Dale Reed transformed aerospace research through his pioneering work on lifting bodies and remotely piloted research vehicles. His innovations laid the foundation for the Space Shuttle’s design and advanced unmanned flight testing, making him one of NASA’s most influential flight research engineers.
Everybody writes about the Singularity now. Almost nobody knows where the word was born.
Not in a lab. Not in a think tank. In the January 1983 issue of Omni magazine, where a mathematician and science fiction writer named Vernor Vinge put a name to the thing the rest of us are still trying to survive.
Think about that. Four decades before ChatGPT, before the AI arms race, before every futurist and their algorithm started forecasting the end of the human era, the framework already existed. Vinge saw the curve. He just needed a word for the point where it goes vertical.
Today, that word is inescapable. Write about AI, about the future of work, about what happens to humanity when the machines get smarter than us, and you are writing about the Singularity, whether you use the term or not. Refuse to, and you owe your reader an explanation for why not. So you are still writing about it.
Thanks to Josh Calder, who dug out and scanned the original page, you can see the exact moment the term entered our vocabulary. A little piece of digital history, hiding in plain sight for 40 years.
Where do you think we are on Vinge’s curve right now? #Singularity #ArtificialIntelligence #Futurism.
Chuck Brooks is the president of Brooks Consulting International and one of Executive Mosaic’s GovCon Experts.
We are on the brink of a transformative era where rising technologies are colliding to create unparalleled innovation. artificial intelligence, nanotechnology and quantum technologies are transforming research and development, expediting prototyping and disrupting various industries.
This convergence, propelled by exponential processing power, molecular precision and intelligent systems, promises trillions in economic value while posing significant concerns in security, ethics and labor preparedness.
Further Reading Perforation-type anchors inspired by skin ligament for robotic face covered with living skinhttps://www.cell.com/cell-reports-physical-scienc…
A PERSONAL SUPER COMPUTER “MACRO-CHIP” WITH PHOTONIC INTERCONNECTS:
This will soon become possible by the cheap nano-imprinting of hundreds of smaller microchips, without the need for laser lithography, onto a single monolithic wafer, with these chips’ communicating with each other at light speed as a single system via silicon photonics. A team at Peking University has set this race in motion in a major way by developing an optical system to boost AI speeds 100-fold by optical interconnects between individual microchips. The next step will be placing all of those chips onto a single monolithic wafer with a similar communication system between them. Nano-imprinting at large node-scale of 15 or 20 nm will make it possible to mass produce wafer scale systems that combine all the best types of computing features, from logic gates to optical AI accelerators in one compact package on a single wafer. Consumers will not care if the computer chips in their computers are not 14-mm wide 2-nm node chips printed by expensive extreme ultraviolet lithography, but are, instead, 8-inch or 12-inch wide super computer “macro-chips” that give 1,000 times the computing power and speed of the best Nvidia computer on the market today, whereon the distance of the individual chiplets on the wafer from the central optical multiplexer becomes part of the ingrained clock feature of the chip, replacing the traditional clock-time limit. The mother boards, GPUs and CPUs of these systems will all exist on the same wafer and communicate at light speed, with the equivalent of something like 1,000 VRAM of unified memory.
These developments come as the shrinking of traditional silicon microchips is facing a final limit. In the same way that the Personal Computer became the game-changer in the 1980’s, it appears that Personal Super-Computers will become the new kid on the block in the 2030’s.
Peking University researchers develop new all-optical interconnect system linking standard electronic chips with specific algorithms.
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Michael Levin is a biologist at Tufts University working on novel ways to understand and control complex pattern formation in biological systems.
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Biological Robots: https://arxiv.org/abs/2207.00880
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Often compared to Frank Herbert’s Dune, this story follows advanced civilizations and their fates across a dark galaxy as they begin to discover the remnants of a far superior progenitor civilization, one that disappeared long before the galactic dating system even began.
This is a full version of Homeworld 1 lore series all put together into one long form video. Enjoy!
Chapters:
00:00 Progenitor Civilization & The Exiles.
05:56 Alien Super-Technology.
09:06 The Great Nebula.
17:49 The Galactic Core.
20:19 Alien Mega-Structures.
22:49 The Homeworld.
Characters and terminology mentioned in the video:
Kharak: The desert planet where Kushan have been exiled 4,000 years ago.
Bentusi: The oldest known alien civilization, and the only one to officially posses hyperspace far-jump technology. Unlike short-jump, it allows near-instantaneous travel to virtually any location in the galaxy.
Taiidan: A powerful empire controlling much of the inner galaxy, including Hiigara.
Kushan: A humanoid civilization exiled from Hiigara 4,000 years ago by the Taiidan. They settled on a desert planet called Kharak. After integrating forbidden hyperspace far-jump core into their Mothership, Kharak was destroyed by the Taiidan.
Kadeshi: The same species as the Kushan. During exile, they settled inside the Great Nebula, which they now call the Garden of Kadesh.
Karan S’jet: The living mind of the Kushan Mothership. Something like a navigator from Dune.
Turanic Raiders: Hostile alien civilization loyal to the Taiidan.
Hyperspace Inhibitors: Technology that shuts down hyperspace engines of any passing ship in a very large radius.
Hiigara: Ancient home of the Kushan, before their exile.
Captain Elson: The leader of Taiidan Rebelion.
You can get the remastered game at: https://store.steampowered.com/
Good Site for Homeworld lore: https://homeworld.fandom.com.
Footage:
To develop new and better peptides, the short amino acid strings behind medicines like GLP-1 drugs, researchers have used AI to generate candidates and to predict their properties.
However, merging these capabilities into a system that generates peptides likely to activate or block specific targets has proven difficult. In part, this is due to the vast number of possible peptides, but also because predicting how readily a peptide will bind to a target—like G protein-coupled receptors (GPCRs), a family of cell-surface proteins targeted by about one-third of approved drugs—is easier than simultaneously forecasting what effect that binding will have.
Now, researchers at the University of Pennsylvania and The Chinese University of Hong Kong have created TD3B, an AI framework that guides peptide generation toward candidates predicted to have a desired effect. The results, which focus on GPCRs, are described in a paper presented as a Spotlight at the 2026 International Conference on Machine Learning.
In this exclusive, long-form interview, Turing Award laureate Geoffrey Hinton—often called the “Godfather of Deep Learning”—opens up about the promise and peril of advanced AI. Hinton explains why he left Google, how close we really are to artificial general intelligence (AGI), and what guard-rails governments, researchers, and ordinary citizens can put in place today to keep powerful neural networks from going off the rails.
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Watch the interview with Yann LeCun on AI and machine learning: • Father of AI: AI Needs PHYSICS to EVOLVE |…
Geoffrey Hinton is a British-Canadian cognitive psychologist and computer scientist best known as the “godfather of deep learning.” As a professor at the University of Toronto and co-founder of Google Brain, he pioneered modern neural networks—work that earned him the 2018 Turing Award alongside Yann LeCun and Yoshua Bengio. Since leaving Google in 2023, Hinton has focused on warning about the societal and existential risks of increasingly powerful AI systems.
Not exactly—but how we use it matters.
A new Trends in Cognitive Sciences perspective argues that AI doesn’t inherently erode human intelligence. Instead, it highlights a well-known principle in cognitive psychology: cognitive offloading.
When we let AI perform tasks that require reasoning, writing, memory, or problem-solving, we reduce the amount of mental practice our brains receive. Like physical exercise, cognitive skills strengthen through use and weaken through disuse.
Skills: learned abilities such as writing, mathematical reasoning, diagnosis, or programming. These are most vulnerable if AI consistently replaces the learning process.
Basic cognitive abilities: foundational functions like working memory, attention, and executive control. Current evidence suggests these may be more resistant to decline, although more research is needed.
The key message isn’t that AI makes people “stupid.” Rather: AI can improve immediate performance. Overreliance may reduce long-term learning and skill retention.
AI is most beneficial when it augments human thinking instead of replacing it. This fits with decades of neuroscience showing that practice drives neuroplasticity. The brain adapts to the cognitive demands we place on it. If.