ZombieML: Reanimating inanimate objects with machine learning

Project Details

Description

Zombies are mythological undead creatures found in horror stories. These stories usually involve a dead animal getting infected with a virus or similar sci-fi mechanism, and coming back to “life” through reanimation. There is some debate in the community about zombies being alive or dead, and in this research we will consider that they lie somewhere in between. Interestingly, a similar process happened four billions years ago, when inanimate matter became alive. Scientists call these theoretical first living entities “protocells”. But were they really alive? What about “proto-protocells”? While today the distinction being “alive” and “dead” is a binary step, was it the same during the origin of life?

To answer this, we will use Generative Artificial Intelligence (AI), a technique used to describe or model datasets, and to generate novel data by sampling from them. This technique has been used to, for example, apply the style of Picasso to a photograph. Thus, the main research question of this proposal is: can AI, in a similar way, transform non-living matter into biology? The focus will be on chemistry experiments with life-like behaviours such as movement or division, and how using AI these behaviours can be enhanced to bring the non-living (chemical) closer to the living (biological) systems. If AI can navigate this gradient of complexity, this research would enable us to answer – as a long-term career goal – one of the biggest questions in science: is the universe a mathematical function? And if so, can AI model it?
Short titleZombieML
AcronymZombieML
StatusActive
Effective start/end date1/04/2230/09/24

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.