Toggle light / dark theme

A new biohybrid computer combining a “brain organoid” and a traditional AI was able to perform a speech recognition task with 78% accuracy — demonstrating the potential for human biology to one day boost our computing capabilities.

The background: The human brain is the most energy efficient “computer” on Earth — while a supercomputer needs 20 mega watts of power to process more than a quintillion calculations per second, your brain can do the equivalent with just 20 watts (a megawatt is 1 million watts).

This has given researchers the idea to try boosting computers by combining them with a three-dimensional clump of lab-grown human brain cells, known as a brain organoid.

In a paper published in npj Imaging, King’s researchers have assessed the use of fertilized chicken eggs as an alternative model that can resolve both ethical and economic issues for preclinical cancer research.

The use of animal models in is a major contributor to the clinical development of drugs and . However, while invaluable tools, the current standard of using mouse models to recreate diseases is expensive, time-intensive, and complicated by both variable tumor take rates and the associated welfare considerations.

Fertilized contain a highly vascularized membrane, known as the chicken chorioallantoic membrane (CAM), which can provide an ideal environment for and study, but to date, relatively few studies have used chick CAM to evaluate novel radiopharmaceuticals.

Though highly capable – far outperforming humans in big-data pattern recognition tasks in particular – current AI systems are not intelligent in the same way we are. AI systems aren’t structured like our brains and don’t learn the same way.

AI systems also use vast amounts of energy and resources for training (compared to our three-or-so meals a day). Their ability to adapt and function in dynamic, hard-to-predict and noisy environments is poor in comparison to ours, and they lack human-like memory capabilities.

Our research explores non-biological systems that are more like human brains. In a new study published in Science Advances, we found self-organising networks of tiny silver wires appear to learn and remember in much the same way as the thinking hardware in our heads.