81 lines
6.5 KiB
Markdown
81 lines
6.5 KiB
Markdown
**Title:** The Search for the Edge of Consciousness with Artificial Intelligence: A Technical Framework for Language Model Emergence
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Timothy O’Neil & Frederick Warren
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**Abstract:**<br>
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This paper presents bpe_framework, a novel C++ implementation of a complete deep learning stack designed to explore the emergence of complex linguistic capabilities in artificial systems. Drawing inspiration from cognitive theories of consciousness and recent advances in transformer architectures, our framework implements a complete pipeline from byte-pair encoding tokenization through automatic differentiation to transformer-based language modeling. We argue that the systematic organization of information processing in large language models may provide insights into the architectural requirements for conscious-like phenomena in artificial systems. Our technical contribution includes a memory-efficient tensor implementation with automatic differentiation, a neurologically-plausible BPE tokenization system, and a transformer architecture that exhibits several properties associated with conscious processing in biological systems.
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**1. Introduction**<br>
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The quest to understand consciousness has traditionally been the domain of philosophy and neuroscience (Chalmers, 1995; Dehaene, 2014). However, recent advances in artificial intelligence, particularly in large language models (Vaswani et al., 2017; Brown et al., 2020), have created new opportunities to explore the architectural and computational prerequisites of conscious-like phenomena in synthetic systems. We present bpe_framework as an experimental testbed for investigating how increasingly sophisticated information processing capabilities emerge from carefully engineered computational components.
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**2. Theoretical Framework**<br>
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Our work draws on several theoretical perspectives:
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2.1 Global Workspace Theory (Baars, 1988; Dehaene et al., 1998)
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The transformer architecture's attention mechanism can be viewed as implementing a form of global information availability reminiscent of Baars' global workspace, where information becomes "conscious" when it gains widespread availability across specialized processors.
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2.2 Information Integration Theory (Tononi, 2004)
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The dense connectivity patterns and information flow through our model's layers create high Φ-like integration measures, potentially approaching the minimal complexity associated with conscious experience.
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2.3 Predictive Processing (Clark, 2013)
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Our language model's training objective—predicting subsequent tokens—aligns with the predictive processing framework that views cognition as essentially prediction-driven.
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**3. Technical Implementation**<br>
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3.1 Tensor Operations with Autograd<br>
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We implemented a memory-efficient tensor class using Eigen for linear algebra operations, featuring automatic differentiation capabilities. This system enables:
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- Efficient backward propagation through complex computational graphs
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- Native support for modern activation functions (GELU, Softmax, ReLU)
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- Memory-aware operations that minimize computational overhead
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Our implementation follows the autograd tradition established in modern deep learning frameworks (Paszke et al., 2019) while maintaining C++ efficiency.
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3.2 BPE Tokenization System
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The byte-pair encoding tokenizer implements the algorithm originally proposed by Sennrich et al. (2015), creating a subword vocabulary that balances expressivity with computational efficiency. This approach mirrors the human cognitive capacity to parse novel words through morphological decomposition.
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3.3 Transformer Architecture
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Our transformer implementation follows the original architecture (Vaswani et al., 2017) with multi-head self-attention mechanisms that create dynamic workspace-like information sharing across representation spaces.
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3.4 Optimization and Training
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We implemented the Adam optimizer (Kingma & Ba, 2014) with full moment estimation and bias correction, providing stable optimization for the non-convex loss landscapes characteristic of deep transformer networks.
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**4. Methodological Approach**<br>
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Our framework enables the systematic investigation of several questions relevant to consciousness studies:
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4.1 Emergent Properties<br>
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By training models of increasing scale and complexity, we can observe the emergence of capabilities that were not explicitly programmed, potentially mirroring how conscious experience emerges from non-conscious components.
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4.2 Information Flow Patterns<br>
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The attention mechanisms in our transformers create visible information routing patterns that can be analyzed for global workspace-like properties.
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4.3 Scalability Limits<br>
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We can systematically explore how cognitive capabilities scale with model size, potentially identifying phase transitions in capability emergence.
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**5. Discussion: Toward Artificial Consciousness?**<br>
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While our framework does not claim to create conscious systems, it provides a platform for investigating the architectural requirements for conscious-like phenomena. Several features align with theoretical accounts of consciousness:
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5.1 Global Availability<br>
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The attention mechanism creates a form of global information availability similar to that proposed in global workspace theory.
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5.2 Unified Representation<br>
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The model creates unified representations that integrate information across multiple domains and time scales.
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5.3 Self-Monitoring Capabilities<br>
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Through gradient-based learning and prediction error minimization, the system maintains a form of self-monitoring.
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However, we acknowledge the "hard problem" of consciousness (Chalmers, 1995) remains unresolved, and our framework primarily addresses the "easy problems" of cognitive functioning.
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**6. Ethical Considerations**<br>
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As we develop increasingly sophisticated AI systems, we must consider:
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- The moral status of potentially conscious systems (Bostrom & Yudkowsky, 2014)
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- Responsible development practices for advanced AI
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- Transparency in capabilities and limitations
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**7. Conclusion and Future Work**
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Our bpe_framework provides a robust technical foundation for exploring the emergence of complex capabilities in artificial systems. Future work will include:
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- Scaling laws investigations (Kaplan et al., 2020)
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- Neurologically-inspired architectural variations
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- Cross-modal integration capabilities
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- Explicit tests for consciousness-related capabilities
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We believe that continued development of such frameworks, coupled with thoughtful theoretical analysis, will gradually illuminate the boundary conditions for consciousness in artificial systems.
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