This is close to my #1 pet peeve: the "if I can do it, you can do it" people. Almost always they start from a major position of unacknowledged privilege that it makes me want to scream. Like, good for you that you fit into the round hole! Still doesn't mean everyone else can.
When someone says „Scientists do not want you to know“ you can dismiss everything from there on. Scientists want you to know. They are desperate that you know. They can’t shut up about what they found out and want you to know.
It says in that piece that the source for concern would be the cyber attacks against Polish energy infrastructure last December (whose source is thought to be Russia). Not clear based on reporting if there is any concrete acute threat.
This skeet brought to you by a very snippy PhD student response to very reasonable coauthor comments because the student is trying to submit by a supervisor suggested (entirely imaginary) deadline.
Why do so many academics keep saying "let's try to submit this next week" when everyone can clearly see that the manuscript is not ready? I see many PhD students so stressed out about this when no one tells them "next week" deadline is aspirational, and not actually a real deadline. #AcademicSky
So the picture will be stretched over 2 pages, am I reading that right? with a few lines at the end for legend
Thanks for a really interesting paper! I would love to take a look at the supplementary material but I can't figure out how to access it. Is that a separate file somewhere? If yes, would you be kind enough to share the link? 🙏
After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data.
If you share a single thing of my lab this year, please make it this competition.
eiko-fried.com/warn-d-machi...
It looks like that that’s how the 150 circle (”meaningful contacts”) of Dunbar’s number is inofficially defined, I must have picked it up from there..
I tend to go by: if I saw them sitting alone in a cafe or a bar, would I go join them or would I find a table of my own? Works for online folks but also imposes some limit of how much I need to like that person in order for them to ”count”. I suspect that that limit scales with introversion though
I went to Metallica gig with a group of professors and, I mean, they weren’t active participants in the mosh pit but they were *right* next to it. yes, theory was discussed.
A photo of a woman in front of a large presentation. On the slide she's presenting, it says The new paradigms Joy has business value Get rid of dumb work If you can't get rid of the work, make computers do the work Don't make humans tell the computer things the computer already knows Get rid of flab from the programming model Measure the important metric, not the one that's easy to measure
Great talk by @hollycummins.com at Lindholmen developer day. As a data person and as someone who used to work with healthcare this resonated a lot: "don't make human tell computer things the computer already knows". It is so key but so often neglected in practice (&definitely a thief of joy) #databs
Hahaha, ok so it's an avalanche of spam and very little ham it sounds like. That sounds better than the inverse to me actually 😂
My best tip is to be very ruthless in having rules for email that automatically gets cleaned out of the inbox, e.g. automatic emails from systems, stuff that gets sent out as FYI, weekly XYZ digests etc. They all go into their own folders and I look at them if/when I feel like it. Maybe never.
In my old job I would get around 20-30 emails per day that required me specifically to react to them (so, no automated emails, no mailing list stuff). In my new job, so far ✨almost nothing✨. But I think email volume is likely a function of your tenure and/or network centrality in an organisation.
Thank you everyone for your suggestions! This is how I ended up plotting my LLM system output vs human annotation vs LLM-as-a-judge evals. Extra thanks to @libbyheeren.bsky.social for boosting my original question. #databs #dataviz
Wow, thank you so much @thoughtfulnz.bsky.social ! That's really pretty & so nice of you to make up a toy example! 🤩🤩🤩
Ooh, that's a thought! Thanks! I think it's good for the audience to have a sense of how prevalent true vs faulty answers are, but that doesn't necessarily need to be in the same plot as the comparisons 🤔
Thanks Libby! Something like this is what I'm leaning towards but with four sections (so that I can show false positives and false negatives separately, they have different business implications)
Fair point! Right now I'm just going : right answer is the one a human gave + false positives and false negatives have different cost (false negatives are much more costly than false positives)
Oh hey thanks, I hadn't really thought of radar plot for this! That's definitely worth considering!
I think the problem is that data science is >10% thinking and you can't really externalize the thinking, just the execution. And even for execution you need to split in suitable sized chunks that you quality control yourself.
I’m really loving it for making and tweaking plots where I kind of know the plotting library but not well enough to remember all the functions and params by heart. @hadley.nz had a wonderful demo of this at the Posit conf earlier this month, it might be on youtube soon!
Maybe sometime down the line! Right I have a number of evals coming from many different models & frameworks and need to figure out how to best visualize them for our dev team.
Any references for good visualizations of LLM evals? We have orig system output (yes/no), a human annotator's y/n and a number of different y/n evaluations. The aim is to see how good our original system is and how much of the issues do the diff LLM evals catch. How would you visualize this? #databs
My mentor once said: strongest results are the ones where you don't even need stats to figure out if there was an effect, you just plot the raw data and look at it with your two human eyes. If it's obvious then, it's a ding-dang strong effect.
#DataBS Conf 2025 preshow! We have two talks that we couldn't fit into the schedule but the speakers pre-recorded their talk for us to share before the main event next week!
Both are really good and give me lots of excitement about what we'll see next week.
ti.to/databsconf/d... <- free tix
🧵1/3
This week on Counting Stuff, the #dataBS conference attendee registration form is open! Also an intro to the 6 of 14 talks we have confirmed!
Tickets are free/pay-what-you-want and there's technically a limit on attendees so grab yours today!
www.counting-stuff.com/databs-conf-...
The weekend is here!
Perfect time to submit your #dataBS talk.
We want your lessons-learned stories from data work, everything from "my data pipeline: the unsung hero" to "how we got that AI system working" to "what we learned when it fell over."
Details and sign-up:
bit.ly/dataBSconf-cfs
