Does AI Mean Algorithmic Interpolation?

The Interpolation Reality

At its core, interpolation is about estimating values between known data points. When you draw a line connecting dots on a graph, you're performing a simple linear interpolation. AI systems do the same thing, just with vastly more complex algorithms that fit sophisticated curves through multidimensional data points.

This mathematical reality explains what's actually happening in AI systems—particularly large language models and diffusion-based image generators. They're connecting dots in a vast, high-dimensional space of possibilities, without any understanding, consciousness, or genuine intelligence.

Want more understanding - read this The stripped down truth-How AI actually works without the fancy talk

Want the maths - read this The mathematical heartbeat of AI

Evidence That AI Is Just Interpolation

Several observations confirm this perspective:

• Training Data Dependency: AI systems cannot generate truly novel concepts that weren't represented in their training data. They can only recombine and blend existing patterns—a hallmark of interpolation, not creative thinking.
• The Uncanny Valley of Generation: When asked to generate content that lies "between" common examples in the training data, AI produces convincing results. However, when pushed to extremes or unusual combinations, the outputs become surreal or nonsensical—clear evidence the system is mechanically interpolating between points without understanding.
• Mathematical Foundations: Every operation in an AI system can be reduced to matrix multiplications and statistical functions. There is no mysterious "thinking" component—just increasingly complex math.

The Illusion of Intelligence

What appears as intelligence in these systems is actually an illusion created by:

• Scale: The sheer scale of parameters and training data creates the impression of understanding, when it's really just more sophisticated interpolation.
• Pattern Matching: AI systems excel at detecting patterns and reproducing similar ones, which can seem like comprehension but is actually statistical matching.
• Human Projection: We naturally anthropomorphize systems that produce human-like outputs, attributing understanding and intention where none exists.

Researcher-Driven Anthropomorphism

Perhaps the most overlooked factor is how AI researchers themselves prime us for anthropomorphism:

• Misleading Terminology: The very term "artificial intelligence" suggests human-like cognition. Researchers routinely use terms like "learning," "understanding," "attention," "memory," and even "thinking" to describe purely mathematical operations.
• Strategic Framing: AI papers and press releases often describe systems in agentive terms: "The model learns to recognize patterns" rather than "Statistical weights are adjusted through backpropagation." This framing makes mathematical processes sound like cognitive ones.
• Benchmark Anthropomorphism: Evaluation benchmarks are frequently described as testing "reasoning," "comprehension," or "problem-solving," implying human-like cognitive faculties rather than pattern recognition and statistical correlation.
• Career Incentives: Researchers benefit professionally from portraying their work as approaching human-like intelligence, creating incentives to use language that suggests consciousness or understanding where none exists.
• Marketing Imperatives: Commercial AI labs frame their technologies using human-like qualities to attract funding, users, and media attention—reinforcing the false perception that these systems think.

This researcher-driven anthropomorphism fundamentally shapes how society perceives AI systems, creating expectations and assumptions about capabilities that are fundamentally misaligned with the mathematical reality of algorithmic interpolation.

The Trillion-Dollar Hype Machine

The financial stakes surrounding AI have created an unprecedented hype machine that further distorts public understanding:

• Astronomical Valuations: When AI companies are valued at hundreds of billions of dollars based on future potential rather than current capabilities, there's enormous pressure to portray systems as more intelligent and capable than they actually are.
• Investor Expectations: Venture capital and public market investors demand narratives of revolutionary progress and human-like capabilities to justify massive investments, pushing companies to exaggerate the cognitive abilities of their systems.
• Media Amplification: Tech and business media, hungry for sensational AI stories, routinely amplify the most dramatic claims about AI "thinking" and "reasoning" without critical examination of the underlying reality.
• Competitive Hyperbole: In the race for AI dominance, companies competitively escalate their anthropomorphic claims, creating a feedback loop where measured technical descriptions are replaced by increasingly dramatic assertions of machine cognition.
• Existential Narratives: Both proponents and critics frame AI in existential terms—either as humanity's savior or destroyer—further cementing the false impression that these systems possess human-like agency rather than performing algorithmic interpolation.

The combination of massive financial incentives with unchecked hype has created a perfect storm where the technical reality of AI—sophisticated statistical interpolation—has been almost completely obscured by narratives of quasi-human machines that "think," "understand," and "reason." This distortion serves the financial interests of the AI industry while leaving the public with fundamentally mistaken impressions about what these systems actually do.

Why This Matters

Understanding that AI is performing algorithmic interpolation rather than thinking has important implications:

• Realistic Expectations: We should calibrate our expectations of AI systems based on what they actually do—interpolate between training examples—rather than imagining them as conscious entities.
• Appropriate Deployment: Recognizing AI as mathematical tools rather than thinking agents helps us deploy them more responsibly in appropriate contexts.
• Ethical Clarity: The narrative that AI systems "think" or "understand" confuses important ethical discussions about their development and use.

How We Got Here

The anthropomorphization of computational systems isn't new:

• Early AI Optimism: In the 1950s and 60s, pioneers like Herbert Simon predicted machines would soon match human intelligence, setting a pattern of overestimation that continues today.
• Cycles of Hype and Winter: AI has experienced multiple cycles of exaggerated claims followed by "winters" of disappointment when promises weren't fulfilled—yet each new cycle seems to forget this history.
• Shift from Symbolic to Statistical: Earlier AI focused on explicit rule-based systems that mimicked logical reasoning. Modern deep learning takes a fundamentally different statistical approach, yet we still describe it using cognitive terms from the symbolic era.
• Silicon Valley Storytelling: The tech industry's culture of visionary narratives and "fake it till you make it" marketing has replaced the more measured academic discourse of earlier AI research.

Real-World Consequences

The mischaracterization of AI as "thinking" creates tangible harms:

• Dangerous Overreliance: Organizations deploy AI systems in critical domains like healthcare, criminal justice, and hiring based on inflated perceptions of their capabilities.
• Misplaced Trust: Users place unwarranted trust in AI outputs, failing to apply appropriate skepticism to what are ultimately statistical predictions.
• Abdication of Responsibility: Decision-makers can hide behind "the algorithm decided" when AI systems produce harmful outcomes, obscuring human responsibility.
• Resource Misallocation: Vast resources flow to approaches framed as advancing toward "thinking machines" rather than more modest but potentially more beneficial applications.
• Distorted Policy Priorities: Concerns about "superintelligent AI" and "artificial general intelligence" dominate policy discussions while more immediate risks from deployed systems receive insufficient attention.

Not All Interpolation Is Equal

Different AI architectures perform different kinds of interpolation:

• Convolutional Neural Networks (CNNs): Perform spatial interpolation, combining local features to recognize patterns in images.
• Transformers: Execute contextual interpolation across sequences, weighting relationships between elements based on learned patterns.
• Diffusion Models: Perform reverse interpolation, learning to recover structure from noise by gradually removing randomness.
• Reinforcement Learning: Interpolate between action-reward pairs to predict optimal behaviors in similar situations.

Understanding these distinctions helps clarify what each system can actually do versus what's beyond its mathematical capabilities.

The Role of Users in Anthropomorphism

Users themselves reinforce the illusion of AI "thinking":

• Conversational Interfaces: The simple act of typing questions to an AI and receiving responses triggers deeply ingrained social cognition.
• Emotional Investment: Users form one-sided emotional attachments to AI systems, projecting personalities and intentions onto mathematical operations.
• Selective Interpretation: People remember AI "successes" that appear thoughtful while dismissing failures as minor glitches rather than fundamental limitations.
• Demand for Narratives: Users often prefer compelling anthropomorphic explanations over accurate technical descriptions of how systems work.

Alternative Framings

More accurate ways to conceptualize and discuss AI include:

• Statistical Prediction Systems: Emphasizing the probabilistic nature of AI outputs rather than implying certainty or understanding.
• Pattern Recognition Tools: Highlighting that AI excels at detecting patterns in data but lacks conceptual understanding of what those patterns represent.
• Computational Media: Viewing generative AI as creating media through mathematical processes rather than through creative intention.
• Stochastic Parrots: Acknowledging that language models reproduce patterns from training data rather than generating original thoughts.
• Automated Decision Support: Positioning AI as supporting human decisions rather than making autonomous choices.

Policy Implications

Mistaking interpolation for thinking distorts policy approaches:

• Misguided Regulations: Policies focused on regulating imagined "thinking machines" miss more urgent concerns about deployed systems.
• Agency vs. Tool Framing: Laws and regulations struggle with whether to treat AI as agents or tools, when in reality they are complex mathematical systems.
• Responsibility Gaps: Anthropomorphic framing creates confusion about who is responsible when AI systems cause harm.
• Focus on Existential Risk: Resources are directed toward speculative far-future risks rather than addressing current harms from deployed systems.
• Overlooked Structural Issues: The focus on AI "capabilities" and "alignment" obscures structural issues of power, ownership, and control over these technologies.

Future Trajectory

Where is this trend heading?

• Escalating Anthropomorphism: As interpolation techniques improve, the gap between technical reality and public perception will likely widen further.
• Inevitable Disillusionment: Eventually, the limitations of statistical interpolation will become apparent, potentially triggering another "AI winter."
• Bifurcated Understanding: A growing divide between technical practitioners who understand the mathematical reality and non-specialists who perceive AI through anthropomorphic lenses.
• Potential Reframing: Growing awareness of the harms of anthropomorphism may eventually lead to more accurate public discourse about AI.
• The Long View: History suggests cycles of hype and disappointment will continue, but gradually systems will improve while expectations become more realistic.

Conclusion

So, does AI mean algorithmic interpolation? Yes—current AI systems are performing sophisticated interpolation in the spaces between their training examples, not engaging in conscious thought or understanding.

The outputs can be impressive and useful, but they emerge from mathematics, not minds. No matter how complex the interpolation becomes or how convincingly human-like the results appear, these systems remain fundamentally different from human intelligence because they lack consciousness, understanding, and true thinking.

As we continue to develop and interact with these systems, maintaining clarity about what they actually do—algorithmic interpolation, not thinking—will help us use them more effectively and ethically while avoiding the pitfalls of misplaced anthropomorphism.

Perhaps most importantly, recognizing the gap between the trillion-dollar hype machine and the mathematical reality allows us to have more grounded conversations about both the genuine capabilities and limitations of these systems. Only then can we make wise decisions about how to develop, deploy, and govern them in ways that truly benefit humanity rather than chasing the mirage of artificial minds that don't actually exist.

Thank you for reading

Related Articles