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The Minds That Left Reality | Diaspora

Greg Egan’s Diaspora is one of the most ambitious and mind-bending science fiction novels ever published. It came out in 1997 and originally started as a short story called “Wang’s Carpets.” That story ended up as a chapter in the novel. Diaspora is: dense, smart, and way ahead of its time.
This is hard science fiction to the core. Egan invents entire new branches of physics. He reimagines life, consciousness, time, space — even what it means to be human. The book doesn’t ease you in. There’s a glossary, invented physics theories like Kozuch Theory, and characters that don’t even have genders. But if you stick with it, what you get isn’t just a story, it’s a look at what the future might actually become.
By the year 2,975, humanity isn’t one species anymore. It’s split into three groups: Fleshers: The biological humans, including the “statics” (unchanged baseline humans) and all sorts of heavily modified versions — underwater people, gene-hacked thinkers, even “dream apes” who gave up speech to live closer to nature. Gleisners: AIs in robotic bodies that live in space. They care about the physical world and experience time like regular humans. They’re kind of old-school — still sending ships to the stars, trying to build things in real space. Citizens: These are digital minds that live entirely in simulated worlds called polises.

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From Turing’s conception of machine intelligence to the evolution of AI in early childhood education: conceptual, empirical, and practical insights

Artificial intelligence (AI) is rapidly permeating many aspects of our everyday lives and nearly every sector of society. In education, AI innovations are increasingly recognized for their transformative potential for enhancing teaching and student learning. In this article, I focus specifically on the evolution of AI in early childhood education (ECE), serving children from birth to age 8. To shed light on this phenomenon, I synthesize pertinent literature to yield conceptual, empirical, and practical insights. I begin with a historical perspective, tracing the origins of Turing’s conception of machine intelligence and the term “AI” to the current practical applications of AI in ECE and AI use by, for, and with children. I then examine developmental appropriateness and ethical considerations surrounding AI use. Next, I identify new opportunities and challenges for early childhood teachers, offering practical recommendations for education leaders and proposing future research directions. Finally, I conclude by reimagining an AI-powered future of ECE, emphasizing the need for supportive practices, active engagement, and the cultivation of positive dispositions among all key stakeholders, who must keep pace with the evolving AI landscape by navigating new opportunities, emerging challenges, and innovative developments. Additionally, I reimagine a transformative educational landscape enriched by student-centered, innovative teaching practices that catalyze learning in an AI-child interactive environment. In this reimagined and progressive educational landscape, the children are empowered with equal opportunities and equitable resources to naturally learn about and from developmentally appropriate AI tools as well as leverage them in ethical and responsible ways to enhance their learning.

Multimodal LLMs and the human brain create object representations in similar ways, study finds

A better understanding of how the human brain represents objects that exist in nature, such as rocks, plants, animals, and so on, could have interesting implications for research in various fields, including psychology, neuroscience and computer science. Specifically, it could help shed new light on how humans interpret sensory information and complete different real-world tasks, which could also inform the development of artificial intelligence (AI) techniques that closely emulate biological and mental processes.

Multimodal large language models (LLMs), such as the latest models underpinning the functioning of the popular conversational platform ChatGPT, have been found to be highly effective computational techniques for the analysis and generation of texts in various human languages, images and even short videos.

As the texts and images generated by these models are often very convincing, to the point that they could appear to be human-created content, multimodal LLMs could be interesting experimental tools for studying the underpinnings of object representations.

Affordable laser could be mass-produced for use in self-driving cars and fiber optics

Laser technology is used in many areas, where precise measurements are required and in communication. This means that they are important for everything from self-driving cars to the fiber optic internet and for detecting gases in the air.

Now, a research group has come up with a new type of laser that solves several problems associated with current-day lasers. The group is led by Associate Professor Johann Riemensberger at NTNU’s Department of Electronic Systems.

“Our results can give us a new type of laser that is both fast, relatively cheap, powerful and easy to use,” says Riemensberger.

Compute-in-memory chip shows promise for enhanced efficiency and privacy in federated learning systems

In recent decades, computer scientists have been developing increasingly advanced machine learning techniques that can learn to predict specific patterns or effectively complete tasks by analyzing large amounts of data. Yet some studies have highlighted the vulnerabilities of some AI-based tools, demonstrating that the sensitive information they are fed could be potentially accessed by malicious third parties.

A machine learning approach that could provide greater data privacy is federated learning, which entails the collaborative training of a shared neural network by various users or parties that are not required to exchange any raw data with each other. This technique could be particularly advantageous when applied in sectors that can benefit from AI but that are known to store highly sensitive user data, such as health care and finance.

Researchers at Tsinghua University, the China Mobile Research Institute, and Hebei University recently developed a new compute-in-memory chip for federated learning, which is based on memristors, non-volatile electronic components that can both perform computations and store information, by adapting their resistance based on the electrical current that flowed through them in the past. Their proposed chip, outlined in a paper published in Nature Electronics, was found to boost both the efficiency and security of federated learning approaches.

Google rolls out text-to-image model Imagen 4 for free

Google Imagen 4, which is the company’s state-of-the-art text-to-image model, is rolling out for free, but only on AI Studio.

In a blog post, Google announced the rollout of the new Imagen 4 model, but reminded users that it’s free for a “limited time” only.

Unlike the old text-to-image model, Imagen 4 offers significant improvements and takes the text-to-image generation quality to the next level.

Confirmed — Texas imposes new rules on autonomous vehicles and will require official permits before they can be driven on its roads

A blue-and-white Waymo van rolls up to a stoplight near Austin’s South Congress Avenue, sensors spinning in the sun. In three months, that van –and every other driverless car in Texas– will need a brand-new permission slip taped to its dash. Governor Greg Abbott has signed SB 2807, a bill that for the first time gives the Texas Department of Motor Vehicles gate-keeper power over autonomous vehicles.

Starting September 1, 2025, any company that wants to run a truly driver-free car—robo-taxi, delivery pod, or freight hauler—must first snag a state-issued permit. To qualify, operators have to file a safety and compliance plan that spells out: