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Speaker 1: I'm Manny, I'm Noah.

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Speaker 2: This is Steven and this is no such thing.

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Speaker 3: The show where we settle our dumb arguments and yours

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Speaker 3: by actually doing the research. On today's episode, are the

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Speaker 3: weather apps actually getting worse? And how random is Spotify's

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Speaker 3: shuffle feature?

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Speaker 2: Really?

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Speaker 1: I no, there's no no such thing, no such thing,

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Speaker 1: no such thing.

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Speaker 4: Such those such.

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Speaker 3: So as the weather seems to be getting more and

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Speaker 3: more unpredictable. With flash flooding overtaking our subways in New

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Speaker 3: York City, you.

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Speaker 5: Can see water spouting out of the walls, people walking

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Speaker 5: across flooded platforms, and that rather dirty water.

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Speaker 3: To the tragic, deadly floods in central Texas, we.

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Speaker 5: Confirm that at least one hundred and thirty five people

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Speaker 5: have been killed. This is in addition to the more

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Speaker 5: than one hundred people who remain missing across several counties.

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Speaker 3: This week, we are turning our attention to our weather

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Speaker 3: apps and then nottedly. Many people in our lives have

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Speaker 3: been complaining that the weather apps are just not accurate anymore.

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Speaker 3: So we're going to get to the bottom of that

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Speaker 3: and answer all your other questions about weather apps, including

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Speaker 3: this one from my friend.

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Speaker 6: Alek Okay, So I want to know why we as

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Speaker 6: society can't come together and use feels like temperatures instead

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Speaker 6: of actual temperatures as the default, because right now, when

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Speaker 6: they tell me it's going to be sixty five degrees,

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Speaker 6: I then have to go and do other homework on

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Speaker 6: like how wendy is it going to be that day?

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Speaker 6: And is it going to be really humid? And it

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Speaker 6: seems like they have come up with one number that

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Speaker 6: can give me the answer that I'm really looking for here,

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Speaker 6: which is do I need a jacket that day?

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Speaker 1: That's a great question, and it reminds me of when

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Speaker 1: you guys used to make fun of me for saying

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Speaker 1: that it feels quote unquote good outside like it feels good,

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Speaker 1: and I maintain that many people say that maybe not

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Speaker 1: from you know, Connecticut or Long Island, but I just

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Speaker 1: wanted to get that off my chest.

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Speaker 4: Yeah.

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Speaker 2: I love the idea of like a Manny weather app that.

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Speaker 3: It just says feels good.

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Speaker 1: That's all I need to know. But just to be

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Speaker 1: clear that the feels like it's a part of the app,

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Speaker 1: I actually don't see it that often. It's a part

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Speaker 1: is on the weather? Which app is.

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Speaker 3: This all the apps kind of have it now, so

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Speaker 3: there's usually app okay.

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Speaker 7: And they don't really have much.

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Speaker 2: So like, let's pull it up now.

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Speaker 3: It's usually you know, the air temp which is up top,

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Speaker 3: and then feels like it's usually below that or very deeper.

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Speaker 3: So like this, this is the default Apple one. You

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Speaker 3: really got to scroll here. So it's eighty seven right

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Speaker 3: now from Brooklyn, and then we scroll all the way down,

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Speaker 3: feels like ninety one.

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Speaker 1: Okay, that is helpful. It's helpful, but I guess I

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Speaker 1: do also want to know the real temperature. That's my

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Speaker 1: guess is that y'all do you want to know the

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Speaker 1: real temp?

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Speaker 3: I don't care about the real temperature. It doesn't matter.

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Speaker 3: This was especially true remember when we had those really

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Speaker 3: really windy days.

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Speaker 7: Oh yeah, it's it's yeah, it's in the hot weather.

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Speaker 7: It's less of a thing I feel for us anymore.

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Speaker 2: I mean, well, no, still thing, because.

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Speaker 7: It can be in the cold. I feel like that

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Speaker 7: the wind can change it so dramatic.

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Speaker 3: They'll be like it's forty degrees out. I'm like, oh,

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Speaker 3: this is kind of warm for winter. And then it

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Speaker 3: was like the wind is making it feel like it's

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Speaker 3: fifteen exactly.

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Speaker 7: But it's like I don't hear what the still cold day? Okay,

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Speaker 7: I can live in this versus like I'm about to

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Speaker 7: die in.

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Speaker 3: Free because why are we checking the weather? You're checking

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Speaker 3: them whether to see what I gotta wear.

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Speaker 1: Yeah, it's true for me, it's.

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Speaker 3: We're not you know, like that's what most people checking

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Speaker 3: other while they're checking the letter.

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Speaker 1: I might wear something different in sixty five degree weather

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Speaker 1: than you might. Yeah, I mean you still need to

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Speaker 1: know what to wear for you. That's why I want

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Speaker 1: to know the real temperature. Yeah, the real temperature is

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Speaker 1: the real temperature, no matter who well look.

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Speaker 2: At no, no, but no.

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Speaker 7: But if it's all right it's sixty degrees but feels

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Speaker 7: like ninety, yes, you you want to dress for ninety yes, yeah, but.

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Speaker 3: It feels like it's actually better for you because you

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Speaker 3: want to know what it's going to feel like, so

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Speaker 3: you should know what to wear.

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Speaker 2: The actual temperature doesn't know what.

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Speaker 7: It actually make a difference if it's actually if it's

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Speaker 7: actually sixty.

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Speaker 1: Yeah, because I want to know if we've breaken up

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Speaker 1: broken any records today.

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Speaker 3: Okay, okay, that's different, that's what you're wearing, breaking records is.

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Speaker 7: Over here.

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Speaker 3: I have a map the historical data. I want to

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Speaker 3: make sure that you can assume we.

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Speaker 7: Are breaking a record every day.

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Speaker 3: So I talked to meteorologists at the Weather Company, okay,

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Speaker 3: which you may know for the Weather Channel brand.

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Speaker 4: So my name is doctor Loriana Gaudett. I am a

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Speaker 4: staff applied meteorological scientist at the Weather Company. I've worked

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Speaker 4: primarily within our forecast sciences team, so I'm not operationally

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Speaker 4: forecasting the weather, but i am working on the science

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Speaker 4: behind the scenes per se to improve our forecasts.

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Speaker 3: All right, So one of the first questions that we

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Speaker 3: got on weather is this idea of real feel. Right,

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Speaker 3: you get our actual temperature and then we get the

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Speaker 3: real field tamp. So can we start by just having

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Speaker 3: you break down what is the real field tamp? How

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Speaker 3: is that calculated?

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Speaker 4: So the real field temperature is more so known as

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Speaker 4: the feels like temperature, and what it's composed of is

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Speaker 4: a combination of the heat index and the windshiel temperature.

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Speaker 4: The heat index itself is a combination of the temperature

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Speaker 4: and the relative humidity and how those two interchangeably play

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Speaker 4: together to affect how we feel when we're outside. And

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Speaker 4: so if it's really humid outside, less of that sweat

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Speaker 4: is able to evaporate off of our bodies, and we

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Speaker 4: are less effectively cooling ourselves down, which makes it feel

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Speaker 4: much warmer. As an example, if it's ninety degrees fahrenheit

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Speaker 4: outside with a relative humidity of seventy percent, the heat

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Speaker 4: index or the feels like temperature in those conditions is

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Speaker 4: around one hundred and five degrees fahrenheit, which is very

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Speaker 4: warm and it can become dangerous with heat stress impacts.

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Speaker 4: And then on the other side of the temperature spectrum,

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Speaker 4: the feels like kind of points to the wind chill temperature.

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Speaker 4: So this time we're considering the effect of wind speed

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Speaker 4: on temperature, and the reason that that's important is because

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Speaker 4: the wind is effectively transporting the heat away from our body,

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Speaker 4: which is cooling down our body or our skin temperature,

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Speaker 4: and then over time core body temperature, which can also

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Speaker 4: come dangerous in its own way through frostbite or through

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Speaker 4: hypothermia effects. So another example here is that if you

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Speaker 4: have a temperature of thirty degrees fahrenheit and a wind

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Speaker 4: speed of fifteen miles an hour, your wind chill or

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Speaker 4: feels like temperature would be nineteen degrees fahrenheit, give or take.

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Speaker 1: Interesting. So basically like seventy degrees with no humidity feels

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Speaker 1: a lot different than seventy degrees with humidity, and some

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Speaker 1: you know, X amount of miles per hour.

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Speaker 3: Wind exactly to take away I got from talking to her,

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Speaker 3: it was like, the feels like takes into account how

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Speaker 3: the temperature affects our body, and it's not just all right,

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Speaker 3: here's the air temp.

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Speaker 7: Yeah, it's not so abstract. I guess it's like kind

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Speaker 7: of an algorithm basically. Yeah, and yeah, it's about how

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Speaker 7: you know your sweat's going to be a vap rated

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Speaker 7: or not or whatever. That's it's more thorough than I thought.

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Speaker 1: Yeah, I kind of thought. A scientist and a lab

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Speaker 1: coat walked outside, I was.

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Speaker 7: Like, okay, sixty five, Yeah, we wan'll make them feel

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Speaker 7: good today.

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Speaker 3: But something is interesting about feels like, which we talked

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Speaker 3: about a little bit already, is that someone like manny,

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Speaker 3: if it's seventy tract Yeah, if it's seventy seven degrees outside,

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Speaker 3: man's complaining as hot?

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Speaker 1: Yeah, well, not that it's hot, it's too It's like,

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Speaker 1: does it's warmer than I prefer?

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Speaker 7: It feels bad?

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Speaker 3: It feels bad, bad, whereas with us that's seventy seven

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Speaker 3: temperature bad, not bad.

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Speaker 7: So well, I.

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Speaker 2: Don't know, Okay, don't put that on that.

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Speaker 3: Okay, what do you what's what seventy seven.

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Speaker 7: Of you that could get pretty hot?

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Speaker 2: Okay?

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Speaker 7: Seventy three is comfy, okay, And I want a little

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Speaker 7: bit of clouds.

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Speaker 3: And speaking of shade versus sun, when they take the

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Speaker 3: temperature outside, did you know that they're taking into shade?

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Speaker 1: Whoa, oh, why is that?

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Speaker 7: I did not know.

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Speaker 2: Well, we're gonna find out.

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Speaker 4: So temperature and heat index are recorded in the shade,

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Speaker 4: and so if oh, yeah, what I know, Yes, it's

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Speaker 4: it's super surprising. But the reason is we don't want

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Speaker 4: the thermometer that's measuring the temperature to heat up and

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Speaker 4: not be able to actually tell us what the air

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Speaker 4: temperature is. We want that not the temperature with the thermometer.

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Speaker 4: So when you were outside doing whatever activity in the sun,

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Speaker 4: it's going to feel warmer than the reported air temperature

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Speaker 4: and even the heat index. So that's important to keep

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Speaker 4: in mind. And then the other component is that scientists

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Speaker 4: when they were deriving these heat indices and the wind chill,

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Speaker 4: they had to make certain approximations and assumptions. So some

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Speaker 4: of those involve like how tall are you, how much

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Speaker 4: do you weigh, how much do you sweat, what kind

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Speaker 4: of clothes are you're wearing, so on and so forth,

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Speaker 4: and that obviously can't account for each of our unique

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Speaker 4: body compositions and circumstances, but it's our best general approximation

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Speaker 4: for what you might experience when you walk out the door.

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Speaker 3: The shade thing really blew my mind because I'm every

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Speaker 3: time I go outside and I'm in the shade, I'm like, oh,

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Speaker 3: this is cooler than what's the app because I'm in

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Speaker 3: the shade. But that's the best case scenario. You're only

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Speaker 3: going to get hotter if it's ninety degrees. That means

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Speaker 3: if you're in the sun, it's hotter than that.

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Speaker 7: Yeah. Yeah, if you're walking around, you're gonna be sucked

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Speaker 7: there sometimes.

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Speaker 3: Yeah.

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Speaker 1: There's so many variables, which is why I just try

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Speaker 1: to distill them all into good or bad. Yeah.

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Speaker 3: I know.

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Speaker 7: Now I'm kind of like many systems well because it's like, well,

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Speaker 7: this makes me want them to have shade, Yeah, because

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Speaker 7: I need to know.

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Speaker 1: You open the weather app. It's just like an Excel

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Speaker 1: sph Yeah.

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Speaker 7: Ever dial in and then you dial in your outfit yeah,

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Speaker 7: and your skin tone calculator.

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Speaker 8: I mean.

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Speaker 7: Imagine you have an or a ring.

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Speaker 2: Yeah, that's that's a lot of data.

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Speaker 8: Yeah.

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Speaker 7: And then you didn't tell weather you take a selfie

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Speaker 7: it skins your clothes.

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Speaker 3: You don't even need to take a selfie, yeah, right,

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Speaker 3: your phone.

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Speaker 1: Yeah, and and you just exists and Peter Tiel buys

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Speaker 1: all of this data. Yeah, exactly, and then he but

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Speaker 1: he tells you it's going to be only seventy two today,

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Speaker 1: or it's only going to feel like seventy.

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Speaker 3: Two when we look at the weather app. Besides many

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Speaker 3: lying and saying he wants to look at historical data,

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Speaker 3: we're looking at it to see what we should wear

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Speaker 3: to go outside. Do I need to bring an umbrella?

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Speaker 3: Why don't we have this as the default? If I

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Speaker 3: open up the app, I want to see feels like

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Speaker 3: and then if I want to search for the actual

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Speaker 3: air temp, I could find that lower down.

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Speaker 2: Why are we doing the reverse?

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Speaker 4: We take a certain approach within our Weather Channel app,

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Speaker 4: and we've supply both temperatures, so not just the temperature,

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Speaker 4: but also the fields like temperature, so that you can

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Speaker 4: have both data points and use those to make decisions

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Speaker 4: about how you want to plan your day or go

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Speaker 4: about your day. But yeah, it's a really good question

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Speaker 4: because it gets down to the core of like the

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Speaker 4: human experience of course, but there's other impacts and implications

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Speaker 4: of the temperature that also don't relate to humans. So

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Speaker 4: some of those might be like if a farmer, for example,

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Speaker 4: is trying to figure out when to plant their crop

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Speaker 4: or protect their crop, they are looking at the actual temperature.

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Speaker 4: So that's what our weather forecast models are actually outputting,

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Speaker 4: is the real air temperature, and that's what we are

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Speaker 4: measuring and looking at when we are monitoring the climate,

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Speaker 4: for example, and it allows us to compare the observations

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Speaker 4: of temperature across the country, across the world, and over time. Whereas,

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Speaker 4: like with everything that we talked about with the feels

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Speaker 4: like temperature, there's a lot of approximations that are being used,

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Speaker 4: and so some weather services given the same environmental setup

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Speaker 4: might come up with different values. That makes it really

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Speaker 4: difficult for that inter comparison component. But if you yourself,

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Speaker 4: as the user of a weather app find that the

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Speaker 4: feels like temperature gives you everything that you need to

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Speaker 4: know you can set that as your own to fault temperature.

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Speaker 4: But we just don't want to make that assumption for

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Speaker 4: all of our users.

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Speaker 2: So they're just very considerate of me at this point.

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Speaker 3: The thing that impacts most of us would be it

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Speaker 3: feels like temperature that should be to default. Make everybody

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Speaker 3: else look for the other stuff.

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Speaker 1: Please contact your company.

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Speaker 3: I guess do you feel I understand all everything that

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Speaker 3: she said. When I'm looking at my app, I'm not

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Speaker 3: looking for climate. You know, over the last ten years,

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Speaker 3: I don't have any crops. I guess I can do

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Speaker 3: it within my own but I feel like as a

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Speaker 3: country we need to we neither do real feel I'm

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Speaker 3: not saying get rid of the real number. I'm just

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Speaker 3: saying put that lower.

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Speaker 7: See why I can't?

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Speaker 1: Why not just side by side?

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Speaker 7: Oh slash yeah, pretty much? Okay, big one, I mean,

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Speaker 7: pick whichever one you want, big one then basically an

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Speaker 7: arrow or slash, Yeah, basically, and you just know that

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Speaker 7: you just know, okay, this is you know, you know

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Speaker 7: the real thing. And here's the thing. It wouldn't take

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Speaker 7: up that much space. They're digits and like the numbers

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Speaker 7: are hopefully not Yeah, yeah, hopefully four digits. You're look

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Speaker 7: at that total.

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Speaker 1: I'm fine with the normal one. I'm fine with the

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Speaker 1: regular Yeah.

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Speaker 7: My main concern with these things is doing an umbrella

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Speaker 7: or not, which I forget to look for most of

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Speaker 7: the time.

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Speaker 3: Anymore, Speaking of rain, Manny, do you want to explain

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Speaker 3: this Twitter argument that people have been having.

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Speaker 1: When it's raining or when it might looks like it

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Speaker 1: might rain. You go on the app and says, let's say,

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Speaker 1: for example, thirty five percent. Doesn't say what the thirty

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Speaker 1: five percent is. So some people think that it's thirty

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Speaker 1: five percent chance it's going to rain where you're standing.

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Speaker 1: Other people think it's in a rain on thirty five

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Speaker 1: percent of the area around you, yes, and a third

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Speaker 1: group of people think that's effectively the same thing.

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Speaker 7: Well, well what do you what do you guys think?

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Speaker 3: Before I did this interview, Yeah, my thought was it's

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Speaker 3: a thirty five percent chance it's going to rain.

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Speaker 7: That's not that's my thought too, because the area doesn't

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Speaker 7: make because it's like, well, what's what area?

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Speaker 1: A lot of people, a lot of people online, these

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Speaker 1: are not experts, but they were like, no, that is

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Speaker 1: what they're telling you. Well, what did you think I

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Speaker 1: was thought, thirty five percent chance is going to rain.

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Speaker 7: Chance, Yeah, where you are? I agree, That's what I'm

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Speaker 7: looking at, and that's how I feel like my lived

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Speaker 7: experience is.

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Speaker 1: You know, it's like, well, that's where it gets tricky.

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Speaker 1: It's it's never right to me.

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Speaker 7: But yeah, I always assumed it's basically there is a

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Speaker 7: percentage of chance that it will rain. Not whatever area

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Speaker 7: I'm taking in, it's going to cover sixty percent of this.

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Speaker 3: Yeah, yeah, that.

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Speaker 7: Doesn't really that would never cross our behind me that much.

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Speaker 4: So this is a common area of confusion for users,

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Speaker 4: and even in our own field of meteorology, it can

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Speaker 4: be really hard to pinpoint what we are actually communicating

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Speaker 4: with probability of precipitation or pop In my experience and

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Speaker 4: knowledge base, I think both of those interpretations are true

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Speaker 4: in their own right, which of course makes it even

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Speaker 4: more confusing. But I can I can share from the

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Speaker 4: Weather Channel app perspective, the way that we're communicating it

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Speaker 4: is really in your surrounding area. What is the likelihood

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Speaker 4: of precipitation or of rain impacting you within whatever time

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Speaker 4: frame you're interested in, So, whether it's today, the next hour,

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Speaker 4: fifteen minutes, and less so the amount of area that

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Speaker 4: is going to be impacted. A couple misconceptions that also

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Speaker 4: come up to are about the intensity of precipitation from pop.

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Speaker 4: So sometimes people will see like, oh, there's a ninety

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Speaker 4: percent chance of rain, it's going to be a downpour.

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Speaker 4: We are indicating the likelihood of rain, of measurable rain,

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Speaker 4: any rain, and so generally it has nothing to do

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Speaker 4: with the intensity of that precipitation, which is important to

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Speaker 4: keep in mind. And then the other component two is

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Speaker 4: you know, sometimes people think, Okay, there's a fifty percent

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Speaker 4: chance of pop in New York City, so only half

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Speaker 4: of the day will be wet, And again you could

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Speaker 4: have that fifty percent chance, there might only be a

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Speaker 4: couple hours that are impacted, and then you have a

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Speaker 4: beautiful remainder of the day. I think that it really

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Speaker 4: lays on us meteorologists to make sure that we're communicating

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Speaker 4: what that likely hood and probability means, especially if it

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Speaker 4: does vary, you know, from a news broadcaster or a

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Speaker 4: meteorologist who's on TV trying to communicate the likelihood of rain,

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Speaker 4: to a push alert from your weather app telling you

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Speaker 4: that there's rain coming within the next thirty minutes.

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Speaker 1: That is really funny because the idea that the percent

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Speaker 1: chance is the total coverage is such a like, actually,

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Speaker 1: I'm smart, and this is what they're really saying, fools

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Speaker 1: in conversation. I've heard it so many times and it's

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Speaker 1: just wrong.

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Speaker 7: And then I mean it's kind of fascinating, like people

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Speaker 7: are just taking this and twisting it in any direction

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Speaker 7: where it's like, okay, ten percent, that's ten percent of

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Speaker 7: the day. Yeah, so yeah, okay, so whatever ten percent

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Speaker 7: of twenty four hours is. It's like, yeah, thinking of

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Speaker 7: all the different ways to map it out, it's kind

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Speaker 7: of that's actually like a SAT question or something.

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Speaker 1: Yeah, I heard the rain is only coming from ten

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Speaker 1: percent of the clouds.

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Speaker 2: Yeah, exactly. Yeah, but there.

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Speaker 3: I thought it was interesting that they're kind of they

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Speaker 3: could both be right depending on your weather.

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Speaker 7: You need explanation.

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Speaker 2: I mean, I think that you know, the apps that

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Speaker 2: we're looking at or telling us, I.

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Speaker 7: Would hope so frankly, I think that you know, maybe

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Speaker 7: they could do a better job than of getting the

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Speaker 7: word out there hopefully. You know, we're going to do

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Speaker 7: our part today with our you know, hundreds of thousands

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Speaker 7: of listeners.

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Speaker 3: Some would say millions, So yeah, you could argue that millions.

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Speaker 3: Maybe all right, we are going to take a quick

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Speaker 3: break and when we get back, we are going to

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Speaker 3: find out are the weather apps actually getting worse? And

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Speaker 3: it's Trump to blame. So in last week's mail bag,

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Speaker 3: we were debating who gets the right of way between

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Speaker 3: a school bus with the stop sign out, an ambulance

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Speaker 3: with the siren on, and a funeral procession, and from

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Speaker 3: North Carolina called in with this insight.

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Speaker 9: I am a state wide law enforcement officer here in

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Speaker 9: North Carolina. If I am running lights and sirens, I

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Speaker 9: am allowed.

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Speaker 10: To go through a soft sign.

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Speaker 9: People legally have to yield to me. But if I

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Speaker 9: run a soft sign and hit someone, it is still

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Speaker 9: my fault, and I, as a driver, would.

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Speaker 10: Not only be responsible to my agency, but I could

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Speaker 10: also be personally legally financially responsible funeral processions.

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Speaker 9: You guys are correct, that is a courtesy.

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Speaker 10: It's not a legal thing, and but it is it

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Speaker 10: drive me.

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Speaker 9: Grace to get down here in the South, they love

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Speaker 9: them at funeral profession.

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Speaker 3: So we're going to try to include more listener responses

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Speaker 3: to our episodes like this in the future, So you

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Speaker 3: can email us at Manny Noladevin at gmail dot com

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Speaker 3: or leave us a voicemail at the number in our

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Speaker 3: show notes and hey, you may includes you in a

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Speaker 3: future episode. All right, let's get back to the show.

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Speaker 3: We do have one final weather question from Mia.

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Speaker 11: I have a question, and this is just bugging me

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Speaker 11: so much, especially this summer, our weather apps getting less

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Speaker 11: accurate because I feel like they are like these days,

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Speaker 11: I just don't feel like I can trust the weather

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Speaker 11: app to tell me exactly what's going to happen, specifically

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Speaker 11: if it's going to rain, like over this the course

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Speaker 11: of the summer, It's been a rainy summer in New York,

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Speaker 11: and now I feel like I'm becoming this kind of

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Speaker 11: like medieval farmer where I'm sort of gauging what's happening

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Speaker 11: in the sky, and I feel like I am becoming

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Speaker 11: a much better predictor of what's actually going to happen

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Speaker 11: than what my phone is telling me. I'm just starting

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Speaker 11: to be like, what's big weather even there? So if

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Speaker 11: you could answer for me, is it getting less accurate?

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Speaker 11: What's the reason should I trust this app anymore?

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Speaker 4: That would be great. Thank you.

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Speaker 2: I put this question to our meteorologists from the weather.

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Speaker 7: She was sweam bullets at this one.

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Speaker 3: Is the weather more unpredictable or is this on in

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Speaker 3: our heads? And you know, has nothing really changed.

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Speaker 4: So that's a super intriguing question. No, as a short answer,

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Speaker 4: the aps are not getting worse at predicting rain. I

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Speaker 4: think there's a there's a few different things at play here,

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Speaker 4: one of which you pointed at. It's psychological, so we're

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Speaker 4: more likely to remember when the weather apps get it wrong,

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Speaker 4: versus that when we are seeing a prediction of, oh,

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Speaker 4: it's going to rain in ten minutes, let me make

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Speaker 4: sure I grab my rain jacket before I go out,

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Speaker 4: and it ends up being correct, and so you're prepared.

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Speaker 4: It becomes a non event and you forget about it.

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Speaker 4: But let's say that if someone is outside it starts pouring,

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Speaker 4: there's absolutely no warning. Of course, you're going to be

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Speaker 4: annoyed that you had no notice that that was going

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Speaker 4: to happen. And there's a few reasons that those specific

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Speaker 4: circumstances can crop up. This example of stepping outside and

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Speaker 4: being met with an unexpected downpour of rain can largely

444
00:23:26,960 --> 00:23:30,280
Speaker 4: happen because even if the radar is able to pick

445
00:23:30,359 --> 00:23:32,679
Speaker 4: up on a storm let's say it just started raining

446
00:23:32,680 --> 00:23:35,760
Speaker 4: within the past few minutes. The weather radar just got

447
00:23:35,760 --> 00:23:38,679
Speaker 4: that data. So for example, that data needs to be

448
00:23:39,320 --> 00:23:42,920
Speaker 4: ingested into our back end systems or any other weather

449
00:23:43,000 --> 00:23:46,639
Speaker 4: app systems to process that data, figure out where that

450
00:23:46,720 --> 00:23:51,800
Speaker 4: storm is using proprietary software and algorithms, predict where that

451
00:23:51,840 --> 00:23:55,879
Speaker 4: storm is headed and if it will intensify or again dissipate,

452
00:23:56,520 --> 00:24:01,280
Speaker 4: get that forecast to our technology system downstream, and then

453
00:24:01,400 --> 00:24:04,639
Speaker 4: those then deliver that information to your app. So that

454
00:24:04,760 --> 00:24:08,040
Speaker 4: takes a bit of wall clock time for all of

455
00:24:08,040 --> 00:24:12,080
Speaker 4: that process to happen. So unfortunately, there are those situations

456
00:24:12,160 --> 00:24:16,359
Speaker 4: where rain can impact you within that window of time

457
00:24:16,400 --> 00:24:18,800
Speaker 4: that we're trying to get that information to you as

458
00:24:18,840 --> 00:24:23,800
Speaker 4: fast as possible, but perhaps missed that window of opportunity.

459
00:24:24,160 --> 00:24:27,000
Speaker 1: I've never thought about a situation where the weather app

460
00:24:27,119 --> 00:24:30,200
Speaker 1: was wrong, because it just was like trying to catch

461
00:24:30,280 --> 00:24:32,960
Speaker 1: up to the real time m hm. You would think.

462
00:24:34,359 --> 00:24:37,640
Speaker 3: You're supposed to be predicting.

463
00:24:37,960 --> 00:24:41,040
Speaker 7: It's like, don't blame me, I'm not the weather company.

464
00:24:41,119 --> 00:24:41,399
Speaker 3: Hello.

465
00:24:41,960 --> 00:24:45,000
Speaker 1: And now that I remember what Mia was complaining about,

466
00:24:45,000 --> 00:24:48,280
Speaker 1: I think her concern is that the apps are more

467
00:24:49,080 --> 00:24:53,120
Speaker 1: frequently incorrect, not that they're ever incorrect, but that it's

468
00:24:53,160 --> 00:24:54,200
Speaker 1: happening more often.

469
00:24:55,280 --> 00:24:58,359
Speaker 3: Yes, yeah, she thinks it's getting the apps are getting worse.

470
00:24:58,640 --> 00:25:04,080
Speaker 3: So the Weather Company said, no, that's you know, the

471
00:25:04,160 --> 00:25:06,879
Speaker 3: apps aren't getting worse, but they work at the Weather Company.

472
00:25:06,960 --> 00:25:10,720
Speaker 3: So I wanted to call up someone who can maybe

473
00:25:10,720 --> 00:25:13,280
Speaker 3: speak a bit more freely about whether or not these

474
00:25:13,320 --> 00:25:16,000
Speaker 3: apps are getting worse and do we need to worry,

475
00:25:16,080 --> 00:25:18,199
Speaker 3: especially with these Doge cuts, about them being worse in

476
00:25:18,240 --> 00:25:18,679
Speaker 3: the future.

477
00:25:18,720 --> 00:25:20,440
Speaker 2: So I called the Mary Glacken.

478
00:25:21,480 --> 00:25:27,040
Speaker 12: I'm the former deputy undersecretary for NOAH for operations there

479
00:25:27,080 --> 00:25:29,320
Speaker 12: and I had worked for Noah for just about thirty

480
00:25:29,320 --> 00:25:34,320
Speaker 12: five years. I also am on an AI board and

481
00:25:34,359 --> 00:25:38,280
Speaker 12: things like that, so today these days I'm mainly in

482
00:25:38,320 --> 00:25:39,800
Speaker 12: an advisory capacity.

483
00:25:40,240 --> 00:25:43,160
Speaker 2: She also worked at the Weather Company back in the day.

484
00:25:43,560 --> 00:25:45,320
Speaker 3: All right, great.

485
00:25:45,160 --> 00:25:46,400
Speaker 7: Job finding, Mary.

486
00:25:46,920 --> 00:25:49,680
Speaker 1: I always have stuff to say about my former employees.

487
00:25:50,440 --> 00:25:51,240
Speaker 1: It should be good to take.

488
00:25:52,280 --> 00:25:54,800
Speaker 3: So, you know, the big thing that we were concerned

489
00:25:54,800 --> 00:25:58,080
Speaker 3: about and seeing the headlines is that with the Doge cuts,

490
00:25:59,160 --> 00:26:02,800
Speaker 3: that there's just less people working, which is why the

491
00:26:02,840 --> 00:26:05,200
Speaker 3: apps are getting worse. So I asked Mary, like, where

492
00:26:05,200 --> 00:26:06,520
Speaker 3: do we currently stand.

493
00:26:06,960 --> 00:26:09,800
Speaker 12: So let me talk about it in terms of personnel first,

494
00:26:10,560 --> 00:26:14,240
Speaker 12: because one of the major things that happened was the exodus.

495
00:26:14,280 --> 00:26:15,720
Speaker 12: You know, there was the Fork in the.

496
00:26:15,720 --> 00:26:20,760
Speaker 13: Road, another unprecedented action from the Trump administration, offering more

497
00:26:20,800 --> 00:26:24,199
Speaker 13: than two million federal workers a buyout. The workers receiving

498
00:26:24,240 --> 00:26:26,520
Speaker 13: an email with the subject line fork in the Road,

499
00:26:26,640 --> 00:26:29,679
Speaker 13: giving them a choice resign and be paid through September

500
00:26:30,080 --> 00:26:31,240
Speaker 13: or risk being fired.

501
00:26:31,680 --> 00:26:35,240
Speaker 12: So the National Weather Service and all of Noah are

502
00:26:35,359 --> 00:26:40,520
Speaker 12: down significantly in terms of people, something approaching as much

503
00:26:40,520 --> 00:26:44,720
Speaker 12: as twenty percent in some locations. So the National Weather

504
00:26:44,840 --> 00:26:48,639
Speaker 12: Service did lose a lot of senior people. In terms

505
00:26:48,640 --> 00:26:52,639
Speaker 12: of funding, they had actually been funded by Congress last

506
00:26:52,720 --> 00:26:56,679
Speaker 12: year for a full year at a reasonable level, but

507
00:26:56,880 --> 00:27:01,119
Speaker 12: the Trump administration has delayed spending of those funds, so

508
00:27:01,240 --> 00:27:04,679
Speaker 12: they've really been slow rolling contracts to go out for

509
00:27:04,840 --> 00:27:08,400
Speaker 12: various things. I don't have any figures here, but it's

510
00:27:08,400 --> 00:27:10,840
Speaker 12: safe to assume there's a fair amount of money still

511
00:27:10,880 --> 00:27:14,360
Speaker 12: on the table. But the direction that we saw this

512
00:27:14,440 --> 00:27:17,639
Speaker 12: week was a spend plan for the rest of this

513
00:27:17,760 --> 00:27:21,360
Speaker 12: fiscal year is clawing back that money. It's saying you're

514
00:27:21,400 --> 00:27:25,560
Speaker 12: not allowed to spend that anymore. So that's that's kind

515
00:27:25,600 --> 00:27:29,399
Speaker 12: of where we are. We've seen some news about them hiring.

516
00:27:29,359 --> 00:27:32,520
Speaker 5: So earlier this year, President Trump's Doze Office cut more

517
00:27:32,560 --> 00:27:36,480
Speaker 5: than five hundred Weather employees. Well now WS says they're

518
00:27:36,480 --> 00:27:39,520
Speaker 5: going to hire four hundred and fifty meteorologists, hydrologists, and

519
00:27:39,680 --> 00:27:40,720
Speaker 5: radar technicians.

520
00:27:41,040 --> 00:27:45,119
Speaker 12: They're all entry level positions, So you know you're not

521
00:27:45,200 --> 00:27:49,760
Speaker 12: going to hire back the expertise that's been lost that

522
00:27:50,480 --> 00:27:53,840
Speaker 12: one of the senior officials that Noah career official had quoted,

523
00:27:53,880 --> 00:27:57,399
Speaker 12: that was twenty seven thousand years of expertise that walked

524
00:27:57,400 --> 00:28:00,720
Speaker 12: out the door between February in April.

525
00:28:02,920 --> 00:28:04,439
Speaker 3: It's just going to be a lot of people, a

526
00:28:04,440 --> 00:28:07,919
Speaker 3: lot of college who don't have the expertise, and now

527
00:28:07,960 --> 00:28:10,600
Speaker 3: they also don't have the leadership to mentor those people.

528
00:28:10,960 --> 00:28:14,200
Speaker 1: They just went from an NFL team to like JV

529
00:28:14,359 --> 00:28:18,000
Speaker 1: high school team.

530
00:28:18,320 --> 00:28:20,520
Speaker 3: So I wanted Mary to answer the question because I

531
00:28:20,520 --> 00:28:22,880
Speaker 3: couldn't take the Weather Company's word for it.

532
00:28:22,960 --> 00:28:24,600
Speaker 2: Are the apps getting worse?

533
00:28:24,720 --> 00:28:27,879
Speaker 12: The apps haven't degraded at all. I think the worries

534
00:28:27,960 --> 00:28:32,400
Speaker 12: that we have with the Trump administration is if they

535
00:28:32,440 --> 00:28:35,680
Speaker 12: move forward with their plans. Literally one of the plans

536
00:28:35,760 --> 00:28:41,200
Speaker 12: is to eliminate all of Noah's research arm So and

537
00:28:41,240 --> 00:28:44,360
Speaker 12: that's all our future. You know, how do we do

538
00:28:44,400 --> 00:28:48,800
Speaker 12: a better job in forecasting that whole section would be eliminated?

539
00:28:49,280 --> 00:28:53,520
Speaker 12: Which is crazy, but that's the plan. I do want

540
00:28:53,520 --> 00:28:57,240
Speaker 12: to kind of remind folks that Congress has a say

541
00:28:57,240 --> 00:29:00,040
Speaker 12: in this whole thing. Both the House and Senate that

542
00:29:00,680 --> 00:29:03,720
Speaker 12: have given their plan on how Noah would be funded.

543
00:29:04,200 --> 00:29:06,880
Speaker 12: The Senate does a really pretty good job and keeping

544
00:29:06,920 --> 00:29:12,040
Speaker 12: Noah funded. The House has some significant cuts in it,

545
00:29:12,080 --> 00:29:15,560
Speaker 12: but nothing as draconian as the administration is asking for.

546
00:29:16,360 --> 00:29:19,240
Speaker 12: I think the problem is that Congress won't be able

547
00:29:19,360 --> 00:29:24,160
Speaker 12: to agree on the overall spending, which will really basically

548
00:29:24,160 --> 00:29:27,320
Speaker 12: allow the Trump administration to continue to do what it

549
00:29:27,360 --> 00:29:28,040
Speaker 12: wants to do.

550
00:29:29,000 --> 00:29:32,160
Speaker 3: I was curious if we've kind of hit the point

551
00:29:32,200 --> 00:29:36,400
Speaker 3: of no return, you know, see if President Biden runs

552
00:29:36,440 --> 00:29:37,280
Speaker 3: again and.

553
00:29:37,280 --> 00:29:45,800
Speaker 2: Wins, yeah, you know which, we're in eight can we

554
00:29:46,040 --> 00:29:47,320
Speaker 2: get back to where we were?

555
00:29:52,240 --> 00:29:54,400
Speaker 12: One of the things I'd like to make clear with

556
00:29:54,560 --> 00:29:56,840
Speaker 12: you know, all of my experience with Noah, both in

557
00:29:56,960 --> 00:30:00,560
Speaker 12: Noah and then working with Noah outside is I would

558
00:30:00,560 --> 00:30:03,400
Speaker 12: not be the one arguing for status quo. You know,

559
00:30:03,600 --> 00:30:06,040
Speaker 12: I'm not here saying oh, it was a perfect world

560
00:30:06,080 --> 00:30:08,520
Speaker 12: and now this has happened. I think it will be

561
00:30:08,680 --> 00:30:11,600
Speaker 12: possible to get us back if we can keep people

562
00:30:11,720 --> 00:30:14,840
Speaker 12: in the field and interested in our problems. You know,

563
00:30:14,880 --> 00:30:19,080
Speaker 12: you have the whole advent of AI, and AI is

564
00:30:19,160 --> 00:30:22,240
Speaker 12: impacting our value chain all the way from taking an

565
00:30:22,240 --> 00:30:27,680
Speaker 12: observation to helping somebody make a decision on what to

566
00:30:27,760 --> 00:30:31,800
Speaker 12: do that day. So when I look at AI, it's

567
00:30:31,800 --> 00:30:35,400
Speaker 12: pretty clear to me and many is the private sector

568
00:30:35,480 --> 00:30:38,280
Speaker 12: is ahead of where Noah is with AI, and that's

569
00:30:38,320 --> 00:30:40,800
Speaker 12: no great chock. You know, the private sector can pay

570
00:30:40,840 --> 00:30:43,560
Speaker 12: somebody a half a million a year because they're a

571
00:30:43,600 --> 00:30:46,440
Speaker 12: genius in AI. You know, if you go to nowhere,

572
00:30:46,440 --> 00:30:49,400
Speaker 12: you're going to be making ninety thousand. So that says

573
00:30:49,440 --> 00:30:52,360
Speaker 12: to me that we need to find a way to

574
00:30:52,400 --> 00:30:57,680
Speaker 12: work with the private sector that meets public good. You know,

575
00:30:57,800 --> 00:31:00,920
Speaker 12: how do we ensure that everybody gets warning that they

576
00:31:00,960 --> 00:31:04,920
Speaker 12: need and how do we ensure people get the basic information.

577
00:31:05,400 --> 00:31:05,959
Speaker 3: To do that?

578
00:31:06,280 --> 00:31:10,000
Speaker 12: So I do think we need to think more creatively

579
00:31:10,080 --> 00:31:13,800
Speaker 12: about public private partnerships. You know, right now, there's not

580
00:31:13,880 --> 00:31:16,840
Speaker 12: a lot of mechanisms for the private sector to work

581
00:31:16,880 --> 00:31:20,640
Speaker 12: with the government sector. And I think if we you know,

582
00:31:20,680 --> 00:31:24,320
Speaker 12: if we started rebuilding tomorrow I think that's what we

583
00:31:24,360 --> 00:31:27,360
Speaker 12: would be thinking about, Well, what really makes sense here

584
00:31:27,840 --> 00:31:29,400
Speaker 12: to do in the future.

585
00:31:32,480 --> 00:31:35,160
Speaker 1: Yeah, I'm like, I'm like your run of the mill

586
00:31:35,360 --> 00:31:38,360
Speaker 1: kind of lefty, right, who has some skepticism about what

587
00:31:38,400 --> 00:31:40,760
Speaker 1: the private sector can do, like for the good of

588
00:31:40,800 --> 00:31:44,040
Speaker 1: the people, quote unquote. But it's interesting how this situation

589
00:31:44,160 --> 00:31:45,960
Speaker 1: is so die or that, like we do just kind

590
00:31:45,960 --> 00:31:46,760
Speaker 1: of have to look at it.

591
00:31:47,400 --> 00:31:47,480
Speaker 10: All.

592
00:31:47,600 --> 00:31:49,760
Speaker 3: Right, So we've been, you know, trying to make sense

593
00:31:49,760 --> 00:31:52,880
Speaker 3: of the world through weather. We're going to take a

594
00:31:52,920 --> 00:31:54,640
Speaker 3: little break. I'm going to leave you guys, and then

595
00:31:54,640 --> 00:31:56,280
Speaker 3: we're going to try to make sense of the world

596
00:31:56,760 --> 00:32:13,680
Speaker 3: through Spotify's shuffle feature. This is Devin, Welcome back to

597
00:32:13,760 --> 00:32:17,560
Speaker 3: no such thing. A few weeks ago, actually, we got

598
00:32:17,600 --> 00:32:22,280
Speaker 3: an email from this listener named Austin. He wrote, after

599
00:32:22,400 --> 00:32:25,240
Speaker 3: listening to your latest mail Bag episode, it got me

600
00:32:25,280 --> 00:32:27,680
Speaker 3: thinking about an issue I've run into over the years.

601
00:32:28,160 --> 00:32:31,240
Speaker 3: Whenever I hit shuffle play for a music playlist on Spotify,

602
00:32:31,600 --> 00:32:36,440
Speaker 3: the shuffle feature never seems to be truly random. I

603
00:32:36,480 --> 00:32:39,000
Speaker 3: want to know how does shuffle work for music apps

604
00:32:39,200 --> 00:32:45,120
Speaker 3: and is it truly random.

605
00:32:45,360 --> 00:32:51,760
Speaker 8: I'm Heather mccoldon and I'm a writer, artist, and sometimes

606
00:32:51,800 --> 00:32:52,840
Speaker 8: a tarot reader.

607
00:32:53,400 --> 00:32:55,840
Speaker 3: All right, So I reached out because we got a

608
00:32:55,960 --> 00:33:00,640
Speaker 3: question from a listener who wanted to know if we

609
00:33:00,640 --> 00:33:04,880
Speaker 3: could figure out how Spotify's shuffle feature works. And I

610
00:33:04,920 --> 00:33:08,040
Speaker 3: came across your article in the Financial Times cool in

611
00:33:08,080 --> 00:33:12,280
Speaker 3: which you dive into this. Would you mind, I guess

612
00:33:12,360 --> 00:33:16,720
Speaker 3: kind of setting the scene for me on what happened

613
00:33:16,760 --> 00:33:20,200
Speaker 3: on December fifteenth, let's say, twenty twenty three.

614
00:33:20,520 --> 00:33:23,240
Speaker 2: Yeah, when all these random acts started coming together.

615
00:33:26,720 --> 00:33:29,440
Speaker 8: Yeah, basically, you know, it's like one of those things.

616
00:33:29,560 --> 00:33:36,360
Speaker 8: It's like holiday time, Brooklyn, like the aras chilled. Everyone's

617
00:33:36,440 --> 00:33:38,920
Speaker 8: excited and happy for like the end of the year

618
00:33:39,080 --> 00:33:42,080
Speaker 8: beginning of the new year. I was visiting some friends

619
00:33:42,120 --> 00:33:47,000
Speaker 8: at a dinner party in Williamsburg and you know, too

620
00:33:47,120 --> 00:33:51,880
Speaker 8: much wine, too much festivities, like everyone leaves on masks

621
00:33:51,920 --> 00:33:55,360
Speaker 8: to go home, and yeah, I was feeling like kind

622
00:33:55,400 --> 00:33:59,760
Speaker 8: of nostalgic, like I had a partner in that neighborhood, like,

623
00:34:00,320 --> 00:34:04,200
Speaker 8: and you know, it's just brought back memories and you know,

624
00:34:04,320 --> 00:34:06,640
Speaker 8: my friends were like, oh, like we opened the door

625
00:34:06,680 --> 00:34:08,960
Speaker 8: from the building, there's a cab right there. And I

626
00:34:09,040 --> 00:34:12,279
Speaker 8: was like great, Like the universe is working out for me.

627
00:34:12,600 --> 00:34:15,600
Speaker 8: So yeah, like I get in and I'm just like, oh,

628
00:34:15,719 --> 00:34:18,719
Speaker 8: like went into my liked songs on Spotify and just

629
00:34:18,800 --> 00:34:25,319
Speaker 8: hit shuffle and the song Maps by the yayaya As

630
00:34:25,400 --> 00:34:29,480
Speaker 8: comes on, which is, you know, like this epic contemporary

631
00:34:29,880 --> 00:34:36,279
Speaker 8: love song and basically like the taxi just like decides to,

632
00:34:36,640 --> 00:34:38,960
Speaker 8: you know, go a certain way and we literally like

633
00:34:39,080 --> 00:34:44,480
Speaker 8: pass my ex's building as like this song is about like,

634
00:34:45,120 --> 00:34:47,080
Speaker 8: you know, saying like you know, they don't love you

635
00:34:47,200 --> 00:34:47,560
Speaker 8: like I.

636
00:34:47,480 --> 00:34:48,080
Speaker 4: Love you.

637
00:34:50,120 --> 00:34:56,480
Speaker 8: Then, and I just had this moment of like, what

638
00:34:56,520 --> 00:35:00,839
Speaker 8: the fuck is happening, not just like in my life

639
00:35:00,840 --> 00:35:04,839
Speaker 8: in the universe, but like what is this app even? So, yeah,

640
00:35:04,880 --> 00:35:08,960
Speaker 8: that led me to investigate those Spotify shuffle works in

641
00:35:08,960 --> 00:35:11,879
Speaker 8: a very randabout way. But you know, like I think

642
00:35:11,960 --> 00:35:16,600
Speaker 8: that's like what's kind of cool about existing now like

643
00:35:16,640 --> 00:35:20,800
Speaker 8: in this era where it's like, how are these technologies

644
00:35:20,920 --> 00:35:26,120
Speaker 8: and their glitches affecting our emotions in our personal lives?

645
00:35:26,239 --> 00:35:30,120
Speaker 8: And you know, is it random? Is it all random?

646
00:35:30,280 --> 00:35:30,760
Speaker 2: Et cetera.

647
00:35:31,719 --> 00:35:34,560
Speaker 3: Can you talk me through I guess starting with the

648
00:35:34,600 --> 00:35:39,000
Speaker 3: first iteration of like what Spotify shuffle used to do.

649
00:35:43,239 --> 00:35:51,759
Speaker 8: So basically there's like a very simple, elegant, mathematical description

650
00:35:52,880 --> 00:35:57,760
Speaker 8: of randomness. That's called the fisher Yates algorithm, and essentially

651
00:35:57,840 --> 00:36:02,720
Speaker 8: this is like from Spotify's insight option. Until somewhere between

652
00:36:02,760 --> 00:36:06,400
Speaker 8: twenty twelve and twenty fourteen, it was using fisher Yates

653
00:36:06,760 --> 00:36:12,560
Speaker 8: from my understanding from what's available fisher Yates basically, it's

654
00:36:12,600 --> 00:36:14,520
Speaker 8: an algorithm that will take an.

655
00:36:14,440 --> 00:36:15,480
Speaker 4: Array of values.

656
00:36:15,760 --> 00:36:19,520
Speaker 8: Let's say, like you have numbers one through ten, it

657
00:36:19,560 --> 00:36:24,040
Speaker 8: removes five, and five becomes the first track of the

658
00:36:24,080 --> 00:36:28,080
Speaker 8: shuffle list, and then it just keeps kind of you know,

659
00:36:29,040 --> 00:36:34,240
Speaker 8: randomly selecting the other values to like create a new list, Sasha,

660
00:36:35,280 --> 00:36:38,600
Speaker 8: So every number in that list has an equal chance

661
00:36:38,800 --> 00:36:43,760
Speaker 8: of appearing in the first position or the consequential one.

662
00:36:43,880 --> 00:36:48,040
Speaker 8: It's great because technically that's completely random.

663
00:36:48,840 --> 00:36:49,120
Speaker 4: Right.

664
00:36:49,320 --> 00:36:54,279
Speaker 8: So Spotify engineers were like, this is awesome. You can

665
00:36:54,320 --> 00:36:57,719
Speaker 8: do this in between like two or three lines of code,

666
00:36:58,239 --> 00:37:00,839
Speaker 8: Like it was so simple. They're like, yeah, we sold this,

667
00:37:01,200 --> 00:37:05,279
Speaker 8: and like kind of instantly the user complaints came in

668
00:37:05,480 --> 00:37:09,320
Speaker 8: of like my shuffle is broken. How is it possible

669
00:37:09,360 --> 00:37:11,440
Speaker 8: that you could screw this up? It's so simple?

670
00:37:11,960 --> 00:37:13,719
Speaker 3: And the complaints were at the time because I remember

671
00:37:13,760 --> 00:37:16,279
Speaker 3: seeing them on social right would be like, how's you

672
00:37:16,280 --> 00:37:20,200
Speaker 3: know Spotify Shuffle Ranmouth. They're playing four Taylor Swift songs.

673
00:37:19,960 --> 00:37:23,399
Speaker 8: In a row one hundred percent. But you can see,

674
00:37:23,520 --> 00:37:26,040
Speaker 8: like based on how I described how it works, Ye,

675
00:37:26,680 --> 00:37:31,640
Speaker 8: randomness does create clusters. So it creates like, you know,

676
00:37:32,200 --> 00:37:36,560
Speaker 8: clusters of songs from one album or clusters of songs

677
00:37:36,560 --> 00:37:39,399
Speaker 8: by the same artist, Like each of those songs has

678
00:37:39,440 --> 00:37:44,240
Speaker 8: the exact same chance of like emerging, right, there's nothing

679
00:37:44,400 --> 00:37:50,160
Speaker 8: preventing that. So what the engineers finally determined was users

680
00:37:50,200 --> 00:37:58,120
Speaker 8: were falling for what's called the Monte Carlo fallacy. So

681
00:37:58,560 --> 00:38:03,000
Speaker 8: this is basically a human perception of randomness that is

682
00:38:03,040 --> 00:38:06,760
Speaker 8: not correct, and it's named after this very specific event

683
00:38:06,840 --> 00:38:11,560
Speaker 8: that occurred August eighteenth, nineteen thirteen, at the famous Monte

684
00:38:11,560 --> 00:38:18,000
Speaker 8: Carlo casino. We're essentially at the roulette table. The ball

685
00:38:18,120 --> 00:38:21,440
Speaker 8: kept falling on black. It starts to create this weird

686
00:38:22,320 --> 00:38:25,600
Speaker 8: chaos and like sensation in the casino because people are

687
00:38:25,640 --> 00:38:29,879
Speaker 8: thinking like this isn't possible, right, like you know, two

688
00:38:29,960 --> 00:38:33,160
Speaker 8: or three times sure, then you get into like five

689
00:38:33,239 --> 00:38:38,239
Speaker 8: to ten times, you know, like whirl, like this is unusual.

690
00:38:39,280 --> 00:38:43,040
Speaker 8: So yeah, something's up, and people kind of come down

691
00:38:43,239 --> 00:38:47,000
Speaker 8: with this fever of just expecting like on the next

692
00:38:47,239 --> 00:38:50,840
Speaker 8: turn of the wheel, red, Red is going to happen.

693
00:38:50,920 --> 00:38:53,359
Speaker 8: Red is going to happen because Black has happened all

694
00:38:53,440 --> 00:39:00,680
Speaker 8: these times of beforehand, and that night all fell on

695
00:39:00,760 --> 00:39:04,080
Speaker 8: Black twenty six times in a row, which is just

696
00:39:04,400 --> 00:39:09,840
Speaker 8: like it's like an astronomical this happened, and yeah, it

697
00:39:10,080 --> 00:39:13,759
Speaker 8: just this whole situation pointed out that like it's also

698
00:39:13,840 --> 00:39:18,120
Speaker 8: called the gambler's fallacy. It's where you think previous events

699
00:39:18,160 --> 00:39:23,760
Speaker 8: somehow affect the current event unfolding. So that's why people

700
00:39:23,800 --> 00:39:29,240
Speaker 8: get angry at like the clusters of you know, Addison

701
00:39:29,320 --> 00:39:29,960
Speaker 8: Ray or whatever.

702
00:39:30,239 --> 00:39:32,759
Speaker 3: Yeah, it's like, okay, you're already played in Addison Ray song.

703
00:39:33,360 --> 00:39:36,799
Speaker 3: Therefore their Monte Carlo Felas is like, yeah, it has

704
00:39:36,840 --> 00:39:38,799
Speaker 3: to be something different now because we've done Black so

705
00:39:38,840 --> 00:39:40,719
Speaker 3: many times in a row. We played the Addison and

706
00:39:40,800 --> 00:39:44,200
Speaker 3: Ray song, so therefore an X song cannot possibly be

707
00:39:44,400 --> 00:39:45,440
Speaker 3: diet pepsi, you.

708
00:39:45,400 --> 00:39:48,520
Speaker 8: Know, right, and then like what the fuck it's diet pepsi?

709
00:39:48,719 --> 00:39:52,760
Speaker 8: Like are you kidding me? Right now? The engineers were like, oh, okay,

710
00:39:52,840 --> 00:39:56,319
Speaker 8: people are having like this weird issue, like they don't

711
00:39:56,320 --> 00:40:02,600
Speaker 8: realize what randomness actually is. So then they created essentially,

712
00:40:02,920 --> 00:40:08,000
Speaker 8: like it's not like a hybrid algorithm, but they created

713
00:40:08,040 --> 00:40:13,560
Speaker 8: a different one that mimics or attempts to mimic, the

714
00:40:13,680 --> 00:40:17,440
Speaker 8: human perception of randomness. So it does this way. It

715
00:40:17,440 --> 00:40:22,399
Speaker 8: takes your playlist and it's like, oh, like, I don't know,

716
00:40:22,560 --> 00:40:25,760
Speaker 8: You've got a lot of like Justin Bieber in here,

717
00:40:26,520 --> 00:40:30,120
Speaker 8: So it's spreading Justin Bieber and then with being Justin Bieber,

718
00:40:30,320 --> 00:40:32,120
Speaker 8: it's spreading out the albums.

719
00:40:32,600 --> 00:40:34,000
Speaker 2: Yeah yeah, yeah, yeah.

720
00:40:33,800 --> 00:40:35,720
Speaker 8: So it's called cluster breaking.

721
00:40:36,000 --> 00:40:38,400
Speaker 3: Yeah, you're not going to get to Justin Bieber songs

722
00:40:38,400 --> 00:40:42,840
Speaker 3: within a ten track shuffle from Swag. We're going to

723
00:40:42,880 --> 00:40:44,640
Speaker 3: make sure if you get to Justin Bieber songs, you're

724
00:40:44,640 --> 00:40:45,840
Speaker 3: from completely different albums.

725
00:40:45,960 --> 00:40:46,640
Speaker 4: One hundred percent.

726
00:40:46,960 --> 00:40:51,080
Speaker 8: Yes. So that was their solution. And keep in mind,

727
00:40:51,239 --> 00:40:53,880
Speaker 8: like what I've just described with the cluster breaks, like

728
00:40:53,920 --> 00:40:57,760
Speaker 8: that's from twenty fourteen, so like we there's been nothing

729
00:40:58,440 --> 00:41:02,439
Speaker 8: really that they put out since then that describes what's

730
00:41:02,480 --> 00:41:06,359
Speaker 8: going on. People are now in this weird position where

731
00:41:06,400 --> 00:41:10,839
Speaker 8: they're like they want, like the Fisheryates algorithm to come

732
00:41:10,880 --> 00:41:15,040
Speaker 8: back as like an additional like like if shuffle could

733
00:41:15,120 --> 00:41:19,040
Speaker 8: have yet exactly, and they're like yeah, like I know

734
00:41:19,080 --> 00:41:23,279
Speaker 8: it's bad, but like it would just be preferable to

735
00:41:23,440 --> 00:41:30,160
Speaker 8: like whatever this weird thing is. Our human minds are

736
00:41:30,200 --> 00:41:35,080
Speaker 8: so small that, you know, we're only able to comprehend

737
00:41:35,200 --> 00:41:37,400
Speaker 8: like a very like tiny wedge.

738
00:41:37,520 --> 00:41:39,480
Speaker 4: Of how the world functions.

739
00:41:39,600 --> 00:41:43,200
Speaker 8: Right, So it could be that like we're seeing all

740
00:41:43,280 --> 00:41:47,799
Speaker 8: these things as random, but really it's like, oh, if

741
00:41:47,800 --> 00:41:51,279
Speaker 8: we could actually take a step back and like have

742
00:41:51,360 --> 00:41:54,239
Speaker 8: more of a bird's eye view, potentially these things could

743
00:41:54,239 --> 00:41:57,959
Speaker 8: be connected. And they just operate at a pace that's

744
00:41:58,000 --> 00:42:00,800
Speaker 8: so slow that like over the core of one human

745
00:42:00,880 --> 00:42:05,040
Speaker 8: lifetime it appears random, but over the course of like

746
00:42:05,200 --> 00:42:09,000
Speaker 8: five hundred human lifetimes, it's actually like one slow process

747
00:42:09,000 --> 00:42:11,560
Speaker 8: that makes perfect sense, but we're just not able to

748
00:42:11,640 --> 00:42:17,840
Speaker 8: comprehend it, right, So you know it just randomness is

749
00:42:17,840 --> 00:42:22,120
Speaker 8: so interesting because in a sense, it's undefinable. Yeah, right,

750
00:42:24,360 --> 00:42:27,600
Speaker 8: So that's why it's so interesting when you have something

751
00:42:27,719 --> 00:42:31,759
Speaker 8: that's essentially like a math problem where it's like, come on,

752
00:42:31,880 --> 00:42:35,360
Speaker 8: like I have like I don't know, thirty five hundred songs.

753
00:42:35,840 --> 00:42:39,520
Speaker 8: It can't like how how complicated could it possibly be?

754
00:42:41,040 --> 00:42:43,000
Speaker 8: And it turns out it's really complicated.

755
00:42:47,239 --> 00:42:50,960
Speaker 2: HEATHERN. Mccalldon is the author of the Observable Universe. We're

756
00:42:50,960 --> 00:42:52,680
Speaker 2: going to put a link to it in our show.

757
00:42:52,520 --> 00:43:01,920
Speaker 3: Notess Hews Hews her howss. No Such Thing is a

758
00:43:01,920 --> 00:43:06,520
Speaker 3: production of Kaleidoscope Content. Our executive producers are Kate Osborne

759
00:43:06,560 --> 00:43:09,840
Speaker 3: in Mangesh Hadi Cardur. The show was created by Many Fidel,

760
00:43:09,920 --> 00:43:13,480
Speaker 3: Noah Friedman and Me Devin Joseph. Our theme in credit

761
00:43:13,560 --> 00:43:17,600
Speaker 3: song or by Manny, mixing by Steve Bone. Additional music

762
00:43:17,680 --> 00:43:20,879
Speaker 3: for this episode by certain Self. Our guests this week

763
00:43:20,880 --> 00:43:24,560
Speaker 3: we're doctor Loriana Gaudette from the Weather Company, Mary Glacken

764
00:43:24,880 --> 00:43:28,880
Speaker 3: and Heather mccauden. Thanks to Alec Mia and Austin for

765
00:43:28,960 --> 00:43:31,840
Speaker 3: the questions. If you have something you want us to

766
00:43:31,840 --> 00:43:33,799
Speaker 3: get at the bottom of you can email us at

767
00:43:33,840 --> 00:43:37,400
Speaker 3: Manny Noah Devin at gmail dot com. Sign up for

768
00:43:37,440 --> 00:43:40,839
Speaker 3: our newsletter No Such Thing that shows We'll

769
00:43:40,840 --> 00:43:45,120
Speaker 1: See you guys, those by those such things