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Scientists Discover Way to Send Information into Black Holes Without Using Energy

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This Artificial Eye Copies Cats, Sheep, and Humans at the Same Time

Nature has spent millions of years perfecting vision. From the round pupils of humans to the slit-shaped eyes of cats and the panoramic vision of sheep, every eye has evolved to solve a specific environmental challenge. Inspired by this extraordinary diversity, scientists are now building artificial vision systems that do more than just “see.” They sense, adapt, and respond—much like living eyes.

In a groundbreaking development, a research team from the University of North Carolina, led by Kun Liang, has introduced a bioinspired artificial vision system that can dynamically change its pupil shape and behavior. This innovation brings machines one step closer to human- and animal-like visual intelligence.

Why Animal Eyes Matter in Artificial Vision

Evolution has produced a wide range of eye designs, each suited to different environments:

  • Human eyes use round pupils to balance clarity and light control.

  • Cats have vertical slit pupils that improve depth perception and hunting ability in low light.

  • Sheep have horizontal pupils that give them a wide field of view to spot predators.

  • Cuttlefish and frogs possess irregular pupil shapes that adapt rapidly to underwater or mixed lighting environments.

These biological solutions inspired engineers to ask a crucial question: Can artificial eyes also adapt their pupil shape and behavior to changing environments?

The Big Challenge: Changing Light Conditions

Modern machine vision systems—used in autonomous cars, drones, and robots—struggle when light conditions change suddenly. A system that works well in daylight may fail at night, in tunnels, or under harsh sunlight.

Current solutions rely on:

  • Complex sensors

  • Heavy computing algorithms

  • High power consumption

Despite their sophistication, these systems still fall short of the effortless adaptability seen in biological eyes.

A Bioinspired Breakthrough

The team at the University of North Carolina proposed a radically different approach. Instead of relying only on software and computation, they built adaptability directly into the hardware—just like nature does.

Their artificial vision system combines three key components:

1. Artificial Retina with a Wide Field of View

At the heart of the system is a hemispherical imaging array that mimics the curved shape of a real retina. This design provides an ultrawide field of view of 108°, allowing the system to capture more visual information at once.

The retina uses an advanced photodetector structure that responds to light across a wide spectrum—from ultraviolet to visible and near-infrared wavelengths. This makes the system highly sensitive and versatile.

2. Liquid Metal as Artificial Neurons

One of the most innovative features is the use of liquid metal (EGaIn, a gallium–indium alloy). This material can change shape when electrically stimulated.

  • The deformation of liquid metal acts like neuronal signals

  • Rapid switching between electrical states mimics biological nerve spikes

  • The system processes visual signals in a way similar to real neural circuits

In simple terms, the vision system doesn’t just detect light—it thinks about it in a biologically inspired way.

3. Adaptive Artificial Pupil

Just like the human eye’s pupillary light reflex, the artificial pupil automatically adjusts when light intensity changes.

  • In bright light, the pupil contracts to reduce exposure

  • In low light, it expands to capture more information

This adaptive pupil significantly improves image recognition accuracy, especially in high-light or uneven lighting conditions.

Programmable Pupil Shapes: One Eye, Many Species

A remarkable advantage of this system is its programmability. By carefully controlling the deformation of the liquid metal, researchers can simulate different animal pupil shapes:

  • Vertical slits like cats

  • Horizontal pupils like sheep

  • Irregular pupils like cuttlefish and frogs

This means a single artificial eye can be reconfigured for different tasks and environments—an ability that traditional cameras simply do not have.

Why This Matters for Future Technology

The implications of this research are vast:

Bioinspired Robotics

Robots operating in unpredictable environments—such as disaster zones or deep-sea exploration—need vision systems that adapt instantly. This technology offers exactly that.

Autonomous Driving

Vehicles must handle glare, shadows, tunnels, and night driving. An adaptive pupil reflex can greatly improve safety and reliability.

Advanced Machine Vision

Industrial inspection, medical imaging, and smart surveillance systems can benefit from wide field-of-view imaging and adaptive light control.

Neuromorphic Electronics

By closely mimicking biological feedback loops, this system provides a foundation for next-generation brain-inspired computing.

Scalable and Customizable Design

Another strength of the system is its scalability. Through structural design, the artificial vision setup can be built in:

  • Rectangular arrays

  • Triangular layouts

  • Multi-camera collaborative systems

This flexibility makes it suitable for a wide range of industries, from intelligent transportation to precision medical tools.

Challenges and the Road Ahead

While promising, the system is not without limitations. The inclusion of mechanical components like liquid metal introduces challenges related to:

  • Long-term stability

  • Response speed

  • Energy efficiency

Additionally, the full potential of simulating different animal pupil shapes is still being explored. Future research will focus on improving robustness and unlocking deeper biological advantages.

A New Era of Seeing Machines

This adaptive artificial vision system represents a major step forward in bridging biology and technology. By integrating a wide-field artificial retina, liquid-metal neural logic, and a shape-shifting pupil, the researchers have demonstrated that machines can do more than record images—they can adapt, respond, and optimize their vision in real time.

As this technology matures, it promises to redefine how machines see the world, bringing us closer to truly intelligent, nature-inspired vision systems for robots, vehicles, and beyond.

Reference: 

  • Kun Liang et al.
    •  
,
Bioinspired adaptive pupil reflex based on liquid-metal shape-shifters for machine vision.Sci. Robot.11,eadx0715(2026).DOI:10.1126/scirobotics.adx0715

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