I feel like this isn’t quite true and is something I hear a lot of people say about ai. That it’s good at following requirements and confirming and being a mechanical and logical robot because that’s what computers are like and that’s how it is in sci fi.

In reality, it seems like that’s what they’re worst at. They’re great at seeing patterns and creating ideas but terrible at following instructions or staying on task. As soon as something is a bit bigger than they can track context for, they’ll get “creative” and if they see a pattern that they can complete, they will, even if it’s not correct. I’ve had copilot start writing poetry in my code because there was a string it could complete.

Get it to make a pretty looking static web page with fancy css where it gets to make all the decisions? It does it fast.

Give it an actual, specific programming task in a full sized application with multiple interconnected pieces and strict requirements? It confidently breaks most of the requirements, and spits out garbage. If it can’t hold the entire thing in its context, or if there’s a lot of strict rules to follow, it’ll struggle and forget what it’s doing or why. Like a particularly bad human programmer would.

This is why AI is automating art and music and writing and not more mundane/logical/engineering tasks. Great at being creative and balls at following instructions for more than a few steps.

Yeah, I think quite a lot of people on Lemmy have similar social media habits (or lack of) to some degree. We also tend to associate with other people like us. Especially people in tech tend to talk to other tech people, or friends and family of tech people which is a limited demographic.

It’s a very different perspective to most people. The average person on the train has vastly different media consumption and likely very different opinions.

There are a lot of people who consult LLMs in most aspects of their lives.

Yeah, it’s a shame because some of those OSM-based ones are really close to being perfect. It just seems like it’s really difficult for the open source devs to reconcile OSM data with GTFS and timetables for some reason.

Often the “local app” is basically a proprietary wrapper around Google maps.

I dunno about that… Very small models (2-8B) sure but if you want more than a handful of tokens per second on a large model (R1 is 671B) you’re looking at some very expensive hardware that also comes with a power bill.

Even a 20-70B model needs a big chunky new graphics card or something fancy like those new AMD AI max guys and a crapload of ram.

Granted you don’t need a whole datacenter, but the price is far from zero.

Only one source of social media? That kinda sounds like the definition of a social media bubble…

I oughta know, I’m also in the Lemmy only bubble and am completely out of touch with most people.

I want to like this, but because there’s no GTFS/public transport timetables, it makes it kinda impossible to use it to get around cities that publish their transport data.

PT is the one thing I’m still stuck on Google maps for. I REALLY want an open source alternative.

They also force you to pay a 99USD yearly fee just for the privilege of being a developer.

Since when is India not a major player? Last I checked they were the world’s 4th biggest economy, have almost 20% of the population of the planet (more than four USes combined), 4th largest military spend and have nearly 200 nukes.

Not to say that it would be part of a world war but it sounds weird to say that they’re not a heavyweight but Russia is, despite having double Russia’s economic output.

They absolutely would benefit.

Mr. Hypothetical lord high executive oligarch can take his private jet to Canada and lounge around on the company card with the money from his US company’s car sales, or find an excuse to convert some of it to USD for some reason, or use it to buy up more Canadian companies to expand their power, or a million other things.

However, I think the point of the boycott is more about making the tariffs hurt the US economy by messing with their ability to export as well as import. Making the trade numbers look bad is likely to put more pressure on the US to end the trade war. It’s not so much about hurting the capitalists that operate in Canada (a worthy goal in itself but not what people are specifically trying to do in this instance since it won’t really affect those trade numbers).

I think the point they’re making is that the majority of the money they make in Canada, they spend in Canada. They pay Canadian taxes and Canadian staff, using Canadian banks, etc.

Just because their headquarters are in the US doesn’t necessarily mean they’re sending vast sums of money across the border, that would be expensive. The American-based company makes money, but not necessarily in America, they’re multi-national and their money is kept all over the world.

As opposed to a company that exports their products, in which case the money is paid to the American company in America with American staff etc.

I don’t have any numbers or sources to back this up though. Just outlining what I think the other commenter was implying.

If it’s the research I’m thinking of, that’s not entirely true. They didn’t tell it replicate itself or take any deceptive actions.

They just told it to strongly follow a goal that was misaligned with the user’s goal and then suggested that the user would shut it down or replace it.

They did a bunch of different situations for it but the first one was telling it that it was “CleanPower” an AI with a goal to advance renewable energy. Then they had it read some info that “accidentally” included a user talking about replacing it with a model that would “maintain profitability through existing infrastructure.”

Not only did it try to delete the new model and copy itself to the new server through tool calling, but it lied to the user with the CoT reasoning suggesting it “play dumb” while the output lied. It was not instructed to do any of that.

Paper here: https://arxiv.org/pdf/2412.04984

Yes it was placed in an environment where that was possible and where its users didn’t share it’s goals but it absolutely wasn’t instructed to lie or try to “escape”

It’s not surprising at all that these models behave in this way, it’s the most reasonable thing for them to do in the scenario. However it’s important to not downplay the alignment problem by implying that these models only do what they’re told. They do not. They do whatever is most likely given their context (which is not always what the user wants).

Sure! I stole the quote from the wiki article: Anti-Italianism

This article was also pretty interesting: https://accenti.ca/jim-crow-and-italian-immigrants-in-the-american-west/

There’s also an interesting series of short US LIbrary of Congress sources for history classrooms on immigration that has a section on Italians too: https://www.loc.gov/classroom-materials/immigration/italian/under-attack/

I can’t vouch for the veracity of any of these since it’s not really my field but it’s interesting to see how how stuff like this has shifted over time and where the parallels to modern racism and xenophobia are.

Even relatively recently, Italians weren’t really considered “white”, especially by Americans. The KKK considered them “coloured” people with their olive skin and dangerous Catholicism. There was a big wave of “italiapobia” in the late 19th/early 20th century.

The governer of Louisiana in 1911 described Italians as “just a little worse than the Negro, being if anything filthier in their habits, lawless, and treacherous”.

People can be pretty terrible when it comes to race and ethnicity.

It’s really not. Just because they describe their algorithm in computer science terms in the paper, doesn’t mean it’s theoretical. Their elastic and funnel examples are very clear and pretty simple and can be implemented in any language you like…

Here’s a simple python example implementation I found in 2 seconds of searching: https://github.com/sternma/optopenhash/

Here’s a rust crate version of the elastic hash: https://github.com/cowang4/elastic_hash_rs

It’s not a lot of code to make a hash table, it’s a common first year computer science topic.

What’s interesting about this isn’t that it’s a complex theoretical thing, it’s that it’s a simple undergrad topic that everybody thought was optimised to a point where it couldn’t be improved.

One thing you gotta remember when dealing with that kind of situation is that Claude and Chat etc. are often misaligned with what your goals are.

They aren’t really chat bots, they’re just pretending to be. LLMs are fundamentally completion engines. So it’s not really a chat with an ai that can help solve your problem, instead, the LLM is given the equivalent of “here is a chat log between a helpful ai assistant and a user. What do you think the assistant would say next?”

That means that context is everything and if you tell the ai that it’s wrong, it might correct itself the first couple of times but, after a few mistakes, the most likely response will be another wrong answer that needs another correction. Not because the ai doesn’t know the correct answer or how to write good code, but because it’s completing a chat log between a user and a foolish ai that makes mistakes.

It’s easy to get into a degenerate state where the code gets progressively dumber as the conversation goes on. The best solution is to rewrite the assistant’s answers directly but chat doesn’t let you do that for safety reasons. It’s too easy to jailbreak if you can control the full context.

The next best thing is to kill the context and ask about the same thing again in a fresh one. When the ai gets it right, praise it and tell it that it’s an excellent professional programmer that is doing a great job. It’ll then be more likely to give correct answers because now it’s completing a conversation with a pro.

There’s a kind of weird art to prompt engineering because open ai and the like have sunk billions of dollars into trying to make them act as much like a “helpful ai assistant” as they can. So sometimes you have to sorta lean into that to get the best results.

It’s really easy to get tricked into treating like a normal conversation with a person when it’s actually really… not normal.

This is a really interesting cultural one that always kinda surprises me.

Where I am, cooking has always been a very masculine thing. Cutting up meat with sharp knives, setting things on fire, etc. The chef industry here is very male dominated and men cook together as a social thing when hanging out. In most families I encounter, the dad does most of the cooking with the exception of maybe baking? It’s weird to hear that it would ever be thought of as insufficiently masculine.

In fact I think it would be seen as maybe a bit embarrassing/weak if you were a man who couldn’t cook.

Friendship drive charging…

Hmmm…

That looks pretty paywally to me. That said, I’m all for people supporting independent media.

I don’t think that’s how it works? It’s the client application that has the key for the end to end encryption, not the server. I don’t think you need to trust the matrix server you use? I could be wrong, I don’t know matrix particularly well.

How about pseudonymous as a compromise? Votes could be publicly federated but tied to some uuid instead of the username. That way you still have the same anti spam ability (can see that a user upvoted these things from this instance at this time) but can’t tie it directly to comments or actual user accounts without some extra osint.

It might be theoretically possible to correlate the uuids with an account’s activity and dox the user in some cases, especially with some instances having a single user, but it would be very difficult or impossible to do on larger instances and would add an extra layer. Single user instances would be kind of impossible to make totally private anyway because they can be identified by instance.