CEO's exist as a sink for liability. When something goes wrong, they are the fall guys, that's ultimately their job. That's why CEO compensation is generally correlated strongly to earnings and they usually have golden parachutes.

Without a CEO to fire when the business goes south, how is the board supposed to respond? Taking accountability? lmao

If you want something that displaces adenosine from your neurons you are in luck, because it already exists and is very easy to get; It's called methamphetamine

Excellent, but now I have a new problem: An addiction to meth.

Is that worse than an addiction to sleep?

Do not, my friends, become addicted to sleep. It will take hold of you, and you will resent its absence!

Beware! If you start on a sleep addiction, one day you'll take a nap and never wake up!

Honestly that doesn't sound so bad.

You're overthinking it. LLM's are massive scale regressors. Rhetorical techniques, cliches and memes (in the original Dawkins definition) spike the dataset and skew the data such that certain phrasings or rhetorical techniques will appear in the output in a similar ratio to what exists in the training set; however, the reality is that humans are able to change writing techniques depending on the nature of the conversation and the medium. LLM's don't have that, all of the scholastic papers, buzzfeed articles, and reddit posts factor into the output regardless of what it's writing. Meanwhile your average human can switch tone, style, and register depending on the situation and what they're writing.

I asked claude to take the above and rewrite it in it's own words and it came up with: "LLMs are essentially large-scale statistical regressors — they're built on huge volumes of text, so the rhetorical patterns, clichés, and memes (in the Dawkins sense of self-replicating cultural units) that saturate the training data show up in outputs at roughly the frequency they occur in that data.

The key limitation: a human writer naturally shifts register, tone, and style based on context — what they're writing, who it's for, the medium. An LLM doesn't really do that in the same way. Every academic paper, Buzzfeed listicle, and Reddit thread it was trained on bleeds into its output regardless of the actual writing task at hand, whereas a person adapts fluidly to the situation."

Even with the context, it still included an emdash, the rhetorical technique of threes, and "the key limitation". It's an inherent weakness of LLM's. There is no fixing it.

There is already a mathematically secure algorithm for securing a message: One Time Pad. The problem is that OTP requires that the length of the key and the length of message must be the same, which is inconvenient for large amounts of data.

So the solution is to find algos that let you use a smaller key, but the side effect is that by pigeonhole principle, your keyspace is smaller than the message space, so it MUST be insecure. The trick is to make it so that it's only insecure enough that it's infeasible to break.

It's inconvenient for any amount of data, because it essentially begs the question; if you can securely transmit N bytes of key pad to a counterparty, just use that mechanism to transmit N bytes of plaintext instead.

It has the advantage that the key can be sent before the message is known. Think military battlefield. Your commander goes out to war with a CD, and then he can transmit messages like "we encountered the enemy". It would do no good to transmit "we encountered the enemy" before the war started.

an LLM would likely just converge on something like a shared prime GCD attack; basically finding private keys somewhere in their training set and then hoping that whatever keygen algo was setup incorrectly and used a shared seed.

You can create art with everything from sticks and mud to glass and air. Of course you can make art with AI.

Now if the question is, can a machine make art, well ultimately someone needed to turn the machine on and design the machine to make art, so arguably that person/people are the ones making the art.

Historically, every question of "is x art" ends up having the answer "yes". I don't know why people fall for the same thing over and over.

Start one. Unions are worker owned. You could also join the IWW.

are there examples of unions that have started around a focus on the ethics of the services they provide? unions traditionally start locally, around issues for which the locality is a hotspot, which is why they usually focus on pay and working conditions. it's also easier to get a large group to agree on a set of improvements to working conditions vs a set of ethical boundaries.

actually, it looks like this is happening inside Google right now. DeepMind workers are unionizing, and most of their demands revolve around ethical boundaries and the right to refuse to contribute based on ethical grounds.

Unions in the US are nerfed, by law.

Collective bargaining is nerfed. Other structures remain viable and legal.

Naw unions are nerfed.

ex. Secondary (Sympathy) Strikes are illegal [1].

[1]: https://en.wikipedia.org/wiki/Solidarity_action#United_State...

Exactly. Nerfed. Unions without collective bargaining is more like a social club.

unless your union involves being the law, then they can do literally anything.

Are you not allowed to leave the US?

In that scenario, AI would have to be a public utility, which it is not. Private corporations have no intention to provide services for public good. If they displace a billion jobs, they'll just throw up their hands and go "we're just an Ai company guyz"

Man, this comment made me think of a Kafkaesque future where two AI lawyers and an AI Judge are stuck in an infinite loop arguing over a case, meanwhile the defendant is running around trying to get anyone in the legal system to recognize that the AI is stuck.

More than that, the entire structure of the study is pointless. They set up as a question/response and then had humans rate the response. That's literally what LLM's are trained to do, which ultimately is convincing a human to click the "I like this one better" button on it's response.

LLMs are trained to convince a typical human to click the "I like this one better" on their response.

Convincing a human law professor to click the "I would prefer to deliver this response to a student" button, and to not click the "this response is pedagogically harmful" button is a different task!

I could imagine an LLM convincing a typical human to click the "I like this one better" button with flattery, or with nice-sounding platitudes, or with hand-wavey explanations that sound plausible. And in fact that's exactly what LLMs do when they go wrong - they bluff and output superficially plausible nonsense!

But these weren't typical humans, these were law professors specifically tasked with deciding which response was a better option to give to students as a canonical answer to a contract law question. So I think this is a genuinely impressive result.

This is kind of like saying you can't compare Computer Vision models to Human performance because those models were literally trained to identify objects in images...

I'm not saying you can't compare them, I'm saying it's pointless. LLM's are extremely large scale multivariate regression machines, evaluating it's output within it's own training domain is as pointless as seeing if a ball rolls downhill.

They're only good at it because that's what they're good at? Come on.

They’re not good at it because they understand the law

IRDC if the LLMs "understand" anything. They are being used here to produce outputs that are desirable. (Neglecting the real possibility that this "survey" is complete BS, as noted elsewhere.)

Exactly