A penny for your thoughts (literally)

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There are companies developing devices that can read your brain’s output and use it to control external devices. The technology used is similar to that used by an MRI scanner; as with an MRI scanner these technologies have the potential of providing a wealth of benefits for health care. An example application is their use to aid people with missing limbs control artificial replacement.

These devices are getting cheaper (in the $100 range). They are being used to allow  people to interact with games and other software applications. The easy and cheap availability of such brain scanning devices however raises some ethical questions. As this emotiv systems presentation shows, these devices can inadvertently read your mind. seeing what are your likes and dislikes.

Such data in the hands of a commision-based salesman is scary. It is easy to imagine a website that can assembles  text and  pictures on the fly (based on your preferences) to get you to buy whatever is being sold. Worse still they can sell your thoughts, such that other companies know how to target you better.

That is scary.

Anonymous vs Scientology

Image by scragz via Flickr

The manufacturers / sellers of these masks must be making a ton of money.

Search, Neutral Evolution and Mapping in Evolutionary Computing:2

Here is a pre-proof copy of my accepted paper: “Search, Neutral Evolution and Mapping in Evolutionary Computing: A Case Study of Grammatical Evolution”.

I would encourage you to read section X  (Analysis of related works) , to see its true implications.

I plan to do a series of posts on what this paper means for Evolutionary Computing, and to post some of the MATLAB code used in this work.

You might want to subscribe to my RSS feed.

“This paper is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.”

Note to Earth, Choose Your Standards Wisely!

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As the year rolls over I am a bit preoccupied with standards, especially that with regard to the measurement of time. There is this amazing Scientific American article that goes through why we have 60 minutes to the hour based on the Egyptian and Sumerian use of bases 12 and 60 respectively.

The point is that some standards do last for a long time. Even those that are ephemeral  get to influence other newer standards due to backward compatibility issues and economies of scale. The recent standards war between Blu-ray and HD DVD shows the politics and economic importance of choices of standards has not changed much since the days of VHS and Betamax. VHS (the poorer standard) won the earlier challenge. Blu-ray (which is more expensive but packs more data on disk than HD DVD) won the more recent battle.

So while we wait for the additional  leap second to usher in 2009, remember that the mechanics of time measurement, as well as a lot of other technical endeavors are based on standards whose choice might not be the most expedient. Such standards will however influence the technologies we use down the road.

Happy New Year folks!

DARPA Math Challenges

These are good times for Artificial Intelligence research; DARPA (the Defense Advanced Research Projects Agency) has put out a request for proposals on 23 contemporary mathematical challenges that has the potential for changing the state of AI as we know it.

The challenges are:

1	The Mathematics of the Brain.
2	The Dynamics of Networks.
3	Capture and Harness Stochasticity in Nature.
4	21st Century Fluids.
5	Biological Quantum Field Theory.
6	Computational Duality.
7	Occam’s Razor in Many Dimensions.
8	Beyond Convex Optimization.
9	What are the Physical Consequences of Perelman’s Proof of Thurston’s. Geometrization Theorem?
10	Algorithmic Origami and Biology.
11	Optimal Nanostructures.
12	The Mathematics of Quantum Computing, Algorithms, and Entanglement.
13	Creating a Game Theory that Scales.
14	An Information Theory for Virus Evolution.
15	The Geometry of Genome Space.
16	What are the Symmetries and Action Principles for Biology?
17	Geometric Langlands and Quantum Physics.
18	Arithmetic Langlands, Topology, and Geometry.
19	Settle the Riemann Hypothesis.
20	Computation at Scale.
21	Settle the Hodge Conjecture.
22	Settle the Smooth Poincare Conjecture in Dimension 4.
23	What are the Fundamental Laws of Biology?

I am partial to challenges 15 and 16 as they fall within areas of my doctoral research work. One result of this work is my paper detailing new perspectives on genotype-phenotype map geometries.

Challenges 3, 14, 20 and 23 also look very appealing. Please email me (dom_wilson at yahoo dot com) if you are looking for collaboration on any of the challenges.

Some ways you can go wrong with Evolutionary Computing

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The second, feeling of his tusk,
Cried, “Ho! What have we here
So very round and smooth and sharp?
To me ’tis mighty clear
This wonder of an Elephant
Is very like a spear”.
by John Godfrey Saxe

The state of Evolutionary Computing is somewhat like the blind men’s observations in Saxe’s poem above. A practitioner’s opinion of what EC is can be influenced based on the sub-area of their specialty; the development of sub-areas themselves being accidents of history.

The basic approach behind Evolutionary Computing algorithms is that they process symbols based on principles that mimic biological evolution mechanisms. The research behind them involves:

• Understanding the science of how they process symbols;

• Figuring out the engineering aspect of how to make such symbol processing problem friendly.

This post is the start to a series on some ways EC practitioners can and do go wrong.

1. One way you can go wrong is to give their symbol processing human attributes. Although EC algorithms can solve problems it does not mean they use the same problem solving techniques humans use. The most prevalent example of the anthropomorphizing of such algorithms is the Building Block Hypothesis. In my opinion the Building Block hypothesis is really an attempt at trying to decipher how EC algorithms (specifically Genetic Algorithms) work based on the common human divide-and-conquer approach.
2. Do not follow biological models too closely, only follow the principles. This advise also works for other biologically inspired optimization algorithms such as neural networks. If the reason for your use of EC is to understand biological phenomena then this advise is not for you. However if you objective is to get an EC algorithm to solve a problem on some digital hardware then you are most likely not constrained by the physics and chemistry of DNA and RNA interactions. As an analogy, consider that airplanes don’t flap their wings though birds do.

The Secrets of Success

This 3 minute video is so transcendental, I have to link to it and review it every month.

more about “The secrets of success“, posted with vodpod