ASpring

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?

It is still the Linux of statistical software. Rstanarm is getting quite good though for R users.

It's pretty common in large tech companies to see this but agreed it is not a super intuitive abbreviation.

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.

[1] https://livinghistory.as.ucsb.edu/tag/uaw-local-2865/ [2] https://payusmoreucsc.com/history-of-students-attempts-to-en...

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.

[1]https://www.sfchronicle.com/realestate/article/most-expensiv...

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 wrote about this exact topic a few years back: "Algorithmic Bias is Not Just Data Bias" (https://aaronlspringer.com/not-just-data-bias/).

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.

It talks directly about this in the article. The service purports to know the difference between prerecorded and live voices

As a PhD candidate in my last years I was paid 2500 gross per month (for 9 months of the year), take-home was 2250 or so.

70% of net is 1575. 1575 is 63% of my gross pay.

Remember, these workers are paid so little they are in the bottom tax brackets at 10-12%.

Can you update the article by holding out a portion of the training set and then using it as an unseen test set?

Otherwise it's impossible to make the comparisons at the end of the article to other results.

Do measurements of 'screentime' normally include talking on the phone or passively listening to music? Or are you implying something different?

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.

In the Materials section: "To assess participants’ level of NFC, we used the NFC scale (Cacioppo et al., 1984)."

It's a pretty well-accepted standard scale.

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?

It's not just you, taking photos of something can impair your actual memory of the moment itself https://www.sciencedirect.com/science/article/pii/S221136811...

Have you considered bracketing it or is this a purposeful marketing point? If a graduate is offered 2 jobs at 39k and 50k, would they make more at the 39k job in the short term?

I don't think they get around it. For most genres except FPS the round trip delay doesn't cause any issues in gameplay. What's an extra 60ms roundtrip when most people have reaction times around 300ms?

To me it looks more like due diligence. If you present only the main result every single reviewer will ask "Well couldn't the instructor's gender/ethnicity/tenure/experience explain this result?" Then you're forced to include this analysis either way.

The real problem here is the lack of UX Research that went into designing the new interface. All of this could have been foreseen before the design project even started.

The whole point of UX research is to avoid expensive redesigns/features/products that users don't want. It is future oriented and pays for itself tenfold in avoided missteps in the product lifecycle.

I agree it is difficult to fault these students who chose majors without fully factoring in lifetime earnings and that we should allow college loans to be wiped out in bankruptcy.

However, I disagree that we should let lenders vary interest rates by institution and major. Our premise was that these students didn't understand the full picture of what they were getting into. I'm not sure complicating that picture by varying interest rates is going to make the situation better overall.

Highly recommend this book. I would consider myself experienced with many statistical methods but this book was still chock full of brilliant examples that let me look at things with fresh eyes. It was helpful also in giving me language to explain technical concepts to less technical folks.

I don't think there are really guidelines on 'how often' UX research should happen. Ideally it is integrated in the development cycle and something like this is found in a usability test and fixed before deployment. Usability testing and UX research are a continual process.

This is unexpected, but now I'm curious. How are you using "AI" here? Are there other things you would point to that indicate IBM is ahead of everyone else?

That's the whole point. Their name is /not/ "Mrs. [husband's first and last name]"

There are definitely people I know that would be okay with being addressed this way. There are many I also know who have expressed that this makes them feel less like an individual human and more like an accessory to their husband.