no. humans can be held accountable and learn from their mistakes.
they can be taught where they went wrong.
if a pattern emerges they can be moved to a role more fitting for them. or removed from a project entirely.
we can judge their effectiveness from past performance.
we can put them in less important roles and gauge whether or not they should be moved up.
so, no, pretending that the correct route forward is to “remove humans from the equation” for a bot that doesn’t learn, is never held accountable, and not held to the same standard as humans is silly tier thinking.
Some kid straight out of highschool being paid minimum wage to answer calls with little or no real training, and performance metrics that reward getting people off the line as quickly as possible whether they've given the right answer or not; or an LLM that by many very reasonable measures provides answers as good as those provided by post-graduate students for difficult problems, and spookily accurate answers for questions that are not... I'll take the LLM please and thank you.
depending on which model one is using, and the error the employee needs to be retrained on, i strongly disagree that you can retrain an llm as easily as you can a person.
particularly most of the commercial models.
again, i’m sure we can all come up with a thousand “hypotheticals”, and tbh, the hypothetical parade is not something i’m interested in having. but i’ll state again, “entirely removing humans” from the situation is silly-tier thinking.
particularly as even the ceos of sota frontier producing models will each and every single one tell you to never trust their model.
“our model is smartest thing in the world…”
…next breath..
“wait, you trusted our model? that was silly of you. always double check it”
If I instruct an employee to prioritize my profit in a way that opens both of us to criminal prosecution, there is a non zero chance of them whistle blowing or even cooperating with the authorities to prosecute me.
We shouldn't use an LLM because...LLMs never improve?
This seems like a great source for domain-specific RL, but even without that, we can expect there to be higher-quality LLMs that can be trivially swapped in within a year, if not a week.
Okay, but I don't understand what you're getting at with them not learning? They acquire more information, get better at providing accurate and relevant information, and acquire new skills. What is the learning they're not doing?
Maybe a human should be available as a backup or something, but not because LLMs don't "learn" -- the system seems to learn in all relevant senses.
(I'd also object to the no accountability, but another subthread is already on that.)
I have read the opening chapters of the book, and honestly I had to put it down after reading 20 percent of the book. I just don't see any new information that I had already known (rightwing effect, propaganda, Fox/Breitbait nonsense, bla bla).
I used to believe Trump winning the 2016 election was a product of rightwing propaganda and fringe theory, but with the 2024 election, I had to revise my belief. What if this is the mainstream mindset of Americans all along, and Trump simply tried to capitalize on that?
Yes of course, the book sets it out that they are covering it in a sort of historical manner, so the first few chapters are covering ground you may well know.
The later chapters are where they go into the more recent details. There is a video of one of the authors (not either Robert Faris or Hal Roberts) is giving a talk at a university panel, and he gives a shorter synopsis of the main mechanism, Its about 11 to 15 minutes in.
The set up of the book puts the evidence for the case they make later, namely that the left and center media are trying to follow journalistic standards, while the machine on the right is entirely different.
I'm pulling from memmory here, bu as an example - a fringe theory will start circulating on the fringes of the internet, the most engaging narrative will be brought up on the podcast circuit. At some point a talking head (Guiliani back in the day) will reference the theory on the news. Which will in turn be referenced by the government who will say this is a topic being discussed on the news.
The other distinction they make is that people on the right who say the narrative is wrong, simply don't get platformed.
They go through this in exhaustive detail. If you know most of the history you can see if skipping to later sections works for you. Its the best description of the actual media mechanics at play.
The 2024 elections were the ones that forced me to really re-examine my priors, and I came to a similar conclusion that Benkler and co did. Their book simply brings all the receipts.
they can be taught where they went wrong.
if a pattern emerges they can be moved to a role more fitting for them. or removed from a project entirely.
we can judge their effectiveness from past performance.
we can put them in less important roles and gauge whether or not they should be moved up.
so, no, pretending that the correct route forward is to “remove humans from the equation” for a bot that doesn’t learn, is never held accountable, and not held to the same standard as humans is silly tier thinking.