SGU Episode 549
|This episode needs: transcription, proof-reading, formatting, links, 'Today I Learned' list, categories, segment redirects.
Please help out by contributing!
|How to Contribute|
|SGU Episode 549|
|January 16th 2016|
|SGU 548||SGU 550|
|S: Steven Novella|
|B: Bob Novella|
|C: Cara Santa Maria|
|J: Jay Novella|
|E: Evan Bernstein|
|Quote of the Week|
|The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them.|
|Sir William Henry Bragg, Nobel Prize for Physics, 1915|
- Two celebrity deaths: Alan Rickman and David Bowie
You're listening to the Skeptics' Guide to the Universe, your escape to reality.
What's the Word (3:21)
S: Cara, we're gonna start this week with What's the Word.
C: Yes, and before we jump into this week's word, I wanted to revisit last week's word. There was a follow up from a listener named Chaz from Roly, North Carolina. He's actually a chemical engineer, and he wrote to correct a pretty important point from last week's definition of the word fugacity.
He said, quote,
“You say in the show that fugacity is the pressure a real gas would need to be at to satisfy an equation as an ideal gas. This isn't true, and actually doesn't much make sense with the thermodynamic relations that I know. You've got it backward. Fugacity is not the pressure you would need to bring a real gas to. Fugacity is the pressure of a hypothetical ideal gas that would correspond to the real gas as it exists at a given temperature, pressure, and composition. There's no change needed to be imposed on the real gas.
So as I was – and I mentioned last week, in my kind of very minimal understanding of chemistry, as I was talking about the relationship between the real gas and the ideal gas, and how that feeds intno the definition of fugacity, I mixed 'em up. So thank you for that correction Chaz. That's one thing that I have to say I love about doing science communication, especially on a show like Skeptic's Guide, because it has such a robust listenership. And when we make
S: Oh yeah
C: mistakes, we cannot get away with it. Someone catches it, and we get to learn from that.
S: The world expert in that one thing is gonna email us
S: and go, “Oh, you made this very subtle error.” But if you remember, Cara, I had concerns about this word,
S: because I thought it was maybe too technical for us ...
C: Too wonky, yeah.
S: Too wonky, and I think this proves my point.
C: I think you might be right.
S: It really was, I mean, I think it was interesting word, and it's okay to push the limits, but when something that technical, it's really hard to get all the nuances correct. To an expert, it's obvious. I still am not really sure what it means, you know what I mean?
C: What I love about it is that it balances in two places, really, technically wonky in this one place that, like you said, neither of us fully understand, although Chaz does. But, it also has this beautiful literary meaning, which comes from this idea
C: of escaping, a gas escaping, and being very evanescent, which I though was quite beautiful. And I do love that, when a science word can really exist on two levels, so that the general public can use it in their poetic conversation. But it still has a pure, technical meaning as well.
S: Right, although I prefer words where understanding the technical meaning gives you sort of a new concept that you can use to understand science in the world.
C: Oh, for sure, that it gives you a deeper understanding
C: of the scientific process.
S: Whereas this one, I think, was so technical that unless you're doing chemical engineering or whatever, it's kind of lost. But, in any case, what do you got for this week?
C: Well, this week, I think, hits a nail on the head in terms of the kinds of words that you like to hear for What's the Word. It is a word that we use every day, but I think many of us don't really understand the technical meaning or the etymology. So this week's word is algorithm.
S: I love that word
C: Of course, yeah, it's such a beautiful
C: word, and off the top of our head, we all know what it is. Like, “Oh, there's an algorithm for that. What's your algorithm for figuring that out?” But really, when we dig deep into what it means, I think there's a lot of cool, surprising things here. So, I went to maybe ten or fifteen different dictionary websites, and sort of conglomerated the definitions to come up with one that I felt was very full and telling. So here it is:
A set of prescribed rules for solving a problem, especially a mathematical or computational one, in a finite number of steps, generally involving repetition of an operation. It's similar to a heuristic, but more finite and unambiguous.
So an algorithm is something that is often coded, or is often utilized in order to solve a mathematical problem. And you should be able to follow the steps to a T, and do it in a redundant way, and always come to the same conclusion. The etymology of the word is amazing. So it seems like a modern word, and actually, it didn't appear in Webster's dictionary until 1957.
C: So you would think, yeah.
C: Really, really recently. So you would think that it's like, “Oh! It's a silicon valley word. Or it was a cool, kind of experimental math out of Harvard word. But it's actually crazy old. It started out as the word “algorism,” with an S-M at the end. But that usage is now obsolete. But they really mean the exact same thing.
Algorism (this is so interesting) is actually a bad translation into Latin of the name (and I'm gonna butcher this, but I'm gonna try it) Al Coartsime, who is an Arab mathematician who lived from 780 to 850 AD. He did the earliest known work in arithmetic using the “new” (at the time, new is in airquotes) Arabic numerals. He famously wrote a treatise called The Algebra.
The distinction later was made between abasists, who did computation via the abacus, kind of in the middle ages, and the algorists. They did computation using algorism, using those Arabic numerals. And so when his name was first translated into Latin, they translated Al Coartsime as “algorism,” and then that actually was the staying definition.
And over the years, there have been a number of misdirected sources of the word algorithm, lots of different leads that were followed, trying to find it. But there were a lot of dead ends, and historians finally agreed that the etymology of the word lies with his name in early Baghdad, around 1825 AD. And so, interestingly, the word algorithm didn't appear in Webster's until 1957, but algorism did up until that point.
C: Interesting, right?
B: Algorism, wow.
S: We use the word in medicine all the time. And you could see how the words like that – I know you love this – get applied in different applications in different disciplines. So in medicine, we talk about diagnostic algorithms. Not all diagnoses can be made algorithmically, but some can. For example, if you come to me with a headache, there are certain diagnostic algorithms you have to follow. If you're over fifty, do you have symptoms of temporal arthritis? If yes, you get a temporal biopsy; if no, you get esedrae. If it's an elevated “yes,” you get a temporal biopsy.
So that's the algorithm. You have a series of questions, and a specific response, whether the question is “yes” or “no,” and everyone has to follow that, otherwise it's malpractice.
S: But of course, medicine doesn't always follow that. Sometimes it's just, “Ah, there's a buncha variables, you sort of weigh them all together, you sprinkle in your experience, and you come up with some judgment call based upon what the patient is concerned about, or whatever. It's much more wishy-washy.
C: But in that case, it's more of a heuristic, exactly,
C: which is kind of a really strong synonym to algorithm. It's just a little less unambiguous.
B: More of an art than science.
S: Or sometimes we use what we call the Gestalt diagnosis. Just look at the whole thing, and just say, “It looks like this,” you know. Somebody walks in, it's like, “That looks like somebody with Parkinson's Disease.
S: That's my diagnosis.” And then there's treatment algorithms too, not just diagnostic algorithms.
S: And the goal is always to do enough research, and get enough evidence that we can boil down practice to as much of an algorithm as we can.
S: But of course, people are complicated, medicine is complicated, we can't always do that. If you could, computers could practice medicine. But of course, you can't do that.
B: Not yet!
S: Yeah, there's ac-
C: 'Cause it's so much more likely that somebody comes in with the flu than some tropical disease, you know.
C: So it's like, you've gotta look at the things that are most likely first, and then as you, I guess, differentiate, as you say, okay, “Now that we know that it's not that, then we'll get into the more rare, complicated things.”
S: Yeah, ideally, we would have an evidence-based algorithmic approach to any situation. That's the goal, but we are a long way from that in many areas of medicine. It's actually a nice exception when we do have that.
B: Oh really.
S: When you actually, you have enough evidence that you actually do have an algorithm that you can fall back on, it's kind of nice.
C: Oh my gosh, I saw this piece of technology the other day (and forgive me, 'cause I don't remember what it was called), I saw it in passing on TV. But this physician had invented this little unit. And it was the craziest thing I had ever seen. You could do a blood drop, you could take heart rate, pulse, ox, all these things, and it would upload it so that your physician could read your vitals at that time. And they could also do all of these rapid tests for different kinds of infections and things. And it was this amazing little pod that they could bring into low income places, to be
C: able to do, basically, an early work up from afar. It was the coolest thing I'd ever seen.
S: There's a lot of work being done on that, trying to develop low-cost, portable, medical diagnostic or treatment instruments that could be taken into the parts of the world that
B: A trichorder.
S: really lack, yeah
C: Yeah, 'cause they could pick up the vast majority of the things that are wrong with people. You
C: can figure out by doing a blood draw, urinalysis, and seeing somebody's heart rate,
C: you know? It's amazing. So that really blew my mind.
C: And it was beautiful. It was like a little Apple product. I mean, it wasn't Apple, but it looked like it.
S: All right, let's move on to some news items.
Bacterial vs Human Cells (12:41)
GMO Labeling (20:11)
Mites Run in the Family (35:21)
(Commercial at 41:27)
Apple Car (42:54)
Gravity Waves (51:47)
Questions and Emails
Question #1: Correction (59:01)
- Infants are obligate nose breathers
Science or Fiction (59:51)
Item #1: Neuroscientists have discovered a mechanism by which our brain compress memories, allowing us to experience them in “fast forward.” Item #2: Researchers have developed a laser light treatment that can be applied to growing crops, killing many common pests and potentially dramatically reducing the need for pesticide. Item #3: Scientists have developed tiny robots that can attach themselves to immobile sperm and swim them to the egg so they can participate in fertilization. https://www.youtube.com/watch?v=Ww-x-VIFh-Q&feature=youtu.be
Skeptical Quote of the Week (1:23:34)
"The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them." - Sir William Henry Bragg, Nobel Prize for Physics, 1915
S: The Skeptics' Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking. For more information on this and other episodes, please visit our website at theskepticsguide.org, where you will find the show notes as well as links to our blogs, videos, online forum, and other content. You can send us feedback or questions to firstname.lastname@example.org. Also, please consider supporting the SGU by visiting the store page on our website, where you will find merchandise, premium content, and subscription information. Our listeners are what make SGU possible.