Speaker 1: I'm Manny, I'm Noah.
Speaker 2: This is Steven and this is no such thing.
Speaker 3: The show where we settle our dumb arguments and yours
Speaker 3: by actually doing the research. On today's episode, are the
Speaker 3: weather apps actually getting worse? And how random is Spotify's
Speaker 3: shuffle feature?
Speaker 2: Really?
Speaker 1: I no, there's no no such thing, no such thing,
Speaker 1: no such thing.
Speaker 4: Such those such.
Speaker 3: So as the weather seems to be getting more and
Speaker 3: more unpredictable. With flash flooding overtaking our subways in New
Speaker 3: York City, you.
Speaker 5: Can see water spouting out of the walls, people walking
Speaker 5: across flooded platforms, and that rather dirty water.
Speaker 3: To the tragic, deadly floods in central Texas, we.
Speaker 5: Confirm that at least one hundred and thirty five people
Speaker 5: have been killed. This is in addition to the more
Speaker 5: than one hundred people who remain missing across several counties.
Speaker 3: This week, we are turning our attention to our weather
Speaker 3: apps and then nottedly. Many people in our lives have
Speaker 3: been complaining that the weather apps are just not accurate anymore.
Speaker 3: So we're going to get to the bottom of that
Speaker 3: and answer all your other questions about weather apps, including
Speaker 3: this one from my friend.
Speaker 6: Alek Okay, So I want to know why we as
Speaker 6: society can't come together and use feels like temperatures instead
Speaker 6: of actual temperatures as the default, because right now, when
Speaker 6: they tell me it's going to be sixty five degrees,
Speaker 6: I then have to go and do other homework on
Speaker 6: like how wendy is it going to be that day?
Speaker 6: And is it going to be really humid? And it
Speaker 6: seems like they have come up with one number that
Speaker 6: can give me the answer that I'm really looking for here,
Speaker 6: which is do I need a jacket that day?
Speaker 1: That's a great question, and it reminds me of when
Speaker 1: you guys used to make fun of me for saying
Speaker 1: that it feels quote unquote good outside like it feels good,
Speaker 1: and I maintain that many people say that maybe not
Speaker 1: from you know, Connecticut or Long Island, but I just
Speaker 1: wanted to get that off my chest.
Speaker 4: Yeah.
Speaker 2: I love the idea of like a Manny weather app that.
Speaker 3: It just says feels good.
Speaker 1: That's all I need to know. But just to be
Speaker 1: clear that the feels like it's a part of the app,
Speaker 1: I actually don't see it that often. It's a part
Speaker 1: is on the weather? Which app is.
Speaker 3: This all the apps kind of have it now, so
Speaker 3: there's usually app okay.
Speaker 7: And they don't really have much.
Speaker 2: So like, let's pull it up now.
Speaker 3: It's usually you know, the air temp which is up top,
Speaker 3: and then feels like it's usually below that or very deeper.
Speaker 3: So like this, this is the default Apple one. You
Speaker 3: really got to scroll here. So it's eighty seven right
Speaker 3: now from Brooklyn, and then we scroll all the way down,
Speaker 3: feels like ninety one.
Speaker 1: Okay, that is helpful. It's helpful, but I guess I
Speaker 1: do also want to know the real temperature. That's my
Speaker 1: guess is that y'all do you want to know the
Speaker 1: real temp?
Speaker 3: I don't care about the real temperature. It doesn't matter.
Speaker 3: This was especially true remember when we had those really
Speaker 3: really windy days.
Speaker 7: Oh yeah, it's it's yeah, it's in the hot weather.
Speaker 7: It's less of a thing I feel for us anymore.
Speaker 2: I mean, well, no, still thing, because.
Speaker 7: It can be in the cold. I feel like that
Speaker 7: the wind can change it so dramatic.
Speaker 3: They'll be like it's forty degrees out. I'm like, oh,
Speaker 3: this is kind of warm for winter. And then it
Speaker 3: was like the wind is making it feel like it's
Speaker 3: fifteen exactly.
Speaker 7: But it's like I don't hear what the still cold day? Okay,
Speaker 7: I can live in this versus like I'm about to
Speaker 7: die in.
Speaker 3: Free because why are we checking the weather? You're checking
Speaker 3: them whether to see what I gotta wear.
Speaker 1: Yeah, it's true for me, it's.
Speaker 3: We're not you know, like that's what most people checking
Speaker 3: other while they're checking the letter.
Speaker 1: I might wear something different in sixty five degree weather
Speaker 1: than you might. Yeah, I mean you still need to
Speaker 1: know what to wear for you. That's why I want
Speaker 1: to know the real temperature. Yeah, the real temperature is
Speaker 1: the real temperature, no matter who well look.
Speaker 2: At no, no, but no.
Speaker 7: But if it's all right it's sixty degrees but feels
Speaker 7: like ninety, yes, you you want to dress for ninety yes, yeah, but.
Speaker 3: It feels like it's actually better for you because you
Speaker 3: want to know what it's going to feel like, so
Speaker 3: you should know what to wear.
Speaker 2: The actual temperature doesn't know what.
Speaker 7: It actually make a difference if it's actually if it's
Speaker 7: actually sixty.
Speaker 1: Yeah, because I want to know if we've breaken up
Speaker 1: broken any records today.
Speaker 3: Okay, okay, that's different, that's what you're wearing, breaking records is.
Speaker 7: Over here.
Speaker 3: I have a map the historical data. I want to
Speaker 3: make sure that you can assume we.
Speaker 7: Are breaking a record every day.
Speaker 3: So I talked to meteorologists at the Weather Company, okay,
Speaker 3: which you may know for the Weather Channel brand.
Speaker 4: So my name is doctor Loriana Gaudett. I am a
Speaker 4: staff applied meteorological scientist at the Weather Company. I've worked
Speaker 4: primarily within our forecast sciences team, so I'm not operationally
Speaker 4: forecasting the weather, but i am working on the science
Speaker 4: behind the scenes per se to improve our forecasts.
Speaker 3: All right, So one of the first questions that we
Speaker 3: got on weather is this idea of real feel. Right,
Speaker 3: you get our actual temperature and then we get the
Speaker 3: real field tamp. So can we start by just having
Speaker 3: you break down what is the real field tamp? How
Speaker 3: is that calculated?
Speaker 4: So the real field temperature is more so known as
Speaker 4: the feels like temperature, and what it's composed of is
Speaker 4: a combination of the heat index and the windshiel temperature.
Speaker 4: The heat index itself is a combination of the temperature
Speaker 4: and the relative humidity and how those two interchangeably play
Speaker 4: together to affect how we feel when we're outside. And
Speaker 4: so if it's really humid outside, less of that sweat
Speaker 4: is able to evaporate off of our bodies, and we
Speaker 4: are less effectively cooling ourselves down, which makes it feel
Speaker 4: much warmer. As an example, if it's ninety degrees fahrenheit
Speaker 4: outside with a relative humidity of seventy percent, the heat
Speaker 4: index or the feels like temperature in those conditions is
Speaker 4: around one hundred and five degrees fahrenheit, which is very
Speaker 4: warm and it can become dangerous with heat stress impacts.
Speaker 4: And then on the other side of the temperature spectrum,
Speaker 4: the feels like kind of points to the wind chill temperature.
Speaker 4: So this time we're considering the effect of wind speed
Speaker 4: on temperature, and the reason that that's important is because
Speaker 4: the wind is effectively transporting the heat away from our body,
Speaker 4: which is cooling down our body or our skin temperature,
Speaker 4: and then over time core body temperature, which can also
Speaker 4: come dangerous in its own way through frostbite or through
Speaker 4: hypothermia effects. So another example here is that if you
Speaker 4: have a temperature of thirty degrees fahrenheit and a wind
Speaker 4: speed of fifteen miles an hour, your wind chill or
Speaker 4: feels like temperature would be nineteen degrees fahrenheit, give or take.
Speaker 1: Interesting. So basically like seventy degrees with no humidity feels
Speaker 1: a lot different than seventy degrees with humidity, and some
Speaker 1: you know, X amount of miles per hour.
Speaker 3: Wind exactly to take away I got from talking to her,
Speaker 3: it was like, the feels like takes into account how
Speaker 3: the temperature affects our body, and it's not just all right,
Speaker 3: here's the air temp.
Speaker 7: Yeah, it's not so abstract. I guess it's like kind
Speaker 7: of an algorithm basically. Yeah, and yeah, it's about how
Speaker 7: you know your sweat's going to be a vap rated
Speaker 7: or not or whatever. That's it's more thorough than I thought.
Speaker 1: Yeah, I kind of thought. A scientist and a lab
Speaker 1: coat walked outside, I was.
Speaker 7: Like, okay, sixty five, Yeah, we wan'll make them feel
Speaker 7: good today.
Speaker 3: But something is interesting about feels like, which we talked
Speaker 3: about a little bit already, is that someone like manny,
Speaker 3: if it's seventy tract Yeah, if it's seventy seven degrees outside,
Speaker 3: man's complaining as hot?
Speaker 1: Yeah, well, not that it's hot, it's too It's like,
Speaker 1: does it's warmer than I prefer?
Speaker 7: It feels bad?
Speaker 3: It feels bad, bad, whereas with us that's seventy seven
Speaker 3: temperature bad, not bad.
Speaker 7: So well, I.
Speaker 2: Don't know, Okay, don't put that on that.
Speaker 3: Okay, what do you what's what seventy seven.
Speaker 7: Of you that could get pretty hot?
Speaker 2: Okay?
Speaker 7: Seventy three is comfy, okay, And I want a little
Speaker 7: bit of clouds.
Speaker 3: And speaking of shade versus sun, when they take the
Speaker 3: temperature outside, did you know that they're taking into shade?
Speaker 1: Whoa, oh, why is that?
Speaker 7: I did not know.
Speaker 2: Well, we're gonna find out.
Speaker 4: So temperature and heat index are recorded in the shade,
Speaker 4: and so if oh, yeah, what I know, Yes, it's
Speaker 4: it's super surprising. But the reason is we don't want
Speaker 4: the thermometer that's measuring the temperature to heat up and
Speaker 4: not be able to actually tell us what the air
Speaker 4: temperature is. We want that not the temperature with the thermometer.
Speaker 4: So when you were outside doing whatever activity in the sun,
Speaker 4: it's going to feel warmer than the reported air temperature
Speaker 4: and even the heat index. So that's important to keep
Speaker 4: in mind. And then the other component is that scientists
Speaker 4: when they were deriving these heat indices and the wind chill,
Speaker 4: they had to make certain approximations and assumptions. So some
Speaker 4: of those involve like how tall are you, how much
Speaker 4: do you weigh, how much do you sweat, what kind
Speaker 4: of clothes are you're wearing, so on and so forth,
Speaker 4: and that obviously can't account for each of our unique
Speaker 4: body compositions and circumstances, but it's our best general approximation
Speaker 4: for what you might experience when you walk out the door.
Speaker 3: The shade thing really blew my mind because I'm every
Speaker 3: time I go outside and I'm in the shade, I'm like, oh,
Speaker 3: this is cooler than what's the app because I'm in
Speaker 3: the shade. But that's the best case scenario. You're only
Speaker 3: going to get hotter if it's ninety degrees. That means
Speaker 3: if you're in the sun, it's hotter than that.
Speaker 7: Yeah. Yeah, if you're walking around, you're gonna be sucked
Speaker 7: there sometimes.
Speaker 3: Yeah.
Speaker 1: There's so many variables, which is why I just try
Speaker 1: to distill them all into good or bad. Yeah.
Speaker 3: I know.
Speaker 7: Now I'm kind of like many systems well because it's like, well,
Speaker 7: this makes me want them to have shade, Yeah, because
Speaker 7: I need to know.
Speaker 1: You open the weather app. It's just like an Excel
Speaker 1: sph Yeah.
Speaker 7: Ever dial in and then you dial in your outfit yeah,
Speaker 7: and your skin tone calculator.
Speaker 8: I mean.
Speaker 7: Imagine you have an or a ring.
Speaker 2: Yeah, that's that's a lot of data.
Speaker 8: Yeah.
Speaker 7: And then you didn't tell weather you take a selfie
Speaker 7: it skins your clothes.
Speaker 3: You don't even need to take a selfie, yeah, right,
Speaker 3: your phone.
Speaker 1: Yeah, and and you just exists and Peter Tiel buys
Speaker 1: all of this data. Yeah, exactly, and then he but
Speaker 1: he tells you it's going to be only seventy two today,
Speaker 1: or it's only going to feel like seventy.
Speaker 3: Two when we look at the weather app. Besides many
Speaker 3: lying and saying he wants to look at historical data,
Speaker 3: we're looking at it to see what we should wear
Speaker 3: to go outside. Do I need to bring an umbrella?
Speaker 3: Why don't we have this as the default? If I
Speaker 3: open up the app, I want to see feels like
Speaker 3: and then if I want to search for the actual
Speaker 3: air temp, I could find that lower down.
Speaker 2: Why are we doing the reverse?
Speaker 4: We take a certain approach within our Weather Channel app,
Speaker 4: and we've supply both temperatures, so not just the temperature,
Speaker 4: but also the fields like temperature, so that you can
Speaker 4: have both data points and use those to make decisions
Speaker 4: about how you want to plan your day or go
Speaker 4: about your day. But yeah, it's a really good question
Speaker 4: because it gets down to the core of like the
Speaker 4: human experience of course, but there's other impacts and implications
Speaker 4: of the temperature that also don't relate to humans. So
Speaker 4: some of those might be like if a farmer, for example,
Speaker 4: is trying to figure out when to plant their crop
Speaker 4: or protect their crop, they are looking at the actual temperature.
Speaker 4: So that's what our weather forecast models are actually outputting,
Speaker 4: is the real air temperature, and that's what we are
Speaker 4: measuring and looking at when we are monitoring the climate,
Speaker 4: for example, and it allows us to compare the observations
Speaker 4: of temperature across the country, across the world, and over time. Whereas,
Speaker 4: like with everything that we talked about with the feels
Speaker 4: like temperature, there's a lot of approximations that are being used,
Speaker 4: and so some weather services given the same environmental setup
Speaker 4: might come up with different values. That makes it really
Speaker 4: difficult for that inter comparison component. But if you yourself,
Speaker 4: as the user of a weather app find that the
Speaker 4: feels like temperature gives you everything that you need to
Speaker 4: know you can set that as your own to fault temperature.
Speaker 4: But we just don't want to make that assumption for
Speaker 4: all of our users.
Speaker 2: So they're just very considerate of me at this point.
Speaker 3: The thing that impacts most of us would be it
Speaker 3: feels like temperature that should be to default. Make everybody
Speaker 3: else look for the other stuff.
Speaker 1: Please contact your company.
Speaker 3: I guess do you feel I understand all everything that
Speaker 3: she said. When I'm looking at my app, I'm not
Speaker 3: looking for climate. You know, over the last ten years,
Speaker 3: I don't have any crops. I guess I can do
Speaker 3: it within my own but I feel like as a
Speaker 3: country we need to we neither do real feel I'm
Speaker 3: not saying get rid of the real number. I'm just
Speaker 3: saying put that lower.
Speaker 7: See why I can't?
Speaker 1: Why not just side by side?
Speaker 7: Oh slash yeah, pretty much? Okay, big one, I mean,
Speaker 7: pick whichever one you want, big one then basically an
Speaker 7: arrow or slash, Yeah, basically, and you just know that
Speaker 7: you just know, okay, this is you know, you know
Speaker 7: the real thing. And here's the thing. It wouldn't take
Speaker 7: up that much space. They're digits and like the numbers
Speaker 7: are hopefully not Yeah, yeah, hopefully four digits. You're look
Speaker 7: at that total.
Speaker 1: I'm fine with the normal one. I'm fine with the
Speaker 1: regular Yeah.
Speaker 7: My main concern with these things is doing an umbrella
Speaker 7: or not, which I forget to look for most of
Speaker 7: the time.
Speaker 3: Anymore, Speaking of rain, Manny, do you want to explain
Speaker 3: this Twitter argument that people have been having.
Speaker 1: When it's raining or when it might looks like it
Speaker 1: might rain. You go on the app and says, let's say,
Speaker 1: for example, thirty five percent. Doesn't say what the thirty
Speaker 1: five percent is. So some people think that it's thirty
Speaker 1: five percent chance it's going to rain where you're standing.
Speaker 1: Other people think it's in a rain on thirty five
Speaker 1: percent of the area around you, yes, and a third
Speaker 1: group of people think that's effectively the same thing.
Speaker 7: Well, well what do you what do you guys think?
Speaker 3: Before I did this interview, Yeah, my thought was it's
Speaker 3: a thirty five percent chance it's going to rain.
Speaker 7: That's not that's my thought too, because the area doesn't
Speaker 7: make because it's like, well, what's what area?
Speaker 1: A lot of people, a lot of people online, these
Speaker 1: are not experts, but they were like, no, that is
Speaker 1: what they're telling you. Well, what did you think I
Speaker 1: was thought, thirty five percent chance is going to rain.
Speaker 7: Chance, Yeah, where you are? I agree, That's what I'm
Speaker 7: looking at, and that's how I feel like my lived
Speaker 7: experience is.
Speaker 1: You know, it's like, well, that's where it gets tricky.
Speaker 1: It's it's never right to me.
Speaker 7: But yeah, I always assumed it's basically there is a
Speaker 7: percentage of chance that it will rain. Not whatever area
Speaker 7: I'm taking in, it's going to cover sixty percent of this.
Speaker 3: Yeah, yeah, that.
Speaker 7: Doesn't really that would never cross our behind me that much.
Speaker 4: So this is a common area of confusion for users,
Speaker 4: and even in our own field of meteorology, it can
Speaker 4: be really hard to pinpoint what we are actually communicating
Speaker 4: with probability of precipitation or pop In my experience and
Speaker 4: knowledge base, I think both of those interpretations are true
Speaker 4: in their own right, which of course makes it even
Speaker 4: more confusing. But I can I can share from the
Speaker 4: Weather Channel app perspective, the way that we're communicating it
Speaker 4: is really in your surrounding area. What is the likelihood
Speaker 4: of precipitation or of rain impacting you within whatever time
Speaker 4: frame you're interested in, So, whether it's today, the next hour,
Speaker 4: fifteen minutes, and less so the amount of area that
Speaker 4: is going to be impacted. A couple misconceptions that also
Speaker 4: come up to are about the intensity of precipitation from pop.
Speaker 4: So sometimes people will see like, oh, there's a ninety
Speaker 4: percent chance of rain, it's going to be a downpour.
Speaker 4: We are indicating the likelihood of rain, of measurable rain,
Speaker 4: any rain, and so generally it has nothing to do
Speaker 4: with the intensity of that precipitation, which is important to
Speaker 4: keep in mind. And then the other component two is
Speaker 4: you know, sometimes people think, Okay, there's a fifty percent
Speaker 4: chance of pop in New York City, so only half
Speaker 4: of the day will be wet, And again you could
Speaker 4: have that fifty percent chance, there might only be a
Speaker 4: couple hours that are impacted, and then you have a
Speaker 4: beautiful remainder of the day. I think that it really
Speaker 4: lays on us meteorologists to make sure that we're communicating
Speaker 4: what that likely hood and probability means, especially if it
Speaker 4: does vary, you know, from a news broadcaster or a
Speaker 4: meteorologist who's on TV trying to communicate the likelihood of rain,
Speaker 4: to a push alert from your weather app telling you
Speaker 4: that there's rain coming within the next thirty minutes.
Speaker 1: That is really funny because the idea that the percent
Speaker 1: chance is the total coverage is such a like, actually,
Speaker 1: I'm smart, and this is what they're really saying, fools
Speaker 1: in conversation. I've heard it so many times and it's
Speaker 1: just wrong.
Speaker 7: And then I mean it's kind of fascinating, like people
Speaker 7: are just taking this and twisting it in any direction
Speaker 7: where it's like, okay, ten percent, that's ten percent of
Speaker 7: the day. Yeah, so yeah, okay, so whatever ten percent
Speaker 7: of twenty four hours is. It's like, yeah, thinking of
Speaker 7: all the different ways to map it out, it's kind
Speaker 7: of that's actually like a SAT question or something.
Speaker 1: Yeah, I heard the rain is only coming from ten
Speaker 1: percent of the clouds.
Speaker 2: Yeah, exactly. Yeah, but there.
Speaker 3: I thought it was interesting that they're kind of they
Speaker 3: could both be right depending on your weather.
Speaker 7: You need explanation.
Speaker 2: I mean, I think that you know, the apps that
Speaker 2: we're looking at or telling us, I.
Speaker 7: Would hope so frankly, I think that you know, maybe
Speaker 7: they could do a better job than of getting the
Speaker 7: word out there hopefully. You know, we're going to do
Speaker 7: our part today with our you know, hundreds of thousands
Speaker 7: of listeners.
Speaker 3: Some would say millions, So yeah, you could argue that millions.
Speaker 3: Maybe all right, we are going to take a quick
Speaker 3: break and when we get back, we are going to
Speaker 3: find out are the weather apps actually getting worse? And
Speaker 3: it's Trump to blame. So in last week's mail bag,
Speaker 3: we were debating who gets the right of way between
Speaker 3: a school bus with the stop sign out, an ambulance
Speaker 3: with the siren on, and a funeral procession, and from
Speaker 3: North Carolina called in with this insight.
Speaker 9: I am a state wide law enforcement officer here in
Speaker 9: North Carolina. If I am running lights and sirens, I
Speaker 9: am allowed.
Speaker 10: To go through a soft sign.
Speaker 9: People legally have to yield to me. But if I
Speaker 9: run a soft sign and hit someone, it is still
Speaker 9: my fault, and I, as a driver, would.
Speaker 10: Not only be responsible to my agency, but I could
Speaker 10: also be personally legally financially responsible funeral processions.
Speaker 9: You guys are correct, that is a courtesy.
Speaker 10: It's not a legal thing, and but it is it
Speaker 10: drive me.
Speaker 9: Grace to get down here in the South, they love
Speaker 9: them at funeral profession.
Speaker 3: So we're going to try to include more listener responses
Speaker 3: to our episodes like this in the future, So you
Speaker 3: can email us at Manny Noladevin at gmail dot com
Speaker 3: or leave us a voicemail at the number in our
Speaker 3: show notes and hey, you may includes you in a
Speaker 3: future episode. All right, let's get back to the show.
Speaker 3: We do have one final weather question from Mia.
Speaker 11: I have a question, and this is just bugging me
Speaker 11: so much, especially this summer, our weather apps getting less
Speaker 11: accurate because I feel like they are like these days,
Speaker 11: I just don't feel like I can trust the weather
Speaker 11: app to tell me exactly what's going to happen, specifically
Speaker 11: if it's going to rain, like over this the course
Speaker 11: of the summer, It's been a rainy summer in New York,
Speaker 11: and now I feel like I'm becoming this kind of
Speaker 11: like medieval farmer where I'm sort of gauging what's happening
Speaker 11: in the sky, and I feel like I am becoming
Speaker 11: a much better predictor of what's actually going to happen
Speaker 11: than what my phone is telling me. I'm just starting
Speaker 11: to be like, what's big weather even there? So if
Speaker 11: you could answer for me, is it getting less accurate?
Speaker 11: What's the reason should I trust this app anymore?
Speaker 4: That would be great. Thank you.
Speaker 2: I put this question to our meteorologists from the weather.
Speaker 7: She was sweam bullets at this one.
Speaker 3: Is the weather more unpredictable or is this on in
Speaker 3: our heads? And you know, has nothing really changed.
Speaker 4: So that's a super intriguing question. No, as a short answer,
Speaker 4: the aps are not getting worse at predicting rain. I
Speaker 4: think there's a there's a few different things at play here,
Speaker 4: one of which you pointed at. It's psychological, so we're
Speaker 4: more likely to remember when the weather apps get it wrong,
Speaker 4: versus that when we are seeing a prediction of, oh,
Speaker 4: it's going to rain in ten minutes, let me make
Speaker 4: sure I grab my rain jacket before I go out,
Speaker 4: and it ends up being correct, and so you're prepared.
Speaker 4: It becomes a non event and you forget about it.
Speaker 4: But let's say that if someone is outside it starts pouring,
Speaker 4: there's absolutely no warning. Of course, you're going to be
Speaker 4: annoyed that you had no notice that that was going
Speaker 4: to happen. And there's a few reasons that those specific
Speaker 4: circumstances can crop up. This example of stepping outside and
Speaker 4: being met with an unexpected downpour of rain can largely
Speaker 4: happen because even if the radar is able to pick
Speaker 4: up on a storm let's say it just started raining
Speaker 4: within the past few minutes. The weather radar just got
Speaker 4: that data. So for example, that data needs to be
Speaker 4: ingested into our back end systems or any other weather
Speaker 4: app systems to process that data, figure out where that
Speaker 4: storm is using proprietary software and algorithms, predict where that
Speaker 4: storm is headed and if it will intensify or again dissipate,
Speaker 4: get that forecast to our technology system downstream, and then
Speaker 4: those then deliver that information to your app. So that
Speaker 4: takes a bit of wall clock time for all of
Speaker 4: that process to happen. So unfortunately, there are those situations
Speaker 4: where rain can impact you within that window of time
Speaker 4: that we're trying to get that information to you as
Speaker 4: fast as possible, but perhaps missed that window of opportunity.
Speaker 1: I've never thought about a situation where the weather app
Speaker 1: was wrong, because it just was like trying to catch
Speaker 1: up to the real time m hm. You would think.
Speaker 3: You're supposed to be predicting.
Speaker 7: It's like, don't blame me, I'm not the weather company.
Speaker 3: Hello.
Speaker 1: And now that I remember what Mia was complaining about,
Speaker 1: I think her concern is that the apps are more
Speaker 1: frequently incorrect, not that they're ever incorrect, but that it's
Speaker 1: happening more often.
Speaker 3: Yes, yeah, she thinks it's getting the apps are getting worse.
Speaker 3: So the Weather Company said, no, that's you know, the
Speaker 3: apps aren't getting worse, but they work at the Weather Company.
Speaker 3: So I wanted to call up someone who can maybe
Speaker 3: speak a bit more freely about whether or not these
Speaker 3: apps are getting worse and do we need to worry,
Speaker 3: especially with these Doge cuts, about them being worse in
Speaker 3: the future.
Speaker 2: So I called the Mary Glacken.
Speaker 12: I'm the former deputy undersecretary for NOAH for operations there
Speaker 12: and I had worked for Noah for just about thirty
Speaker 12: five years. I also am on an AI board and
Speaker 12: things like that, so today these days I'm mainly in
Speaker 12: an advisory capacity.
Speaker 2: She also worked at the Weather Company back in the day.
Speaker 3: All right, great.
Speaker 7: Job finding, Mary.
Speaker 1: I always have stuff to say about my former employees.
Speaker 1: It should be good to take.
Speaker 3: So, you know, the big thing that we were concerned
Speaker 3: about and seeing the headlines is that with the Doge cuts,
Speaker 3: that there's just less people working, which is why the
Speaker 3: apps are getting worse. So I asked Mary, like, where
Speaker 3: do we currently stand.
Speaker 12: So let me talk about it in terms of personnel first,
Speaker 12: because one of the major things that happened was the exodus.
Speaker 12: You know, there was the Fork in the.
Speaker 13: Road, another unprecedented action from the Trump administration, offering more
Speaker 13: than two million federal workers a buyout. The workers receiving
Speaker 13: an email with the subject line fork in the Road,
Speaker 13: giving them a choice resign and be paid through September
Speaker 13: or risk being fired.
Speaker 12: So the National Weather Service and all of Noah are
Speaker 12: down significantly in terms of people, something approaching as much
Speaker 12: as twenty percent in some locations. So the National Weather
Speaker 12: Service did lose a lot of senior people. In terms
Speaker 12: of funding, they had actually been funded by Congress last
Speaker 12: year for a full year at a reasonable level, but
Speaker 12: the Trump administration has delayed spending of those funds, so
Speaker 12: they've really been slow rolling contracts to go out for
Speaker 12: various things. I don't have any figures here, but it's
Speaker 12: safe to assume there's a fair amount of money still
Speaker 12: on the table. But the direction that we saw this
Speaker 12: week was a spend plan for the rest of this
Speaker 12: fiscal year is clawing back that money. It's saying you're
Speaker 12: not allowed to spend that anymore. So that's that's kind
Speaker 12: of where we are. We've seen some news about them hiring.
Speaker 5: So earlier this year, President Trump's Doze Office cut more
Speaker 5: than five hundred Weather employees. Well now WS says they're
Speaker 5: going to hire four hundred and fifty meteorologists, hydrologists, and
Speaker 5: radar technicians.
Speaker 12: They're all entry level positions, So you know you're not
Speaker 12: going to hire back the expertise that's been lost that
Speaker 12: one of the senior officials that Noah career official had quoted,
Speaker 12: that was twenty seven thousand years of expertise that walked
Speaker 12: out the door between February in April.
Speaker 3: It's just going to be a lot of people, a
Speaker 3: lot of college who don't have the expertise, and now
Speaker 3: they also don't have the leadership to mentor those people.
Speaker 1: They just went from an NFL team to like JV
Speaker 1: high school team.
Speaker 3: So I wanted Mary to answer the question because I
Speaker 3: couldn't take the Weather Company's word for it.
Speaker 2: Are the apps getting worse?
Speaker 12: The apps haven't degraded at all. I think the worries
Speaker 12: that we have with the Trump administration is if they
Speaker 12: move forward with their plans. Literally one of the plans
Speaker 12: is to eliminate all of Noah's research arm So and
Speaker 12: that's all our future. You know, how do we do
Speaker 12: a better job in forecasting that whole section would be eliminated?
Speaker 12: Which is crazy, but that's the plan. I do want
Speaker 12: to kind of remind folks that Congress has a say
Speaker 12: in this whole thing. Both the House and Senate that
Speaker 12: have given their plan on how Noah would be funded.
Speaker 12: The Senate does a really pretty good job and keeping
Speaker 12: Noah funded. The House has some significant cuts in it,
Speaker 12: but nothing as draconian as the administration is asking for.
Speaker 12: I think the problem is that Congress won't be able
Speaker 12: to agree on the overall spending, which will really basically
Speaker 12: allow the Trump administration to continue to do what it
Speaker 12: wants to do.
Speaker 3: I was curious if we've kind of hit the point
Speaker 3: of no return, you know, see if President Biden runs
Speaker 3: again and.
Speaker 2: Wins, yeah, you know which, we're in eight can we
Speaker 2: get back to where we were?
Speaker 12: One of the things I'd like to make clear with
Speaker 12: you know, all of my experience with Noah, both in
Speaker 12: Noah and then working with Noah outside is I would
Speaker 12: not be the one arguing for status quo. You know,
Speaker 12: I'm not here saying oh, it was a perfect world
Speaker 12: and now this has happened. I think it will be
Speaker 12: possible to get us back if we can keep people
Speaker 12: in the field and interested in our problems. You know,
Speaker 12: you have the whole advent of AI, and AI is
Speaker 12: impacting our value chain all the way from taking an
Speaker 12: observation to helping somebody make a decision on what to
Speaker 12: do that day. So when I look at AI, it's
Speaker 12: pretty clear to me and many is the private sector
Speaker 12: is ahead of where Noah is with AI, and that's
Speaker 12: no great chock. You know, the private sector can pay
Speaker 12: somebody a half a million a year because they're a
Speaker 12: genius in AI. You know, if you go to nowhere,
Speaker 12: you're going to be making ninety thousand. So that says
Speaker 12: to me that we need to find a way to
Speaker 12: work with the private sector that meets public good. You know,
Speaker 12: how do we ensure that everybody gets warning that they
Speaker 12: need and how do we ensure people get the basic information.
Speaker 3: To do that?
Speaker 12: So I do think we need to think more creatively
Speaker 12: about public private partnerships. You know, right now, there's not
Speaker 12: a lot of mechanisms for the private sector to work
Speaker 12: with the government sector. And I think if we you know,
Speaker 12: if we started rebuilding tomorrow I think that's what we
Speaker 12: would be thinking about, Well, what really makes sense here
Speaker 12: to do in the future.
Speaker 1: Yeah, I'm like, I'm like your run of the mill
Speaker 1: kind of lefty, right, who has some skepticism about what
Speaker 1: the private sector can do, like for the good of
Speaker 1: the people, quote unquote. But it's interesting how this situation
Speaker 1: is so die or that, like we do just kind
Speaker 1: of have to look at it.
Speaker 10: All.
Speaker 3: Right, So we've been, you know, trying to make sense
Speaker 3: of the world through weather. We're going to take a
Speaker 3: little break. I'm going to leave you guys, and then
Speaker 3: we're going to try to make sense of the world
Speaker 3: through Spotify's shuffle feature. This is Devin, Welcome back to
Speaker 3: no such thing. A few weeks ago, actually, we got
Speaker 3: an email from this listener named Austin. He wrote, after
Speaker 3: listening to your latest mail Bag episode, it got me
Speaker 3: thinking about an issue I've run into over the years.
Speaker 3: Whenever I hit shuffle play for a music playlist on Spotify,
Speaker 3: the shuffle feature never seems to be truly random. I
Speaker 3: want to know how does shuffle work for music apps
Speaker 3: and is it truly random.
Speaker 8: I'm Heather mccoldon and I'm a writer, artist, and sometimes
Speaker 8: a tarot reader.
Speaker 3: All right, So I reached out because we got a
Speaker 3: question from a listener who wanted to know if we
Speaker 3: could figure out how Spotify's shuffle feature works. And I
Speaker 3: came across your article in the Financial Times cool in
Speaker 3: which you dive into this. Would you mind, I guess
Speaker 3: kind of setting the scene for me on what happened
Speaker 3: on December fifteenth, let's say, twenty twenty three.
Speaker 2: Yeah, when all these random acts started coming together.
Speaker 8: Yeah, basically, you know, it's like one of those things.
Speaker 8: It's like holiday time, Brooklyn, like the aras chilled. Everyone's
Speaker 8: excited and happy for like the end of the year
Speaker 8: beginning of the new year. I was visiting some friends
Speaker 8: at a dinner party in Williamsburg and you know, too
Speaker 8: much wine, too much festivities, like everyone leaves on masks
Speaker 8: to go home, and yeah, I was feeling like kind
Speaker 8: of nostalgic, like I had a partner in that neighborhood, like,
Speaker 8: and you know, it's just brought back memories and you know,
Speaker 8: my friends were like, oh, like we opened the door
Speaker 8: from the building, there's a cab right there. And I
Speaker 8: was like great, Like the universe is working out for me.
Speaker 8: So yeah, like I get in and I'm just like, oh,
Speaker 8: like went into my liked songs on Spotify and just
Speaker 8: hit shuffle and the song Maps by the yayaya As
Speaker 8: comes on, which is, you know, like this epic contemporary
Speaker 8: love song and basically like the taxi just like decides to,
Speaker 8: you know, go a certain way and we literally like
Speaker 8: pass my ex's building as like this song is about like,
Speaker 8: you know, saying like you know, they don't love you
Speaker 8: like I.
Speaker 4: Love you.
Speaker 8: Then, and I just had this moment of like, what
Speaker 8: the fuck is happening, not just like in my life
Speaker 8: in the universe, but like what is this app even? So, yeah,
Speaker 8: that led me to investigate those Spotify shuffle works in
Speaker 8: a very randabout way. But you know, like I think
Speaker 8: that's like what's kind of cool about existing now like
Speaker 8: in this era where it's like, how are these technologies
Speaker 8: and their glitches affecting our emotions in our personal lives?
Speaker 8: And you know, is it random? Is it all random?
Speaker 2: Et cetera.
Speaker 3: Can you talk me through I guess starting with the
Speaker 3: first iteration of like what Spotify shuffle used to do.
Speaker 8: So basically there's like a very simple, elegant, mathematical description
Speaker 8: of randomness. That's called the fisher Yates algorithm, and essentially
Speaker 8: this is like from Spotify's insight option. Until somewhere between
Speaker 8: twenty twelve and twenty fourteen, it was using fisher Yates
Speaker 8: from my understanding from what's available fisher Yates basically, it's
Speaker 8: an algorithm that will take an.
Speaker 4: Array of values.
Speaker 8: Let's say, like you have numbers one through ten, it
Speaker 8: removes five, and five becomes the first track of the
Speaker 8: shuffle list, and then it just keeps kind of you know,
Speaker 8: randomly selecting the other values to like create a new list, Sasha,
Speaker 8: So every number in that list has an equal chance
Speaker 8: of appearing in the first position or the consequential one.
Speaker 8: It's great because technically that's completely random.
Speaker 4: Right.
Speaker 8: So Spotify engineers were like, this is awesome. You can
Speaker 8: do this in between like two or three lines of code,
Speaker 8: Like it was so simple. They're like, yeah, we sold this,
Speaker 8: and like kind of instantly the user complaints came in
Speaker 8: of like my shuffle is broken. How is it possible
Speaker 8: that you could screw this up? It's so simple?
Speaker 3: And the complaints were at the time because I remember
Speaker 3: seeing them on social right would be like, how's you
Speaker 3: know Spotify Shuffle Ranmouth. They're playing four Taylor Swift songs.
Speaker 8: In a row one hundred percent. But you can see,
Speaker 8: like based on how I described how it works, Ye,
Speaker 8: randomness does create clusters. So it creates like, you know,
Speaker 8: clusters of songs from one album or clusters of songs
Speaker 8: by the same artist, Like each of those songs has
Speaker 8: the exact same chance of like emerging, right, there's nothing
Speaker 8: preventing that. So what the engineers finally determined was users
Speaker 8: were falling for what's called the Monte Carlo fallacy. So
Speaker 8: this is basically a human perception of randomness that is
Speaker 8: not correct, and it's named after this very specific event
Speaker 8: that occurred August eighteenth, nineteen thirteen, at the famous Monte
Speaker 8: Carlo casino. We're essentially at the roulette table. The ball
Speaker 8: kept falling on black. It starts to create this weird
Speaker 8: chaos and like sensation in the casino because people are
Speaker 8: thinking like this isn't possible, right, like you know, two
Speaker 8: or three times sure, then you get into like five
Speaker 8: to ten times, you know, like whirl, like this is unusual.
Speaker 8: So yeah, something's up, and people kind of come down
Speaker 8: with this fever of just expecting like on the next
Speaker 8: turn of the wheel, red, Red is going to happen.
Speaker 8: Red is going to happen because Black has happened all
Speaker 8: these times of beforehand, and that night all fell on
Speaker 8: Black twenty six times in a row, which is just
Speaker 8: like it's like an astronomical this happened, and yeah, it
Speaker 8: just this whole situation pointed out that like it's also
Speaker 8: called the gambler's fallacy. It's where you think previous events
Speaker 8: somehow affect the current event unfolding. So that's why people
Speaker 8: get angry at like the clusters of you know, Addison
Speaker 8: Ray or whatever.
Speaker 3: Yeah, it's like, okay, you're already played in Addison Ray song.
Speaker 3: Therefore their Monte Carlo Felas is like, yeah, it has
Speaker 3: to be something different now because we've done Black so
Speaker 3: many times in a row. We played the Addison and
Speaker 3: Ray song, so therefore an X song cannot possibly be
Speaker 3: diet pepsi, you.
Speaker 8: Know, right, and then like what the fuck it's diet pepsi?
Speaker 8: Like are you kidding me? Right now? The engineers were like, oh, okay,
Speaker 8: people are having like this weird issue, like they don't
Speaker 8: realize what randomness actually is. So then they created essentially,
Speaker 8: like it's not like a hybrid algorithm, but they created
Speaker 8: a different one that mimics or attempts to mimic, the
Speaker 8: human perception of randomness. So it does this way. It
Speaker 8: takes your playlist and it's like, oh, like, I don't know,
Speaker 8: You've got a lot of like Justin Bieber in here,
Speaker 8: So it's spreading Justin Bieber and then with being Justin Bieber,
Speaker 8: it's spreading out the albums.
Speaker 2: Yeah yeah, yeah, yeah.
Speaker 8: So it's called cluster breaking.
Speaker 3: Yeah, you're not going to get to Justin Bieber songs
Speaker 3: within a ten track shuffle from Swag. We're going to
Speaker 3: make sure if you get to Justin Bieber songs, you're
Speaker 3: from completely different albums.
Speaker 4: One hundred percent.
Speaker 8: Yes. So that was their solution. And keep in mind,
Speaker 8: like what I've just described with the cluster breaks, like
Speaker 8: that's from twenty fourteen, so like we there's been nothing
Speaker 8: really that they put out since then that describes what's
Speaker 8: going on. People are now in this weird position where
Speaker 8: they're like they want, like the Fisheryates algorithm to come
Speaker 8: back as like an additional like like if shuffle could
Speaker 8: have yet exactly, and they're like yeah, like I know
Speaker 8: it's bad, but like it would just be preferable to
Speaker 8: like whatever this weird thing is. Our human minds are
Speaker 8: so small that, you know, we're only able to comprehend
Speaker 8: like a very like tiny wedge.
Speaker 4: Of how the world functions.
Speaker 8: Right, So it could be that like we're seeing all
Speaker 8: these things as random, but really it's like, oh, if
Speaker 8: we could actually take a step back and like have
Speaker 8: more of a bird's eye view, potentially these things could
Speaker 8: be connected. And they just operate at a pace that's
Speaker 8: so slow that like over the core of one human
Speaker 8: lifetime it appears random, but over the course of like
Speaker 8: five hundred human lifetimes, it's actually like one slow process
Speaker 8: that makes perfect sense, but we're just not able to
Speaker 8: comprehend it, right, So you know it just randomness is
Speaker 8: so interesting because in a sense, it's undefinable. Yeah, right,
Speaker 8: So that's why it's so interesting when you have something
Speaker 8: that's essentially like a math problem where it's like, come on,
Speaker 8: like I have like I don't know, thirty five hundred songs.
Speaker 8: It can't like how how complicated could it possibly be?
Speaker 8: And it turns out it's really complicated.
Speaker 2: HEATHERN. Mccalldon is the author of the Observable Universe. We're
Speaker 2: going to put a link to it in our show.
Speaker 3: Notess Hews Hews her howss. No Such Thing is a
Speaker 3: production of Kaleidoscope Content. Our executive producers are Kate Osborne
Speaker 3: in Mangesh Hadi Cardur. The show was created by Many Fidel,
Speaker 3: Noah Friedman and Me Devin Joseph. Our theme in credit
Speaker 3: song or by Manny, mixing by Steve Bone. Additional music
Speaker 3: for this episode by certain Self. Our guests this week
Speaker 3: we're doctor Loriana Gaudette from the Weather Company, Mary Glacken
Speaker 3: and Heather mccauden. Thanks to Alec Mia and Austin for
Speaker 3: the questions. If you have something you want us to
Speaker 3: get at the bottom of you can email us at
Speaker 3: Manny Noah Devin at gmail dot com. Sign up for
Speaker 3: our newsletter No Such Thing that shows We'll
Speaker 1: See you guys, those by those such things