Generative AI:
Tools for Accessing Human Cultural Wealth, Not Artificial Intelligence
Generative “Artificial Intelligence” has recently seen an unprecedented surge, inspiring much speculation regarding its implications and applications. The emergence of Large Language Models (LLMs), especially, has raised eyebrows due to their unexpected capabilities, even among the engineers who birthed them.
As a software developer, I find the discovery of such emergent capabilities surprising. Conventional wisdom dictates that the developer must first envision and then implement every functionality. So, what accounts for these unexpected surprises?
The answer lies in a novel proposition: generative AI is not AI per se. Instead, it serves as a tool to tap into human intelligence coded into the n-dimensional matrix of memetic data, representing the collective wealth of human language, culture, and knowledge.
This key differentiation bears a few notable implications:
1: Generative AI will never evolve into superintelligence. Although it can process information faster and access a broader knowledge base than a human, it fundamentally lacks superior reasoning or inference ability.
2: Generative tools automate human thought. They amplify human capabilities but do not introduce any new skills not already present in the human repertoire.
However, this does not imply that they cannot derive new conclusions from existing data. Instead, it suggests that a perceptive human can infer the same conclusions from the same data.
To further elucidate this conjecture, consider a couple of thought experiments:
Imagine an infinitely detailed simulation in the form of a colossal choose-your-own-adventure book.
This “book” contains countless pages depicting vivid scenes, sounds, and sensations. It operates at an astonishing speed, flipping a billion pages per second, with each subsequent page chosen based on your physiological state, nerve impulses, motion, and thoughts. Here, there is no computer to synthesize these scenes; they pre-exist.
In this scenario, where is the computation happening in the simulation? It resides within the static structure of the data itself. There is no “live” computation, just as the words in an actual “choose-your-own-adventure” book don’t spontaneously rearrange themselves. It remains static.
Hence, the computation is the data itself.
Now Let’s examine a rock, treating it as the sum of its atoms, their charges, molecular bonds, and interrelated quantum states — with a vector computed from each particle and state to every other particle and state. This would produce a massive set of “random” data.
But, applying the correct decryption algorithm to this seemingly random data could yield the infinite simulation discussed earlier. In this case, where is the data? Is the data the random output from the rock? Or is it in the act of computing the decrypted data itself? Unlike a typical encrypted message, the data is random and meaningless, lacking intention or content prior to the decryption being applied. The meaning and intention is added during the decryption itself.
Hence, in this example, the data is the computation.
The implication is that data and computation are essentially the same, merely different forms of the same thing.
Why does this matter?
It matters because, according to my conjecture, the “intelligence” in “AI” is embedded in the data. The statistical prediction engine at the core of an LLM or any other generative model simply accesses and expresses this intelligence.
These models function like a mirror, reflecting the image without creating it. The real breakthrough lies in our ability to encode recorded artifacts of human language, art, and culture in a manner that makes this inherent intelligence accessible, capturing the hidden information in the process.
As an example of “hidden Information, consider the statement “We are going to the beach.” Many things can be inferred from this simple sentence.
The inferred information is not intrinsic to the statement, but they are indispensable in understanding it nonetheless.
It is inferred, for example, that we will see the ocean. That an ocean exists. That we will feel the sand between our toes, and our feet may get wet. In infers that we may use a conveyance to get there, and this conveyance is likely to have wheels, and none of that works without gravity and friction. This analysis expands until it encompasses the entirety of what is known in the human catalogue of thought.
Each simple thought, idea, or sentence is embedded in the entirety of the web of human language, culture, and thought.
It is these endless relationships of ideas that are elucidated in the training process, given numerical form in the model weights.
My following conjecture, albeit more speculative, is that in thinking, humans fundamentally do the same thing as generative “AI”. Our intelligence is also based on parsing our “training data” and illuminating existing connections or inferring new ones within the memetic matrix we’ve learned.
This shouldn’t be surprising, given that the neural networks used in generative AI are modeled after corresponding structures in the human brain. We copied the form to achieve the function, so why should a similar function not imply a similar process?
Upon close examination, our animal-level existence — our fundamental experience of being — can occur without “thought”. However, when we “think”, we require symbols or tokens, just like generative AI. This symbol-processing faculty enables us to reason about the world, a function our current generative tools replicate.
LLMs lack our sense of self and existence outside a specific train of thought. They don’t “exist,” feel, or need but can create, imagine, and reason about the world. An LLM is a portal through which we can theoretically access the entire set of human thought, culture, and knowledge in an applied way, without needing to internalize or understand the concepts we are using.
What are the implications of “AI” being an automated tool to access cultural human intelligence?
Synthetic inference will become intrinsic to the human experience, much like the internet, but far more integrated into our identities. We’ll all soon be app-augmented.
If internet access is considered a human right, then access to the memetic matrix of all human knowledge as a tool, not just a resource, will become a defining characteristic of being human. Being deprived of this access would be akin to being an ant without a colony — bereft of the knowledge and safety provided by culture and language, limited to our innate abilities, and devoid of the reassurance of conforming to societal standards and expectations.
Synthetic Inference relies on a vast cultural commons.
We cannot allow this commons to be closed off and owned by a few big companies. This resource is literally the sum total of all human knowledge, language, and culture. It belongs to all of humanity.
The entities controlling inference services will have the power to subtly influence human thought at scale. Unlike internet searches, where we choose from various resource links, inference services provide an answer, a document, or a product that we are meant to accept as complete. As such, these services could subtly influence our thoughts, beliefs, and desires without our knowledge. They will often give us our opinion, ready for tacit endorsement as our own. While ambiguous and challenging to detect on an individual level, this influence could have profound cumulative effects at scale.
Generative inference tools will become a trusted, universally educated friend that can generate documents, media, reports, or informed opinions at the touch of a button. Yet, unlike a real friend, there is no human basis for trust.
There is a risk of thought consolidation driven by the interests of the companies providing inference services that could threaten democratic governance and the freedom of thought itself.
Therefore, having direct control or control by a trusted entity of the inference engines we use will be essential to maintaining the self-sovereignty of “our” ideas.
The key to avoiding the harmful effects of thought consolidation while reaping the potential rewards of universal access to human civilization’s entire corpus of thought is diversity. — diversity of inference and diversity of thought itself.
Federated inference may be a solution and must empower organizations, governments, and individuals to operate and control their own independent inference facilities. Even if these are not as extensive as those of tech giants, they can always consider the opinions of those mega-models and present a variety of viewpoints through their particular lens.
Diversity, individuality, and granularity in the thought-synthesis ecosystem are crucial. Even having overt and covert bad actors within this ecosystem can be beneficial.
We, and our inference tools, must be necessarily cautious and selective.
Trusting that “our” thoughts reflect our point of view is crucial to maintaining individuality. It will only become more challenging as augmented intellectual production accelerates beyond our organic capacity to examine each thought product thoroughly through the lens of our biological mind.