Monthly Archives: May 2013

Collected Quotes Apr-May 2013

My main page on quotes is Quotes to Make You Think. Additional ones can be found under the Quotes tag.


Everyone sees what you appear to be, few experience what you really are. — Niccolò Machiavelli

Do not look for approval except for the consciousness of doing your best. — Bernard M. Baruch

When I was young, I used to admire intelligent people; as I grow older, I admire kind people. — Abraham Joshua Heschel

His books were the closest thing he had to furniture and he lived in them the way other men live in easy chairs. — Laura Hillenbrand

Thanks to Janice, heirloomgrit.

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Your Guesses On My Origin Story

I have written before on this blog on people trying to figure out my skin color or my accent. These are details which make very little sense. My skin is not dark enough to be black and not pale enough to be white. The weird ways I pronounce words provides few clues to where I was raised.

So I am re-reading The Great Gatsby. One of the things that entertained me were the various scenes of people trying to figure Gatsby’s origin story through gossip and inference. This makes me curious. I know people similarly talk about me because they eventually ask. So review details you know about me that make little sense. What are your inferences? I’ll let you know yes or no. Plus I’ll fill in the gaps.

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Accounting Predictions

In my Prediction Accountability, I ranted on how no one really knows whether predictions are accurate and ended with it really does not matter because no one is going to really stop using these services because they are usually wrong. Basically, I thought it futile to even try. In retrospect that is probably the perfect reason to do it.

So I came up with a scoring system:

    • Good Recommendation= 3 points
    • Not interested= -1 points
    • Wishlist/Queue= -2 points
    • Dislike= -3 points

Would you score these differently? Why?

My reasoning goes something like this. Something I agree I should watch should equal the inverse number of points of something I know I will dislike from previous experience. Anything I am not really interested in definitely is not a win, so it should be a negative, but not too close to a dislike. Suggesting something already on that company’s records that I am interested in wastes my time because they already know I am interested in it, so lose two points.

First pass, Amazon sent me an email today saying,

Are you looking for something in our <x> department? If so, you might be interested in these items.

One item I have thought I should watch based on TV ads but not put on my wishlist yet, so I agree with Amazon, I might be interested in it. It gets three points. (3) Five items already were in my wishlist so that is negative two points each. (3 -10= -7) One item is the 6th season of a television series I have only seen part of the first season and not gotten around to completing even that so not interested and negative one point. (-7 -1= -8) Another item is the 3rd season of a TV series I where I have not watched even the first yet. If the recommendation had been the first, then I would count it as a good one so instead I’ll award halfway between good and not interested (-8 + 1 = -7) Out of eight items in the email, the score is a -7. That is just one email. I track this for a couple months and see where it goes. And do the same for Netflix.

I think this exercise points out the possibility that these “predictions” are basically nudges more to buy something.

If your Learning Management System vendor claimed they have a 90% plus correct prediction rate for whether students will fail a class, then how would you assess it? The obvious start would be track the predictions for classes but do not provide the predictions to instructors. Compare the predictions to actual results. Of course, these things are designed around looking at past results. What is the investment company statement they have to put in so they do not get sued for fraud? Oh, right, “Past success does not guarantee future performance.” So I would not rely too much on just historical data. I would want a real world test the system is accurately working.

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Prediction Accountability

The technology buzzword standard for prediction appears to be Netflix and Amazon. Everyone wants to get to where they make recommendations customers will buy. But are these predictions any good?

Out of the slew of emails you get from Amazon, how percentage do you actually buy? How many do you sneer at it and hit delete in disgust that they could get it that wrong? For me, the latter is more common than the former. Certainly it is not from a lack of data, I buy more off that site than I do all bricks and mortar stores excepting groceries combined. (And that makes me re-think how I buy groceries.) Maybe Amazon has too much data that confuses it mixed with correct data. I look things I have no interest in buying such as someone mentioned having problems with a product. Though I have to question Amazon recommending I buy the camera I bought from them a couple months prior.

Netflix really is not any better. Their top 10 recommendations change weekly for me. In my current top 10, one was already rated 5 stars. Another four were already in my queue. The remaining five predicted I would like them between about 3.0 and 3.3 stars. That is out of five. There are 27 items in my queue with higher predictions than these.

Before I start tracking these predictions to gauge how effectiveness, do I even really care? Am I going to stop consuming from companies that overstate their claims? Or should I close my ears when clueless people spout the prediction buzzword? Not really. No. Guess that is what I am left doing.

I think the standard comes not from them being any good. Instead decision makers are aware of them, so they understand wanting to emulate them.

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Frying the Brain

Weird day yesterday. The security guard at work decided I have a girlfriend because I rushed to my car instead of hanging around to chat. Of course, he says not to have children because they ruin everything.

Next, a woman I only really know from conversations on Tumblr suggested a couple Helen Fisher books. I told her about Loneliness where Cacioppo says social isolation causes pain in similar parts of the brain as physical pain. Fisher talks about love occupying the same parts of the brain that cocaine manipulates.

If the below video does not work, then try Helen Fisher: The brain in love.

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Movie Before Book? Or Book Before Movie?

She asked what I was reading. So I told her Dust of Dreams and showed her the book. She said I am smart. This launches into a weird conversation culminating in the question. She asked, “Is it weird I watch the movie before reading the book?” A little over a year ago a friend asked me the same question.

My response was I have done the same thing. I pointed out I watched the first three Harry Potter movies without reading the books. But, then I really was confused about the story of the third one enough, I ended up getting the books and reading them before catching up on the movies. But that is not really a good example.

So what do you do? Movie first? Book first? Both ways depending on mood?

My thoughts…

    • Some movies made no sense to me, so I went back to read the book, and watched the movie again. The 2nd time around, the movie made much more sense. So often I try to read the book first, so I can enjoy the movie without feeling lost.
    • Some stories seem not that interesting. Why invest 10-20 hours reading a book when I can just watch a 2-3 hour movie?
    • Books allow me to give my own visual identity to characters, places, and things.
    • Watching the movie gives a director’s visual identity to characters, places, and things.
    • For me, movie first or middle relies on the director’s visual identity instead of my own. I guess it depends on whether I like the director’s take over my own?

Anyway, I also showed her Goodreads as she was interested in what I thought about a book. (I have not read it. But maybe it can hook her up with friends who have?)

Oh, and it is rather intelligent to call others smart. It feels nice.

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