Flock isn't just a private mass surveillance corporation that leases cameras and infra to police departments. We've had that since digital cameras and the internet.

What Flock sells as its core product is "AI" surveillance and policing. What that actually means is that Flock trains their own statistical models on the data their cameras passively collect. Flock sells access to those models as a "one click" crime solver.

The result is not only the massive expansion of public surveillance, but the replacement of actual detective work with statistical modeling. By using a statistical model to "solve crime", police departments get to launder their responsibility away to the magic AI black box.

Only if you set aside the passage of time.

An LLM continues the prompt it is given. What is more likely to come after a question? An answer, not an "oh sorry I'm not sure". Sure, you could make the latter more likely, but then the model would be unusable. Larger models simply contain more answers, more ways to stumble into them, and a granular enough geography to stay on the trail.

Flock collects these images, and uses them to train statistical models, and sells those models to police departments as a one-click crime solving solution.

That means that their primary function is to passively aggregate driver behavior into an automated judgement system, where the entire judicial system (designed to check and balance itself) is laundered away. Flock cameras exist to fundamentally replace police work, not to assist with it.

Disgusting copy. We all know that's not their primary function.

Their goal is to find a "good fit", not to find a good engineer. That's why their software is so incredibly disfunctional: it's just a reflection of their workplace culture.

Your speech doesn't just fail to stay with you, it's implications hang over you. What you have said is treated structurally like a magical incantation: directly relevant to reality itself, no matter how little sense comes out of that relationship. This is the vain objectivity I'm talking about: everything you say is interpreted as if you meant to state an objective fact.

Context is a fundamental feature of communication. Objectivity just means factoring as much context as we can out of the interpreter, and into the expression. It's a useful tradeoff, but it has limitations. At some point, there must be subjective interpretation. Without it, you are just talking to a brick wall.

> I think this is exactly your point

Yes, it is.

Another useful word would be "direct", or maybe "concrete". A metaphor allows us to get right to the point, instead of going up the ladder of abstraction. Alternatively, we can use well-defined abstractions, like formal mathematics, to be precise in an abstract way.

I tried to lay it out, but I'm (ironically) using objectively defined abstractions to do so...

Metaphor is what allows a story's implications to be taken out of an expression, and placed into the interpreter itself. An interpreter that arbitrarily decides what an expression can mean does so by bringing its own implications. This way, instead of forcing an expression to articulate those implications, the expression can imply them instead. The interpreter fills in the gap.

If we formalize a story's implications, that means we create a decided interpreter: a collection of assumptions that impose a specific perspective on the reader. This is how programming languages work: any expression has a clearly defined meaning, because it will only be given to that language's interpreter. The disadvantage is that you cannot write ambiguous expressions, because ambiguity would require the potential for more than one perspective, which would require more than one interpreter.

My idea is to create many ad-hoc interpreters, so that any of them can be chosen to interpret a given expression. This way, the chosen perspective is computable, while the expression can be ambiguous and metaphorical. The only "hard" problem left is the selection of interpreter, which I expect to be manageable.

I was saying the opposite. The core feature of an LLM is that it is subjective. The implications of a written expression (prompt) are not well-defined objective truth, but instead a vague probability. We can compute the probability, but that doesn't ever intersect with logical reduction or arithmetic; so the implications we get are just vague guesses on the progression of the story. With enough examples and training, the LLM can guess correct arithmetic answers, but critically, it does not actually perform the arithmetic that verifies them.

The meaning of an expression is its implication, not its definition. The implication must be decided by the reader. This is either done by strict definition, or by arbitrary inference. Inference is a powerful feature of language, because it expands the set implications that can be expressed without abstracting the language. Without inference and metaphor, we must instead create a tower of well-defined abstractions that rule over us; having already determined what implications are relevant: objective truth. With inference and metaphor, the writer/speaker can articulate their implications with familiar language; relying on the reader/listener to infer from the expected relevant perspective: subjective truth.

Essentially the tradeoff isn't whether or not you get precision, but whether that precision can be shared by the writer and the reader, or be managed entirely by the writer. Defined abstractions allow the writer to be unambiguous to anyone (objective), while inference allows the writer to offload that responsibility to the context and perspective of the reader (subjective).

Does that make sense?

This is something I've been thinking a lot about. We have trended from subjective language to objective language. Why?

Computing. Software is written with objective language. Everything is clearly unambiguously defined. Blue is no longer a category, it's #0000FF. Logic must always reduce to a binary truth value. Most of what we have to talk about is somehow relative to software. Software even structures most of what we write! We don't just talk to each other, we tweet, email, message, post, search, etc. These structures each imply a specific set of phrase structures that can make sense.

Lately, it's hard to go even a day without reading some complaint that such and such was written by "AI" (an LLM). Why is this so obvious? Well, the core advantage that LLMs provide is that they don't compute. Inside an LLM, there is no arithmetic, no logical branches, no truth values. Phrases aren't generated to define or to resolve. They are generated to continue. Sure, we can direct the story to follow the steps of logical deduction, but that isn't anything like calculation. An LLM simply isn't invested in logic, precision, correctness, etc. the way we expect modern writers to be. It's not the em-dashes or the word choice that illustrates this, it's the fundamental perspective of the system.

We are sorely missing subjectivity. Natural language never was, and never will be, computable. You can't reduce a natural story to binary truth values without choosing an arbitrary perspective that resolves its ambiguity. The more precisely abstract our language gets, the more detached from reality our stories become. The more objective our assertions about reality are, the less relevant they can be.

My answer to this is to make the arbitrary choice of perspective a first-class feature. If we can explicitly decide what meaning is relevant, we should be able to weakly solve natural language processing. It seems like a pretty simple and obvious idea, but so far is easier said than done.

Examples provide more than syntax. It's the semantics that we care about most.

The deeper underlying problem is that we treat arbitrary UI/UX implementations as objective truth. There is one decided way your computer can be interacted with, and you the user must accommodate it. This is what I want to change.

What we are really missing is the ability for expression to be subjective by default. When we participate in socially global contexts, everything we read and write must be coherent to the generalized expectations of the entire group of people who are participating in that context. Instead of your words being taken out of context, they are constantly assumed into the context, implying your own interpretation is objectively wrong. The meaning of every expression is decided and relevant, even when it shouldn't be.