Icon Animation Blend Spaces without Triangulation

 

Icon Quaternion Weighted Average

 

Icon BVHView

 

Icon Dead Blending Node in Unreal Engine

 

Icon Propagating Velocities through Animation Systems

 

Icon Cubic Interpolation of Quaternions

 

Icon Dead Blending

 

Icon Perfect Tracking with Springs

 

Icon Creating Looping Animations from Motion Capture

 

Icon My Favourite Things

 

Icon Inertialization Transition Cost

 

Icon Scalar Velocity

 

Icon Tags, Ranges and Masks

 

Icon Fitting Code Driven Displacement

 

Icon atoi and Trillions of Whales

 

Icon SuperTrack: Motion Tracking for Physically Simulated Characters using Supervised Learning

 

Icon Joint Limits

 

Icon Code vs Data Driven Displacement

 

Icon Exponential Map, Angle Axis, and Angular Velocity

 

Icon Encoding Events for Neural Networks

 

Icon Visualizing Rotation Spaces

 

Icon Spring-It-On: The Game Developer's Spring-Roll-Call

 

Icon Interviewing Advice from the Other Side of the Table

 

Icon Saguaro

 

Icon Learned Motion Matching

 

Icon Why Can't I Reproduce Their Results?

 

Icon Latinendian vs Arabendian

 

Icon Machine Learning, Kolmogorov Complexity, and Squishy Bunnies

 

Icon Subspace Neural Physics: Fast Data-Driven Interactive Simulation

 

Icon Software for Rent

 

Icon Naraleian Caterpillars

 

Icon The Scientific Method is a Virus

 

Icon Local Minima, Saddle Points, and Plateaus

 

Icon Robust Solving of Optical Motion Capture Data by Denoising

 

Icon Simple Concurrency in Python

 

Icon The Software Thief

 

Icon ASCII : A Love Letter

 

Icon My Neural Network isn't working! What should I do?

 

Icon Phase-Functioned Neural Networks for Character Control

 

Icon 17 Line Markov Chain

 

Icon 14 Character Random Number Generator

 

Icon Simple Two Joint IK

 

Icon Generating Icons with Pixel Sorting

 

Icon Neural Network Ambient Occlusion

 

Icon Three Short Stories about the East Coast Main Line

 

Icon The New Alphabet

 

Icon "The Color Munifni Exists"

 

Icon A Deep Learning Framework For Character Motion Synthesis and Editing

 

Icon The Halting Problem and The Moral Arbitrator

 

Icon The Witness

 

Icon Four Seasons Crisp Omelette

 

Icon At the Bottom of the Elevator

 

Icon Tracing Functions in Python

 

Icon Still Things and Moving Things

 

Icon water.cpp

 

Icon Making Poetry in Piet

 

Icon Learning Motion Manifolds with Convolutional Autoencoders

 

Icon Learning an Inverse Rig Mapping for Character Animation

 

Icon Infinity Doesn't Exist

 

Icon Polyconf

 

Icon Raleigh

 

Icon The Skagerrak

 

Icon Printing a Stack Trace with MinGW

 

Icon The Border Pines

 

Icon You could have invented Parser Combinators

 

Icon Ready for the Fight

 

Icon Earthbound

 

Icon Turing Drawings

 

Icon Lost Child Announcement

 

Icon Shelter

 

Icon Data Science, how hard can it be?

 

Icon Denki Furo

 

Icon In Defence of the Unitype

 

Icon Maya Velocity Node

 

Icon Sandy Denny

 

Icon What type of Machine is the C Preprocessor?

 

Icon Which AI is more human?

 

Icon Gone Home

 

Icon Thoughts on Japan

 

Icon Can Computers Think?

 

Icon Counting Sheep & Infinity

 

Icon How Nature Builds Computers

 

Icon Painkillers

 

Icon Correct Box Sphere Intersection

 

Icon Avoiding Shader Conditionals

 

Icon Writing Portable OpenGL

 

Icon The Only Cable Car in Ireland

 

Icon Is the C Preprocessor Turing Complete?

 

Icon The aesthetics of code

 

Icon Issues with SDL on iOS and Android

 

Icon How I learned to stop worrying and love statistics

 

Icon PyMark

 

Icon AutoC Tools

 

Icon Scripting xNormal with Python

 

Icon Six Myths About Ray Tracing

 

Icon The Web Giants Will Fall

 

Icon PyAutoC

 

Icon The Pirate Song

 

Icon Dear Esther

 

Icon Unsharp Anti Aliasing

 

Icon The First Boy

 

Icon Parallel programming isn't hard, optimisation is.

 

Icon Skyrim

 

Icon Recognizing a language is solving a problem

 

Icon Could an animal learn to program?

 

Icon RAGE

 

Icon Pure Depth SSAO

 

Icon Synchronized in Python

 

Icon 3d Printing

 

Icon Real Time Graphics is Virtual Reality

 

Icon Painting Style Renderer

 

Icon A very hard problem

 

Icon Indie Development vs Modding

 

Icon Corange

 

Icon 3ds Max PLY Exporter

 

Icon A Case for the Technical Artist

 

Icon Enums

 

Icon Scorpions have won evolution

 

Icon Dirt and Ashes

 

Icon Lazy Python

 

Icon Subdivision Modelling

 

Icon The Owl

 

Icon Mouse Traps

 

Icon Updated Art Reel

 

Icon Tech Reel

 

Icon Graphics Aren't the Enemy

 

Icon On Being A Games Artist

 

Icon The Bluebird

 

Icon Everything2

 

Icon Duck Engine

 

Icon Boarding Preview

 

Icon Sailing Preview

 

Icon Exodus Village Flyover

 

Icon Art Reel

 

Icon LOL I DREW THIS DRAGON

 

Icon One Cat Just Leads To Another

How Nature Builds Computers

Created on April 23, 2013, 5:32 p.m.

At a basic level life could be seen as any self-replicating system. A physical system in the universe which manages to remain stable for long enough that it can use the resources around it to create replicates. We stack many other qualities for life too, but self-replication is the ultimate requirement. Otherwise it is nothing more than a chemical happenstance in the vast universe.

Computer Shell

At first this self-replicating biology may have happened by chance, like some form of chemical chain reaction or equilibrium. Systems with a miniscule lifetime, and no real ability to fight against entropy and change. Complicated behavior is required if these systems are to last more than a few seconds. Movement and feeding, some protection against the elements of the outside world.

Fighting against the weathering and unpredictability of the environment is the biggest challenge to life. On our blue planet evolution has stepped up to this challenge. It has created life that is incredible and diverse. There are many lessons to learn from it. Not just lessons in creating stable systems, but creating beautiful ones too. The scorpion is one of my favorites.

When we create computer programs meant to be left to their own devices, we are in a sense creating these autonomous systems, much like animals operating in a simple environment. Can we analyze Mother Nature to see how she manages to adapt so well to so many complex and unpredictable environments?

One approach is to take conservative action, observe the environment carefully, and only act when there are predictable outcomes. Another is to use formal verification or powerful simulation to prove that no components of the system can fail in any situation. But I am talking about something more fundamental and devoid of these high level mathematical and logical concepts.

A radical idea comes from scientists, which in 2010 compared the functionality of an E. Coli bacteria to that of the Linux Kernel. They compared the "call graph" of the systems.

 

Call Graph

 

In an E. Coli there are a few basic functions that can be performed at the very top level. These are the overall states, or behaviors, of the bacteria. These all use some middle level systems and controllers, which then use a very large number of different basic physical functions for creating proteins and enacting biological needs.

The E. Coli has a few simple goals in life. A few simple high level things it tries to achieve. But in trying to achieve these there are a large number of logical and physical processes going on at the low level. This muddle usually does what is required by the high level goals - but the method is convoluted and somewhat imprecise. It is no design any sensible person would use.

The Linux kernel is the opposite. At the top level is a number of user programs that perform all the tasks needed by a modern computer. They call on many different mid-level controllers which manage the major systems such as networking, audio and video. These finally call a small set of important functions which run the core of the machine. The Linux kernel is imprecise and fuzzy at the high level - while well defined and simple the closer it gets to the machinery.

In this way the systems are somewhat reflected vertically. The E. Coli resembling a pyramid and the Linux Kernel representing an upside down pyramid.

Their reliability is also mirrored. In the Linux kernel it is most important that the core functionality at the bottom always performs correctly and can be relied on. Otherwise all the other systems break. At the middle level it is still important that everything functions, but one thing breaking does not make the system completely dysfunctional. At the top programs often crash. While annoying there are usually other ways to achieve the same task.

In the bacteria evolution has rendered the bottom layer somewhat unpredictable. Some of the basic functions don't work, some only work occasionally and some do something only similar to what was intended. The middle layer which manages behavior is also unpredictable, but will tend toward the correct behavior. The top layer is most important for a correctly functioning organism.

 

Computer Knobs

The reason this backward design is so successful in nature is due to mutation. Mutation always happens at the bottom most level - the genetic machinery - the DNA. If a fundamental component were to change at the bottom level in the Linux kernel the whole system would be broken. This isn't true in the bacteria. A break in one bottom level piece of machinery doesn't make much difference. There are so many other routes to perform behavior that it will probably still function the same.

These design schemes are reflected in both all of nature and computer science.

In computer science we love perfect abstractions. Those components we can use without knowing the internals, and that we can use without them ever unexpectedly failing. The computer in my mobile phone has been clicking over for five years and never got an addition wrong. Another example is the internet. One can use it without any idea of how it works. It is vast and complicated, but (almost) always just works.

In nature things are softer. Animals tend to have a few simple behaviors. But the systems which carry those behaviors out are huge and complex with many flaws and faults. They have simple, unreliable ways of overcoming those issues. Somehow it almost always works out in the end.

To the guided hand of a watchmaker the upside down design of an E. Coli is abhorrent. The complexity is intractable and chaotic. It is the design of a Jackson Pollock madman.

But Mother Nature is no mere watch maker. Evolution laughs at a system that has run for just five years. There is another ledge to climb onto in system design. Eventually programmers must assign her role. The computers will have to start doing the designing for us. And they will do a horrible job of it. But somehow it will feel more familiar. They will have the quirks of the flora and fauna we love. Somehow those systems will start to feel alive.

_________

Sources

github twitter rss