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Hexbear Code-Op (europe.pub)
submitted 4 months ago* (last edited 4 months ago) by RedWizard@hexbear.net to c/technology@hexbear.net
 
 

Where to find the Code-Op

Wow, thanks for the stickies! Love all the activity in this thread. I love our coding comrades!


Hey fellow Hexbearions! I have no idea what I'm doing! However, born out of the conversations in the comments of this little thing I posted the other day, I have created an org on GitHub that I think we can use to share, highlight, and collaborate on code and projects from comrades here and abroad.

  • I know we have several bots that float around this instance, and I've always wondered who maintains them and where their code is hosted. It would be cool to keep a fork of those bots in this org, for example.
  • I've already added a fork of @WhyEssEff@hexbear.net's Emoji repo as another example.
  • The projects don't need to be Hexbear or Lemmy related, either. I've moved my aPC-Json repo into the org just as an example, and intend to use the code written by @invalidusernamelol@hexbear.net to play around with adding ICS files to the repo.
  • We have numerous comrades looking at mainlining some flavor of Linux and bailing on windows, maybe we could create some collaborative documentation that helps onboard the Linux-curious.
  • I've been thinking a lot recently about leftist communication online and building community spaces, which will ultimately intersect with self-hosting. Documenting various tools and providing Docker Compose files to easily get people off and running could be useful.

I don't know a lot about GitHub Orgs, so I should get on that, I guess. That said, I'm open to all suggestions and input on how best to use this space I've created.

Also, I made (what I think is) a neat emblem for the whole thing:

Todos

  • Mirror repos to both GitHub and Codeberg
  • Create process for adding new repos to the mirror process
  • Create a more detailed profile README on GitHub.

Done

spoiler

  • ~~Recover from whatever this sickness is the dang kids gave me from daycare.~~
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cross-posted from: https://ibbit.at/post/8784

spoilerDonald Trump on Wednesday signed a trio of executive orders that he vowed would turn the United States into an “AI export powerhouse”, including a directive targeting what the White House described as “woke” artificial intelligence models.

The anti-woke order is part of the administration’s broader anti-diversity campaign that has also targeted federal agencies, academic institutions and the military. “The American people do not want woke Marxist lunacy in the AI models, and neither do other countries,” Trump said during remarks at an AI summit in Washington on Wednesday.

Trump also signed orders aimed at expediting federal permitting for datacentre infrastructure and promoting the export of American AI models. The executive actions coincide with the Trump administration’s release of a broader, 24-page “AI action plan” that seeks to expand the use of AI in the federal government as well as position the US as the global leader in artificial intelligence.

“Winning this competition will be a test of our capacities unlike anything since the dawn of the space age,” Trump told an audience of AI industry leaders, adding: “We need US technology companies to be all-in for America. We want you to put America first.”

The metrics of what make an AI model politically biased are extremely contentious and open to interpretation, however, and therefore may allow the administration to use the order to target companies at its own discretion.

The action plan, titled “Winning the Race”, is a long-promised document that was announced shortly after Trump took office and repealed a Biden administration order on AI that mandated some safeguards and standards on the technology. It outlines the White House’s vision for governing artificial intelligence in the US, vowing to speed up the development of the fast-growing technology by removing “red tape and onerous regulation”.

During his remarks, Trump also proposed a more nominal change. “I can’t stand it,” he said, referring to the use of the word “artificial”. “I don’t even like the name, you know? I don’t like anything that’s artificial. So could we straighten that out, please? We should change the name. I actually mean that.”

“It’s not artificial. It’s genius,” he added.

A second order Trump signed on Wednesday calls for deregulating AI development, increasing the building of datacentres and removing environmental protections that could hamper their construction.

Datacentres that house the servers for AI models require immense amounts of water and energy to function, as well as produce greenhouse gas emissions. Environmental groups have warned about harmful increases to air and noise pollution as tech companies build more facilities, while a number of local communities have pushed back against their construction.

In addition to easing permitting laws and emphasizing the need for more energy infrastructure, both measures that tech companies have lobbied for, Trump’s order also frames the AI race as a contest for geopolitical dominance. China has invested billions into the manufacturing of AI chips and datacentres to become a competitor in the industry, while Chinese companies such as Deepseek have released AI models that rival Silicon Valley’s output.

While Trump’s plan seeks to address fears of China as an AI superpower, the Trump administration’s move against “woke” AI echoes longstanding conservative grievances against tech companies, which Republicans have accused of possessing liberal biases and suppressing rightwing ideology. As generative AI has become more prominent in recent years, that criticism has shifted from concerns over internet search results or anti-misinformation policies into anger against AI chatbots and image generators.

One of the biggest critics of perceived liberal bias in AI is Elon Musk, who has vowed to make his xAI company and its Grok chatbot “anti-woke”. Although Musk and Donald Trump are still locked in a feud after their public falling out last month, Musk may stand to benefit from Trump’s order given his emphasis on controlling AI’s political outputs.

Musk has consistently criticized AI models, including his own, for failing to generate what he sees as sufficiently conservative views. He has claimed that xAI has reworked Grok to eliminate liberal bias, and the chatbot has occasionally posted white supremacist and antisemitic content. In May, Grok affirmed white supremacist conspiracies that a “white genocide” was taking place in South Africa and said it was “instructed by my creators” to do so. Earlier this month, Grok also posted pro-Nazi ideology andremoved fantasies while identifying itself as “MechaHitler” until the company was forced to intervene.

Despite Grok’s promotion of Nazism, xAI was among several AI companies that the Department of Defense awarded with up to $200m contracts this month to develop tools for the government. OpenAI, Anthropic and Google, all of which have their own proprietary AI models, were the other recipients.

Conservatives have singled out incidents such as Google’s Gemini image generator inaccurately producing racially diverse depictions of historical figures such as German second world war soldiers as proof of liberal bias. AI experts have meanwhile long warned about problems of racial and gender bias in the creation of artificial intelligence models, which are trained on content such as social media posts, news articles and other forms of media that may contain stereotypes or discriminatory material that gets incorporated into these tools. Researchers have found that these biases have persisted despite advancements in AI, with models often replicating existing social prejudices in their outputs.

Conflict over biases in AI have also led to turmoil in the industry. In 2020, the co-lead of Google’s “ethical AI” team Timnit Gebru said she was fired after she expressed concerns of biases being built into the company’s AI models and a broader lack of diversity efforts at the company. Google said she resigned.

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The case for sabotage (collectiveactionintech.substack.com)
submitted 4 days ago by chobeat@lemmy.ml to c/technology@hexbear.net
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https://github.com/sapientinc/HRM

Hierarchical Reasoning Model is a new architecture that's inspired by neural computation principles observed in the brain, such as hierarchical processing, temporal separation of neural rhythms, and recurrent connectivity.

The bio-inspired design demonstrates significantly improved efficiency and accuracy on complex reasoning tasks compared with current LLMs.

The HRM architecture is designed to achieve significant computational depth while maintaining stability and efficiency during training. It consists of two interdependent recurrent modules operating at different speeds.

The High-Level module operates slowly and is responsible for abstract planning and deliberate reasoning. The Low-Level module functions rapidly, handling detailed computations.

A dual-module system allows the HRM to perform sequential reasoning tasks in a single forward pass without needing explicit supervision of intermediate steps. The model is also designed to be Turing-complete, meaning it can theoretically simulate any Turing machine, overcoming the computational limits of standard Transformer models.

Another interesting feature is the use of one-step gradient approximation, which improves efficiency by avoiding the computationally intensive backpropagation through time method typically used for recurrent networks. Avoiding backpropagation offers a constant memory footprint, making the model more scalable.

The model also incorporates an Adaptive Computation Time mechanism, inspired by the brain's ability to switch between fast, automatic thinking and slow, deliberate reasoning. The HRM is thus able to dynamically allocate computational resources based on the complexity of the task.

Despite having only 27 million parameters, the HRM achieves nearly perfect performance on difficult tasks like complex Sudoku puzzles and finding optimal paths in large mazes, areas where even advanced models using Chain-of-Thought (CoT) methods fail completely.

The HRM also outperforms much larger models on the Abstraction and Reasoning Corpus benchmark for artificial general intelligence. It achieved a 40.3% accuracy, surpassing models like 03-mini-high (34.5%) and Claude 3.7 8K (21.2%).

The model's design means that its training phase is much cheaper as well. It can be trained effectively with a small number of examples (around 1,000) and does not require pre-training or CoT data.

HRM conducts computations within its internal hidden state space which is more efficient than CoT where reasoning is externalized into token-level language. The externalization process can be brittle and requires extensive data to work.

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At the high-profile summit on Tuesday—where, in addition to Sacks, panelists and attendees included Anthropic CEO Dario Amodei, Google president and chief investment officer Ruth Porat, and ExxonMobil CEO Darren Woods—companies announced $92 billion in investments across various energy and AI-related ventures. These are just the latest in recent breakneck rollouts in investment around AI and energy infrastructure. A day before the Pittsburgh meeting, Mark Zuckerberg shared on Threads that Meta would be building “titan clusters” of data centers to supercharge its AI efforts. The one closest to coming online, dubbed Prometheus, is located in Ohio and will be powered by onsite gas generation, SemiAnalysis reported last week.

For an administration committed to advancing the future of fossil fuels, the location of the event was significant. Pennsylvania sits on the Marcellus and Utica shale formations, which supercharged Pennsylvania’s fracking boom in the late 2000s and early 2010s. The state is still the country’s second-most prolific natural gas producer. Pennsylvania-based natural gas had a big role at the summit: The CEO of Pittsburgh-based natural gas company EQT, Toby Rice—who dubs himself the “people’s champion of natural gas”—moderated one of the panels and sat onstage with the president during his speech.

All this new demand from AI is welcome news for the natural gas industry in the US, the world’s top producer and exporter of liquefied natural gas. Global gas markets have been facing a mounting supply glut for years. Following a warm winter last year, Morgan Stanley predicted gas supply could reach “multi-decade highs” over the next few years. A jolt of new demand—like the demand represented by massive data centers—could revitalize the industry and help drive prices back up.

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