Scientific Speed Reading: How to Read 300% Faster in 20 Minutes. (Photo: Dustin Diaz) How much more could you get done if you completed all of your required reading in 1/3 or 1/5 the time? Increasing reading speed is a process of controlling fine motor movement—period. This post is a condensed overview of principles I taught to undergraduates at Princeton University in 1998 at a seminar called the “PX Project.” The below was written several years ago, so it’s worded like Ivy Leaguer pompous-ass prose, but the results are substantial.
In fact, while on an airplane in China two weeks ago, I helped Glenn McElhose increase his reading speed 34% in less than 5 minutes. I have never seen the method fail. The PX Project The PX Project, a single 3-hour cognitive experiment, produced an average increase in reading speed of 386%. It was tested with speakers of five languages, and even dyslexics were conditioned to read technical material at more than 3,000 words-per-minute (wpm), or 10 pages per minute. The Protocol 1) Trackers and Pacers (to address A and B above)
A Non-Mathematical Introduction to Using Neural Networks. The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. And like many things, sci-fi writers have created a vast, but somewhat inaccurate, public idea of what a neural network is. Most laypeople think of neural networks as a sort of artificial brain. Neural networks would be used to power robots or carry on intelligent conversations with human beings. Neural networks are one small part of AI. The human brain really should be called a biological neural network (BNN). There are some basic similarities between biological neural networks and artificial neural networks. Like I said, neural networks are designed to accomplish one small task.
The task that neural networks accomplish very well is pattern recognition. Figure 1: A Typical Neural Network Neural Network Structure Programming hash tables use keys and values. 1200+ educational videos covering math, physics, chemistry etc. All made by Salman Khan. Khan Academy. <em>g</em>, a Statistical Myth. G, a Statistical Myth Attention Conservation Notice: About 11,000 words on the triviality of finding that positively correlated variables are all correlated with a linear combination of each other, and why this becomes no more profound when the variables are scores on intelligence tests. Unlikely to change the opinion of anyone who's read enough about the area to have one, but also unlikely to give enough information about the underlying statistical techniques to clarify them to novices.
Includes multiple simulations, exasperation, and lots of unwarranted intellectual arrogance on my part. Follows, but is independent of, two earlier posts on the subject of intelligence and its biological basis, and their own sequel on heritability and malleability. To summarize what follows below ("shorter sloth", as it were), the case for g rests on a statistical technique, factor analysis, which works solely on correlations between tests. The origin of g: Spearman's original general factor theory.