Bio-Intelligence
Details
Robotic Design, Microbiological Culture Media,
Physical Computing and calibration
Tools
Arduino, Grasshopper, Adobe After Effects,
Client
Hyundai Motor Group (HMG)
~ Hyundai Future Experience Research Collaborative 2022
Date
Spring 2022
Mentor
Paula Gaetano Adi
Team
Manini Banerjee (Industrial Design + Computation) &
Mehek Vohra (Cognitive Neuroscience + Graphic Design)
︎︎︎ Final Project Video
︎︎︎Feature on Popular Science Magazine
︎︎︎Feature on Kia’s Global Media Center
︎︎︎Feature on Livid Magazine
Robotic Design, Microbiological Culture Media,
Physical Computing and calibration
Tools
Arduino, Grasshopper, Adobe After Effects,
Client
Hyundai Motor Group (HMG)
~ Hyundai Future Experience Research Collaborative 2022
Date
Spring 2022
Mentor
Paula Gaetano Adi
Team
Manini Banerjee (Industrial Design + Computation) &
Mehek Vohra (Cognitive Neuroscience + Graphic Design)
︎︎︎ Final Project Video
︎︎︎Feature on Popular Science Magazine
︎︎︎Feature on Kia’s Global Media Center
︎︎︎Feature on Livid Magazine
"Driverless Cars' Need for Data is sparking a new Space Race" Autonomous vehicles will generate as much as 40 terabytes of data an hour from cameras, radar, and other sensors—equivalent to an iPhone's use over 3,000 years.... (Bloomberg)
Data processing, storage and production requires carbon emitting data centers.
Through Bio-intellgence, an alternative paradigm to Artificial Intelligence, we challenge an opportunity for sustainable, data free navigation.
How would a car work if it were driven by organisms instead of algorithms?
What if we moved from nature-inspired into nature-collaborated?
For many years, we rode horse carriages and controlled the horse rather than giving it freedom to operate using its own intelligence. This freedom is the essence of Biointelligence.
Through Bio-intellgence, an alternative paradigm to Artificial Intelligence, we challenge an opportunity for sustainable, data free navigation.
How would a car work if it were driven by organisms instead of algorithms?
What if we moved from nature-inspired into nature-collaborated?
For many years, we rode horse carriages and controlled the horse rather than giving it freedom to operate using its own intelligence. This freedom is the essence of Biointelligence.
We researched the fungal animal and living memristor: Slime Mold, Mycetoza, Physerium Polycephalum, or
"the blob" during this project. We chose this organism as it:
"the blob" during this project. We chose this organism as it:
1) Uses Phototaxis and Chemotaxis instead of sensors, cameras and data.
2) Does not rely on electricity for data storage, and memorizes past routes.
3) Operates as a collective and learns past routes.
4) Demonstrates generalisability in navigating new + old terrains
Slime mold demonstrates intelligence - it can grow, learn, predict, and adapt depending on its environment. It can remember
where it has previously been, and can navigate new terrains without having to be manually trained.
The mould shoots out protoplasmic tubes searching for an efficient path toward oat flakes.
In one of the experiments we conducted, we placed its favorite food - oats - at specific locations in a petri dish and recorded its
growth over 3 days. It ended up creating efficient paths between each oat, proving its navigational intelligence.
Autonomous vehicles require a lot of training to memorise and learn routes, is there a way slime mould can collaborate with these
vehicles to make them more efficient? We set up an experiment to test this our theory
1) Robot navigation: Has to navigate through every section of the maze before finding the most efficient path. This process takes time,
a lot of computation and data storage.
growth over 3 days. It ended up creating efficient paths between each oat, proving its navigational intelligence.
Autonomous vehicles require a lot of training to memorise and learn routes, is there a way slime mould can collaborate with these
vehicles to make them more efficient? We set up an experiment to test this our theory
1) Robot navigation: Has to navigate through every section of the maze before finding the most efficient path. This process takes time,
a lot of computation and data storage.
2) Slime Navigation: The same maze being solved by the Slime mold - uses no sensors, processing algorithms or data storage.
However, whilst working with living matter, there are challenges regarding speed of growth and life spans.
Is there a way we can immortalise Biointelligence?
Is there a way we can immortalise Biointelligence?
To tackle such questions we created a grasshopper simulation to mimic slime growth.
In the simulation, we would input the start and end points, and allow the simulated Slime Mold to calculate the most effective path.
This path would then be fed into the robot.
This path would then be fed into the robot.
Such algorithms are more efficient and require less data storage.
we imagine computer vision being used to read the slime mould's movements and informing the vehicles navigation. The smart petri contains
self healing agar and programmable oats to simulate a range of paths using harnessing the Bioingelligence of the organism.
- was made possible through the support of Hyundai Motor Group and RISD Nature Lab along with my mentors: Paula Gaetano-Adi and Felipe Santos Shibuya. Special thanks to my collaborator: Mehek G. Vohra
