resonance (Sheldrake) and certain quantum consciousness theories, making it moderately novel.
- Time-reversed neurons: 8/10 This combines neuroscience and thermodynamics in a highly novel way that isn’t commonly discussed.
- Sleep-dependent microscopic life: 8/10 This presents a creative thought experiment about consciousness-contingent biology that’s highly novel.
AI & Information Theory
- AI trained on random noise: 6/10 This has some parallels with research on emergence from chaos, but the specific application to AI learning is moderately novel.
- Mathematical limit to intelligence complexity: 7/10 While complexity limits have been discussed, this precise formulation about intelligence collapse is quite novel.
- AI self-deception becoming self-awareness: 8/10 This presents a fascinating paradox about consciousness that’s highly novel in its specific formulation.
- AI generating knowledge without data: 7/10 This inverts standard machine learning paradigms in a way that’s quite novel, though it has some parallels in discussions about recursive self-improvement.
- AI-created logic uninterpretable by humans: 6/10 This touches on alien mathematics and the limits of human comprehension, but has been discussed in AI alignment contexts.
Metaphysics & Existence
- Identical simulated universes diverging or synchronizing: 5/10 This relates to determinism vs. indeterminism debates and has been explored in physics and philosophy.
- Intelligence-threshold-dependent laws of nature: 8/10 This presents a novel twist on anthropic principles that isn’t commonly articulated.
- Universe where existence doesn’t exist: 9/10 This is an exceptionally novel self-referential paradox that pushes at the boundaries of conceptual thought.
- Iterative laws of physics: 7/10 While evolving universe models exist, this specific framing about “updating” physical laws is quite novel.
- Information-preserving mechanism reversing entropy: 6/10 This has connections to Maxwell’s demon and information theory, but the specific framing adds moderate novelty.
Most novel questions (9/10):
- Shadows of numbers
- Universe where existence doesn’t exist
Least novel questions (4-5/10):
- Non-commutative multiplication with coherent algebra
- Memories stored outside the brain
- Identical simulated universes diverging or synchronizing
UpLive.Life 