The End of Objectivity as We Knew It

When machines reveal truths beyond human comprehension, who decides which patterns to trust. The call for a negotiated epistemology.

In fields from drug discovery to weather prediction and quantum systems, early examples suggest a potential inversion of the traditional knowledge-creation process.

In addition to theory-guided discovery, scientific insights are coming from patterns that AI systems detect but humans cannot initially comprehend. AI is producing operational knowledge that works in practice before humans can explain why it works in theory.

The implication is that reality may contain aspects fundamentally inaccessible to human cognition alone.

If these insights exist outside of the human theoretical corpus, how do we evaluate the veracity of this type of knowledge? What does this mean for our understanding of knowledge itself?

In order to navigate a world where transformative discoveries emerge from patterns beyond human comprehension, we need a new kind of theory of knowledge a.k.a epistemology: a negotiated-epistemology. An epistemology that shifts from “what is true?” to “whose truth matters?” A concept that doesn’t demand complete human comprehension but still preserves human agency and values in deciding which AI-discovered patterns to adopt.

AI gets us from essence to resemblance

For millennia, we've operated under the rationalist paradigm of scientific discovery: that there is something to learn about reality. An objective truth lying somewhere and accessible through human reasoning. Objectivity, the cornerstone of rationalist thinking, has propelled scientific progress by assuming that things have essential properties waiting to be discovered.

In philosophical terms, this is the classical paradigm that things have a form of essence (remember Plato’s Cave) and that our job as humans is to uncover these truths. The quest is to forever get closer to that truth.

AI ditches this paradigm and embraces a view more closer to Wittgenstein: that of a family of resemblances. AI systems in the form of Large Language Models (LLMs) map resemblances. They group things based on statistical similarities and predict what comes next based on that, and not on any essential property.

This resemblance-based approach is producing knowledge that functions effectively in the world while existing partially beyond human comprehension.

II. The Fracturing of Objectivity: AI (Sometimes) Sees More Than We Can

This shift from theory to pattern recognition isn't merely theoretical, below I list two recent examples where AI identified effective knowledge that initially exceeded human theoretical understanding:

1. Drug discovery: Halicin - The Antibiotic Human Theory Missed

Halicin is an antibiotic drug discovered in partnership with AI.

MIT researchers trained an AI on 2,000 molecules, to learn about the ability of molecules to inhibit bacterial growth. The system then evaluated over 60,000 candidate molecules from a chemical library, looking for compounds with potential antibiotic properties that wouldn't be toxic to humans.

The AI found one: Halicin.

What struck me about this finding is that the AI identified relationships that were outside the concepts and theories humans have devised. It didn’t need to know why the molecule could work. It merely had to make a prediction based on the fabric of relationships it had encoded during training.

“The AI that MIT researchers trained did not simply recapitulate conclusions derived from the previously observed qualities of the molecules. Rather, it detected new molecular qualities - relationships between aspects of their structure and their antibiotic capacity that humans had neither perceived nor defined. Even after the antibiotic was discovered, humans could not articulate precisely why it worked” (Source: Kissinger, Schmidt & Huttenlocher, 2021)

2. Weather prediction

In 2023, DeepMind's AI weather forecasting models GraphCast was released to the public displaying “10-day weather predictions at unprecedented accuracy in under one minute.” (Source: Google DeepMind)