Often I like the material presented at www.informationisbeautiful.net, but a recent posting (http://www.informationisbeautiful.net/2012/rhetological-fallacies/) fell short. The posting is an ‘infographic’ (a stretch of the term) of Rhetological Fallacies. Nice. I have spent a bit of time looking at these types of things when doing Current Reality Trees / Future Reality Trees (part of Goldratt’s Theory of Constraints).
However, reading through the list, I started to see that this list was a bit incomplete or incorrect (at least in the explanations of the entries). I could even use entries in the list to refute other entries. For example:
“Gambler’s Fallacy: Assuming the history of outcomes will affect future outcomes”
Now, I know what the author was getting at, but the way this is stated is incorrect. The example given was:
“I’ve flipped this coin 10 times in a row and it’s been heads therefore the next coin flip is more likely to come up tails”
So in the example, the author is correct — those events are independent (where the probability of the subsequent flip is not dependent on the outcome of previous flips.. aka Bernoulli Trial). However, in the ‘definition’ of Gambler’s Fallacy, the author left out the critical word ‘independent’. If the events are not independent (e.g. the weather conditions observed at the start of an hour), then the future outcomes are different depending on the outcomes observed in the past. For example, we are more likely to observe rain at 2pm if we have observed rain at 1pm, with some measurable increase in probability.
Using the author’s own list, they fell prey to ‘Composition Fallacy: Assuming that a characteristic or beliefs of some or all of a group applies to the entire group’. (That sentence needs some work). Not all events are independent, and would lead someone to fall prey to a ‘Gambler’s Fallacy’, even though some events are independent.
There are a few others in this list which annoy me (such as ‘Appeal to Probability’), not because of the ‘idea’ behind it, but because of how it is vaguely expressed.
Analog, Web, Kindle simul-read
Last month I was in a back-and-forth ‘did you see the article..’ scenario with a colleague.
(You know you have been there.. if you don’t get what I mean, check out this Portlandia comedy sketch). Everything I sent him, he claimed he already knew about and decided to try to one-up me. Not having infinite monkeys to do all my reading for me (they were busy typing the works of Shakespeare on an infinite number of typewriters), I was almost always lagging behind him.
When I came across this LifeHacker post today, the video from Portlandia made me smile and I had to share it with him and the rest of the team. Of course, I realized that it was pointless to share it with him, since he obviously had already seen it..
True to form, I am reposting the article without having actually read it. ( I think it mentions something about that in the article, but not having read it fully, I am just guessing..)
Someone recently threw out a challenge to identify the artist of a particular painting /photograph which was hanging on the wall in the movie Iron Man 2. What she provided was this image:
- ‘unknown’ art from Iron Man 2
This appears to be two frames from the movie? I can’t recall seeing it in the movie, but ultimately it didn’t matter, since I wasn’t going to go down that path. (I assume she already tried that avenue, googling ‘iron man 2 office art’ or something similar).
With machine learning on the brain, I realized this was clearly a machine learning problem. Thankfully, I remembered I had already found an existing tool to do just this — take an input image and find other images which are the same. Here is where I first talked about TinEye
All I did was pop the image into photoshop and chop off the right side, since I expected to match to a head-on image of the art and not a combined graphic. That being done, I uploaded the graphic to TinEye and viola it returned:
From this website, it appears it is a 1997 photograph of Tonnay, France’s Esser River by German photographer Ursula Schulz-Dornburg
. It looks like the photo was in Lichfield Studios in Notting Hill.
I spent a second trying to validate the information, but the artist’s site doesn’t have all her works posted.
Still, I think there is fairly high likelihood that I have found the artist.
WAY TO GO MACHINE LEARNING!
(BTW, it took me way longer to write the blog post than to do the search.. )
I came across this site.. http://www.tineye.com/ didn’t explore it very far, but I am curious to dig into it and see how it works..
Lumosity - Brain Training website
One of the things I did prior to AMP (and have started to do again) is to try to stretch my brain in ways which are different than what I encounter at my job. In the past I have periodically read brain-teasers or thought exercises, but this time I went a different route.
I am not sure how I was turned on to this site, but somehow I ended up at Lumosity. They have a free trial to let you explore the site and their games, which gives you a good sense of how their ‘brain training’ actually works. I am not sure I completely buy their propaganda, but I can tell you that it is both:
a) a diversion (i.e fun) and
b) a boost in confidence about your mental acuity (through improvements seen in the games)
Clearly a big jump in one’s improvement is due to practice and a deeper understanding of how the games are structured. However, I found a few games very interesting. One was the fast-food name/order matching game, which attacks one of my weaknesses – name recall. I found that after focusing on that game, I was more apt to actively attempt to remember someone’s name when I meet them, and therefore have a higher probability of remembering the name.
Again, I am not completely sold on ‘brain training’, but I think it does have some positive benefits. Give it a try and see what you think.
Amazingly cool. The interactive 360 degree view is quite well done, and certainly a “time waster”.
The Photopic Sky Survey is a 5,000 megapixel photograph of the entire night sky stitched together from 37,440 exposures. Large in size and scope, it portrays a world far beyond the one beneath our feet and reveals our familiar Milky Way with unfamiliar clarity. When we look upon this image, we are in fact peering back in time, as much of the light—having traveled such vast distances—predates civilization itself
Thanks to Flowing Data for the post.
So I have finally broke down and succumbed to the pressures of Social Media and set up a blog. I feel a bit dirty.. need to go wash my hands..