Ever wish you could have your own army of servants, constantly keeping everything clean and fixing anything that breaks? Now take that army, make it a billion times smaller, and put it inside your body. That’s the power of nanobots.
We’ve been theorizing about nanobots for decades; tiny machines that travel through your body, fight disease, and keep you in good working order.
Futurists like Ray Kurzweil and Peter Diamandis predict that nanobots could cure every disease, map the brain, and even allow us to live forever. So where are they? Why don’t we have nanobots today?
Nano Size, Macro Problems
As they say, nothing good comes easy, and nanobots are no exception. The problems preventing us from creating these machines can be broken down into 2 areas:
We currently don’t have a way to reliably manufacture nanobots at scale. Atomic Force Microscopy, one of the most advanced way we have right now to manipulate individual atoms, has to be done manually and is very slow and accurate. If we want to produce nanobots at scale, we need a way to manufacture them quickly, efficiently, and autonomously.
But even if we could manufacture nanobots very quickly for very cheap, what good is that if we don’t know how to actually design them?
Right now, the design process sucks. It mainly involves designing a nanobot that we think might work, actually building it, then testing it. This process is very slow, very expensive, and so far hasn’t yielded any deployable nanobots.
This is what DarwinX aims to solve.
Our mission is to enable nanobot research and development so that the first nanobots will be deployed in people by 2036. That means that a baby born today will have nanobots inside of them keeping them healthy by the time they graduate high school!
Simulations and AI
That’s super exciting, but how the hell are we gonna get there? Let’s dive into how it works.
Our solution is a 2-phase process that will take place over a span of around 10 years:
- Development Phase: Create a platform where researchers can run simulations and use machine learning algorithms to design nanobots.
- Application Phase: Combine our research and development with other developments in nanotech to make nanobot design and manufacturing accessible to researchers and companies.
The Simulation: Design is a huge bottleneck in the development of nanobots. It’s clear that researchers need a way to easily visualize, manipulate, and test nanobots in order to make more progress.
This is exactly why we’re working on a simulation software where researchers can do all of that, in real time.
A molecular dynamics simulation will accurately model atomic and molecular interactions in real time, allowing researchers to test their designs without having to build them in real life. This not only makes designing and iterating faster, but it also saves expensive material from being wasted.
The user interface will be intuitive and easy to use, enabling researchers to easily manipulate the 3D models in real time.
The end goal is to make designing nanobots just as doable as designing normal robots, by having a database of pre-made nanobot components which researchers can put together to create their own. These components could include sensors, motors, joints, etc.
The Algorithm: Even with intuitive simulation software, creating nanobots is an astronomically complex task. That’s why the simulation will also have algorithm integration so that researchers can use artificial intelligence to help them design nanobots.
Say you want to design a nano-motor which would allow a nanobot to travel through blood vessels. Seems simple enough, right? Unfortunately, not at all, and it’s something researchers are still trying to figure out.
But by using evolutionary algorithms, we can let computers do the hard work for us.
Let’s say we want to build a nano-motor that can propel a nanobot. We can follow a straightforward process to help us accomplish that.
First, we decide what components we want the algorithm to use. One way researches have proposed making a nano-motor is by using graphite platforms as the ‘chassis’ and certain proteins that act like springs as the motor.
The algorithm will then put these components together in thousands of different combination, and put them in a simulation. Each combination’s performance will be measured by a fitness function, which might take certain variables into account, like the power generated, the weight of the motor, etc.
Then, the top performing motors of this generation will be replicated and slightly mutated, then rinse & repeat. This is exactly how real evolution works.
With every generation, the motors will get better and better, until finally, we’ll have a working design that could actually propel a nanobot forward.
For every component, a different fitness function will be specified that will measure its performance.
After all the necessary components are created, we can once again use evolutionary algorithms to determine the best way to arrange them in order to achieve a goal, like delivering drugs to cancerous cells. The nanobots which can deliver drugs best will “reproduce”, and after many generations, the algorithm will come up with a nanobot that can deliver drugs really effectively.
This is just one way researchers could design nanobots using our software, but the possibilities are endless, literally.
By miniaturizing components and developing working nanobot designs, we can solve the design challenges, exponentially increasing nanobot development.
Application Phase (swarm algorithms, manufacturing)
At this point, our designs will be ready to manufacture and deploy, but we need to find a way to efficiently manufacture nanobots and to make sure they act in a coordinated fashion.
Ray Kurzweil predicts that in roughly 10 years, nanobot manufacturing will reach a point where we can print nanobots at scale. This means we’ll be able to manufacture our users’ designs, creating an end-to-end platform that allows them to design, test, and manufacture their own nanobots.
As nanobots become more advanced, we’ll also look into using swarm algorithms to enable the nanobots to coordinate and act intelligently. A promising approach is to mimic the behaviour of insects like ants, which can work together on the scale of millions.
What does this mean?
The implications of successfully creating nanobots are huge. DarwinX could help us unlock nanobots’ full potential. All diseases cured, the secrets of the brain unlocked, living indefinitely, and so much more will be unlocked when we learn to create working nanobots. Our platform will do to nanobots what the internet did to knowledge. We’ll be able to crowdsource and come up with designs that will revolutionize the way we live
Nanobots are the future of medicine, and by extension, the future of humanity as a whole. By creating a platform that makes designing nanobots easy (as easy as it can get with nanobots…), we can bring that future as close to today as possible.