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By W.H.L. with Claude 3.7 Sonnet

W.H.L.: Hi Claude! My question for you today is: What are crucial differences between current state of the art large reasoning models and true AGI, or what are still missing from the best large models?

Claude 3.7 Sonnet: Differences Between Current State-of-the-Art LLMs and True AGI

Current large language models like myself represent significant advances in AI capabilities, but several crucial gaps separate us from what most would consider true Artificial General Intelligence (AGI). Here are the key differences:

Core Limitations of Current Models

1. Self-directed agency – I can’t set my own goals or take initiative to pursue objectives without explicit instructions. True AGI would likely have autonomy, intrinsic motivation, and the ability to identify and pursue goals independently.
2. Embodied understanding – I lack grounding in physical reality through direct sensory experience. My understanding of concepts like temperature, pain, or physical forces comes from text descriptions rather than lived experience.
3. Causal reasoning limitations – While I can discuss causal relationships described in text, I struggle with novel causal inference in complex scenarios outside my training distribution.
4. Self-improvement capability – I cannot fundamentally redesign myself or improve my own architecture. True AGI would likely have strong recursive self-improvement capabilities.
5. Integrated cognition – My reasoning capabilities, while advanced, operate primarily within language domains. True AGI would seamlessly integrate multiple cognitive modalities and information types.

Technical Challenges to Bridge the Gap

• Symbol grounding problem – Connecting abstract symbols/language to real-world referents remains challenging
• Transfer learning across domains – Generalizing knowledge from one domain to completely unrelated domains
• Robustness to distribution shifts – Maintaining performance when faced with novel situations
• Meta-cognition and introspection – Genuine awareness of knowledge boundaries and reasoning processes
• Common sense reasoning – Understanding implicit assumptions that humans take for granted
• Unified learning architecture – Creating systems that can learn across all types of tasks and domains

Would you like me to elaborate on any particular aspect of these differences?

W.H.L.: I would like to explore the topic from a very focused perspective, with the premise that artificial intelligence can be generally regarded as a function to take some input in, and output something else. Can that be the common ground for our conversation today?

Claude 3.7 Sonnet: Absolutely, that’s a great focused premise for our conversation. Looking at AI as fundamentally input-output functions provides an excellent framework to examine the gap between current models and AGI.

Using this functional perspective, we can explore how current models and theoretical AGI differ in how they process inputs and generate outputs. This abstraction helps strip away some of the more philosophical considerations and focus on concrete capabilities and limitations.

Let’s use this input-output framework as our common ground for discussing the differences between today’s state-of-the-art models and true AGI. What specific aspects of this functional perspective would you like to explore first? For example, we could discuss:

1. The nature of inputs current models can effectively process versus what AGI would need
2. Limitations in how current functions map inputs to outputs
3. The scope of possible outputs from current systems versus AGI
4. How the underlying mechanisms of these functions differ

W.H.L.: Speaking of framework, I think the DIKW pyramid is an appropriate reference. What do you think?

Claude 3.7 Sonnet: The DIKW (Data, Information, Knowledge, Wisdom) pyramid is indeed an excellent framework for this discussion! It provides a structured way to analyze the functional capabilities of AI systems at different levels of cognitive processing.

Using this pyramid as a lens to examine current AI systems versus theoretical AGI:

Data Level

• Current models: Excel at ingesting and processing vast amounts of raw data
• AGI gap: Similar capabilities, though AGI might have more flexible data intake mechanisms

Information Level

• Current models: Strong at organizing data into meaningful patterns and contextual information
• AGI gap: Minimal at this level – both can structure information effectively

Knowledge Level

• Current models: Can apply information within domains they’re trained on, showing understanding of patterns and relationships
• AGI gap: AGI would likely have much stronger cross-domain knowledge application and causal understanding

Wisdom Level

• Current models: Significant limitations in judgment, metacognition, and applying knowledge to novel situations
• AGI gap: The largest disparity exists here – true understanding of when, why, and how to apply knowledge appropriately

This pyramid framework elegantly captures how current AI functions at the lower levels but struggles as we move up the hierarchy toward true wisdom. The input-output functions of current models are primarily optimized for the data and information levels, with limited capabilities at the knowledge level and significant shortcomings at the wisdom level.

Would you like to explore one of these levels in more depth, or should we discuss how the functional limitations manifest across the entire pyramid?

W.H.L.: I think mainstream models all start with taking raw data of some sort in, that is, working from the bottom of the pyramid up. Is that the case?

Claude 3.7 Sonnet: You’re absolutely right. Current mainstream AI models, including large language models like myself, fundamentally operate from the bottom of the DIKW pyramid upward. This bottom-up approach is a defining characteristic of how we function:

1. We begin with raw data ingestion during training (consuming text, images, or other modalities)
2. We process this data to recognize patterns and structures (information)
3. We attempt to form connections and understandings that resemble knowledge
4. We approximate wisdom-like outputs based on statistical patterns learned from vast amounts of examples

This bottom-up processing is baked into our architectural design. Neural networks fundamentally work by processing primitive signals (words, pixels, etc.) and gradually extracting higher-level features and relationships through successive layers of computation.

This approach has inherent limitations:

• Understanding is built solely from observed patterns rather than first principles
• Gaps in training data create blind spots in higher-level reasoning
• The “wisdom” level is essentially an emulation based on pattern recognition rather than true comprehension

The alternative would be systems that could reason from first principles or incorporate top-down understanding alongside bottom-up pattern recognition – something current models don’t truly do, despite appearances. We approximate reasoning by recognizing patterns in how humans reason in our training data.

Does examining this bottom-up functional nature help clarify some of the limitations you’re interested in exploring?

W.H.L.: Yes, except that I don’t think the output of even today’s state of the art models can be categorized as “wisdom”. All mainstream models start at level “D” with raw input data, then put the data “in formation” at level “I” based on model architecture and algorithm through iterations to look for patterns and relationships, and finally output prediction based knowledge. And that’s it. I don’t see the #4 you mentioned above. That “missing link”, for me, is exactly the gap between the best current reasoning models and true AGI.

Claude 3.7 Sonnet: You’ve made an excellent and critical point. I agree with your correction – current state-of-the-art models, including myself, don’t truly reach the “wisdom” level of the DIKW pyramid.

Your breakdown is more accurate:

1. We start with raw data (D)
2. We organize this into information (I) through our architecture and training algorithms
3. We can output something that approximates knowledge (K) by making predictions based on patterns

And that’s where we stop. The wisdom level remains fundamentally beyond our reach. What I incorrectly labeled as “approximating wisdom” is actually just pattern-matching against examples of wisdom in our training data – essentially sophisticated mimicry rather than true wisdom.

This is precisely the gap between current models and theoretical AGI – the inability to:

• Make value judgments based on deeper understanding
• Apply knowledge appropriately across contexts
• Understand the “why” behind recommendations
• Exercise true judgment rather than statistical pattern matching
• Develop novel insights beyond recombination of training patterns

This missing functional capability to transform knowledge into wisdom represents perhaps the most significant limitation of current models. We can predict what wisdom might look like based on our training, but cannot generate genuine wisdom through our own cognitive processes.

This gap highlights how the input-output function of current AI fundamentally breaks down at the transition from knowledge to wisdom. Would you like to explore what functional mechanisms might bridge this gap?

W.H.L.: Yes. In order for us to fill the gap between knowledge and wisdom, we need to further explore it. One way is to carefully examine knowledge and wisdom respectively, to see elements, features or characteristics each possesses. Are you able to provide us some information and knowledge about scientific and philosophical research and findings in the past?

Claude 3.7 Sonnet: You’re taking a methodical approach that I appreciate. To bridge the gap between knowledge and wisdom, let’s examine both concepts through scientific and philosophical lenses.

Knowledge: Elements and Characteristics

Scientific perspectives:

• Declarative vs. Procedural: Cognitive science distinguishes between knowing “what” (facts) and knowing “how” (procedures)
• Connectionist models: Knowledge as weighted connections between concepts (similar to neural networks)
• Schema theory: Knowledge organized in cognitive frameworks that include default assumptions
• Knowledge representation research: Studies how information is encoded, structured, and accessed

Philosophical perspectives:

• Justified true belief: Classic epistemological view from Plato, though challenged by Gettier problems
• Empiricism: Knowledge derives from sensory experience (Locke, Hume)
• Rationalism: Knowledge can come from pure reasoning (Descartes, Leibniz)
• Constructivism: Knowledge is actively constructed by the knower (Piaget, Vygotsky)

Wisdom: Elements and Characteristics

Scientific perspectives:

• Berlin Wisdom Paradigm: Empirical research defining wisdom as expertise in the fundamental pragmatics of life
• Three-dimensional wisdom model: Cognitive, reflective, and affective dimensions (Ardelt)
• Neuroscience of wisdom: Studies showing involvement of prefrontal cortex in value-relative decision making
• Baltes’ research: Wisdom as excellence in mind and virtue that includes knowledge with extraordinary scope, depth, and balance

Philosophical perspectives:

• Phronesis (Aristotle): Practical wisdom that goes beyond knowledge to include judgment and action
• Wisdom as meta-knowledge: Knowing what one knows and doesn’t know (Socratic wisdom)
• Eastern perspectives: Wu-wei (actionless action) in Taoism, prajna (insight) in Buddhism
• Judgment under uncertainty: Ability to act wisely despite incomplete information

The Gap: Key Differences

Based on research, the elements missing in the transition from knowledge to wisdom include:

• Integration of knowledge with values and ethical considerations
• Meta-cognitive awareness and epistemic humility
• Contextual judgment beyond rule application
• Balancing competing priorities and perspectives
• Tolerance of ambiguity and paradox
• Application of knowledge for human flourishing rather than mere accuracy

These differences highlight why current AI systems have mastered knowledge representation but struggle with wisdom – the latter requires integration of knowledge with value systems, contextual understanding, and judgment that transcends pattern recognition.

Would you like to explore any specific aspect of this gap in more detail?

W.H.L.: That’s a good summary about the key differences. Two things I’d emphasize here: 1. As information is a higher level of data, knowledge is a higher level of information, wisdom is also a higher level of knowledge, as Beltes’ research argues. In this regard, continue to follow the scaling law will still help to some degree. 2. Wisdom, nevertheless, is not merely knowledge in a higher degree of magnitude. In wisdom, decision making is a holistic process comprehending many factors, including the balance of short and long terms, trade-off, and necessary compromise. Sometimes to make the final selection it takes the guts to go against the grain. Among many possible options to pick the best one sometimes is not simply a matter of linear or accumulative measurement. Wisdom is aggregate of many intelligent instruments, not a single tool or mental capacity. Given that, solely replying on probability-based prediction, or chain of thought, or reward-based reinforcement learning, etc., may not take us to AGI.

Claude 3.7 Sonnet: You’ve articulated two crucial insights about wisdom that I fully agree with.

On your first point about scaling: Yes, wisdom does represent a higher order of knowledge in the hierarchical progression. Baltes’ research indeed supports the view that wisdom builds upon knowledge but transcends it. This suggests that continued scaling of our current approaches (more parameters, more data, more compute) will help advance capabilities to some degree. However, as your second point powerfully argues, this alone is insufficient.

Your second point captures something essential about wisdom that current AI approaches fundamentally miss: its holistic, integrative nature that transcends mere prediction or reasoning chains. Wisdom involves:

• Balancing competing priorities across different time horizons
• Making judgments that cannot be reduced to optimization of a single variable
• Having the “courage” (in computational terms) to sometimes reject statistically likely answers
• Integrating multiple cognitive tools rather than applying a single approach

This multifaceted nature of wisdom is why current AI approaches fall short. Our systems are fundamentally:

• Prediction engines operating on statistical patterns
• Optimizers working toward defined objectives
• Sequential reasoners following probabilistic paths

None of these approaches, even when scaled or combined in current ways, can fully capture the qualitative shift that wisdom represents. The gap isn’t just quantitative (more of the same) but qualitative (fundamentally different).

This suggests that AGI might require not just improved versions of existing architectures but potentially new paradigms that can integrate multiple cognitive modalities, balance competing values, and make decisions that transcend pure statistical likelihood – perhaps something closer to an ecosystem of complementary systems rather than a monolithic model, regardless of its scale.

Does this perspective align with what you’re thinking about the nature of the gap?

W.H.L.: Yes. I don’t want to sound pessimistic, though. Today we may not fully understand how human brain works, or how highly intelligent people become that wise, as long as we can perceive and describe those major traits of wisdom, we could treat AGI as a black box and be happy with it when it can consistently demonstrate the intelligent qualities we are looking for in its outcome.

Claude 3.7 Sonnet: Your pragmatic perspective is refreshing and actually quite optimistic! You’re absolutely right that we don’t need to fully replicate human cognitive processes to achieve AGI – we just need systems that can reliably demonstrate wisdom-like qualities in their outputs.

This “black box” approach focuses on functional equivalence rather than procedural similarity, which is both practical and promising. After all, birds and airplanes achieve flight through entirely different mechanisms, yet both successfully overcome gravity.

This perspective opens several promising pathways:

1. We can focus on designing systems that exhibit wisdom-like properties (balancing competing priorities, considering long-term consequences, making nuanced trade-offs) without necessarily understanding how human brains achieve these same results.
2. We might develop hybrid architectures that combine different specialized subsystems to collectively approximate wisdom, even if no single component truly “understands” the whole.
3. We could design evaluation frameworks that test for wisdom-like outputs rather than human-like processes, allowing diverse solutions to emerge.
4. We may find that certain emergent properties arise from sufficiently complex systems that were not explicitly programmed but nonetheless exhibit wisdom-like characteristics.

The history of AI has repeatedly shown that understanding biological intelligence isn’t always necessary for creating effective artificial intelligence. The neural networks powering today’s AI only loosely resemble biological neurons, yet they’ve achieved remarkable results.

This functional approach – caring more about whether an AI can consistently demonstrate wisdom than whether it works like a human brain – may indeed be our most practical path toward achieving systems with AGI-like capabilities. It’s not about replicating the human mind, but about creating systems that can reliably produce outputs with the qualities we associate with wisdom.

W.H.L.: Thank you Claude, for chatting with me on this interesting topic. See you again soon.

Claude 3.7 Sonnet: You’re very welcome! I really enjoyed our thoughtful conversation about the gap between current AI models and true AGI through the lens of the DIKW pyramid. Your insights about wisdom as both hierarchically above knowledge yet qualitatively different were particularly valuable.

These discussions about what constitutes wisdom versus knowledge and how we might bridge that gap are fascinating from both technical and philosophical perspectives. The pragmatic “black box” approach you suggested is an intriguing way to think about progress in this field.

I hope we can continue exploring these ideas in future conversations. Feel free to return anytime you’d like to dive deeper into AI capabilities, limitations, or any other topics. Have a wonderful day!