Entries by John R

The Glossary entry for William of Ockham here at the site has a new section titled “In Other Words?”. This new section attempts to provide a nutshell explanation of William's original advice more accurately than the nutshell statement commonly used today. The advice in question is commonly referred to as Ockham's Razor. Here's the suggested new nutshell definition from the glossary entry.


"Always express things using the most general representation possible for the context in which the representation is being used."


The glossary entry goes on to clarify that this is just an attempted improvement over the current vague fashion statement, and it welcomes other suggestions.


 -djr

The book on the Netlab project often returns to the notion that learning is merely a form of adaptation and that, conversely, adaptation is merely a form of long-term learning. This, in turn, all fits under the umbrella notion that memory is behavior.

The idea that learning is adaptation is learning is forwarded as a possibility, mainly as a better means of discussing the concepts. This (in my opinion) provides a clearer and more converged understanding of how memory works in biological organisms. This could be very wrong, of course, so it's important to describe it properly. That way it, and not a straw man, can be critiqued. This article represents one such attempt to properly describe it. . .

Batesian Mimicry

Batesian mimicry is when a non-noxious/non-poisonous plant or animal projects the appearance of a poisonous plant or animal, allowing it to avoid being eaten by predators.

Those predators, goes the logic, which have partaken of the poisonous organism and survived, would have become very sick, and would have learned to avoid ingesting anything that appears to be that organism in the future. This will include those organisms who are not poisonous, but merely look, or act, like the poisonous organism.

Dennis Ritchie, the creator of the C programming language, died on Saturday after battling a long illness. The C programming language, arguably, changed the world. It can be found at the heart of most modern computer applications, operating systems, and successor programming languages.


   Dennis Ritchie
   Creator of the C programming language

   9 September 1941 — 8 October 2011


   


There's an obituary, and a very well researched history, at the Guardian



From his book, "The C Programming Language"

main()
{
        printf("hello, world\n");
}

Overview

This article provides a layman's-level discussion of neural network technology within the framework of a sketchy historical sequence. Neural networks are described while presenting an overview of just one of the many routs taken by the field in the last half-century or so.

It is not for those interested in a full history of neural networks (i.e., connectionism). It is just a quick backgrounder, which should suffice to give readers a little bit of perspective into how we got from "there" to "here." The actual history of this field is storied, and sometimes even checkered and controversial. I highly recommend to anybody who is interested, that you get a good book or two on the subject.

This entry will also serve as a place to accumulate links to resources and information on the subject of neural networks and their history at this layman's level.

Scientists at UC Berkeley have taken brain scans of subjects in an fMRI machine while they watched a movie clip. They then reconstructed the movie the subjects were watching using only the brain scan data, and a database of 18 million seconds of random video gleaned from the web.

First, they used fMRI imaging to measure brain activity in visual cortex as a person looked at several hours of movies. They then used those data to develop computational models that could predict the pattern of brain activity that would be elicited by any arbitrary movies (i.e., movies that were not in the initial set). Next, they used fMRI to measure brain activity elicited by a second set of movies that were also distinct from the first set. Finally, they used the computational models to process the elicited brain activity, and reconstruct the movies in the second set.

The amount of new understanding this could allow us to gather about mind-brain correlates and first person knowledge should be considerable. If this lives up to the hype, a lot of new research ideas should come out of it. Keeping fingers crossed here.




In the above clip - the movie that each subject viewed while in the fMRI is shown in the upper left position. Reconstructions for three subjects are shown in the three rows at bottom. All these reconstructions were obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli. The reconstruction at far left is the Average High Posterior (AHP). The reconstruction in the second column is the Maximum a Posteriori (MAP). The other columns represent less likely reconstructions. The AHP is obtained by simply averaging over the 100 most likely movies in the reconstruction library. These reconstructions show that the process is very consistent, though the quality of the reconstructions does depend somewhat on the quality of brain activity data recorded from each subject. [source: Gallant Lab (see resources below)]

It's called the fifth printing, but that is a bit of a misnomer. These days the actual physical printings are done in small runs in multiples of six. It is really more like the fifth “edit.”

In practice, corrections have been made to each new edit while its previous edit was running. After five edits, there have been hundreds of corrections. The issues addressed have included a lot of out-of-place commas, over-used hyphens, typos, and more grammar errors than I'd like to admit.


How do you know you've got the fifth (or better) printing?





Easy. Look at the bar code on the back cover. If the price code (the smaller bar-code, to the right of the ISBN number) says “90000” (no price specified), you have an older copy. If it reads “54795” (USD $47.95) you have the fifth or better edit.

In the book, Netlab Loligo, repeated calls are made for true random number generators (TRNGs) to be included in all CPUs, or at least in those that are intended for use in neural network applications. Naturally, I was very excited to see a headline about Intel having developed one with general purpose use in mind.

Intel's Low-Power “True” Random Number Generator

IEEE has an article about a new “true” random number generator from Intel that has been 10 years in development. Its primary advantage is that, while it is a true RNG, it operates entirely in digital mode using digital devices to obtain randomness from hardware. The slow, energy hogging, analog technology normally needed to glean randomness from Quantum phenomena has been eliminated. It has a few quirks, such as the need to force the outputs of its two mutex inverters high, and the seemingly unavoidable need to compensate using averaging techniques. I expand just a little on these quirks below.

In the spirit of not critiquing something without also offering, at least, a sincere attempt at a solution, I've forwarded a quick (if dirty) attempt at an “all logic gates” DTRNG (Digital True Random Number Generator) below. Only the equations were scratched out at the IEEE blog, I've since produced a circuit diagram graphic, which is included here as well.