Notices by Paul Cantrell (inthehands@hachyderm.io)

@aral why, in this great year of 2024…

Oh joy! Went to get my COVID vax update this morning (timing makes sense for me: students pouring back with cases at peak, yuck), and…

…the pharmacist tells me HealthPartners insurance is refusing the updated vaccine.

SPOILER: The end of this thread is that I got the shot, but private health insurance should be burned to the ground.

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They have a solution:

- Pay for the shot out of pocket now (which is $200).
- Save the receipt.
- Bring the receipt back to the pharmacy after Saturday (physically, this can’t happen over the phone, not that CVS is reachable over the phone anyway), and ask them to refund and rebill, and then it should go through.
- If that doesn’t work, upload receipt to HealthPartners and ask for reimbursement by going to their web site > Log in > My plan > Overview > Find a Form❮post truncated for length❯

3/

Called HealthPartners. Time to human: ~10 min. To their credit, the human was super nice. Turns out HealthPartners did some massive internal corporate faceplant, and the vaccine wasn’t entered into their computer systems properly. It’s wrongly refusing across the board.

Problem will be fixed “by Saturday.” My appointment is now.

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Consider:

- I can afford to pop $200 out of pocket and get reimbursed later. Not everyone can.
- I can afford 15 minutes wasted on the phone. Not everyone can.
- I can get back to the pharmacy easily for the billing retry. Not everyone can.
- I’m sufficiently fluent, pushy, and culturally privileged to navigate this whole stupid situation. Not everyone is.

Each of those “not everyone”s is somebody who doesn’t get vaccinated — at who knows what cost to themselves and to us all.

4/

Conversely, if some writing process •is• so full of pro forma boilerplate that it can be automated by LLMs — and I have serious doubts about police reports fitting this criterion, but if so — what is wrong with that process? Why are we making people jump through purposeless hoops, add filler material, check non-information-bearing boxes? In communication, automatability correlates with bullshit.

@ct_bergstrom’s observation about police writing reports with LLMs [shudder] generalizes well:

“Yes, [human processes] aren't always the most accurate, but introducing an additional layer of non-accountability is bad.”

Communication has consequences. Who is responsible for those consequences?

My wise English prof mother always says: “Good writing is good thinking.” When we automate the writing, who is doing the thinking?
https://fediscience.org/@ct_bergstrom/113028760435643985

Having a fluff-filled communication process and then using an LLM to make it efficient is like putting the flush handle 3 blocks away from the toilet and then buying a Humvee to go flush it.

Lest it get lost in that longer post:

Assume Telegram is compromised. Not just vulnerable. Compromised.

So…there is a concerted campaign, with Musk as its mouthpiece, to discredit Signal and get people to switch to Telegram. It’s disinformation, but there’s also useful information in it. The useful information is that a hideous, powerful, right-wing crank — or whoever’s yanking his chain — really, really wants people to use Telegram.

We’ve long known Telegram’s security is weak. But now, in light of this new information, we should move forward assuming that Telegram is actively compromised.

This essay from @jenniferplusplus is very good, and very important.

It’s good enough and important enough that I’m just going to QFT the heck out of it here on Mastodon until I annoy you into readying the whole thing.

https://jenniferplusplus.com/losing-the-imitation-game/

This essay isn’t the last word on AI in software — but what it says is the ground level for having any sort of coherent discussion about the topic that isn’t all hype and panic.

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“Artificial Intelligence is an unhelpful term. It serves as a vehicle for people's invalid assumptions. It hand-waves an enormous amount of complexity regarding what ‘intelligence’ even is or means.“

“Our understanding of intelligence is a moving target. We only have one meaningful fixed point to work from. We assert that humans are intelligent. Whether anything else is, is not certain. What intelligence itself is, is not certain.”

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“While the capabilities are fantasy, the dangers are real. These tools have denied people jobs, housing, and welfare. All erroneously. They have denied people bail and parole, in such a racist way it would be comical if it wasn't real.

👇👇👇
“And the actual function of AI in all of these situations is to obscure liability for the harm these decisions cause.”

3/

“What [LLM] parameters don't represent is anything like knowledge or understanding. That's just not what LLMs do. The model doesn't know what those tokens mean. I want to say it only knows how they're used, but even that is over stating the case, because it doesn't •know• things. It •models• how those tokens are used.

“…The model doesn't know, or understand, or comprehend anything about that data any more than a spreadsheet containing the same information would understand it.”

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“The hard part of programming is building and maintaining a useful mental model of a complex system. The easy part is writing code.”

Do you see where this is going? Have I convinced you to read the whole thing yet?

https://jenniferplusplus.com/losing-the-imitation-game/

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Here it is: the One Weird Thing that people who aren’t programmers (or are bad programmings) just don’t understand about writing software. This is it. If you miss this, you’ll miss what LLMs can and can’t do for software development. You’ll be prey to the hype, a mark for the con.

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@foone @jenniferplusplus
I…that…what?!

@foone @mcc
Don’t worry, there will be a next time

This is good (from @shriramk): https://mastodon.social/@shriramk/110040524796761802

The skill of recognizing and diagnosing broken code only becomes •more• important in the face of LLM code generators.

Any experienced programmer worth their salt will tell you that •producing• code — learning syntax, finding examples, combining them, adding behaviors, adding complexity — is the •easy• part of programming.

The hard part: “How can it break? How will it surprise us? How will it change? Does it •really• accomplish our goal? What •is• our goal? Are we all even imagining the same goal? Do we understand each other? Will the next person to work on this understand it? Should we even build this?”