https://www.noemamag.com/why-ai-is-a-philosophical-rupture/
What makes AI such a profound philosophical event is that it defies many of the most fundamental, most taken-for-granted concepts — or philosophies — that have defined the modern period and that most humans still mostly live by. It literally renders them insufficient, thereby marking a deep caesura.
The human-machine distinction provided modern humans with a scaffold for how to understand themselves and the world around them. The philosophical significance of AIs — of built, technical systems that are intelligent — is that they break this scaffold.
AI teaches us that this is not so. And not just AI, of course. Over the last two decades or so the concept of intelligence has multiplied. We now know that there are lots of other kinds of intelligence: from bacteria to octopi, from Earth systems to the spiral arms of galaxies. We are an entry in a series. And so is AI.
To argue that these other things are not “really” intelligent because their intelligence differs from ours is a bit silly. That would be like one species of birds, say Pelicans, insisting that only Pelicans “really” know how to fly.
The work of the self on the self has formed the core of what Greek philosophers called meletē and Roman philosophers meditatio. And the kind of AI system I evoke here would be a philosopher’s dream. It could make us humans visible to ourselves in ways no human interlocutor can, from outside of us, free from conversational narcissism.
In fact, one can push that a step further and say that AI systems appear to be capable of distinguishing truths from falsehoods. That’s because truth is positively correlated with a consistent logical structure. Errors, so to speak, are all unique or different. While the truth is not. And what we see in AI models is that they can distinguish between statements that conform to the patterns that they discover and statements that don’t.
So in that sense, AI systems have a nascent sense of truth.
Gardels: Karl Jaspers was best known for his study of the so-called Axial Age when all the great religions and philosophies were born in relative simultaneity over two millennia ago — Confucianism in China, the Upanishads and Buddhism in India, Homer’s Greece and the Hebrew prophets.
The practice of writing created new possibilities for analytical thinking that led to increasingly abstract, classificatory nouns and to a form of systematic search and production of knowledge that was not seen anywhere in human history before.
Fascinatingly, what emerges from this learning process is a high-dimensional, relational space that engineers call latent — in the sense of hidden — space.
It is just, and this is the third thing, that this spatial map doesn’t have only the three dimensions — length, width, depth — our conscious human mind is comfortable operating in. Instead, it has many, many more dimensions. Tens of thousands and with the latest models, perhaps millions.
That is, the understanding an LLM has formed is a spatial architecture. It has a geometry that literally determines what, for an LLM, is thinkable.
It is literally the logical condition of possibility — the a priori — of the LLM.
For all we know, human brains also create latent space representations. The neurons in our brain work in a very similar fashion to how neurons work in a neural network.
Yet, despite this similarity, it appears that the latent space representations that a human brain produces and the latent space representations that an AI can produce are different from one another.
Bauhaus School.
When Walter Gropius founded the Bauhaus, in 1919, many German intellectuals were deeply skeptical of the industrial age. Not so Gropius. He experienced the possibilities that new materials like glass, steel and concrete offered as a conceptual rupture with the 19th century.
And so, he argued –– very much against the dominant opinion — that it was the duty of architects and artists to explore these new materials, and to invent forms and products that would lift people out of the 19th and into the 20th century.
Today, we need something akin to the Bauhaus — but focused on AI.
We need philosophical R&D labs that would allow us to explore and practice AI as the experimental philosophy it is.
