The quanta article referenced at [1] used the term "Anthropologist of Artificial Intelligence"; folks appear to have issues [2] with the use of 'anthro-' since that means human. Submitted these alternative terms for the potential field of study elsewhere [3] in the discussion; reposting here at the top-level for visibility:

Automatologist: One who studies the behavior, adaptation, and failure modes of artificial agents and automated systems.

Automatology: the scientific study of artificial agents and automated-system behavior.

[1] https://www.quantamagazine.org/the-anthropologist-of-artific...

[2] https://news.ycombinator.com/item?id=47957933

[3] https://news.ycombinator.com/item?id=47958760

Automatologist: One who studies the behavior, adaptation, and failure modes of artificial agents and automated systems.

Automatology: the scientific study of artificial agents and automated-system behavior.

Greek word derivatives all seem to be a bit unwieldy; Latin might work better.

While the names aren't set yet, the field of study is apparently already being pushed forward. [1]

[1] https://www.quantamagazine.org/the-anthropologist-of-artific...

Sir, I would have you know that we are discussing English terms, not Greek

AInthropologist works fine for me, and is a lot funnier

LoL

OP is hedging bets in case the future overlords review forum postings for evidence of bias against machine beings. [1]

[1] https://knowyourmeme.com/memes/i-for-one-welcome-our-new-ins...

That's fair. Was trying to be funny, so glossed over the difference. Leaving my post above unedited/undeleted as a testament to your precision, and evidence of my folly.

Onwards; more appropriate rebuttals:

"English is a precision instrument assembled from spare parts during a thunderstorm." --ChatGPT

“If the English language made any sense, a catastrophe would be an apostrophe with fur.” -- Doug Larson

Have an upvote :)

*thropologist: study of beings

“The problem with defending the purity of the English language is that English is about as pure as a cribhouse wh***. We don’t just borrow words; on occasion, English has pursued other languages down alleyways to beat them unconscious and rifle their pockets for new vocabulary.”* --James D. Nicoll

* Does not generally apply to scientific papers

And may I introduce you to organized religion :)

I might have to hard disagree on this one, since my understanding of state machines (the technical term [1] [2]) is that they are determistic, while LLMs (the ai topic of discussion) are probabilistic in most of the commercial implementations that we see.

[1] https://en.wikipedia.org/wiki/Finite-state_machine

[2] have written some for production use, so have some personal experience here

And may I introduce you to "groupthink" :))

no no no, don't stop there, just go full AItheologian, pronounced aetheologian :)

May I introduce you to homo sapiens, a species so vulnerable to such subtle (or otherwise) biases (and affiliations) that they had to develop elaborate and documented justice systems to contain the fallouts? :)

I see you took the prudent approach of recognizing the being-ness of our future overlords :) ("being" wasn't in your first edit to which I responded below...)

Still, a bit uninspired, methinks. I like AInthropologist better, and my phone's keyboard appears to have immediately adopted that term for the suggestions line. Who am I to fight my phone's auto-suggest :-)

So you, for one, do not welcome our new robot overlords?

A rather risky position to adopt in public, innit ;-)

> The rewards were applied only in the Nerdy condition, but reinforcement learning does not guarantee that learned behaviors stay neatly scoped to the condition that produced them

> Once a style tic is rewarded, later training can spread or reinforce it elsewhere, especially if those outputs are reused in supervised fine-tuning or preference data.

Sounds awfully like the development of a culture or proto-culture. Anyone know if this is how human cultures form/propagate? Little rewards that cause quirks to spread?

Just reading through the post, what a time to be an AInthropologist. Anthropologists must be so jealous of the level of detailed data available for analysis.

Also, clearly even in AI land, Nerdz Rule :)

PS: if AInthropologist isn't an official title yet, chances are it will likely be one in the near future. Given the massive proliferation of AI, it's only a matter of time before AI/Data Scientist becomes a rather general term and develops a sub-specialization of AInthropologist...

I agree with the outcome of your premise (i.e., openness), but for different reasons:

First, isn't it the case that these bleeding edge 'newfangled' LLMs are basically variations on the same core ideas from "Attention Is All You Need" from 2017? [1]. Different scale, but still the same basic architecture. Even the "MoE" innovation keeps the Transformer attention stack while replacing or augmenting the dense feed-forward/MLP part with routed expert blocks.

And, I would argue that Engineers aren't working on new architectures. That would be Researchers, working on

  State-space models/Mamba (CMU/Princeton ecosystem), 
  Diffusion Language Models (Inception Labs), 
  Long-convolution architectures/Hyena (Stanford etc.), 
  RWKV/Recurrent LLMs (open-source community), 
  Memory-augmented architectures (Google Research/DeepMind?), 
  World models/spatial intelligence (LeCun/Fei-Fei Li/DeepMind), 
  Symbolic/neurosymbolic alternatives, 
  Thousand brains (Numenta).

[1] https://arxiv.org/abs/1706.03762

[2] https://x.com/ylecun/status/1795589846771147018

> The collision between those two facts — that American capital paid for a moat, and that the technology no longer provides one — is the most important force in the AI industry today.

> The open-weight ecosystem did not arrive in stages. It arrived in a wave. In late 2024, a Chinese lab named DeepSeek released a model

Looking at the assertions above, anyone passingly familiar with AI over the past few years will tell you that open weights and open research were the norm until OpenAI GPT-3 came along, and even then they were forced to release GPT-OSS by the market. So what technology moat? There has never been one in AI. Training 100B+ or trillion+ parameter models in expensive runs was potentially a moat, until the chinese startups showed in short order that it could be done for $6 million a run. Even the CUDA monopoly seems to be ending.

Also, no evidence referenced to back up any of the assertions. How do they know that the bet was that the frontier models would be the next great monopoly business? Especially when there were many from the outset: GPT, Anthropic, Llama, Deepmind, etc. etc.

I'd argue that the wholesale replacement of labor was and is the driver behind the capex, not monopoly dreams.

The starting premises appear to be, well, faulty. Whither the rest of the article?

> Excepting for trivial-size, freshly formed startups, companies cannot be "Agile", because finance and legal and HR and even marketing have constrains setting the tempo - you cannot just drive them with a sprint as if it was a clock signal.

Implementations of Agile at different companies can be an issue, yes. But that is to be expected in any large organization, simply because of scale. It doesn't change the fact that the on-the-ground teams at agile orgs work to a different cadence and approach than historically traditionally structured companies.

There are a few different ways to manage interfacing with parts of the org that need to march to a different beat. That always creates friction, and has to be managed properly. Any large org can suffer from hubris, middling management skills and capacity, wasted effort. Problems of scale, I guess.

Folks from those companies will have to speak up, but my understanding is that yes, internally these large tech orgs use the Agile Methodology, as opposed to the 'traditional' 'Waterfall' development methods.