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Category: mapping

Beyond words: Study maps the cognitive force of metaphor

Metaphors are a fundamental aspect of human language and cognition, allowing us to understand complex concepts and relationships by mapping them onto more familiar and concrete domains. However, the nature of metaphors and how they work is still not well understood.

In a new paper published in PLOS Complex Systems, Max-Planck-Institute for Mathematics in the Sciences researchers Marie Teich and Wilmer Leal together with director Jürgen Jost have developed a formal framework and large-scale empirical methodology to analyze metaphors and their role in conceptual theory.

The study confirms the fundamental assumption in conceptual metaphor theory that metaphors are enduring linguistic and cognitive structures, not merely rhetorical figures. Using complex systems tools, the researchers identified a metaphor network with distinctions between abstract and concrete categories, and two significant metaphorical processes: mappings from concrete to abstract topics and the emergence of new mappings between concrete domains.

Sweeping survey maps hundreds of satellite systems orbiting dwarf galaxies

We usually think of satellites as small objects orbiting planets or stars. But in the broader universe, galaxies themselves can have satellites—smaller galaxies bound by gravity that orbit a larger host, carrying with them stars, gas, dust, and dark matter.

Most of what we know about satellite galaxies comes from studying the Milky Way and other similarly large galaxies. But a new study led by Dartmouth astronomers broadens that understanding by exploring the satellites of dwarf galaxies—systems less than a tenth the size of the Milky Way.

The multi-institutional survey triples the number of dwarf galaxies surveyed for satellites, the researchers report in The Astrophysical Journal. The study identifies 355 candidate satellite galaxies, including 264 that were previously undocumented. The researchers suggest that 134 of these candidates are highly likely to be satellite galaxies.

Google DeepMind says its new AI can map the entire planet with unprecedented accuracy

Google DeepMind unveils AlphaEarth Foundations, an AI system that processes satellite data 16x more efficiently to create detailed Earth maps for tracking deforestation, climate change, and environmental shifts.

Nanometer-Resolved Images from Superconducting Technology

An imaging method provides unprecedented resolution for studies of quantum materials by relying on superconductors’ extreme sensitivity to light.

The energy deposited in a superconductor by a single photon can register a detectable signal, which is why superconductors are employed in some extremely sensitive detectors. Now researchers have shown how to use this sensitivity to create maps of the superconducting properties of a material with nanometer resolution [1]. The technique can also detect polaritons—hybrid light–matter excitations that may be useful in quantum technologies—with higher resolution than earlier methods. The researchers expect the new technique to be useful in fields as diverse as quantum information and nanophotonics.

When a superconductor held just below its critical temperature absorbs a single photon, the superconductivity can be destroyed in a small region of the material, triggering a small electrical signal. Recent advances have expanded the operating temperatures of such detectors and improved their sensitivities to photons over a wide range of frequencies, enabling many new applications. Mengkun Liu of Stony Brook University in New York and colleagues wondered if the same sensitivity might be employed to build high-resolution spatial maps of the properties of superconducting samples. “Spatial variations often influence superconducting strength and coherence, so an ability to image these properties locally would bring valuable insight,” says Stony Brook team member Ran Jing.

Scientists create a “time crystal” using giant atoms, a concept long thought to be impossible

Recent studies have already used Rydberg vapors to detect radio‑frequency fields with extreme sensitivity.

Persistent, phase‑locked oscillations promise low‑phase‑noise signals useful for clock recovery, precision spectroscopy, and perhaps gravitational‑wave detection, where any self‑referencing oscillator could serve as a phase tag.

On the theory side, researchers now have a platform for mapping phase diagrams that include stationary, bistable, and time‑crystalline regimes.

Tesla FSD Competitors Admit DEFEAT: “Elon Was Right”

Questions to inspire discussion.

Safety and Performance.

🛡️ Q: How does Tesla’s full self-driving system compare to human driving in terms of safety? A: According to Elon Musk, Tesla’s end-to-end neural networks trained on massive video datasets have been proven to be dramatically safer than average human driving.

⚡ Q: What recent hardware upgrade has improved Tesla’s full self-driving capabilities? A: Tesla’s AI4 hardware has been upgraded to 150–200 watts, enabling more complex neural networks and faster decision-making, achieving 36 frames per second processing.

Scalability and Efficiency.

📈 Q: Why is Tesla’s vision-only approach considered more scalable than competitors’ methods? A: Tesla’s vision-only approach is more scalable than competitors’ use of multiple sensors, sensor fusion, and high-definition maps, as stated by BU’s Robin Lee.

Continue reading “Tesla FSD Competitors Admit DEFEAT: “Elon Was Right”” | >

MIT’s new AI can teach itself to control robots by watching the world through their eyes — it only needs a single camera

This framework is made up of two key components. The first is a deep-learning model that essentially allows the robot to determine where it and its appendages are in 3-dimensional space. This allows it to predict how its position will change as specific movement commands are executed. The second is a machine-learning program that translates generic movement commands into code a robot can understand and execute.

The team tested the new training and control paradigm by benchmarking its effectiveness against traditional camera-based control methods. The Jacobian field solution surpassed those existing 2D control systems in accuracy — especially when the team introduced visual occlusion that caused the older methods to enter a fail state. Machines using the team’s method, however, successfully created navigable 3D maps even when scenes were partially occluded with random clutter.

Once the scientists developed the framework, it was then applied to various robots with widely varying architectures. The end result was a control program that requires no further human intervention to train and operate robots using only a single video camera.

Can AI really code? Study maps the roadblocks to autonomous software engineering

Imagine a future where artificial intelligence quietly shoulders the drudgery of software development: refactoring tangled code, migrating legacy systems, and hunting down race conditions, so that human engineers can devote themselves to architecture, design, and the genuinely novel problems still beyond a machine’s reach.

Recent advances appear to have nudged that future tantalizingly close, but a new paper by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and several collaborating institutions argues that this potential future reality demands a hard look at present-day challenges.

Titled “Challenges and Paths Towards AI for Software Engineering,” the work maps the many software-engineering tasks beyond code generation, identifies current bottlenecks, and highlights research directions to overcome them, aiming to let humans focus on high-level design while routine work is automated. The paper is available on the arXiv preprint server, and the researchers are presenting their work at the International Conference on Machine Learning (ICML 2025) in Vancouver.

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