#bigai — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bigai, aggregated by home.social.
-
BIG AI'S INFLUENCE ON REGULATION A CONCERN FOR LAW
Study finds Big AI companies may be influencing laws and public talk about AI. Researchers are concerned about fairness and safety for people.
#BigAI, #AIRegulation, #LawConcerns, #TechInfluence, #CorporatePower
https://newsletter.tf/big-ai-influence-on-law-worries-researchers/
-
A new study shows Big AI companies might be influencing laws, similar to how tobacco and oil companies acted in the past. This could affect what rules are made for AI.
#BigAI, #AIRegulation, #LawConcerns, #TechInfluence, #CorporatePower
https://newsletter.tf/big-ai-influence-on-law-worries-researchers/ -
New research shows over 90% of gamers find playing with AI-powered NPCs to be "enjoyable and rewarding"
This is what I've been saying ever since I first played around with GPT-2 for a while (I found GPT-3 and later versions actually rather boring in comparison, and they don't even write any better meaningless Dada poetry than ancient GPT-2): #transformer type #LLM powered NPCs can make gaming so much more fun. Just use rather small LLMs that have been trained on all the lore and run locally on the GPU, and you get NPCs to whom you can actually talk. Also, if they hallucinate non-existing lore, real humans often do things like that as well. Give every inportant NPC a couple of scripted lines which contain the important information, and use an LLM so the player can (through their character) talk to the NPCs about topics that aren't in the script.
If you write such an NPC you put a list of things that character happens to know or believe in the NPC prompt, just after the general character description part of the prompt, and as soon as the player deviates from the scripted parts, the LLM drives the conversation. You will most likely have a UI that gives you screen mask with separate text boxes: Character description, description of the character's knowledge and beliefs, description of the character's situation, and a box filled with all conversations with any player character so far. If you as a player character talk to some random stranger from the street, the LLM generates everything. Natural feeling speech synthesis has been a thing for a couple of years now, just like generating a natural sounding human voice from nothing but a prompt describing the speaker (age, gender, accent, personality, current emotional state). It is therefore possible to give each randomly generated NPC their own voice, their own personality, each of them completely unique yet completely generic. A mesh #diffusion type 3D model generator can be used to automatically generate variations of original 3D models from a prompt, generating slightly changed hairstyles, clothes, jewelry, tools, etc. on the fly when needed, using the GPU.
All the AI models needed for this can be made small enough to run on the GPU, although you'll probably need a computer in the >900€ range to run it, or maybe more like >1200€ now that #BigAI are buying all the hardware. If people can't get any decent gaming hardware, computer games will become either much simpler than today or very much dependent on external computing centres to do much of the compute, even if they don't much AI. We don't even need humongous AI models to make gaming better, only small ones that do one thing good enough for the game. Of course NVdia will do everything they can to monopolise the gaming AI by putting their own models in their GPU firmware, with game developers having to pay fees in order to be able to train their own LoRAs so they can actually use the NVidia AI for their own games. However, open source machine learning models aren't restricted in that way, and they can also be used in games.
Oh, by the way, many games already use latent diffusion type models for graphics. This is how you get realtime raytracing with lots of detail in 2k or even 4k and things like that: Part of the GPU is rendering the scene with accurate lighting in low resolution, then the AI is used for upscaling and adding/reconstructing the finer details. The AI in question has been trained on high quality renderings of the same scenes. You need to make a couple of seconds of Pixar quality animation from each scene in the game, train a LoRA for each scene for a video upscaler/filter, and then run the low resolution, low detail raytraced video stream through the upscaler, that's basically how they do it. The actual workflow is much more complicated, but it's not really important if you just want to understand the basic idea. The trick is just to render accurately what needs to be accurate, like lighting, and then use AI to generate a good enough approximation of everything else.
-
New research shows over 90% of gamers find playing with AI-powered NPCs to be "enjoyable and rewarding"
This is what I've been saying ever since I first played around with GPT-2 for a while (I found GPT-3 and later versions actually rather boring in comparison, and they don't even write any better meaningless Dada poetry than ancient GPT-2): #transformer type #LLM powered NPCs can make gaming so much more fun. Just use rather small LLMs that have been trained on all the lore and run locally on the GPU, and you get NPCs to whom you can actually talk. Also, if they hallucinate non-existing lore, real humans often do things like that as well. Give every inportant NPC a couple of scripted lines which contain the important information, and use an LLM so the player can (through their character) talk to the NPCs about topics that aren't in the script.
If you write such an NPC you put a list of things that character happens to know or believe in the NPC prompt, just after the general character description part of the prompt, and as soon as the player deviates from the scripted parts, the LLM drives the conversation. You will most likely have a UI that gives you screen mask with separate text boxes: Character description, description of the character's knowledge and beliefs, description of the character's situation, and a box filled with all conversations with any player character so far. If you as a player character talk to some random stranger from the street, the LLM generates everything. Natural feeling speech synthesis has been a thing for a couple of years now, just like generating a natural sounding human voice from nothing but a prompt describing the speaker (age, gender, accent, personality, current emotional state). It is therefore possible to give each randomly generated NPC their own voice, their own personality, each of them completely unique yet completely generic. A mesh #diffusion type 3D model generator can be used to automatically generate variations of original 3D models from a prompt, generating slightly changed hairstyles, clothes, jewelry, tools, etc. on the fly when needed, using the GPU.
All the AI models needed for this can be made small enough to run on the GPU, although you'll probably need a computer in the >900€ range to run it, or maybe more like >1200€ now that #BigAI are buying all the hardware. If people can't get any decent gaming hardware, computer games will become either much simpler than today or very much dependent on external computing centres to do much of the compute, even if they don't much AI. We don't even need humongous AI models to make gaming better, only small ones that do one thing good enough for the game. Of course NVdia will do everything they can to monopolise the gaming AI by putting their own models in their GPU firmware, with game developers having to pay fees in order to be able to train their own LoRAs so they can actually use the NVidia AI for their own games. However, open source machine learning models aren't restricted in that way, and they can also be used in games.
Oh, by the way, many games already use latent diffusion type models for graphics. This is how you get realtime raytracing with lots of detail in 2k or even 4k and things like that: Part of the GPU is rendering the scene with accurate lighting in low resolution, then the AI is used for upscaling and adding/reconstructing the finer details. The AI in question has been trained on high quality renderings of the same scenes. You need to make a couple of seconds of Pixar quality animation from each scene in the game, train a LoRA for each scene for a video upscaler/filter, and then run the low resolution, low detail raytraced video stream through the upscaler, that's basically how they do it. The actual workflow is much more complicated, but it's not really important if you just want to understand the basic idea. The trick is just to render accurately what needs to be accurate, like lighting, and then use AI to generate a good enough approximation of everything else.
-
New research shows over 90% of gamers find playing with AI-powered NPCs to be "enjoyable and rewarding"
This is what I've been saying ever since I first played around with GPT-2 for a while (I found GPT-3 and later versions actually rather boring in comparison, and they don't even write any better meaningless Dada poetry than ancient GPT-2): #transformer type #LLM powered NPCs can make gaming so much more fun. Just use rather small LLMs that have been trained on all the lore and run locally on the GPU, and you get NPCs to whom you can actually talk. Also, if they hallucinate non-existing lore, real humans often do things like that as well. Give every inportant NPC a couple of scripted lines which contain the important information, and use an LLM so the player can (through their character) talk to the NPCs about topics that aren't in the script.
If you write such an NPC you put a list of things that character happens to know or believe in the NPC prompt, just after the general character description part of the prompt, and as soon as the player deviates from the scripted parts, the LLM drives the conversation. You will most likely have a UI that gives you screen mask with separate text boxes: Character description, description of the character's knowledge and beliefs, description of the character's situation, and a box filled with all conversations with any player character so far. If you as a player character talk to some random stranger from the street, the LLM generates everything. Natural feeling speech synthesis has been a thing for a couple of years now, just like generating a natural sounding human voice from nothing but a prompt describing the speaker (age, gender, accent, personality, current emotional state). It is therefore possible to give each randomly generated NPC their own voice, their own personality, each of them completely unique yet completely generic. A mesh #diffusion type 3D model generator can be used to automatically generate variations of original 3D models from a prompt, generating slightly changed hairstyles, clothes, jewelry, tools, etc. on the fly when needed, using the GPU.
All the AI models needed for this can be made small enough to run on the GPU, although you'll probably need a computer in the >900€ range to run it, or maybe more like >1200€ now that #BigAI are buying all the hardware. If people can't get any decent gaming hardware, computer games will become either much simpler than today or very much dependent on external computing centres to do much of the compute, even if they don't much AI. We don't even need humongous AI models to make gaming better, only small ones that do one thing good enough for the game. Of course NVdia will do everything they can to monopolise the gaming AI by putting their own models in their GPU firmware, with game developers having to pay fees in order to be able to train their own LoRAs so they can actually use the NVidia AI for their own games. However, open source machine learning models aren't restricted in that way, and they can also be used in games.
Oh, by the way, many games already use latent diffusion type models for graphics. This is how you get realtime raytracing with lots of detail in 2k or even 4k and things like that: Part of the GPU is rendering the scene with accurate lighting in low resolution, then the AI is used for upscaling and adding/reconstructing the finer details. The AI in question has been trained on high quality renderings of the same scenes. You need to make a couple of seconds of Pixar quality animation from each scene in the game, train a LoRA for each scene for a video upscaler/filter, and then run the low resolution, low detail raytraced video stream through the upscaler, that's basically how they do it. The actual workflow is much more complicated, but it's not really important if you just want to understand the basic idea. The trick is just to render accurately what needs to be accurate, like lighting, and then use AI to generate a good enough approximation of everything else.
-
New research shows over 90% of gamers find playing with AI-powered NPCs to be "enjoyable and rewarding"
This is what I've been saying ever since I first played around with GPT-2 for a while (I found GPT-3 and later versions actually rather boring in comparison, and they don't even write any better meaningless Dada poetry than ancient GPT-2): #transformer type #LLM powered NPCs can make gaming so much more fun. Just use rather small LLMs that have been trained on all the lore and run locally on the GPU, and you get NPCs to whom you can actually talk. Also, if they hallucinate non-existing lore, real humans often do things like that as well. Give every inportant NPC a couple of scripted lines which contain the important information, and use an LLM so the player can (through their character) talk to the NPCs about topics that aren't in the script.
If you write such an NPC you put a list of things that character happens to know or believe in the NPC prompt, just after the general character description part of the prompt, and as soon as the player deviates from the scripted parts, the LLM drives the conversation. You will most likely have a UI that gives you screen mask with separate text boxes: Character description, description of the character's knowledge and beliefs, description of the character's situation, and a box filled with all conversations with any player character so far. If you as a player character talk to some random stranger from the street, the LLM generates everything. Natural feeling speech synthesis has been a thing for a couple of years now, just like generating a natural sounding human voice from nothing but a prompt describing the speaker (age, gender, accent, personality, current emotional state). It is therefore possible to give each randomly generated NPC their own voice, their own personality, each of them completely unique yet completely generic. A mesh #diffusion type 3D model generator can be used to automatically generate variations of original 3D models from a prompt, generating slightly changed hairstyles, clothes, jewelry, tools, etc. on the fly when needed, using the GPU.
All the AI models needed for this can be made small enough to run on the GPU, although you'll probably need a computer in the >900€ range to run it, or maybe more like >1200€ now that #BigAI are buying all the hardware. If people can't get any decent gaming hardware, computer games will become either much simpler than today or very much dependent on external computing centres to do much of the compute, even if they don't much AI. We don't even need humongous AI models to make gaming better, only small ones that do one thing good enough for the game. Of course NVdia will do everything they can to monopolise the gaming AI by putting their own models in their GPU firmware, with game developers having to pay fees in order to be able to train their own LoRAs so they can actually use the NVidia AI for their own games. However, open source machine learning models aren't restricted in that way, and they can also be used in games.
Oh, by the way, many games already use latent diffusion type models for graphics. This is how you get realtime raytracing with lots of detail in 2k or even 4k and things like that: Part of the GPU is rendering the scene with accurate lighting in low resolution, then the AI is used for upscaling and adding/reconstructing the finer details. The AI in question has been trained on high quality renderings of the same scenes. You need to make a couple of seconds of Pixar quality animation from each scene in the game, train a LoRA for each scene for a video upscaler/filter, and then run the low resolution, low detail raytraced video stream through the upscaler, that's basically how they do it. The actual workflow is much more complicated, but it's not really important if you just want to understand the basic idea. The trick is just to render accurately what needs to be accurate, like lighting, and then use AI to generate a good enough approximation of everything else.
-
New research shows over 90% of gamers find playing with AI-powered NPCs to be "enjoyable and rewarding"
This is what I've been saying ever since I first played around with GPT-2 for a while (I found GPT-3 and later versions actually rather boring in comparison, and they don't even write any better meaningless Dada poetry than ancient GPT-2): #transformer type #LLM powered NPCs can make gaming so much more fun. Just use rather small LLMs that have been trained on all the lore and run locally on the GPU, and you get NPCs to whom you can actually talk. Also, if they hallucinate non-existing lore, real humans often do things like that as well. Give every inportant NPC a couple of scripted lines which contain the important information, and use an LLM so the player can (through their character) talk to the NPCs about topics that aren't in the script.
If you write such an NPC you put a list of things that character happens to know or believe in the NPC prompt, just after the general character description part of the prompt, and as soon as the player deviates from the scripted parts, the LLM drives the conversation. You will most likely have a UI that gives you screen mask with separate text boxes: Character description, description of the character's knowledge and beliefs, description of the character's situation, and a box filled with all conversations with any player character so far. If you as a player character talk to some random stranger from the street, the LLM generates everything. Natural feeling speech synthesis has been a thing for a couple of years now, just like generating a natural sounding human voice from nothing but a prompt describing the speaker (age, gender, accent, personality, current emotional state). It is therefore possible to give each randomly generated NPC their own voice, their own personality, each of them completely unique yet completely generic. A mesh #diffusion type 3D model generator can be used to automatically generate variations of original 3D models from a prompt, generating slightly changed hairstyles, clothes, jewelry, tools, etc. on the fly when needed, using the GPU.
All the AI models needed for this can be made small enough to run on the GPU, although you'll probably need a computer in the >900€ range to run it, or maybe more like >1200€ now that #BigAI are buying all the hardware. If people can't get any decent gaming hardware, computer games will become either much simpler than today or very much dependent on external computing centres to do much of the compute, even if they don't much AI. We don't even need humongous AI models to make gaming better, only small ones that do one thing good enough for the game. Of course NVdia will do everything they can to monopolise the gaming AI by putting their own models in their GPU firmware, with game developers having to pay fees in order to be able to train their own LoRAs so they can actually use the NVidia AI for their own games. However, open source machine learning models aren't restricted in that way, and they can also be used in games.
Oh, by the way, many games already use latent diffusion type models for graphics. This is how you get realtime raytracing with lots of detail in 2k or even 4k and things like that: Part of the GPU is rendering the scene with accurate lighting in low resolution, then the AI is used for upscaling and adding/reconstructing the finer details. The AI in question has been trained on high quality renderings of the same scenes. You need to make a couple of seconds of Pixar quality animation from each scene in the game, train a LoRA for each scene for a video upscaler/filter, and then run the low resolution, low detail raytraced video stream through the upscaler, that's basically how they do it. The actual workflow is much more complicated, but it's not really important if you just want to understand the basic idea. The trick is just to render accurately what needs to be accurate, like lighting, and then use AI to generate a good enough approximation of everything else.
-
After scraping all that #copyright, #bigai deserves this #karma. And We The People get all the open weight models. Hey, publishers are not your friends either, remember the #mpaa trying to send Moms to prison? #distillation is all kinds of comeuppance. #AI #LLM It all is leaking into the #publicdomain !!!
https://www.theregister.com/2026/02/14/ai_risk_distillation_attacks/
-
After scraping all that #copyright, #bigai deserves this #karma. And We The People get all the open weight models. Hey, publishers are not your friends either, remember the #mpaa trying to send Moms to prison? #distillation is all kinds of comeuppance. #AI #LLM It all is leaking into the #publicdomain !!!
https://www.theregister.com/2026/02/14/ai_risk_distillation_attacks/
-
After scraping all that #copyright, #bigai deserves this #karma. And We The People get all the open weight models. Hey, publishers are not your friends either, remember the #mpaa trying to send Moms to prison? #distillation is all kinds of comeuppance. #AI #LLM It all is leaking into the #publicdomain !!!
https://www.theregister.com/2026/02/14/ai_risk_distillation_attacks/
-
After scraping all that #copyright, #bigai deserves this #karma. And We The People get all the open weight models. Hey, publishers are not your friends either, remember the #mpaa trying to send Moms to prison? #distillation is all kinds of comeuppance. #AI #LLM It all is leaking into the #publicdomain !!!
https://www.theregister.com/2026/02/14/ai_risk_distillation_attacks/
-
After scraping all that #copyright, #bigai deserves this #karma. And We The People get all the open weight models. Hey, publishers are not your friends either, remember the #mpaa trying to send Moms to prison? #distillation is all kinds of comeuppance. #AI #LLM It all is leaking into the #publicdomain !!!
https://www.theregister.com/2026/02/14/ai_risk_distillation_attacks/
-
#BigAI is #climate poison. It steals from the future of all life.
https://www.theguardian.com/environment/2026/jan/29/gas-power-ai-climate
-
An enviro defense group reached out asking for my security take on using BigAI in activism work. After giving them the take (basically "don't"), I took liberty to talk about the ethics of using it in the first instance, especially in the context of human rights and climate activism.
Sharing here in case useful on the same grounds elsewhere.
-
I have posted about how #AI, well #bigai, has been won by #Google. Not only #gemini, it is their infrastructure. But forget that. Here is a beautiful video about how #openai MUST become #Facebook, well #meta, well, #rayban, err, point is, #Zuckerberg, a guy who once lucked into the future is no visionary, and not even a good fraud. All while serial #fraud #samaltman is aimed straight at Mark's empire. Watch out Zuck, #chatgpt is coming for you, buddy.
-
Heed:
The best way to stay on top of the resistance is to join/lead the resistance. -
Generative AI is not necessarily emancipatory or liberating. It has a significant environmental impact, violates copyrights, destroys jobs, and in fact increases job insecurity, is neo-colonial, feeds pigs of #BigTech and #BigAI, and even the definition of open source AI with OSAID is too flexible. There are much better and more useful things to do with AI and Generative AI than to maintain a productivist logic.
Tools are meant to help, not replace nor destroy.
-
Generative AI is not necessarily emancipatory or liberating. It has a significant environmental impact, violates copyrights, destroys jobs, and in fact increases job insecurity, is neo-colonial, feeds pigs of #BigTech and #BigAI, and even the definition of open source AI with OSAID is too flexible. There are much better and more useful things to do with AI and Generative AI than to maintain a productivist logic.
Tools are meant to help, not replace nor destroy.
-
Generative AI is not necessarily emancipatory or liberating. It has a significant environmental impact, violates copyrights, destroys jobs, and in fact increases job insecurity, is neo-colonial, feeds pigs of #BigTech and #BigAI, and even the definition of open source AI with OSAID is too flexible. There are much better and more useful things to do with AI and Generative AI than to maintain a productivist logic.
Tools are meant to help, not replace nor destroy.
-
Generative AI is not necessarily emancipatory or liberating. It has a significant environmental impact, violates copyrights, destroys jobs, and in fact increases job insecurity, is neo-colonial, feeds pigs of #BigTech and #BigAI, and even the definition of open source AI with OSAID is too flexible. There are much better and more useful things to do with AI and Generative AI than to maintain a productivist logic.
Tools are meant to help, not replace nor destroy.
-
Generative AI is not necessarily emancipatory or liberating. It has a significant environmental impact, violates copyrights, destroys jobs, and in fact increases job insecurity, is neo-colonial, feeds pigs of #BigTech and #BigAI, and even the definition of open source AI with OSAID is too flexible. There are much better and more useful things to do with AI and Generative AI than to maintain a productivist logic.
Tools are meant to help, not replace nor destroy.
-
Denmark to tackle deepfakes by giving people copyright to their own features.
“Everybody has the right to their own body, their own voice and their own facial features.”
-
At its core, #CCSignals is an attempt by Creative Commons, a Silicon Valley-based organisation, to legitimise the AI grifts of its donors – Google, Microsoft, and Meta (Zuckerberg).
Creative Commons was always a thinly-veiled attempt at enabling Big Tech data farmers to get more data (that’s why the whole “open data” realm is so well funded/popular – open as in “open for business” not free as in “freedom”) but at least their original licenses (non-commercial, share-alike, no derivatives, and yes, sometimes even just attribution) were genuinely useful for people as well as for corporations.
I like to think (perhaps naïvely, I don’t know) that Lawrence Lessig had his heart in the right place when he came up with it all. But I’m biased. I learned how to present from him (including how to use my presentation display) and we even presented a session together back in the day when I was running Open Source Flash. I’m also a big fan of his concept of “institutional corruption”. But I have no illusions that we see eye to eye on all things and I haven’t spoken to him in over a decade.
Anyway, that’s neither here nor there.
This is Creative Commons jumping the shark (is jumping the shark the original enshittification?) and destroying their credibility with a scheme that doesn’t benefit people, only their corporate donors.
Keep using the existing licenses, as they have value, but don’t help them legitimise this latest land grab by the same trillion-dollar corporations and billionaires who are busy destroying our habitat, human rights, and democracy.
In fact, if they go ahead with this, it might be an idea to fork the original Creative Commons licenses and publish them under a different name in an effort to counter the use of their legitimacy to whitewash Big AI.
#CreativeCommons #SiliconValley #BigTech #BigAI #AI https://mastodon.cloud/@raymondpert/114754532045016792
-
-
I shared this on FB but wanted to post here too. Prompted by ppl like Paris Marx recent posts.
Technosocial contracts could gate #bigtech access, and mitigate the unsustainable stealing of open web knowledge by extractive #BigAI for vast profit.
All #universities shld run prof dev in #DigitalSovereignty, what it means, affects, how it works. Its part of basic #digitalliteracy for #academia to have an informed criticality about what is going on in the world.
-
2/2 https://www.news.co.uk/latest-news/make-it-fair/
Crucially, the approach of the UK Creative Industries’ Make It Fair campaign - of resisting AI ‘piracy’ by preserving or strengthening copyright law - ignores what Bellos and Montagu make clear in Who Owns This Sentence? A History of Copyrights and Copywrongs:
• Copyright is a major driver of inequality in the twenty-first century.
• It plays a pivotal but often overlooked role when it comes to understanding the roots of disparities of wealth in modern societies.
• the wealthiest corporations globally derive their power primarily from owning copyright and patents, with ‘sixteen of the fifty richest people in the world’ amassing their fortunes entirely or partially from copyright-related industries.What’s so fair about this?
-
2/2 https://www.news.co.uk/latest-news/make-it-fair/
Crucially, the approach of the UK Creative Industries’ Make It Fair campaign - of resisting AI ‘piracy’ by preserving or strengthening copyright law - ignores what Bellos and Montagu make clear in Who Owns This Sentence? A History of Copyrights and Copywrongs:
• Copyright is a major driver of inequality in the twenty-first century.
• It plays a pivotal but often overlooked role when it comes to understanding the roots of disparities of wealth in modern societies.
• the wealthiest corporations globally derive their power primarily from owning copyright and patents, with ‘sixteen of the fifty richest people in the world’ amassing their fortunes entirely or partially from copyright-related industries.What’s so fair about this?
-
2/2 https://www.news.co.uk/latest-news/make-it-fair/
Crucially, the approach of the UK Creative Industries’ Make It Fair campaign - of resisting AI ‘piracy’ by preserving or strengthening copyright law - ignores what Bellos and Montagu make clear in Who Owns This Sentence? A History of Copyrights and Copywrongs:
• Copyright is a major driver of inequality in the twenty-first century.
• It plays a pivotal but often overlooked role when it comes to understanding the roots of disparities of wealth in modern societies.
• the wealthiest corporations globally derive their power primarily from owning copyright and patents, with ‘sixteen of the fifty richest people in the world’ amassing their fortunes entirely or partially from copyright-related industries.What’s so fair about this?
-
2/2 https://www.news.co.uk/latest-news/make-it-fair/
Crucially, the approach of the UK Creative Industries’ Make It Fair campaign - of resisting AI ‘piracy’ by preserving or strengthening copyright law - ignores what Bellos and Montagu make clear in Who Owns This Sentence? A History of Copyrights and Copywrongs:
• Copyright is a major driver of inequality in the twenty-first century.
• It plays a pivotal but often overlooked role when it comes to understanding the roots of disparities of wealth in modern societies.
• the wealthiest corporations globally derive their power primarily from owning copyright and patents, with ‘sixteen of the fifty richest people in the world’ amassing their fortunes entirely or partially from copyright-related industries.What’s so fair about this?
-
2/2 https://www.news.co.uk/latest-news/make-it-fair/
Crucially, the approach of the UK Creative Industries’ Make It Fair campaign - of resisting AI ‘piracy’ by preserving or strengthening copyright law - ignores what Bellos and Montagu make clear in Who Owns This Sentence? A History of Copyrights and Copywrongs:
• Copyright is a major driver of inequality in the twenty-first century.
• It plays a pivotal but often overlooked role when it comes to understanding the roots of disparities of wealth in modern societies.
• the wealthiest corporations globally derive their power primarily from owning copyright and patents, with ‘sixteen of the fifty richest people in the world’ amassing their fortunes entirely or partially from copyright-related industries.What’s so fair about this?
-
Centralised #BigAI thinks that centralised nuclear plants will save their business model while the people are moving towards decentralised electricity production, storage, distribution and use. What approach will centralised government decide to promote after talking to the centralised grid owners that they created through privatisation? #FilmAt11
-
#AI #GenerativeAI #BigTech #BigAI #Competition #Monopolies #Oligopolies #Competition: "Artificial intelligence (AI) is shaping how we live, work and connect with the world. From chatbots to image generators, AI is transforming our online experiences. But this change raises serious questions: Who controls the technology behind these AI systems? And how can we ensure that everyone — not just traditional big tech — has a fair shot at accessing and contributing to this powerful tool?
To explore these crucial issues, Mozilla commissioned two pieces of research that dive deep into the challenges around AI access and competition: “External Researcher Access to Closed Foundation Models” (commissioned from data rights agency AWO) and “Stopping Big Tech From Becoming Big AI” (commissioned from the Open Markets Institute). These reports show how AI is being built, who’s in control and what changes need to happen to ensure a fair and open AI ecosystem."
https://blog.mozilla.org/en/mozilla/ai/unlocking-ai-research/
-
#BigAI reminds me of that kid in high school who knocked on everyone's door asking to pirate every floppy disc they had in the house. Except the asking part.