I know the core of that desire is for a game industry unmoored from the graphical arms race and hardware growth above all else, which doesn't necessitate the former.

But nah fuck it breaking it down to its base economic critiques isn't as fun as saying that "I just wish the DS/PSP were still viable low end platforms and that phone gaming didn't eat its lunch in that space". I love those silly little handhelds and wish devs could release games on platforms where graphical expectations weren't unsustainable.

C'mon... I know that, just let me have my fun and imagine a world where these handhelds are still viable >.>

portable pentium 4 handheld that's called the steam deck, but it's not Valve® Steam™, it's the water in your hands turning to vapor from being only a few inches from a desktop pentium 4

MSNBC x Super Smash Bros.

(not mine ofc)

to view the comments of all of my pullquotes from this, please go to the most recent post

The enclosure of information access as a private exchange with a language model creates its own self-perpetuating cyclone whose impacts will be difficult even for the most fastidious tech vegan to avoid. Some proportion of people turning to their LLM assistants rather than public forums or peer production systems like Stack Overflow or Wikipedia means some smaller proportion of questions asked or information shared in public. That decreases the quality of information on those sites, incentivizing more people to turn to LLMs, and so on. Why bother with pesky problems like governance and moderation and other people when you could just ask the godhead of all knowledge itself?

Cultivation of dependence comes wrapped in the language of trust and safety. The internet is full of untrustworthy information, spam, hackers, and only a new generation of algorithmically powered information platforms can rebuild some sense of trust online. It seems awfully convenient that the same companies that are promising to save us are also the ones that create the incentive systems recklessly deploy LLMs to clog the internet with SEO clickbait in the first place. We’re being made an offer we can’t refuse: it’s a shame that you can’t find anything on the internet anymore, but the search companies are here to help. Ever more sophisticated spam creates a strong comparative advantage for those companies that can afford to develop the systems to detect it, and Google and Microsoft are substantially larger than, say, DuckDuckGo.

An endless expanse of data traded out of sight, crudely filtered like coffee through a cloth napkin between layers of algorithmic opacity, rented drop by drop from a customer service prompt that’s a little too intent on being our friend. Information is owned by fewer and larger conglomerates, we are serfs everywhere, data subjects to be herded in gig work, crowdsourcing content for the attention mines to drown ourselves in distraction. It’s all made of us, but we control nothing. Our lives are decided by increasingly opaque flows of power and computation, the Cloud Orthodoxy mutates and merges with some unseemly neighbors, the new normal becomes the old normal. The floor of our future rusts out from beneath our feet while we’re chasing the bouncing ball on the billboard ahead.

KG-LLMs [Knowledge Graph based Large Language Models] augment traditional enterprise platforms with the killer feature of data laundering. The platforms are at once magical universal knowledge systems that can make promises of provenance through their underlying data graphs, but also completely fallible language models that have no reasonable bounds of expectation for their behavior. Because it is unlikely that these models will actually deliver the kind of performance being promised, vendors have every incentive to feed the models whatever they can to edge out an extra 1% over SOTA93 — who’s going to know? The ability for LLMs to lie confidently is again a feature not a bug. Say we were an information conglomerate who didn’t want to acknowledge that we have collected or rented some personal wearable data in our clinical recommendation product94. We could allow our model to be conditioned by that data, but then censor it from any explanation of provenance: the provenance given is in terms of proteins and genes and diseases rather than surveillance data, and that might be all the clinician is looking for. If we want to use another company’s data, we might just use it to train our models rather than gaining direct access to it. That is literally the model of federated learning (eg. [218, 219]), where a data collector can make a promise that the data “never leaves your device” (even if a model trained on it can.) The ability to resolve matching entities across knowledge graphs makes this even easier, as the encoding of the fine tuning data can be made to match that of the original model.

We face a stark choice for our future. The Cloud is circling, will it eat us alive? Will we build a space of universalizing knowledge graphs that allow the seamless linking and trade of every element of our society, powering algorithmic systems from information organization through medical systems, governance, and policing? Will we continue to let information conglomerates farm us for our data and feed it back to us, reprocessed, as Content and Knowledge™? Will we be hooked by the lip by barbed convenience that promises us magic, but delivers us only greater surveillance, control, and dependence? Will our attempts at resistance only ever amount to a never ending treadmill of startups and publicly-funded projects that can’t break from the gravitational pull of The Cloud Orthodoxy, retreading its worldview of asymmetrical power concentration, inevitably shuttered or bought as they fail to compete on the same territory as the information giants?

Or will we build a better world?

(not mine ofc)

to view the comments of all of my pullquotes from this, please go to the most recent post

The enclosure of information access as a private exchange with a language model creates its own self-perpetuating cyclone whose impacts will be difficult even for the most fastidious tech vegan to avoid. Some proportion of people turning to their LLM assistants rather than public forums or peer production systems like Stack Overflow or Wikipedia means some smaller proportion of questions asked or information shared in public. That decreases the quality of information on those sites, incentivizing more people to turn to LLMs, and so on. Why bother with pesky problems like governance and moderation and other people when you could just ask the godhead of all knowledge itself?

Cultivation of dependence comes wrapped in the language of trust and safety. The internet is full of untrustworthy information, spam, hackers, and only a new generation of algorithmically powered information platforms can rebuild some sense of trust online. It seems awfully convenient that the same companies that are promising to save us are also the ones that create the incentive systems recklessly deploy LLMs to clog the internet with SEO clickbait in the first place. We’re being made an offer we can’t refuse: it’s a shame that you can’t find anything on the internet anymore, but the search companies are here to help. Ever more sophisticated spam creates a strong comparative advantage for those companies that can afford to develop the systems to detect it, and Google and Microsoft are substantially larger than, say, DuckDuckGo.

An endless expanse of data traded out of sight, crudely filtered like coffee through a cloth napkin between layers of algorithmic opacity, rented drop by drop from a customer service prompt that’s a little too intent on being our friend. Information is owned by fewer and larger conglomerates, we are serfs everywhere, data subjects to be herded in gig work, crowdsourcing content for the attention mines to drown ourselves in distraction. It’s all made of us, but we control nothing. Our lives are decided by increasingly opaque flows of power and computation, the Cloud Orthodoxy mutates and merges with some unseemly neighbors, the new normal becomes the old normal. The floor of our future rusts out from beneath our feet while we’re chasing the bouncing ball on the billboard ahead.

KG-LLMs [Knowledge Graph based Large Language Models] augment traditional enterprise platforms with the killer feature of data laundering. The platforms are at once magical universal knowledge systems that can make promises of provenance through their underlying data graphs, but also completely fallible language models that have no reasonable bounds of expectation for their behavior. Because it is unlikely that these models will actually deliver the kind of performance being promised, vendors have every incentive to feed the models whatever they can to edge out an extra 1% over SOTA93 — who’s going to know? The ability for LLMs to lie confidently is again a feature not a bug. Say we were an information conglomerate who didn’t want to acknowledge that we have collected or rented some personal wearable data in our clinical recommendation product94. We could allow our model to be conditioned by that data, but then censor it from any explanation of provenance: the provenance given is in terms of proteins and genes and diseases rather than surveillance data, and that might be all the clinician is looking for. If we want to use another company’s data, we might just use it to train our models rather than gaining direct access to it. That is literally the model of federated learning (eg. [218, 219]), where a data collector can make a promise that the data “never leaves your device” (even if a model trained on it can.) The ability to resolve matching entities across knowledge graphs makes this even easier, as the encoding of the fine tuning data can be made to match that of the original model.