Oh absolutely, I’m pretty sure I’m on the same page with this. I only pose that to someone who believes they’ve found people who respect them, and particularly those who have felt for a long time that their voice didn’t matter, it is counterproductive to approach them and their group with outward hostility.

Telling them the people who took them in and listened to them are vile, abusive, disgusting people and are exactly the problem they say everyone says you are, is just reinforcing of their views.

Consider the comment originally replied to; paraphrase because mobile is hard, “those loudest about being victimized are the most eager to take their pound of flesh”. This can easily sound like:

1. (Man) I’ve been victimized and nobody lets me voice this except for this gang/cult/militia. Cult says they should be allowed to “get support” and they know the way (it’s bad).
2. (Outsider) Claiming to be a victim usually means you are a terrible person.
3. (Man) So according to outsiders, if I seek help, I’m a bad person. According to my (cult etc) if I tell them, they will offer a form of support. I can stay with these people and get something of support, or I can leave them, be ostracized, and any attempts to voice my feelings will lead me to being labeled someone eager to take a pound of flesh.

They need to be shown that those on the outside understand them and are better people than those who took them in. They are with people whose form of empathy and respect is so distorted and toxic, but it’s the only model of that experience they know.

Your comment, upon my read, felt like anyone in that position would feel justified in their gang telling them that everyone on the outside is out to get them. If they already think everyone else is a predator, what is attacking their friends, their family, and their opinions, going to do?

They will only leave when they know they will arrive somewhere with the respect they craved without those toxic feelings they repressed during their time with a hateful group.

So I guess it’s less about the content of the comment, more of the way it represented the ideas, the timing, and the perceived intention.

I agree that much of the problem is men on men and this patriarchy - men who do not want to uphold patriarchal values can often be ostracized and demonized by those who do - but I believe OP was specifically noting that then those men who get abused and ostracized cannot speak out of seek help because many people will simply snap back at them saying that they are part of the problem and resources need to be given elsewhere. They cannot endure the abuse, and their own cohort becomes abusive, and the only way to avoid the abuse from all sides (in their view) becomes joining the “social excrement” they wanted to escape in the first place.

Angry screams tend to mask sad and lonely tears. Hatred does not end hatred; hatred ends through non-hate alone. Non-hate is not inaction, though. If we do not look at them, and ourself, with empathy and kindness and understanding and patience, they will continue living in a world devoid of and therefore ignorant to empathy and kindness and understanding and patience.

I think centralization played a big role in this, at least for software. When messaging meant IRC, AIM, Yahoo, MSN, Xfire, Ventrilo, TeamSpeak, or any number of PHP forums, you had to be able to pick up new software quickly and conceptualized the thing it’s doing separate from the application it’s accomplished with. When they all needed to be installed from different places in different ways you conceptualize the file system and what an executable is to an extent. When every game needs a bit of debugging to get working and a bit of savvy to know when certain computer parts are incompatible, you need a bit of knowledge to do the thing you want to do.

That said, fewer people did it. I was in highschool when Facebook took off, and the number of people who went from never online to perpetually online skyrocketed.

I teach computer science, I know it isn’t wholly generational, but I’ve watched the decline over the past decade for the basics. Highschool students were raised on Chromebooks and tablets/phones and a homogenous software scene. Concepts like files, installations, computer components, local storage, compression, settings, keyboard proficiency, toolbars, context menus - these are all barriers for incoming students.

The big difference, I think, is that way more people (nearly everyone) has some technical proficiency, whereas before it was considered a popular enough hobby but most people were completely inept, but most of students nowadays are not proficient with things past a cursory level. That said, the ones who are technically inclined are extremely technically inclined compared to my era, in larger numbers at least.

Higher minimum and maximum thresholds, but maybe lower on average.

Yeah, that’s definitely the way to see it, and as that I think it’s great. I think it might overload the term dark patterns a bit too much, and would have liked to have seen a different name used (as a game design academic), but I absolutely agree with and appreciate the approach otherwise.

Edit to include, I guess why I have that hesitation with an example - I couldn’t link this in a class I’m teaching without loads of caveats because suddenly 80% of the curriculum gets seen as abusive when it’s really just experience design and explain the grey (which we do, so this is quite helpful for that particular purpose), and I would need to caveat that when they see the term out in the wild it will be used differently.

All I’m commenting on, as a game design researched and professor, is that it’s an established term in a discipline which means something else to those actually within the discipline. These are still patterns, and they can absolutely be harmful patterns, but the terminology is being overloaded and there is some interesting nuance within it.

Also, just to comment on the last quip there, and yes - to those I’ve spoken to, they are okay with those because they (being actively involved in the industry) know more than most people to educate and supervise and ensure that playing games with these patterns doesn’t turn into harmful behaviours. They also call them out for what they are - often, very bad design.

I guess that’s really the line they drew - these patterns are more gray than the examples they presented. Most are good sometimes and terrible other times depending on how it is used. The term “dark patterns” as used professionally refers to always bad, always deceptive, always harmful. I do like having that line, even if it means the dark side is a much smaller subset of the greater space, then you can easily say, “If this uses a single dark pattern, it’s out. If it uses a lot of ‘grey’ patterns, be cautious. If it’s nothing but grey patterns, it’s purely abusive trash.”

Interesting. I was chatting with a lot of big name AAA designers and indie designers discussing dark patterns, and they’ve got a very different opinion on what constitutes a dark pattern. To them, largely, it needs to be more technical deception - like having a fake “X” button, or immediately popping up an ad over where a button was to trick you into clicking it, or bait-and-switching pricing before the user notices.

I tried to raise these kinds of patterns as problematic, and it was a mixed bag. The general vibe from them was that they’d only call it a dark pattern if it deceives the player to get more money than they were prepared to spend (or similar for ads). If the player knows what they’re getting into, and they are presented with a choice to stop or continue, it’s on them.

And I’ll admit, while I don’t go that far (and there were designers in both camps), I can at least understand how all game design is manipulation, in the same way that teaching and storytelling is manipulation, and drawing the lines can be very hard. Your job is to convince the player that they are having fun and want to keep playing. Resources in a game have no real value, only valued by the scarcity and utility of them, which the designer intentionally assigns to convince the player it’s more or less valuable.

Curiously, the examples listed in the OP were exactly the patterns I see designers discuss, but don’t seem to be the patterns on the website (like “illusion of control”, artificial scarcity, which is like, game designs while thing).

Either way, nice to have this as a resource because honestly a lot of these elements are what I’d put in the “bad / abusive design” category rather than purely dark patterns, but still great to highlight, but I can agree that we should probably be careful blanket calling these dark patterns; examples: It mentions illusion of control being separating you into shards of leader boards so that you can be in the top 500 of a shard rather than top 200,000 world ranking or whatever, or claw machines choosing whether you successfully grab an item rather than relying on skill. How does this compare to Uncharted not letting enemies successfully shoot you in the first few seconds of an action sequence to give you time to ground yourself, or Resident Evil spawning different loot and enemies based on how good/bad you play?

I’d say, is it to extract money from you in the short term, but it’s more grey than a non-designer might read into from lists like these.

I love that friend group A is a gradient.

I’m Canadian, but the point they are making is that there won’t be another election; VP is the backup for a dead president. Not to tinfoil too hard, but placing an obvious puppet in as VP with a clearly declining candidate who is popular seems like a strategy straight out of House of Cards.

Geode colouring matched Ace Flag colouring.

Insane compute wasn’t everything. Hinton helped develop the technique which allowed more data to be processed in more layers of a network without totally losing coherence. It was more of a toy before then because it capped out at how much data could be used, how many layers of a network could be trained, and I believe even that GPUs could be used efficiently for ANNs, but I could be wrong on that one.

Either way, after Hinton’s research in ~2010-2012, problems that seemed extremely difficult to solve (e.g., classifying images and identifying objects in images) became borderline trivial and in under a decade ANNs went from being almost fringe technology that many researches saw as being a toy and useful for a few problems to basically dominating all AI research and CS funding. In almost no time, every university suddenly needed machine learning specialists on payroll, and now at about 10 years later, every year we are pumping out papers and tech that seemed many decades away… Every year… In a very broad range of problems.

The 580 and CUDA made a big impact, but Hinton’s work was absolutely pivotal in being able to utilize that and to even make ANNs seem feasible at all, and it was an overnight thing. Research very rarely explodes this fast.

Edit: I guess also worth clarifying, Hinton was also one of the few researching these techniques in the 80s and has continued being a force in the field, so these big leaps are the culmination of a lot of old, but also very recent work.

Lots of good comments here. I think there’s many reasons, but AI in general is being quite hated on. It’s sad to me - pre-GPT I literally researched how AI can be used to help people be more creative and support human workflows, but our pipelines around the AI are lacking right now. As for the hate, here’s a few perspectives:

• Training data is questionable/debatable ethics,
• Amateur programmers don’t build up the same “code muscle memory”,
• It’s being treated as a sole author (generate all of this code for me) instead of like a ping-pong pair programmer,
• The time saved writing code isn’t being used to review and test the code more carefully than it was before,
• The AI is being used for problem solving, where it’s not ideal, as opposed to code-from-spec where it’s much better,
• Non-Local AI is scraping your (often confidential) data,
• Environmental impact of the use of massive remote LLMs,
• Can be used (according to execs, anyways) to replace entry level developers,
• Devs can have too much faith in the output because they have weak code review skills compared to their code writing skills,
• New programmers can bypass their learning and get an unrealistic perspective of their understanding; this one is most egregious to me as a CS professor, where students and new programmers often think the final answer is what’s important and don’t see the skills they strengthen along the way to the answer.

I like coding with local LLMs and asking occasional questions to larger ones, but the code on larger code bases (with these small, local models) is often pretty non-sensical, but improves with the right approach. Provide it documented functions, examples of a strong and consistent code style, write your test cases in advance so you can verify the outputs, use it as an extension of IDE capabilities (like generating repetitive lines) rather than replacing your problem solving.

I think there is a lot of reasons to hate on it, but I think it’s because the reasons to use it effectively are still being figured out.

Some of my academic colleagues still hate IDEs because tab completion, fast compilers, in-line documentation, and automated code linting (to them) means you don’t really need to know anything or follow any good practices, your editor will do it all for you, so you should just use vim or notepad. It’ll take time to adopt and adapt.

As someone who researched AI pre-GPT to enhance human creativity and aid in creative workflows, it’s sad for me to see the direction it’s been marketed, but not surprised. I’m personally excited by the tech because I personally see a really positive place for it where the data usage is arguably justified, but we either need to break through the current applications of it which seems more aimed at stock prices and wow-factoring the public instead of using them for what they’re best at.

The whole exciting part of these was that it could convert unstructured inputs into natural language and structured outputs. Translation tasks (broad definition of translation), extracting key data points in unstructured data, language tasks. It’s outstanding for the NLP tasks we struggled with previously, and these tasks are highly transformative or any inputs, it purely relies on structural patterns. I think few people would argue NLP tasks are infringing on the copyright owner.

But I can at least see how moving the direction toward (particularly with MoE approaches) using Q&A data to support generating Q&A outputs, media data to support generating media outputs, using code data to support generating code, this moves toward the territory of affecting sales and using someone’s IP to compete against them. From a technical perspective, I understand how LLMs are not really copying, but the way they are marketed and tuned seems to be more and more intended to use people’s data to compete against them, which is dubious at best.

Not to fully argue against your point, but I do want to push back on the citations bit. Given the way an LLM is trained, it’s not really close to equivalent to me citing papers researched for a paper. That would be more akin to asking me to cite every piece of written or verbal media I’ve ever encountered as they all contributed in some small way to way that the words were formulated here.

Now, if specific data were injected into the prompt, or maybe if it was fine-tuned on a small subset of highly specific data, I would agree those should be cited as they are being accessed more verbatim. The whole “magic” of LLMs was that it needed to cross a threshold of data, combined with the attentional mechanism, and then the network was pretty suddenly able to maintain coherent sentences structure. It was only with loads of varied data from many different sources that this really emerged.

My guess was that they knew gaming was niche and were willing to invest less in this headset and more in spreading the widespread idea that “Spatial Computing” is the next paradigm for work.

I VR a decent amount, and I really do like it a lot for watching TV and YouTube, and am toying with using it a bit for work-from-home where the shift in environment is surprisingly helpful.

It’s just limited. Streaming apps aren’t very good, there’s no great source for 3D movies (which are great, when Bigscreen had them anyways), they’re still a bit too hot and heavy for long-term use, the game library isn’t very broad and there haven’t been many killer app games/products that distinct it from other modalities, and it’s going to need a critical amount of adoption to get used in remote meetings.

I really do think it’s huge for given a sense of remote presence, and I’d love to research how VR presence affects remote collaboration, but there are so many factors keeping it tough to buy into.

They did try, though, and I think they’re on the right track. Facial capture for remote presence and hybrid meetings, extending the monitors to give more privacy and flexibility to laptops, strong AR to reduce the need to take the headset off - but they’re first selling the idea, and then maybe there will be a break. I’ll admit the industry is moving much slower than I’d anticipated back in 2012 when I was starting VR research.

Yeah, I may be wrong but I think it usually comes down to a very specific kind of precision needed. It’s not meant to be hostile, I think, but meant to provide a domain-specific explanation clearly to those who need to interpret it in a specific way. In law, specific jargon infers very specific behaviour, so it’s meant to be precise in its own way (not a law major, can’t say for sure), but it can seem completely meaningless if you aren’t prepped for it.

Same thing in other fields. I had a professor who was very pedantic about {braces} vs [brackets] vs (parentheses), and it seemed totally unnecessary to be so corrective in discussions, but when explaining where things went wrong with a student’s work it was vital to be able to quickly differentiate them in their work so they could review the right areas or understand things faster during a lecture later down the line.

But that noise takes longer to teach through, so if it is important, it needs it’s own time to learn, and it will make it inaccessible to anyone who didn’t get that time to learn and digest it.

Absolutely! One of the difficulties that I have with my intro courses is working out when to introduce the vocabulary correctly, because it is important to be able to engage with the industry and the literature, but it adds a lot of noise to learning the underlying concepts and some assessments end up losing sight of the concept and go straight to recalling the vocab.

Knowing the terms can help you self-learn, but a textbook glossary could do the same thing.

There was a lovely computer science book for kids I can’t remember the name of, and it was all about the evil jargon trying to prevent people from mastering the magical skills of programming and algorithms. I love these approaches. I grew up in an extremely non/anti-academic environment, and I learned to explain things in non-academic ways, and it’s really helped me as an intro lecturer.

Jargon is the mind killer. Shorthands are for the people who have enough expertise to really feel the depths of that shorthand and use it to tickle the old familiar neurons they represent without needing to do the whole dance. It’s easy to forget that to a newcomer, the symbol is just a symbol.

Functional programming is much more math oriented and I think works well here, as it likes to violate a lot of these rules as a rule. I think it’s what makes it so challenging and so obvious for different folks.

I really want to see if worker owned cooperatives plus AI could do help democratize running companies (where appropriate). Not just LLMs, but a mix of techniques for different purposes (e.g., hierarchial task networks to help with operations and pipelining, LLM for assembling/disseminating information to workers).

It is real, you just have to have sufficient funds already to be able to pay someone else to do the active part of the income and make sure they are earning less than their worth so that you can pick up the excess. Most effective if there are many layers in between, so that the income becomes increasingly passive as you move up the chain, so that those under you have something to strive for, because you don’t want to be in charge of hiring all of those people, so you hire people to hire those people, each taking a cut of the value along the way.

But don’t worry, the American Dream™ is that, as long as you keep working about 10 layers deep in value cuts, eventually you might be able to get into layer 3 or 4 and get your kid into the job early so that they can get to layer 5 or 6, and maybe they’ll have enough money to get their kid to 6 or 7.