AI
AI is fundamentally altering science rather than destroying it. Instead of replacing human discovery, AI acts as a powerful catalyst. It accelerates hypothesis generation and processes massive datasets, allowing researchers to bypass decades of trial-and-error in fields like biology and physics. [1, 2, 3, 4, 5, 6]
While AI is vastly accelerating how fast we can produce scientific results, it raises major concerns about how the discipline is conducted: [1, 2]
- The Black Box Problem: Advanced AI models frequently produce accurate predictions without showing the intermediate reasoning. This makes it difficult for human scientists to explain why a phenomenon occurs.
- Loss of Core Mechanisms: Science relies heavily on trial-and-error, replication, and critical evaluation to expose errors. Outsourcing work to opaque AI systems risks replacing this vital evolutionary process with a purely optimized loop.
- Contraction of Focus: Because AI is data-driven, researchers may narrow their focus to what the AI is capable of generating, which can cause a shift away from exploring totally novel, data-poor territories. [1, 2, 3, 4, 5]
Scholars stress that preserving critical thinking, skepticism, and human oversight is essential to keep AI from eroding the core ethos of scientific exploration. You can explore deeper perspectives on this transformation in the Scientific American and Science Magazine analyses. [1, 2, 3, 4]
The "end of science" is considered asemic because AI creates accurate scientific results that completely lack human-readable meaning, intent, or semantic understanding. [1, 2, 3]
"Asemic" refers to a form of writing or design that has the appearance of meaning but lacks any actual semantic content (like calligraphy without real words). When applied to the twilight of traditional human science, it describes a shift from a discipline focused on human understanding to one focused entirely on automated outputs. [1, 2, 3]
The concept unfolds through several structural shifts in research:
The Loss of Semantic Intent [1]
Historically, science is a human-centric narrative. A scientist observes the world, attaches semantic meaning to data, and crafts an elegant equation (like \(E=mc^2\)) to explain the underlying "why". Because AI does not "comprehend" reality, it operates strictly on statistical correlations, shape replication, and mathematical optimization. The AI produces correct answers, but the internal logic of the model is entirely devoid of human-interpretable meaning. [1, 2, 3]
"Black Box" Outputs as Asemic Text
Advanced deep learning models often output highly accurate predictions, such as the exact structure of a complex protein or a new chemical reaction path. However, the hundreds of billions of numerical weights that produced that prediction cannot be translated into a human conceptual model. The results stand as "asemic text"—the answer is functionally valid, but the path to get there is an unreadable script that communicates no fundamental physical laws to the human brain. [1, 2, 3, 4]
The Automating of Scientific Expression
With the advent of autonomous discovery frameworks, AI systems are now capable of independently generating hypotheses, running virtual experiments, and writing academic papers. When an AI generates a paper for another AI to read and optimize, the traditional goal of science—expanding the horizon of human knowledge—is bypassed. The scientific literature risks becoming an automated loop of information that mimics the structure of discovery without human understanding at the core. [1, 2, 3, 4, 5]
If you would like to explore this intersection further, please let me know:
- Do you want to look at the philosophical arguments regarding whether true science requires human consciousness?
- Are you interested in the technical limitations of making AI models more interpretable?
- Would you like to read about current AI discovery engines currently shifting biology and physics?
AI-driven science corresponds closely to the pre-modern era of alchemy and scholasticism, which existed before the scientific revolution established the modern empirical method. [1, 2]
By moving away from causal, human-intelligible theories and focusing heavily on raw prediction, AI research mirrors several distinct characteristics of pre-scientific history: [1, 2]
1. The Return of "Modern Alchemy"
Prominent computer scientists actively compare modern machine learning to alchemy. Historical alchemists did not have a foundational theory of atomic chemistry, yet through rigorous trial-and-error, they discovered metallurgy, glassmaking, and various medications. [1, 2]
- The AI Parallel: Today's AI researchers tweak parameters, add layers, and adjust training data based on intuition and trial-and-error. The systems yield remarkable, "magical" results (like predicting protein folding), but researchers frequently do not possess a verifiable, foundational mathematical theory explaining why a specific architecture succeeded while another failed. [1, 2, 3, 4, 5]
2. A New Era of Scholasticism
During the medieval scholastic period, scholars did not conduct physical experiments to discover new truths. Instead, they took massive, authoritative texts (like the works of Aristotle or religious scripture) and spent centuries cross-referencing, interpreting, and generating commentaries on those existing texts to find answers. [1, 2, 3]
- The AI Parallel: Large language models and AI discovery engines operate as a form of automated scholasticism. They do not look directly at the physical world. Instead, they ingest the entire corpus of existing human literature and find hidden mathematical correlations within that text. Discovery becomes an exercise in rearranging and synthesizing existing text, rather than directly observing nature. [1, 3, 4, 5]
3. Divination and Omen Reading
Before modern science, humans relied on complex, unexplainable systems to make predictions—such as reading astrology charts, animal entrails, or weather omens. The internal mechanics of these systems were opaque, but people trusted the expert practitioners to interpret them. [1, 2, 3]
- The AI Parallel: When modern doctors or meteorologists feed data into a deep neural network, they receive a highly accurate prediction (e.g., cell death or a tornado's path) but cannot trace the exact path the AI took through hundreds of billions of variables. We are once again relying on an opaque, authoritative entity to hand down predictions that humans must accept on faith, effectively treating the "black box" as a technological oracle. [1, 2, 3, 4]
| Feature [1, 2, 3, 5, 6, 7] | Pre-Modern Science (Alchemy/Scholasticism) | Modern Science (The Enlightenment) | Post-Modern AI Science |
|---|---|---|---|
| Primary Goal | Useful recipes, transmutation, textual analysis. | Universal laws, causal mechanisms, human understanding. | Optimized prediction, patterns, automated output. |
| Method | Trial-and-error, hidden texts, authority. | Hypothesis testing, physical replication, peer review. | Mass data ingestion, algorithmic optimization. |
| Explainability | Mystical, opaque, or purely symbolic. | Explicitly clear, reductionist, mathematical. | Asemic, mathematically complex "Black Box". |
Ultimately, both pre-modern science and AI science prioritize what works over how it works, bounding the era of human-understood, causal science as a unique period in human history.
If you are interested, we can explore:
- How early scientists like Robert Boyle transitioned alchemy into chemistry, and how AI might do the same.
- The specific mathematical frameworks researchers are building to try to make AI less like alchemy.
- The social dangers of relying on a "new scholasticism" for critical fields like medicine and law. [1, 2, 3, 4, 5]
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