Bob McGinley::J2Evolution

Blogging at Terra Nova

I'll be a guest blogger over at Terra Nova this month.  TN is home to some of the world's top scholars on the science, business, and law of virtual worlds.  Lots of great opinions and discussion. 

Terra Nova

Another step forward for Virtual Medicine

The University of Calgary (go Flames eh!) and private interests have developed a 4D full-scale rendition of the human body.  They used the Java 3D API and object oriented design patterns.  The model resides in the "Cave", a research holodeck where the full-scale model can be viewed in 4D space.   

The 4D human atlas is built upon data from basic anatomy textbooks. Fundamental body systems and organs were rendered into animated drawings by a graphic artist, and converted into Java 3DTM to bring them to life in the CAVE environment. “CAVEman is designed to look like a real human, but can also be sized to any scale we want,” says Sensen. “We can display all or only a few select components of the model at any given time."

CAVEman unveiled

The potential for this type of technology is amazing.  I can see these sorts of models easily being rendered in Virtual Worlds.  Should be easy to gain even more insight into animated motion and natural movement in our simulations. 

Predicting Crowd Behavior

Panic is contagious, and human contact in crowded conditions can create mob aggression.  This is a great example of the different methods for modeling human behavior and the relationships affected by that behavior.

The goal of this project is to develop a reusable and behaviorally-founded computer model of pedestrian movement and crowd behavior amid dense urban environments, to serve as a test-bed for experimentation. The idea is to use the model to test hypotheses, real world plans and strategies that are not very easy or are impossible to test in practice, Torrens says.

New Computer Model Predicts Crowd Behavior

This new model incorporates individual behavior among objects, which I would expect.  I think this would be a great experiment to run in a virtual world, let people play with the parameters and mold the behavior of the pedestrians and the environment.  I know this is a slightly twisted thought, but I find the idea of simulating a panicked crowd fairly amusing.  Just something about all those bots spreading chaos I guess.

Artificial Intelligence Sees Market Dimensions Far Beyond that of the Human Brain

Legend Advisory Corp. announced today that they are now using an Asset Allocation Neural Network (AANN) to forecast the strength of global asset classes.

New Breakthrough Unveiled in Investment Management

"AANN analyzes 32 customized inputs, each of which is       compressed from huge volumes of financial data, in order to rank the       relative strength of seven asset classes: large and small capitalization       domestic stocks, investment grade and high-yield domestic debt,       international debt and equity, and cash. Her recommendations are then       fed into a separate optimization program, which creates weightings to       suit the individual risk tolerances of Legend’s       model portfolios. Yet another set of screens picks out the appropriate       mix of mutual funds to fill the designated asset allocations."

This is a great example of how neural nets are being used in society to solve everyday business problems.  Neural nets are also used by banking companies to recognize changes in spending to detect credit card fraud.  This neural net with only 32 inputs is still rather small and basic, but this is where things start.  Humans can interpret millions of  inputs in our neural electrochemical brains. It wont be long before 32 inputs are considered prehistoric, kinda like the 1 megabyte harddrive.

Robots that learn

Dartmouth researchers have developed a basic learning robot called Brainbot.  It is part of some of the cutting-edge research carried out by the Neukom Institute for Computational Science. 

The Neukom Institute’s central purpose is to apply computational science to a wide array of subject areas, ranging from music to public policy. Computational science is the process through which scientists apply mathematical models to real-world events, and computational models often are important to test hypotheses when standard scientific experiments would not be possible.

Institute develops a robot that learns

This is still a pretty primitive attempt at machine learning, but the imagery questions are compelling.  I have always been amazed at how well I can read things that are written backwards, upside down, or in reverse.  We associate things based on a 3-dimensional image, an impression.  We record the contrast of colors, and associate them, along with the correlation of the color to the shape.  Think of a Coca-Cola soda can.  Red, White, swirls, letters, we all know that can, no matter what direction it is facing, whether empty or full, we know what that can looks like.

I'm also taken by how humans can associate sounds of words spoken in foreign languages to words in their own languages.  These will always be challenging tasks for the binary world.  The associations we create, and how we dynamically compare them to our other experiences is a very powerful and advanced level of processing.  Its going to take a while before we can program machines to do comparative associations.

When was the last time you saw a total stranger that reminded you of someone you knew, but hadn't thought of in a long time?

Neural Nets

I've been doing a lot of reading lately about Neural Nets and the different learning processes they use.  Neural Nets are rather crude artificial representations of the human brain that try to simulate the learning process.  Our brains are made up of billions of tiny neurons that can connect to thousands of other neurons by emitting electrochemical signals.  Signals are received along synapses, which can be attached or detached to the dendrites of other neurons.  When a neuron receives an input electrical signal, the neuron processes the information and determines whether to move it forward as an output to other neurons.  There are a couple great tutorials on the concepts behind neural nets at AI-Junkie.com and Jochen Frohlich.

I have a couple problems with Neural Nets.  The first being that they are a serial simulation of parallel processing.  Our brains are not very fast in the electrical, computational sense, but they are very efficient parallel processors.  Each cycle of our brain function processes millions of instructions, whereas today's CPU's only process a single instruction per cycle.  So trying to equate the processing architecture of our brains to a CPU is a stretch.  IBM has taken the approach of actually trying to replicate the parallel processing capabilities of a rat's brain using the Blue Gene L supercomputer.  Mouse brain simulated on computer

Second,  neural nets are restricted to binary inputs, whereas our brains are capable of taking many types of inputs such as the senses, touch, sight, smell, sound, and taste.  Our brains also act differently depending on hormone balances, nutrition, and what mood we happen to be in.  All of these factors alter how the neurons in our brain fire and transmit information.

Third, and most important is memory.  It is still a mystery how we remember certain events, people that we meet, or experiences.  It is theorized that our memories are nothing more than dynamic connections between neurons that occur during a particular experience.  As the memory fades and becomes less used, the connections across neurons degenerate, and eventually break, causing the memory to lapse.  It is also theorized that certain parts of our brain are highly tuned to manage these connections between neurons and index these memories using other neurons, memory neurons.

Neural nets simply do not have the ability to record a pleasant experience, or recognize an old friend in a crowd who has aged or changed their hair style.  Our memories and sub conscience are truly unique in this regard.  We have movie memory, or what some refer to as holographic memory.  We can actually re-run events in our brain as if they were occurring for a second time.  We can even make up events in our brains that never occurred, and we can dream.

I have an idea for improving the architecture of neural nets.  The parallel processing aspects of our brains are not going to be equaled by CPU's anytime soon, if ever.  We need a way then to leverage the serial nature of a CPU, and still perform the mass of computations required to simulate billions of neurons.  My thought for this approach is to make smarter neurons.  Instead of having neurons just take inputs, do some magic, and provide an output, what the neurons were given more options, say to record memory, dynamically associated or disassociate with other neurons, and to provide negative as well as positive responses to input.  Neurons should also have a weight for application to the senses, and the ability to prioritize work based on the state of the senses, say the 'hunger' sense is abnormally high, other senses would be diminished so that the organism could focus.