Posted by Tom Moertel
Wed, 02 Nov 2011 02:55:00 GMT
Back in 2007, I repaired an aging and fairly obscure A/V receiver that had lost the ability to respond to its remote control. This I did by re-soldering some hard-to-find solder joints that had broken on its circuit board.
On the chance that someone else had a similar problem, I posted some instructions and photos on my blog. I didn’t think much of it at the time.
But since then, every week or so, another comment shows up, thanking me for writing it. Some typical examples:
Fixed my Kenwood V6030D 10 minutes ago. Life is good again, Thanks mate….
Ditto! Worked on my VR-209 like a champ! Thanks!
Thank you for this posting, which I stumbled upon when I was researching the problem of my remote control no longer working for this receiver (VR-507). These pictures were invaluable to locate the faulty pins. (They sure are small.) Re-soldering them restored full functionality to the receiver and the original remote control. Good job!
Thanks, Fixed my KRF-8010D with this, been 5 years fighting with remote working now and then.
There are now about 60 comments like that. I never would have imagined that 60 people would have read the post let alone get out a soldering iron because of it. But they did! And it helped them!
Now, every time I’m feeling down, I Google up that post and read the thank-yous. It cheers me up.
So here’s the lesson: Write it down. If you’ve figured something out, even if it seems unimportant, write it down. Maybe someone else will find it helpful. Maybe a lot of someone elses will find it helpful.
You never know. It might even cheer you up someday.
Posted in interesting stuff
Tags helping, lessons, surprising, writing
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Posted by Tom Moertel
Fri, 04 Feb 2011 03:32:00 GMT
Yesterday, I offered some completely uninformed speculation about why Google may have accused Microsoft of using Google’s search results for Bing. My hunch was that Google’s real concern wasn’t copy-catting but something else: Bing’s increasingly competitive search results, powered in good part by “clickstream” data that Microsoft captures by monitoring users as they surf the web with its software.
I wrote:
[M]aybe what’s really going on is that Google is trying to focus the public’s attention on how Bing is getting its relevance evidence. If it turns out that Bing is watching over people’s shoulders as they surf, I can imagine a lot of people, including citizens’ groups, raising a fuss over it. Maybe some politicians take notice. You see where I’m going?
Today, Google’s Matt Cutts offered his thoughts on the Google-Bing debate. After the expected rehash of the copy-catting evidence, he introduces something new:
I don’t think an average consumer realizes that if they say “yes, show me suggested sites” that they’re granting Microsoft permission to send their queries and clicks on Google to Microsoft, which will then be used in Bing’s ranking. I think my Mom would be confused that saying “Yes” to that dialog will send what she searches for on Google and what she clicks on to Microsoft. I don’t think that IE8′s disclosure is clear and conspicuous enough that a reasonable consumer could make an informed choice and know that IE8 will send their Google queries/clicks to Microsoft.
I’d now say that my speculation isn’t completely uninformed. (I’m counting the above as +15 dB of evidence toward my hypothesis being true. Of course, I’m not telling you what my prior probability was. ;-)
Posted in interesting stuff
Tags bing, google, microsoft, search, speculation
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Posted by Tom Moertel
Wed, 02 Feb 2011 15:08:00 GMT
The recent spat between Google and Microsoft over Bing’s search results is entertaining as it is, but what it could really use is some completely uninformed speculation. Like this.
The story so far is that Google accused Microsoft of using Google’s search results for Bing. As evidence of this claim, Google seeded its search index with nonsense terms that appeared nowhere else on the Internet. For each of these terms, Google provided one search result, pointing to some legitimate page on the Internet. Then some Googlers did Google searches on those terms using new Windows laptops with IE8 and the Bing Toolbar enabled. Later, when searching on Bing for those same nonsense terms, Bing provided the same search results, results that could have come only from Google’s search engine.
Busted!
Or could there be another explanation? Another possibility, one that Google doesn’t seem to have considered (more on this later), is that Bing uses IE8 and the Toolbar to observe user behavior and, from this observational evidence, infer page relevance in general, not just when users are surfing Google’s search results. That is, if Microsoft sees you reading a page and notes that you typed “potato chips” into a form a bit earlier, it could take the relationship as a tiny piece of evidence that the current page is relevant to potato chips.
Now, I don’t know if Bing actually infers page relevance from user behavior, but if you’re watching what users are doing as they surf, it seems obvious that you could mine those observations for relevance evidence. Google uses its celebrated PageRank algorithm to mine relevance evidence from the web’s link graph, but Bing needs some other way. Why not watch users and see what they think is relevant?
If Bing does mine user behavior for relevance evidence, that puts Google’s sting operation in a new light.
In Google’s experiment, the only evidence of what was relevant to its nonsense search terms was the Googlers’ own surfing behavior. When they clicked on their own fake search results and surfed the pages those results pointed to, they sent Microsoft evidence that those pages were relevant to the earlier nonsense terms. And Bing recorded this evidence.
But, because those terms were nonsense, nobody on the Internet was searching for them, let alone visiting pages that might be “relevant” to them – except for the Googlers. Therefore, the only thing Bing had learned about those terms was what the Googlers had told it through their surfing behavior. It was denied all other evidence.
So, when somebody searched Bing for those terms, Bing tried to figure out what results were relevant and found those tiny pieces of evidence gathered from the Googlers’ surfing. And, in absence of other evidence, those tiny pieces won out, and Bing coughed up the most relevant pages, which just happened to be the fake results from Google’s own search engine.
The point is that Google’s sting operation would have had the same outcome, regardless of whether Bing was trying to borrow Google’s search results specifically or trying to mine user behavior in general. To put it in Bayesian terms, the following likelihood ratio is pretty close to one:
P(Google’s sting “busts” Bing | Bing tries to reuse Google results)
divided by
P(Google’s sting “busts” Bing | Bing mines user behavior in general)
But that’s not the interesting thing.
The interesting thing is that there are folks at Google who know how to use Bayes’ rule. And my guess is that they pointed out to their fellow Googlers that the sting, as evidence of Bing deliberately trying to use Google results, wasn’t persuasive. But Google went public anyway.
Why?
Here comes the completely uninformed speculation I promised.
Maybe Google is worried that Bing’s relevance evidence, if it comes directly from users, might in time become more reliable than Google’s own PageRank-derived evidence, now that SEO and content farms have thrown so much noise into the calculations.
So – more speculation – maybe what’s really going on is that Google is trying to focus the public’s attention on how Bing is getting its relevance evidence. If it turns out that Bing is watching over people’s shoulders as they surf, I can imagine a lot of people, including citizens’ groups, raising a fuss over it. Maybe some politicians take notice. You see where I’m going?
If monitoring user behavior lets Bing do an end-run around PageRank, Google might want to shut that play down by appealing to the referees. But Google can’t be seen as trying to take a competitive advantage away from Microsoft. So, what if Google put Microsoft into a position where it had to defend Bing’s results, and the only way it could make a credible defense was by admitting, in the public spotlight, it was spying on its users?
Anyway, it’s just completely uninformed speculation. What else have you got?
Posted in interesting stuff
Tags bing, google, microsoft, search, speculation
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Posted by Tom Moertel
Sun, 23 Jan 2011 22:11:00 GMT
Writing for The New Yorker, Ken Auletta offered his insights about Larry Page taking over for Eric Schmidt as Google’s C.E.O. Auletta’s article is full of the unexpected, but what surprised me most was this claim about one of the obstacles Larry Page must overcome to lead Google:
He will have to rid himself of a proclivity most engineers have: they are really bad at things they can’t measure.
It’s hard to dispute that engineers are really good at things they can measure. But are they especially bad at things they can’t?
And isn’t everybody “really bad” at things they can’t measure? (Try running through a forest with your eyes closed. Let me know how it goes.)
So what makes Auletta single out engineers for this failing? Is there a grain of truth in there somewhere?
Posted in interesting stuff
Tags engineering, measurement, society
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Posted by Tom Moertel
Mon, 20 Dec 2010 14:35:00 GMT
Recently, I asked how much evidence was contained in a single coin toss:
After seeing the outcome of this single coin toss [which came up heads], how much more should you believe my claim that the coin always comes up heads, compared to what you believed before the coin toss?
Many people submitted answers here on the blog and also on Hacker News, where the question led to an interesting discussion. Before I get to my answer, however, let’s talk about the question.
I like this question because it’s simple yet offers ample opportunity to explore something valuable but often unappreciated: weak evidence. Here we have the evidence of a single coin toss that comes up heads. That’s not much to go on. But it is something, and we would be wrong to ignore it.
Nevertheless, I’ve witnessed many experts ignore weak evidence, doctors in particular. The problem with ignoring weak evidence is that it’s abundant. Think of it as “long-tail” evidence: there’s so much of it that even if each piece is worth only a tiny bit, as a whole it’s worth a ton. So, if you don’t know how to mine it, you’re leaving a ton of potential knowledge buried within that long tail.
Interpreting evidence (weak or otherwise)
So, let’s talk about the evidence of our coin toss. My question was how much your prior beliefs about my claim (that the coin always comes up heads) should be swayed by the outcome of that single coin toss. I’m not asking about the coin, but about your state of knowledge about the coin and, more specifically, how that state should change in light of the coin toss.
There are many ways to approach the question, but to start, let’s define some notation. We’ll let P(X) denote our degree of belief in the proposition X, some statement that can be either true or false. Let P(X) = 0 represent our absolute conviction that X is false, and P(X) = 1 our absolute conviction that X is true. When P(X) = 1/2, it represents that we have no reason to believe that X is more likely to be true than false. If we know nothing about X, then, our default value for P(X) must be 1/2.
Let’s be clear that X is either true or false, regardless of what we think. Our X represents some real property of the universe, and the universe doesn’t alter itself just because our thoughts about it change or because we do a mathematical calculation that we think describes it in some way. That’s why we write P(X): the P notation represents that we’re not talking about X itself but rather our belief in X. The P(·) can be read as “the probability of” (or “the plausibility of”), so P(X) represents “the probability of X.”
Instead of some placeholder X, let’s define some real propositions that relate to our coin toss:
- S: the coin is a special coin that always comes up heads when tossed
- H: we observe the coin to come up heads in a coin toss
- T: we observe the coin to come up tails in a coin toss
- K: our prior knowledge about the coin, the universe, and everything
That last one, K, is important. It’s a massive proposition, the logical conjunction of many smaller propositions that represent everything we already know – that the Earth is approximately spherical, that gravity pulls things toward one another, that the author of this blog post is exceedingly handsome, and so on. This massive proposition is often left out of probability calculations with the understanding that it’s implied, but I’m going to include it because it makes our assumptions more explicit.
Now, the probabilities we’re interested in:
- P(S|K): our belief that the coin is a special heads-always coin, in light of our prior knowledge
- P(S|H∧K): our belief that the coin is a special heads-always coin, in light of our prior knowledge and the knowledge that we observed the coin to come up heads in a coin toss
I’ve introduced some new notation. The vertical bar (|) is read as “given” and can be interpreted to mean “in light of the following.” The ∧ operator is new, too. It represents logical conjunction and can be read as “and.” For instance, A∧B represents the proposition that both propositions A and B are true; and P(S|H∧K) represents the probability that S is true, in light of both H and K being true.
The first probability, P(S|K), is sometimes called our prior probability because it represents how much we believe S before considering new evidence, when we have only our prior knowledge K to go on. The second, P(S|H∧K), is sometimes called our posterior probability because it represents how much we believe S after considering the new evidence H, too.
Now, how do we update our prior beliefs about the coin to arrive at our posterior beliefs, in light of having witnessed the coin toss come up heads? Let’s think about this updating process for a moment.
Our new beliefs about the plausibility of some proposition X, in light of new evidence E, ought to be the same as our prior beliefs about X, but adjusted to account for observing the new evidence. The adjustment factor, according to Bayes’ rule (and justified by Cox’s theorem), is given by a quotient: the plausibility of observing the new evidence, given that X is true, divided by the plausibility of observing the new evidence in any case. (And, of course, all of these adjustments occur in light of our prior knowledge K about the universe in general.)
As a pseudo-English equation, Bayes’ rule is surprisingly intuitive:
(new plausibility) = (old plausibility) × (evidence adjustment),
or, equivalently, using our probability notation:
P(X|E∧K) = P(X|K) × [ P(E|X∧K) / P(E|K) ].
The evidence adjustment itself may not seem so intuitive, but it does make sense. It is the quotient of two plausibilities: that of observing the evidence E given that the proposition X is true, and that of observing E regardless. You can think of the adjustment as quantifying how well the proposition uniquely explains the evidence.
For example, if the proposition being true is the only reasonable explanation for the evidence, observing the evidence ought to provide strong support for the proposition. If rain is the only way that every house in the neighborhood gets wet at the same time, knowing that every house in the neighborhood is currently getting wet provides strong support to the proposition that it is raining. On the other hand, knowing that somebody is carrying an umbrella provides weaker support because things besides rain can also explain that evidence, the anticipation of rain, for one.
Getting back to my original question, I asked how much more you should believe my claim S (that the coin always comes up heads) after observing the evidence H (that the coin did come up heads when you tossed it). That is, I’m asking you to characterize the new plausibility in light of the old. The relative change between the two is given as follows:
[ (new plausibility) / (old plausibility) ] – 1
This quantity, we can see from Bayes’s rule, is merely our evidence adjustment less one. But to calculate this value, we’ll first need the probabilities the calculation is likely to require. Let’s see, what do we already know?
Representing our knowledge
First, our prior knowledge K informs us that a coin toss is understood to have only two potential outcomes: heads and tails. A coin toss is considered invalid, for example, if the coin stands on edge or is tossed into a chasm. Therefore, a coin toss must result in heads or tails:
P(H∨T|K) = 1,
and getting tails is the same as not getting heads:
P(T|K) = P(¬H|K).
More notation: we use ¬ to denote “not” and ∨ to denote logical disjunction, read “or.”
Next, we know that if the coin is special, it will come up heads when tossed:
P(H|S∧K) = 1.
But what if the coin is not special? In that case, do we have any reason to believe it is more likely to come up heads than tails, or vice versa? No. So, we must consider each proposition equally likely:
P(H|¬S∧K) = P(¬H|¬S∧K).
Further, because there are no other possibilities – the coin must come up heads or tails – their total probability must be one:
P(H|¬S∧K) + P(¬H|¬S∧K) = 1.
If the two probabilities are equal and must sum to one, each must be one half:
P(H|¬S∧K) = P(¬H|¬S∧K) = 1/2.
At this point, you may be tempted to object that our beliefs, being overly subjective, have led us to an unjustified conclusion. Even if the coin isn’t special, how can we say it has an even chance of coming up heads (or tails), in other words, that it’s fair? What justifies this claim?
In truth, we can’t justify it. But we didn’t make it, either.
Remember, we are not making any claims about the coin. Our equations make claims only about our knowledge of the coin. If the coin isn’t special, maybe it is still biased somehow. Even so, we have no reason to believe it is more likely to be biased one way or the other. Therefore, by symmetry, we can assign only one degree of belief to either proposition H or ¬H, and that is 1/2.
The evidence-adjustment factor
With our prior beliefs represented as probability equations, let’s get back to computing that evidence adjustment.
(evidence adjustment) = P(H|S∧K) / P(H|K)
The numerator on the right-hand side we already know: P(H|S∧K) = 1.
The denominator, P(H|K), we do not. We must find some way to break it into terms that we do know.
The nice thing about propositions, like H, is that we can use Boolean logic to manipulate them. So, let’s break H into pieces that are more likely to be useful:
H = H∧(S ∨ ¬S) = (H∧S) ∨ (H∧¬S).
What I did was split the proposition that the coin comes up heads into a disjunction of two mutually exclusive propositions: that the coin comes up heads and is special, or that the coin comes up heads and is not special. That first term of the disjunction, however, is redundant: if a coin is special, our prior knowledge already tells us that it must come up heads; therefore, we can simplify H∧S to S. Now we have,
H = S ∨ (H∧¬S), given K,
and, therefore,
P(H|K) = P((S ∨ (H∧¬S))|K).
We can break up the disjunction on the right-hand side using the sum rule for probabilities, which is given as:
P(A∨B) = P(A) + P(B) – P(A∧B).
Since our disjunction is of mutually exclusive propositions, the final term of the sum-rule expansion drops out; therefore,
P(H|K) = P(S|K) + P(H∧¬S|K).
Now let’s crack that new final term, P(H∧¬S|K). To do so, we’ll use the product rule for probabilities:
P(A∧B) = P(A|B) P(B) = P(B|A) P(A).
So:
P(H∧¬S|K) = P(H|¬S∧K) P(¬S|K).
And, already knowing that P(H|¬S∧K) = 1/2, we can simplify the right-hand side:
P(H∧¬S|K) = P(¬S|K)/2.
And substituting this reduction back into the equation for P(H|K) gives,
P(H|K) = P(S|K) + P(¬S|K)/2.
We can further simplify the equation by noting that P(S|K) + P(¬S|K) must equal 1 and, therefore, that the ¬S term can be rewritten in terms of S to give,
P(H|K)
= P(S|K) + (1 – P(S|K))/2
= (1 + P(S|K)) / 2.
Now, to bring it all home, let’s plug these values into our evidence-adjustment formula:
(evidence adjustment)
= P(H|S∧K) / P(H|K)
= 1 / P(H|K)
= 1 / [(1 + P(S|K)) / 2]
= 2 / (1 + P(S|K)).
And that’s our evidence-adjustment factor. Now, what does it do?
Adjusting our beliefs in light of the new evidence
To better understand what the evidence adjustment does, let’s recall the original belief-adjustment equation:
(new plausibility) = (old plausibility) × (evidence adjustment)
So the adjustment factor nudges our initial degree of belief, whatever it may be, one way or the other, depending on the evidence. To see the effect of this nudge for various initial degrees of belief, consider the following plot:
Looking at the plot, let’s see if the nudge agrees with our intuition. First, if we were absolutely convinced that the coin is (or is not) special, no amount of evidence should sway our beliefs. Looking at the plot, we see that when our prior probability is 0 or 1, so is our adjusted (posterior) probability, exactly what we expected.
But what if our initial knowledge is complete ignorance about the coin being special? In that case, upon seeing the coin toss, our prior probability of 1/2 gets nudged to the posterior probability of 2/3 – toward the belief that the coin is indeed special. Again, it’s what we would expect.
In fact, the evidence adjustment is always going to push us toward confirming the belief that the coin is special because the evidence supports that belief. The force of that push, however, depends on how surprising we find the evidence, that is, how much it challenges our prior beliefs. The following plot shows this relationship:
Note that the evidence provides the strongest push – a factor of 2 – when our prior knowledge makes us doubt most strongly that the coin is special. On the other extreme, when we are already convinced that the coin is special, observing that the coin comes up heads when tossed isn’t surprising at all, and correspondingly the evidential push of that observation is nothing: an adjustment factor of unity.
Answering the original question
Finally, with our evidence adjustment well in hand, we can answer the original question: After seeing the outcome of this single coin toss, how much more should you believe my claim that the coin always comes up heads, compared to what you believed before the coin toss?
The answer, we reasoned earlier, is the evidence adjustment less one:
(relative plausibility increase)
= [evidence adjustment] – 1
= [2 / (1 + P(S|K))] – 1
= [2 / (1 + (prior plausibility))] – 1
= (1 – (prior plausibility)) / (1 + (prior plausibility))).
So, if we let p represent our prior degree of belief that the coin is a special, heads-always coin, we should be
100% × (1 – p) / (1 + p)
more confident in our belief after seeing the coin come up heads when tossed.
And that’s the answer.
But there are other ways of arriving at it. One of the more convenient is to use odds instead of probabilities. But let’s save that discussion for next time.
Posted in interesting stuff
Tags bayesian, coin, evidence, odds, probability, reasoning, toss
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Posted by Tom Moertel
Fri, 11 Apr 2008 15:58:00 GMT
Via Chris:
$ history | awk '{print $2}' | sort | uniq -c | sort -rn | head
196 git
110 l
102 cd
70 make
34 darcs
30 pushd
23 ssh
23 m
23 ls
20 rm
The l and m commands are aliases:
Posted in interesting stuff
Tags life, memes, programming
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Posted by Tom Moertel
Sun, 15 Jul 2007 04:40:00 GMT
Yesterday, in the middle of a beautiful, sunny afternoon, a storm
front came out of nowhere and cut across southern Pittsburgh. I was
in my backyard at the time, and I could tell by the sudden icy wind
that something unusual was happening. A few moments later I heard a
sharp thwack! as something struck my deck and bounced into the
yard – a nickel-sized hailstone. Then, another one. Thwack!
Then the hail fell like rain – thwack! thwack! thwack! – faster
and faster, until the air was filled with icy missiles, some bigger
than quarters, streaking to the earth around me. As the
deluge intensified, I was deafened by the sound of a million berserk
carpenters hammering away at my home, my deck, and my garden.
In a few short minutes it was over. The ground was littered with ice,
leaves, and small branches. My home’s window screens had holes
punched through them. My garden was torn to shreds.
If you haven’t experienced the combined effects of hailstones and high winds,
count yourself lucky.
Photos
If you wonder what the storm and its aftermath looked like, I put a
set of hailstorm photos on
flickr.
My neighbor and fellow programmer Casey
West had his new iPhone handy and snapped
some great photos,
too.
I also captured the storm on my front-yard webcam, which looked into the
oncoming hail. For reference, here is what the webcam saw just before the
storm:
If you look in the upper-right corner of that photo, you can see the
first few hailstones streaking into the frame.
Then, the storm hit in force:
The wind was so strong that it drove the hail horizontally at times.
When the storm ended, just 3 and a half minutes later, the ground
was littered with ice. In the photo below, that’s not snow in the
driveway:
In the garden, it was easier to see just how much ice
the storm had dropped upon us:
The garden, itself, was shredded:
Amazing.
Posted in interesting stuff
Tags damage, garden, hail, hailstorm, photography, storm
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Posted by Tom Moertel
Tue, 27 Mar 2007 03:23:00 GMT
Recently, Reg Braithwaite wrote about the ad hominem
fallacy. His
article reminded me that debating – the art and science of
constructing sound arguments in the face of opposition – is a valuable
skill.
Though many online debates devolve into name calling and other
foolishness, most are rich opportunities to learn – if learning is
your goal. So make learning your goal.
Look at each debate you enter as a chance to discover something new.
When you participate, assume the other participants are good people,
who deserve an honest argument from you. If you learn the
fundamentals of logic and clear thinking, it’s easy to stay in the
debate, contribute, and increase your (and their) chances to learn.
Many people, however, overlook the opportunity to learn in order to
pursue the opportunity to win. What a mistake. If the price of
winning is ignorance, can you afford the purchase?
Therefore, when I debate, I make considerable efforts to be rational
and reasonable. Even so, it’s hard not to say the wrong thing when
a debate gets heated. To help keep me in the right frame of mind,
I use a simple, idealized debating model.
I wrote about this model six years ago on Kuro5hin, but it’s worth
revisiting. The model is not magic, and I doubt it’s
novel, but it has helped me. Maybe it can help you, too.
Here it is:
- The motivation for debating is to arrive at a better understanding of reality (i.e., the truth).
- All participants share this motivation.
- All participants are intelligent, rational human beings, each fully capable of drawing logical conclusions from facts.
- The reason for disagreements is not because participants want to disagree but rather because their understandings of the facts differ.
- Therefore, the objective of debating is to share information until the participants can bring their understandings of the facts into alignment, which will allow for agreement or at least consensus.
I know that the model and reality part ways at the outset. When I
debate, however, I pretend the model is reality. I do this is
because it allows me to participate earnestly. Forcing myself to make
meaningful contributions increases the chance that debates will end
in somebody learning something useful.
Nevertheless, debates often go wrong. That’s the second reason I use
the model. It gives me something to compare real debates with so that
problems are easy to spot and classify. If a key participant in a
debate makes personal attacks or refuses to accept demonstrated facts,
for example, the problem is easy to see and classify: It is a debate
killer. Time to move on.
Another tool that has helped me stay on track is D. Q. McInerny’s
wonderful introduction to logic, Being Logical: A Guide to Good
Thinking.
This short book, inspired by Strunk and White’s classic, tiny text on
writing, The Elements of
Style,
introduces the foundations of logic, explains how to construct sound
arguments, and prepares you to recognize and avoid illogical thinking
(fallacies). The book is a pleasure to read and makes a handy reference (I
keep mine within arm’s reach). If you need a quick refresher on clear
thinking, add this delightful book to your toolbox.
Don’t forget: Every debate is an opportunity to learn. So when
debating, make learning your goal. And if you learn to debate, you
will have an easier time debating and learning.
Debate to learn. Learn to debate.
Posted in interesting stuff
Tags arguments, debating, learning, life, logic, people, thinking
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Posted by Tom Moertel
Fri, 07 Jul 2006 20:39:00 GMT
Last week I was vacationing with the in-laws in upper Michigan.
They live on Lake Huron in a wooded area. One of their neighbors
pointed out an unusual growth on a nearby wooden fence:
“matchstick moss,” he called it.
Intrigued, I grabbed my camera and tripod
and took some pictures.
Man, are these things weird. And tiny. For scale, that’s a woman’s
wedding band in the pictures below.
Some recent
Googling revealed
that matchstick moss not moss at all but rather
lichen, in particular “British
soldier lichen”, Cladonia cristatella.
Here’s some more information on the fascinating little guys:
Now that’s interesting stuff!
Posted in photography, interesting stuff
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Posted by Tom Moertel
Thu, 20 Apr 2006 21:36:00 GMT
Last year, I wrote about some beekeeping stories on Kuro5in.org. Interesting stuff.
The stories, like the bees, stopped when winter came. Now that spring is here, the bees have emerged from their hive, and the beekeeping guy, xC0000005, has resumed his writing.
At this time, he has a story on the front page: Tales from the Hive – Birth of a Package. But the good stuff to come will appear in his diary. Keep an eye on it.
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