People have been botting on Runescape since the early 2000s. Obviously not quite at the Claude level :). The botting forums were a group of very active and welcoming communities. This is actually what led me to Java programming and computer science more broadly--I wrote custom scripts for my characters.
I still have some parts of the old Rei-net forum archived on an external somewhere.
> Remember that test where you ask a LLM whether 9.11 or 9.9 is the bigger number? [Just checked gpt-4o still gets it wrong]
Interesting, 4o got this right for me in a couple different framings including the simple "Which number is larger, 9.9 or 9.11?". To be a full apologist, there are a few different places (a lot of software versioning as one) where 9.11 is essentially the bigger number so it may be an ambiguous question without context anyway.
Aside from the methodological concerns (very valid) this is also published in a set of journals of controversial repute: https://en.wikipedia.org/wiki/MDPI
I'm not sure I'm fully understanding your point. Is it that constructing confidence intervals using t-statistics is inappropriate for a lot of real data that isn't distributed somewhat normally?
The wildcat strike is exactly what I was referring to as "We at UCSC didn't always agree with the course of the larger UAW 2685..."
The wildcat strike was led by the local union leadership after they abdicated their official positions iirc. Having that previous level of organization and identified leadership certainly made organizing wildcat actions easier.
Unions are more than just the highest level of leadership.
Nice to see a fellow slug! I think you are correct on the timeline being further back. The narrative I recalled was that there was a major victory around health care fee remission before I joined but it looks as if that was part of the original contract the union negotiated [1].
I spent my final years at UCSC working through the systems they had set up internally (administration meetings with GSA, getting on committees of administrators as a grad student voice, working with on campus housing developers[2]) in order to improve housing availability and cost. We had marginal wins if anything. The strike the next year won everyone thousands of dollars toward housing every year. I understand the nuance of it being a wildcat strike but the entire organizing infrastructure there was from the union.
I agree with your final points and hope stipends will follow upwards in the near future.
It was great for us at UC Santa Cruz. The reason I had healthcare during grad school was because the union won it right before I joined. The reason they have a housing stipend now (in the most expensive rental market in the US[1]) is because the union fought for a cost-of-living allowance. We at UCSC didn't always agree with the course of the larger UAW 2685 but they did a lot for us.
I'm not sure what the system was like in the union in Wisconsin but I'm surprised that more STEM students didn't join and change the course of the union if they were that negatively affected. Our union was democratic almost to a fault but maybe the structure in Wisconsin was different.
Maybe I wasn't very clear, I don't think every single machine learning model should be subject to regulation.
Rather I view it more along the lines of how the US currently regulates accessibility standards for the web or enforces mortgage non-discrimination in protected categories. The role of government here is identify a class of tangible harms that can result from unfair models deployed in various contexts and to legislate in a way to ensure those harms are avoided.
I think the author is generally correct but there is a lot of focus on algorithmic design and not on how we collectively decide what is fair and ethical for these algorithms to do. Right now it is totally up to the algorithm developer to articulate their version of "fair" and implement it however they see fit. I'm not convinced that is a responsibility that belongs to private corporations.
In my mind there are 2 defining axes of data science:
The first is Statistics <-> Machine Learning.
On one hand, you can be a data scientist that primarily uses statistics to model user behavior, create metrics, test hypotheses that then inform product design. On the other hand, you can prototype and develop machine learning systems (recommendation engines, predictive analytics etc).
The second axis is developing production code. Some data scientists live in their notebooks and analyses and never write code that directly makes it into production. Others are expected to sit alongside the SWEs and write production level code for the models they have created.
Examples: Data Scientists at Google are statistics heavy and not often writing production code (though this varies by team). On the other hand Quora Data Scientists are mostly developing machine learning models and are expected to write production level code to implement these.
But that's worth exploring, right? I'm sure many people would prefer a lucid but shorter life than a longer one that is highly impacted by dementia symptoms
Thanks for this comment, it's well thought out and elucidates what the original poster is probably critiquing.
I want to make sure I'm understanding, are you saying that there is no sample size with which you would be comfortable making a conclusion about this? That's what I'm taking away from this comment "the assumption that you can use any formula to establish a reasonable population size Y is absurd."
I completely agree that the questions that we choose to ask determine the knowledge that we get. This experiment is clearly situated within the positivist view of research.
However I don't think that invalidates the result, the whole point of randomization of people into the conditions (here the names) is to control for these latent variables like you've talked about (way of reading, mood, etc).
Are you critiquing randomization in general or this specific experiment?
I still have some parts of the old Rei-net forum archived on an external somewhere.