Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 40

AI digital twins struggle to predict human behavior, creating ‘funhouse mirror’ distortions

While many fear artificial intelligence will replace humans, using AI to take over some human roles has benefits. Companies can use the technology to conduct surveys and polls, while behavioral scientists can run experiments on digital twins to gather faster insights without risking harm or distress to real participants.

But there’s a huge snag.

Today’s digital twins are just not up to it. New research published in the journal Science Advances found that digital twins seriously misrepresent human behavior.

The composer still making music four years after his death — thanks to an artificial brain

I love this kind of synergy between art and technology — another demonstration of how these two concepts really aren’t separate after all. People build up so many experiences over their lives, each moment layering on the previous one to create a uniquely complex tapestry. When we die, all of this is lost to thermal noise. While this particular artistic iteration may not exactly capture memory engrams, I hope that future posthumous neuro-arts find a way to transmit more of the original person’s experiential tapestry. I think it is a lovely idea to try to keep at least part of a person’s uniqueness growing, changing, and creating even after death.

In Australia, a team of artists and scientists have resurrected the US composer Alvin Lucier. It raises a storm of questions about AI and authorship – and it’s also incredibly beautiful.

The statement means that the $12.93 billion acquisition price is a piece of technical inside humor

It literally encodes the “Hugging Face” emoji (🤗) and a nod to NVIDIA’s brand color directly into the financial figure, serving as a geeky tribute to the community that made Hugging Face strategically valuable.

What is the Easter egg? The $12,930,300,000 price announced for Nvidia’s acquisition of Hugging Face contains two references to the companies. Hugging Face co-founder Thomas Wolf invited readers to find both meanings in the number, turning an otherwise unusually precise acquisition price into a small piece of technical and visual wordplay.

The Hugging Face reference: The sequence 129,303 is the decimal representation of Unicode code point U+1F917, the 🤗 emoji officially named “Hugging Face”. The number therefore encodes the company’s distinctive name and icon directly into the announced consideration. It is an especially fitting reference for a company whose brand is built around the emoji and whose platform has become a central meeting place for the open machine-learning community.

The Nvidia reference: The same number also works as a hexadecimal colour code: #129303. That produces a vivid green close to Nvidia’s familiar brand colour. Earlier online theories split the digits into 12, 93 and 3, suggesting references to Nvidia’s $12 IPO price, its 1993 founding and its three founders; however, Clément Delangue pointed readers towards the colour-code explanation. He also said the detail showed that the price was never the most important thing for either side.

Japan Is Launching a Probe to Collect the FirstEver Samples From a Martian Moon

Once on the surface, a robotic arm will drill cylindrical tubes into the surface to collect samples. The probe is also equipped with a device first developed by NASA that uses a stream of nitrogen gas to collect fine particles from the very top layer of dust. Whether or not Phobos originated from an impact with the planet, experts believe Martian sand can be obtained from the lunar surface. Over time, meteorite impacts have blasted materials off Mars, coating its moons. Scientists expect about 0.1 percent of the samples collected will be debris from Mars itself.

Costing about $345 million US, this mission marks Japan’s first Mars probe launch in 28 years. Beyond the scientific insights it’s expected to provide, from an engineering perspective, the mission aims to test new technologies that could aid future sampling and round-trip travel to Mars.

MMX is scheduled to launch from the Tanegashima Space Center on October 20, 2026.

Samuel Butler and the Technological Singularity

1863, a sheep farmer in New Zealand wrote that machines were evolving, that the process would not stop, and that we were busy building our own successors.

No computers. No electricity grid. No code. Steam engines and mechanical looms.

His name was Samuel Butler, and if you read his sentences cold today you would swear they were written the week after ChatGPT shipped.

Then he did the thing almost nobody remembers him for. He proposed a remedy. It was not regulation. It was not alignment. It was not a six-month pause.

Nine years later he built a whole fictional civilization around that remedy, complete with a rule about which machines were allowed to survive and which had to go. The cutoff he chose is oddly specific, and once you see the logic behind it you cannot unsee it.

A century later, another man arrived at a similar diagnosis and reached for a very different set of tools. One of them only ever wrote. The other did not stop at writing.

Butler was not a crank. He saw the arc, he ran it forward, and he was willing to say the part that most people in #AI today still swallow. The questions he left behind are the ones sitting on our desks right now: can machines become conscious, is this the next step in evolution, and what does that make us?

Continue reading “Samuel Butler and the Technological Singularity” | >

Brain activity patterns could help sharpen LLM deductive reasoning

Large language models (LLMs), the artificial intelligence systems underpinning the functioning of ChatGPT, Gemini and other similar conversational agents, are now widely used worldwide. In addition to processing, interpreting and generating texts, some of these models can solve basic logical problems and answer some user questions with striking accuracy.

While various past studies assessed the reasoning capabilities of some LLMs, how these models encode information to make logical predictions is poorly understood. In particular, the extent to which their activity patterns while processing reasoning problems resemble those in areas of the human brain linked with reasoning remains unclear.

Researchers at Peking University and Tsinghua University recently carried out a study comparing brain activity patterns during reasoning with the numerical patterns through which LLMs process information while generating logical answers to questions. Their paper, published in Nature Machine Intelligence, suggests that language models and the human brain represent deductive reasoning in similar, but not identical, ways, while also showing that LLM representations could potentially be steered using brain imaging data.

Quantum-optical spin glass could improve how AI remembers and learns

A new study has demonstrated that it is possible to make a network of atoms and photons that could improve how artificial intelligence stores and recalls memories. This network, called a quantum-optical spin glass, works as an associative memory, a form of AI that enables the recall of full memories from partial information—much like how humans can recognize a person’s face in a blurred photograph.

The advance, published in Science, shows that this new type of spin glass has a greater capacity to hold and recall memories than a traditional AI network of the same size. The atom-and-photon network also exhibited short-term plasticity, a phenomenon that resembles how synaptic connections between neurons in the brain change when learning new information.

“We can now make neural networks at the atomic level, and they adjust themselves in a way that is somewhat similar to how we believe our brains learn,” said Benjamin Lev, the study’s senior author and the Stanford Fortitude Professor and professor of physics and applied physics in the School of Humanities and Sciences.