Correction: According to https://en.wikipedia.org/wiki/Danya_Ruttenberg Ruttenberg is no longer affiliated with the Conservative movement, and it seems she does not have a specific movement she is connected to right now.

Danya Ruttenberg, a prominent Conservative Rabbi wrote a detailed piece arguing that Jews should ask for exemptions from using AI https://www.lifeisasacredtext.com/the-jewish-case-for-ai-work-exemptions/. I found both the halachic (Jewish law) reasoning and elements of how American secular law handles such exemptions to be unconvincing. (I've sent Rabbi Ruttenberg's piece to a bunch of people as one of the better arguments for a moral problem with using AI in general, even as I find it unconvincing.) But it is notable there's a trend here. It will be interesting to see if any Christian clergy try to make a similar argument.

I use AirBNBs as locations to produce adult content. I'm not joking.

An AirBNB is perfect for my needs; it's a literal godsend. I want a clean home-like setting that is relatively generic and free from personal items, that also doesn't look like a hotel and can be unique for the amount of content being produced there. I believe this is a relatively common practice.

That being said, I bring my own bedding, towels, pillows, cleaning products and tools and I take my trash with me when I leave and dispose of it elsewhere. I am conscientious that I'm staying at another individual's property.

In one case, I found out the property owner was still home, in his basement, while I was working with performers upstairs. He wanted to know what we were doing and didn't actually have a problem with it, but talking with him informed what I should do going forward. Even if I wasn't responsible for messy human activities, I'd still choose to leave as little work as I can for service staff.

I realize that many AirBNBs are owned by property management services and in some cases even hotel groups, but what I'm paying for is exactly the thing I want: clean and not-a-hotel.

You know you don't have to install Adobe Reader, right?

FWIW, I had an interaction with the person responsible for PDFGear online somewhere and I've been using it since I found out about it. It's a free (gratis) PDF viewer and editor. Maybe the full, paid Acrobat product can do more things, but if so I haven't noticed.

That's why it is using it to look over drafts. You take the AI output and then go through its suggested edits or concerns. Essentially it functions as a very fast, half-way decent proof-reader. For example, my spouse looks over a lot of drafts of things I write, but if I give her a 15 page paper, there's good work on the first 8 or 9 pages, probably a joke on page 10 about my notation, one or two comments on the next page, and then a few comments on the bibliography. Claude is not as good a proof-reader as she is, but Claude doesn't poop out after a few pages. And Claude is much faster. So reliability goes up without having to rely on the AI being reliable itself.

And sometimes it catches subtle things. For example, not too long ago, I gave a paper draft to Claude, and it noted in the bibliography that the journal volume number seemed unlikely given the year; it turned out that the volume was correct, but I had actually written the wrong journal down in the entry. Another time, Claude noted that one entry in a bibliography was from a "borderline predatory" (its words but it wasn't wrong) journal, and since I wasn't using it for much other than as part of a list of "hey, here's prior literature" and the paper in question didn't prove anything interesting, I discussed just dropping it from the citation list. Claude then without me asking, pulled up preprint I already had on the arXiv and noted it saw that paper cited there and suggested we remove the paper there also. I didn't do so, but this is the sort of thing that it can suggest. One still has the human make the decisions, but the input itself is useful. And for the most part, verification by a human for most issues is much faster than finding the issues one self.

Not really. It turns out that specialized models do not do as well as large general models. This is essentially a variant of the Bitter Lesson https://en.wikipedia.org/wiki/Bitter_lesson. In this case, there's other reason for it also. There's a lot of reasoning science and math discussions in non-specialized training data. For example, one paper I wrote with two coauthors started as a Reddit discussion. But there's also just a lot of subtle interaction that specialized models don't necessarily get. Math is easier to understand if one has a good command of physics. Engineering is better if one has math and physics. Etc. And sometimes a technique gets directly pulled over. For example, in the large OpenAI paper drop, one of the graph theory problems relied on a trick involving a weighing on the graph that involved complex numbers designed to cancel, where the trick came from a physics context that did something similar. A math specialized model would have been unlikely to find and import that trick.

Right, and the line here isn't clear. One take on NFTs is that they are nominally a tech but functioned purely as a commodity bubble. Or that NFT is a tech but it was used to make commodities which had a bubble. And the line between tech and commodity was blurry historically. When Spain entered the "platinum age," that was based in part on how to make nearly pure platinum was a tech, but the platinum itself was a commodity.

Since per a token output seems to be getting better also for open-weight models, this suggests this is more than just a business decision. And some of it is specific changes; we know that larger models with more training data for example will often produce higher quality output. But also aside from scaling, there have been architectural improvements. For example, one insight that occurred was that having lower precision weights which allowed more weights was often a positive tradeoff. That lead to changes in both the software and hardware ends, as the GPUs were modified as a result. Similar, there's been changes in what functions are used for the feedforward function.

The business case for LLMs is a bit broader than that. They don't need to replace that many workers if they make enough workers more efficient or more reliable (such as simply having LLMs look over drafts). There's also at this point, clear usage cases for scientific and mathematical research but that's using the very high end models, and admittedly is a pretty small market, not enough to justify massive expenditure at this level. The tulip example is interesting; what you are touching on their is the distinction between a "tech bubble" (like railroads or internet) and a "commodities bubble" (tulips, housing, Beanie Babies). Note that the degree to which the tulip bubble was real is questionable. See https://theconversation.com/tulip-mania-the-classic-story-of-a-dutch-financial-bubble-is-mostly-wrong-91413, and the line between a tech bubble and a commodity bubble itself can be blurry (see for example Bitcoin and NFTs). One of the difficulties here is that the LLMs keep improving, and the cost per token is going down, and the amount one can do per a token is going up. But that's happening in part due to massive investment in both infrastructure and research. How sustainable any of that is is unclear.

The thing you are looking for is the Group Policy Editor. There are Group Policies that can disable the prompts to use Microsoft accounts and the ability to use OneDrive or Windows AI features, the hook Bing has in Windows Search (i.e. no more searching in the start menu and having it list web sites before the file or application you wanted) and all the "consumer experience" features in non-Enterprise Windows. There are a few GPO settings that get ignored unless you're on Windows Enterprise or Education. Most of them related to Microsoft Datamining.

It is also possible to transition a Microsoft account back to a local one without losing your Profile. This is done in Settings > Accounts > Your Info in Windows 11.

gpedit.msc is normally only available to non-Home Windows SKUs, but it is unlockable with a tiny bit of Powershell:

Get-ChildItem @(
        "C:\Windows\servicing\Packages\Microsoft-Windows-GroupPolicy-ClientTools-Package*.mum",
        "C:\Windows\servicing\Packages\Microsoft-Windows-GroupPolicy-ClientExtensions-Package*.mum"
) | ForEach-Object { dism.exe /online /norestart /add-package:"$_" }

Open an Administror-elevated Powershell or Terminal session, paste that in and even your $200 Acer vomit-grade notebook can work the way you want it again.

The reason we need GPOs for this stuff is that Microsoft's software updates will occasionally re-set registry changes. If the changes are made with GPOs, the settings get reapplied and overwrite changes no matter what state Windows is in. This is particularly annoying for stuff like OneDrive, CoPilot and Edge preferences. You either have to regularly re-run scripts to alter the registry to the state you like it and hope nothing makes a structural change to where those settings live, or you set a GPO.

There are at least four problems with this. First, linear algebra is from a math standpoint insanely powerful. For example, all of representation theory https://en.wikipedia.org/wiki/Representation_theory is using linear algebra to understand groups. Much of the study of modular forms https://en.wikipedia.org/wiki/Modular_form involves understanding the behavior of modular forms over SL_2(Z) which has heavy linear algebra elements to it. People who see linear algebra as somehow a weak thing are often people who took a single semester of it and didn't do much else with it.

Second, the idea that these AI systems are just linear algebra isn't accurate anyhow. While there's a lot of linear involved, a major part of how transforms like LLMs work is the intervening non-linear layers, which use ReLu https://en.wikipedia.org/wiki/Rectified_linear_unit, Softplus, or a similar function. So, summarizing this as just linear algebra isn't accurate. It is linear algebra with some spice.

Third, even if this were somehow just linear algebra, that shouldn't therefore be a reason to think that there's any limits to what is going on. At the end of the day, the empirical success is what matter, and systems which can do things like prove the quasi-Riemann Hypothesis or solve other major math problems are pretty impressive regardless of what is under the hood. So, if one does think this is just linear algebra, that's not a reason to dismiss the systems but a reason to increase your estimate of what can be done with mere linear algebra.

Fourth, slowing down connections like this may make crashes *less likely.* In particular, the main way we could have a serious bubble here is over-investment. Delays including power grid delays force bottlenecks on the investment level, and make that sort of problem less likely.

We may be in a bubble here. Certainly some elements look very bubble-like. In the late 1990s, during the dot-com boom, one had people making perfectly normal businesses but just tacking on "on the internet" to it and it would get millions of dollars of funding automatically. See for example pets.com . And we're seeing some of that now; we've had a whole bunch of businesses see their stock price shootup just because they mention AI. So we may be in a bubble, and it may crash now, or might crash never or decades from now. (Arguably the US had an 80 year automobile bubble that didn't crash until the late 2000s which then forced the auto bailout.) But the other thing to keep in mind is that a crash does not mean the tech goes away. In 1873, the US had a devastating collapse of the railroad bubble, and yet railroads continued to grow, with the amount of track not peaking until around 1910 https://transportgeography.org/contents/chapter5/rail-transportation-pipelines/rail-track-mileage-united-states/. Similarly, although the internet bubble burst, by 2015, one had more internet use than even the most internet hyping person in 1998 would have likely dreamed of. A bubble bursting does not mean the tech itself goes away.

The set of problems that only a human can solve is getting rapidly smaller. The set of puzzles that a human can solve, that an AI cannot, and which take a small amount of time for a human and aren't highly inconvenient may already be empty.

I mean it's a cut and paste of three lines of powershell.

I wouldn't call the ins and outs of operating gpedit.msc trivial, but enabling on Windows Home is a couple clicks and maybe six key strokes.

Jumping a car with compressed air isn't a technological miracle. It is a useless stunt, and one which if went into production cars would be dangerous to both those in the car and people around them.

I really do agree.
I maintain system images as part of my job, but it always made sense for me to extend that to individual devices since I am asked to help with them anyway. For me, this has been just adding a tweak here or there that grew over time. GPOs for Windows Home didn't become standard in my image management until Microsoft started inflicting Onedrive on everyone. Now I view it mandatory.

For what it's worth, I have a similarly long list of things I do when I configure a Linux system or MacOS. I definitely have to do the most arguing with MacOS since I hate some of the OS default software and Apple has a long history of breaking things that should be scriptable.

You can exclude them from installing through an autounattend.xml (see Schneegans.de for help building one) and prevent them from display via a GPO. You really want to use a group policy object because otherwise a Windows Update might put them back; it'll override a plain old registry change or user preference any time it wants but GPO changes persist.

It is trivial to restore Local Group Policy editing on Windows Home SKUs with a little powershell.

Get-ChildItem @(
        "C:\Windows\servicing\Packages\Microsoft-Windows-GroupPolicy-ClientTools-Package*.mum",
        "C:\Windows\servicing\Packages\Microsoft-Windows-GroupPolicy-ClientExtensions-Package*.mum"
) | ForEach-Object { dism.exe /online /norestart /add-package:"$_" }

Some GPO settings are ignored outside the context of Windows Education or Enterprise SKUs, but most of those are related to user behavior data mining. I can verify that if you're just trying to supress OneDrive, Bing Search and Microsoft Account sign-ins, those are all maintained just fine even on Windows Home.

I don't know what field you are in, but in my area, math, this is wildly not the case. They are much, much better. And the next line of the public frontier models are obviously even better judging from everything that we've seen drop from OpenAI and Anthropic. The large OpenAI release would by itself would have four or five results which would arguably be a Fields Medal if a human had done that, and quasi-RH which would if a human did it be almost definitely a Fields Medal result.

It's highly unlikely that anything you'd do in the course of normal desktop use would make much of a subjective difference from a SATA SSD to an NVMe drive. Most sustained IO operations don't take so long that you'll realize the difference. I can tell when I'm copying 30GB files from my camera card or doing full database dumps, but for just loading up a game or booting an OS? They're subjectively almost exactly the same experience.

That being said, there is such a thing as a cacheless drive. SSDs used to have a certain amount of DRAM or SLC NAND as a faster intermediary interface for storage. Low-cost drives might not have THAT, and that seriously reduces the utility of the SSD in the first place, especially on no-name drives. Those guys are sometimes no better than mechanical drives. Since your system is a refurb, it's possible they did just stick a POS drive in.