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Category: robotics/AI – Page 6

Who Will Buy Starcloud for Datacenters In Space? đŸ›°ïž

Questions to inspire discussion.

💰 Q: Which companies are most likely to acquire StarCloud and why? A: Microsoft, Amazon, Google, OpenAI, and Nvidia are prime candidates to acquire StarCloud for billions to gain first-mover advantage in the space data center industry, allowing them to immediately access 20 of the smartest satellite engineers and an operational program to expand rather than building from scratch.

Technical Achievement.

⚡ Q: What technical milestone did StarCloud achieve that validates the space datacenter concept? A: StarCloud’s recent launch demonstrated a space-based NVIDIA H100 data center that is 100 times more powerful than anything previously launched and successfully ran Gemini AI model, marking the dawn of a new era and proving the viability of AI compute power in space.

🛾 Q: What launch advantage does SpaceX have in the space datacenter industry? A: SpaceX currently holds a monopoly on launching data centers into space with competitors like Blue Origin and Rocket Lab still needing to develop capable rockets, while SpaceX’s Starship is expected to remain the cheapest and best option for orbital launches.

Strategic Value.

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The Robot Revolution Is Closer Than You Think

A robot revolution, driven by advancements in robotics and AI, is imminent and will drastically transform the economy, labor, and society, leading to a post-labor, post-scarcity system with abundant energy and labor ##

## Questions to inspire discussion.

Investment & National Strategy.

🚀 Q: Why should governments prioritize humanoid robot investment now? A: Governments must treat humanoid robots as a national priority for transforming productivity and defense, with enormous investments justified because there’s no time to lose as both the US and China have already recognized this imperative.

💰 Q: What economic growth rates become possible with early humanoid robot adoption? A: Spinning up the humanoid robot flywheel early enables exponential economic growth rates of 20–100% per year, unlocking unprecedented prosperity and catapulting societies up the curve over the next 15 years.

⚡ Q: Which countries or entities will likely lead the humanoid robot transformation? A: Outsiders rather than incumbents or centers of power will lead the transformation to a new economic paradigm, as history shows leadership typically comes from the edge rather than the status quo.

Continue reading “The Robot Revolution Is Closer Than You Think” | >

Why Supersonic Jet Engines Will Power AI Data Centers

⚙ Q: What makes supersonic engines better than subsonic for ground power generation? A: Supersonic engines designed for 160°F operation at 60,000 ft and Mach 1.7 handle high ground temperatures without requiring water cooling, throttling, or water spray that subsonic engines need to avoid melting in hot conditions.

Deployment Timeline.

📅 Q: When will Boom’s engines start powering AI data centers? A: Boom’s supersonic engines are scheduled to begin providing power to AI customers in 2027, becoming the most tested new jet engine ever before carrying passengers.

Business Model.

💰 Q: How does powering data centers benefit Boom’s supersonic aircraft development? A: The symbiotic relationship creates a financial bridge where engines generate revenue from data center operations before deployment in commercial passenger aircraft, funding the experimental-to-commercial transition.

Environmental Considerations.

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How we know Aliens will be AI

Astronomer Royal Lord Martin Rees predicts AI-Alien discovery in the next 10 years. And he thinks he can figure out whether they’ll let us live or not. Inspired by Elon Musk’s exploits (yep he’s everywhere ain’t he) me and Lord Martin go on a deep dive into the minds of AI-Aliens. Massive thank you to Lord Martin for granting me an interview and massive thanks to Rik for the brilliant soundtrack.

Support me on Patreon: / astrobiscuit.

Further reading: “The End of Astronauts” by Lord Martin Rees: https://amzn.to/3l7NqlP

Riktenstein’s album: https://riktenstein.bandcamp.com/foll
 discord server: https://astrobiscuit.com/discord/ Astrobiscuit Shop: https://astrobiscuit.com/shop.

Astrobiscuit discord server: https://astrobiscuit.com/discord/

Astrobiscuit Shop: https://astrobiscuit.com/shop

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Heavy-tailed update distributions arise from information-driven self-organization in nonequilibrium learning

Like human decision-making under real-world constraints, artificial neural networks may balance free exploration in parameter space with task-relevant adaptation. In this study, we identify consistent signatures of criticality during neural network training and provide theoretical evidence that such scaling behavior arises naturally from information-driven self-organization: a dynamic balance between the maximum entropy principle that promotes unbiased exploration and mutual information constraint that relates updates with task objective. We numerically demonstrate that the power-law exponent of updates remains stable throughout training, supporting the presence of self-organized criticality.

Crazy: Scientists Compute With Human Brain Cells

Go to https://ground.news/sabine to get 40% off the Vantage plan and see through sensationalized reporting. Stay fully informed on events around the world with Ground News.

Human brains are roughly 100,000 times more energy-efficient than current AI systems. So why don’t we build computers using human brain cells? Don’t worry, researchers are one step ahead of you there – different teams across the globe are racing to develop neuron computers; processors that integrate living brain neurons into their chips. Let’s take a look at how this technology is developing and when we might see brain cells chips in the future.

Paper 1: https://www.cell.com/neuron/fulltext/.
 2: h https://www.frontiersin.org/journals/.
 👕T-shirts, mugs, posters and more: ➜ https://sabines-store.dashery.com/ 💌 Support me on Donorbox ➜ https://donorbox.org/swtg 👉 Transcript with links to references on Patreon ➜ / sabine 📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/ đŸ“© Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle
 👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl
 🔗 Join this channel to get access to perks ➜ / @sabinehossenfelder 📚 Buy my book ➜ https://amzn.to/3HSAWJW #science #sciencenews #tech #neuroscience.
Paper 2: h https://www.frontiersin.org/journals/.


👕T-shirts, mugs, posters and more: ➜ https://sabines-store.dashery.com/
💌 Support me on Donorbox ➜ https://donorbox.org/swtg.
👉 Transcript with links to references on Patreon ➜ / sabine.
📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/
đŸ“© Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle

👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl

🔗 Join this channel to get access to perks ➜
/ @sabinehossenfelder.
📚 Buy my book ➜ https://amzn.to/3HSAWJW

#science #sciencenews #tech #neuroscience

Darwin Gödel Machine Explained: Self-Improving AI Agents

In this video, we dive into Darwin Gödel Machine (DGM), introduced in a recent paper from Sakana AI and the University of British Columbia.

Darwin Gödel Machine takes self-improving AI a step froward, by introducing a mechanism for an AI agent to self-improve itself.

Paper — https://arxiv.org/abs/2505.22954
Written Review — https://aipapersacademy.com/darwin-go
 🔔 Subscribe for more AI paper reviews! đŸ“© Join the newsletter → https://aipapersacademy.com/newsletter/ Patreon — / aipapersacademy The video was edited using VideoScribe — https://tidd.ly/44TZEiX ___________________ Chapters: 0:00 Introduction 1:54 Darwin Gödel Machine 3:59 Results.
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Patreon — / aipapersacademy.

The video was edited using VideoScribe — https://tidd.ly/44TZEiX

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KFSHRC Uses AI Enabled Brain Implants in Advanced Neurological Care

The device is also used in selected neurological cases where accurate signal detection and responsive stimulation are critical to managing symptoms over time. Its application forms part of a broader treatment pathway rather than a standalone intervention.

Implantation is performed using minimally invasive techniques and typically takes three to five hours. The approach avoids large surgical incisions and supports shorter recovery periods, allowing patients to resume daily activities more quickly.

Rather than marking a single milestone, the continued use of this technology reflects KFSHRC’s integration of artificial intelligence into routine neurological care, where adaptability and long-term management are central to patient outcomes.

RIYADH, SAUDI ARABIA, December 28, 2025 /EINPresswire.com/ — At King Faisal Specialist Hospital & Research Centre (KFSHRC) in Riyadh, artificial intelligence enabled brain implants are used as part of advanced care for patients with neurological conditions, including Parkinson’s disease and selected movement disorders.

The implant functions by continuously analyzing brain signals and responding to abnormal activity through targeted electrical stimulation. This adaptive approach allows treatment to adjust in real time based on the patient’s neural patterns, reducing reliance on fixed stimulation settings and limiting the need for frequent manual recalibration.

In clinical practice, the technology has supported improved symptom control for patients whose conditions require precise neuromodulation. As treatment progresses, some patients have been able to reduce their dependence on medication under clinical supervision, while maintaining daily function and stability.

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Machine learning helps robots see clearly in total darkness using infrared

From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of these environments lack natural or artificial light, making it difficult for robotic systems, which usually rely on cameras and vision algorithms, to operate effectively.

A team consisting of Nathan Shankar, Professor Hujun Yin and Dr. Pawel Ladosz from The University of Manchester is tackling this challenge by teaching robots to “see” in the dark. Their approach uses machine learning to reconstruct clear images from infrared cameras—sensors that can “see” even when no visible light is present.

The breakthrough, published in a paper on the arXiv preprint server, means that robots can continue using their existing vision algorithms without making changes, reducing both computational costs and the time it takes to deploy them in the field.

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