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Brain circuit links memory to hearing, revealing how learned sounds guide behavior

Short-term memories are thought to be formed deep within the brain in structures such as the hippocampus, but little is known about how and where memory-related information is kept in the brain or the process of drawing on this information. A good example is the sound of a car horn—most of us recognize it as a warning and know how to respond, even though not all horns sound the same and the circumstances in which we might hear a horn are different each time.

New research led by Professor Lucy Palmer from The Florey’s Neural Network Group has uncovered new insights into how and where memory-related information is stored and how these memory banks are used. These findings improve our fundamental understanding of how the brain works, providing a springboard for other scientists to make further, disease-specific discoveries. The paper is published in the journal Science Advances.

“Using mice that we trained to respond to similar, but slightly altered sounds, we identified a long-range cortical circuit that links memory and sensory systems,” Professor Palmer said. “Our findings provide valuable insights into the cellular and network mechanisms that support learning and memory-guided sensory behavior.

The New Gold Standard: When AI Tokens Become the Currency of the Future

I’ve spent years watching finance and technology slowly adapt to one another, but the shift we’re looking at right now is going to change the entire landscape overnight. We need to stop thinking of AI as just a software tool or a cool shortcut for writing emails. We are officially entering an era where computational power is a foundational global commodity—and the standard unit of that commodity is the AI token.

Think of it like digital energy. Just as factories consume kilowatt-hours of electricity, modern enterprises now have to “burn” tokens to power their workflows. In my latest piece, I break down the massive hidden risk of letting a few Big Tech hyperscalers control both the production of this raw material and the infrastructure of exchange. This is where the banking sector has to step in, not just to cut their own costs, but to act as the ultimate market makers for artificial thought.

I dive deep into how banks will soon offer token futures markets—allowing companies to hedge their computing costs the exact same way airlines hedge aviation fuel—and how autonomous AI agents will soon be transacting with each other using tokenized value. The institutions that build these financial rails now will own the next century of commerce, while the rest risk being left behind in an aging system.

Click through to read the full breakdown on how the machine-to-machine economy is actually going to work!

(https://www.linkedin.com/pulse/new-gold-standard-when-ai-tok…Resilience over Political Influence: History shows that attempting to lobby a system to be “less exploitative” rarely works because the system is designed for extraction. True survival in this model might mean finding “off-grid” pockets where the resource demand is low enough to fly under the AI’s radar, or where the land is unsuitable for massive data centers.


I have spent a significant portion of my career watching the tectonic plates of finance and technology grind against each other. Usually, it is a slow, methodical process—a gradual shifting of legacy systems adapting to new digital realities. But every so often, a shift occurs that is so profound, it completely redefines the landscape overnight. We are standing on the precipice of one of those shifts right now.

Ex-Google CEO: What Artificial Superintelligence Will Actually Look Like w/ Eric Schmidt & Dave B

Get access to metatrends 10+ years before anyone else — https://qr.diamandis.com/metatrends.

Eric Schmidt is the former CEO of Google.

Dave Blundin is the founder of Link Ventures.

Chapters.

00:00 — The Rise of Digital Superintelligence.
09:26 — AI and Energy: The Power Behind Progress.
18:34 — The Future of Work: AI’s Impact on Jobs.
28:02 — Navigating the AI Landscape: Opportunities and Risks.
37:13 — The Role of Education in an AI-Driven World.
46:41 — The Ethics of AI: Balancing Innovation and Responsibility.
56:12 — The Future of Creativity: AI in Arts and Media.

Engineered for the Future

Buildings account for 30–40 percent of global energy expenditure and more than half of global electricity consumption. But the most advanced smart buildings—those with full automation, AI controls, and on-site generation—can achieve energy reductions of 50–70 percent. Scaled across the built environment, that translates to 60–110 exajoules of energy saved per year—that’s more than the entire current energy consumption of the United States, or the total output of all the world’s nuclear power plants combined.

Transforming the buildings we already live and work in to become a part of the system itself that generates, stores, and manages energy efficiently could be the blueprint for the future of energy use, creation, and management.

Finding the best ways for humans and robots to work together requires ‘swarm’ thinking

If the future of warehouse work belongs to humans and robots working side by side, a key question remains: What is the most effective way for them to collaborate?

Research published in Transportation Science suggests that the answer may be more flexible than many warehouse operators expect. The study, “Picking the Best Bot: Collaboration Strategies for Humans and Bots in Order Pick Systems with Traveling Salesman Problem Routing,” found that under many real-world conditions, warehouse workers achieve higher productivity when they dynamically switch among multiple autonomous mobile robots rather than work exclusively with a single robot.

The findings challenge a common assumption that fixed human–robot pairings are the most efficient approach.

Artificial General Intelligence: So Close Yet So Far?

Despite its rapid development and widespread adoption, AI is a nascent technology with vast potential for enormous growth in the coming years.

Decades of science fiction make it easy to imagine a future in which AI evolves beyond task-focused point applications to offer broad, human-like intelligence. Although artificial general intelligence (AGI) is theoretical, the road to real AGI is fraught with serious technological and societal challenges. AGI developers face the daunting hurdles of making AGI work effectively, accurately, reliably — and, most of all, safely.

Chip-scale ‘acoustic atom’ controls sound waves to imitate atomic energy levels and advance computing

For every action, there is an equal and opposite reaction. What goes up must come down. Physical laws like these govern all of the natural world—except for the tiny internal components of today’s microprocessors, which operate according to the unique and complicated rules of quantum physics.

As the microprocessors that power computers, medical equipment, sensors, and more continue to shrink in size, engineers face challenges controlling quantum-scale systems. But in a step forward for the technology, researchers at Virginia Tech have developed an “acoustic atom”—a chip-scale device that traps and controls sound waves in ways that mimic the behavior of real atoms. Long term, these advances could influence technologies connected to quantum artificial intelligence (AI), telecommunication, medical imaging, GPS, and more.

The research is published in Physical Review Letters by Linbo Shao, assistant professor in Virginia Tech’s Bradley Department of Electrical and Computer Engineering, along with colleagues at the university’s Center for Power Electronic Systems, Department of Physics, and Center for Quantum Information Science and Engineering and the Oak Ridge National Laboratory.

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