#it's just. how big of a difference claire's memories from bonus stage could make in the end...

[slightlytoastedbagel]

Something so fucked in how it's Wilardo specifically who ends up doodling with Claire and Sirius, who gives them both some of his preserved flowers as a thank you for letting them stay in the mansion/just out of kindness. He's the one that struggles for around a day debating if it's right to kill Claire for his wish and ultimately decides letting them both live is the easier (happier) option for him. Even if he'd be dead soon after. How when it all ends it isn't Noel or Ashe who walks away with them both but Wilardo, who had killed them both in Sirius's initial scenario.

[lingthusiasm]

Transcript Episode 40: Making machines learn language - Interview with Janelle Shane

This is a transcript for Lingthusiasm Episode 40: Making machines learn language - Interview with Janelle Shane. It’s been lightly edited for readability. Listen to the episode here or wherever you get your podcasts. Links to studies mentioned and further reading can be found on the Episode 40 show notes page.

[Music]

Lauren: Welcome to Lingthusiasm, a podcast that’s enthusiastic about linguistics! I’m Lauren Gawne.

Gretchen: I’m Gretchen McCulloch. Today, we’re getting enthusiastic about artificial intelligence – teaching computers language – with special guest Dr Janelle Shane, who runs the blog A.I.weirdness.com and is the author of You Look Like a Thing and I Love You, which is a fun new book about A.I. But first, we have some announcements.

Lauren: It’s a new year and we have new, big, exciting plans for the Lingthusiasm Patreon page. We are introducing a Discord, which is an online chat space, for patrons to share their lingthusiasm with their fellow lingthusiasts.

Gretchen: We’ve heard from a lot of you that you got into linguistics because of Lingthusiasm or it reawakened your memories of how much you like linguistics because you did some courses on it way back when and now you wish you could talk about linguistics more. We’re giving you a space where you can talk about linguistics, share your interesting linguistics links that you come across, and talk about them in a space with other lingthusiasm fans. We’re really excited to see what this community becomes. It’s a bit of an experiment, but we think it’ll be really fun to do. You can join the Patreon at the tier where you get bonus episodes as well, and you also have a space to talk about those bonus episodes and the regular Lingthusiasm episodes and any other linguistics things you wanna talk about.

Lauren: We want to see more Lingthusiasm not just online but also on all kinds of things, which is why we are also sending stickers over the next few months to patrons at the Ling-phabet tier. Patrons who are at that tier for three months or more will get stickers that say, “Lingthusiast” on them.

Gretchen: You can stick that to your laptop, your water bottle, your notebook, anything else in your life. Because the original trial run of stickers that we did with the special offer last year were really popular, we thought we’d provide a way for you to do that around the year. You can join that tier on Patreon as well.

Lauren: You can get other items at our lingthusiasm.com/merch page, but the stickers are an exclusive for our patrons.

Gretchen: Thanks to everybody who’s been a patron so far. We’re really excited to see you in the Discord. And we’re excited to get to try that out.

Lauren: Our last exciting announcement is that our patrons also helped us meet a new funding goal, which means that we now have some additional ling-ministration support.

Gretchen: Our fantastic producer Claire, who’s been with us since the very beginning, is also going to be taking on some more of the administration for the podcast, so you’ll see her around a bit on social media and on Patreon. You can listen to a bonus episode with Claire if you’d like to get to know her better as well.

Lauren: Our current bonus is on the future of English and what English might look like in a couple of centuries from now, inspired by Gretchen’s New York Times article.

Gretchen: You can get access to this episode and 34 other bonus episodes – that’s twice as much Lingthusiasm that you can listen to – at patreon.com/lingthusiasm.

[Music]

Gretchen: Hello, Janelle. Welcome to Lingthusiasm!

Janelle: Hi, it’s great to be here.

Lauren: Janelle, we are so excited to have you on the show today to talk about how we can make machines do language.

Gretchen: I think one of the things that we have in common, definitely one of the reasons I enjoy following your blog and Twitter feed and so on, is that both linguists and your approach to A.I. like poking at systems and seeing where they break.

Janelle: Yeah, for sure.

Gretchen: In case some people aren’t already following you on all of the internets, I wanna give people an idea of some of the stuff that you have tried to make break.

Lauren: Janelle, in your work, for people who haven’t seen it, you take large data sets of particular sets of terms or particular language genres, I guess, and then you feed them into an artificial intelligence, and we’ll talk about what that is later, and then it spits out these delightfully whimsical outputs. It takes inspiration from the data set that it’s given. I have a sustained history of laughing inappropriately loudly on public transport while reading your blog because the results are always so entertaining. Gretchen, do you have a favourite to share with us so I can chortle inappropriately?

Gretchen: Lauren, I think we should start with ice cream because I know you have a deep and abiding love of ice cream, and Janelle has come up with ice cream flavours.

Lauren: Yes! Yes, yes, yes. Janelle, where did the ice cream data come from? Did you have a list of ice cream flavours that someone gave you or…?

Janelle: Yeah. In this case, it was a group of middle-schoolers, actually. There’s a school in Austin, Texas, called Kealing Middle School where there is a group of students in the coding classes who decided that – they saw my blog. They wanted to do it too, and they wanted to generate ice cream flavours.

Lauren: Aww.

Gretchen: That’s so great!

Janelle: The thing is, I had looked at that, and I’m like, “Oh, this would be cool.” Then, I looked online and I say, “I need examples of existing ice cream flavours” because the A.I. has to have something to imitate. It doesn’t know about ice cream flavours unless I have some to tell it about. They’re scattered around. There wasn’t any big master list of them. So, I kinda said, “Oh, well. I guess that’s not gonna work.” Then, these middle-schoolers kicked my butt because they went and there was, I dunno, dozens of them – 50, 60 of them. Like, a lot of them. Each of them went and collected a few from this site or that site. Each one site would only have a few at a time. They had to manually copy and paste to this data set. They just, through the sheer numbers and having the time to do it, they put together this amazing data set of existing ice cream flavours. These middle-schoolers ended up getting about 1600 different ice cream flavours. Whereas, I only managed to get together 200. With the data set that much bigger, it made a huge difference. They started generating pretty amusing flavours.

Gretchen: I’ve got the blogpost up about the ice cream flavours from the middle school students, and some of them are really good. There are these whimsical flavours like “It’s Sunday” and “Cherry Poet” and “Brittle Cheesecake” and “Honey Vanilla Happy.” These seem like kind of reasonable ice cream flavours, right?

Lauren: I’d be open to ordering a “Vanilla Nettle.”

Gretchen: “Cherry Cherry Cherry.” If you like cherries, this is the flavour for you. There are also some weirder flavours from this data set like, “Chocolate Finger” and “Caramel Book” and –

Lauren: “Washing Chocolate.”

Gretchen: “Texas Charlie Covered Stunt.” Then, there’s this even weirder category, “Nuts with Mattery,” “Brown Crunch,” “Cookies and Green.”

Lauren: Aww, so close, and yet…

Gretchen: “Mango Cats.”

Lauren: They’re weird to us because of the semantics of them – just to be linguist-y and spoil the moment for a second – but they still are English words, or they look like something we’d recognise as English words, even though I don’t think “mattery” is a word that I know of. I think it’s worth saying artificial intelligence doesn’t know what ice cream is, right, it’s just using this list of flavours to figure out what kind of patterns could fit into that list.

Janelle: Exactly. It’s doing it at a very basic level. Like, what kinds of letters tend to come after other letters? What letters are we often finding in combination? Which letters are we never finding in combination? It’ll learn frequent words like “chocolate” or something. It’ll learn how to spell that after some false starts during training, but, yeah, without any concept of what chocolate is.

Gretchen: If it ends up with something like “Vervette’s Caramel Borfle,” it learned “caramel” but who “Vervette” and “borfle” are, I don’t know. That’s just randomly combining some letters in ways that are probable as English words.

Janelle: Yeah, it’s like a kid who learns how to write and immediately starts putting down letters on paper like, “Is this a word? Is this a word? How do you pronounce this?”

Lauren: Because obviously we train the neural nets that are children’s brains by talking to them a lot and giving them more input and taking them to school and doing those kind of things, but a neural net-type artificial intelligence that we’re doing this kind of training by giving it lots of data, how does it know if it is generating something that is more or less English? Is there a little thing in the computer saying, “Good work, Computer”?

Janelle: What it’s trying to do, how it knows it’s making any progress at all, is its job is to try and predict the next letter or the next combination of letters. Then, it just checks its prediction against some example of real texts that it hasn’t seen before that it saved aside to check itself with and said, “Okay, did I guess close or am I still way off? Am I going to have to change my internal structure so that my guess would’ve been better and see if, going forward, that’s gonna be an improvement?” It’s like a trial and error, guess and check.

Gretchen: When you look at the different sorts of stages – because it goes through several different generations, right? It might start out with just “Here’s a bunch of Es because E is really common.” And then the check is like, “Yeah, but you could do better.”

Janelle: Yeah. It’s like guessing lots of Es is more correct than guessing lots of question marks or lots of Qs. Yeah, it has to say, “Oh, well, maybe I could work in an S from time to time. What do you know? That’s slightly more correct,” and proceeds from there.

Lauren: So, that’s how it learns “chocolate”? Because it might go in with CH and HC, and every time it goes, “Is HC right? Is HC right?” And the data set is like, “Naw, not really.” But when it’s got the CH for an ice cream list, it’s like, getting lots of positive feedback that that’s gonna appear in “chocolate” and “chip” and “cherry.”

Janelle: Yeah, exactly. The process, yeah, it is a lot different from the human child learning language because it’s taking place, really, in isolation with no other context. It’s as if you are setting somebody in a room with just a few dictionaries or a few encyclopaedias written in a language that they don’t understand. It’s even harder for the A.I. because it doesn’t have a concept of what language even is to start out with. It’s all just guessing what comes next in this sequence of arcane symbols.

Gretchen: It doesn’t have a sense of what’s probable in the world either, right?

Janelle: Yeah.

Gretchen: Because you have some of these flavours like “Peanut Butter Slime,” which those are all English words, it’s just it would make a terrible ice cream because slime and peanut butter and ice cream are not things that go together.

Janelle: Yeah, exactly. Or, if I’m getting it to generate Halloween costumes, it’ll come up with “zombie school bus.” It’s like, “Okay, zombie school bus. There’s magic school bus. Why is that more likely than zombie school bus?” We know. It doesn’t.

Gretchen: It doesn’t have any of that real-world knowledge that you can do – or like “Mango Cats.” What does it mean for a cat to be mango? I don’t know.

Lauren: If an artificial intelligence gained sentience, it’s likely it actually wouldn’t be a very good linguistics student because it doesn’t really understand the concept of sounds. It doesn’t seem to have a lot of understanding of the structure of a sentence. We talk in one episode about syntax essentially being this structure that we can hang other bits of sentences off. It has much more of a flat, just looking at the patterns on the surface kind of approach to language.

Janelle: Yeah. Keep in mind, too, the amount of computing power it has to work with is so much less than what it takes for sentience or anything near human level. If you’re looking at raw computing power, the neural nets we have today are somewhere around the level of an earthworm.

Gretchen: Maybe an earthworm would like peanut butter slime-flavoured ice cream.

Janelle: I’ll give all my Peanut Butter Slime to the earthworm.

Lauren: That’s very generous of you.

Gretchen: This was one of the analogies that I liked in your book, which I enjoyed very much. You Look Like a Thing and I Love You – the title of this book was named after another neural net, right?

Janelle: Mm-hmm. This was a phrase generated from a neutral net that was trying to do pick-up lines.

Gretchen: I guess that could be a pick-up line.

Lauren: We have things like ice cream names, and you’ve done death metal names, and Halloween costumes, and colours, and these are all three or four words at most. Pick-up lines is moving into more of the sentence/couple of sentences-type of thing. As the amount of words you’re trying to generate grows longer, how much more difficult does that make it for the artificial intelligence?

Janelle: It makes it a lot more difficult. When I was generating the ice cream flavours and things, I was deliberately going exclusively for these kinds of problems where it would just have to do a couple words at a time because when it tried to do longer sentences or phrases, it would not make sense. One of the things is that the A.I. I was working with at the time didn’t have very much memory at all. So, it would kind of lose track of things that happened a couple of words ago. It wasn’t really able to figure out then how to make a sentence work or make phrases work. It was a bit beyond it. The pick-up lines was definitely a case of, “This is too hard for the A.I.” It struggles, okay, not just the “How do you make a grammatical phrase?” but also “How do you do puns? How do you do innuendo?” These were all things that require a lot of background knowledge that this thing just did not have.

Gretchen: Another example that you use in the book is with recipes, right? It can figure out that you need to list some ingredients, you need to list some instructions, but then those instructions won’t contain the ingredients that were previously mentioned, necessarily, because it doesn’t remember that those are what it listed before.

Janelle: Yeah, we’ll see that. You’ll get something that on the surface at first glance looks like a recipe and then, when you actually read more closely, you’re like, “Wait a second. It has no idea what’s going on. It’s forgotten its ingredients. It’s telling me to chop the milk into cubes. Something’s going on here.”

Lauren: There’s something very confident about the way it fakes its ability.

Janelle: Yeah. Well, I mean, part of the reason it sounds so confident is that it’s copying what humans have written, and humans generally didn’t tend to write in the middle of a recipe, “Uhh, wait a second. I have no idea what’s going on.” It learns that is not a phrase that appears in a recipe, so it’s going to express any kind of confusion. It’s just going to plough ahead with its best guess at what a human would say.

Gretchen: This is where, I think, your famous giraffe question comes from.

Janelle: Ah, yes. I love this chatbot. It’s a chatbot called Visual Chatbot. It’s designed to answer questions about an image. You show it an image and then it comes up with a caption, and then you can have this back and forth conversation with the bot about what it sees in the image. You think that premise would be fairly straightforward, but there are weird quirks that arise just because this thing is trying to copy how humans ask and answer questions about images. The training data is important. In this case, the training data is a whole bunch of people hired through Amazon Mechanical Turk to take turns asking and answering questions about images. Then, the chatbot was trained on answers. So, given this kind of image, given what the question is, what would humans tend to answer in this situation? Some weird quirks emerge just from that premise. One of the things that they wanted to make sure to avoid was this thing called priming. People tend to ask questions to which the answer tends to be “yes.” They found in an early version of this chatbot that they could get 80% accuracy just by answering “yes” to every single yes-or-no question.

Gretchen: Uh-oh!

Janelle: They ended up having to hide the image from the person who was asking questions, so that helped a little bit. Now, it’s about 50/50 if you ask a given question whether it’s going to answer yes or no to that. One of the things that they weren’t able to correct was this interesting thing with the giraffes. What happens is, if you ask the question, “How many giraffes do you see?” the chatbot will almost always return a non-zero answer. It can be doing great about an image and, “Oh, yeah. This is a person on a snowboard. There’s snow,” up until the point where you ask, “And how many giraffes are there?” It will answer, “Three” or “Two” or “Too many to count.”

Lauren: I think it’s just worth clarifying, just to really make this clear, this is not a data set in which giraffes appear in every image.

Janelle: True. Yes. I would love to see that data set – snowboarding with giraffes.

Lauren: “Yeah, there are three giraffes.”

Gretchen: Giraffe snowboarders – this is possible. Because I know this is an ongoing joke that you have, I tested with an image of the cover of my book which, as I think as everyone knows, contains zero giraffes because it’s not about giraffes. Visual chatbot told me that it is a sign that says, “Unknown, unknown, unknown,” on the side of it which I guess is not the worst for a cover that has text in it. It just can’t read the text – sure. Then, I said, “How many giraffes?” and Visual Chatbot said, “Two.”

Janelle: It comes from this thing is copying how humans tend to answer this question. In its examples of humans hired through Amazon Mechanical Turk, the humans had not tended to ask the question, “How many giraffes are there?” when they didn’t know if there were any giraffes.

Gretchen: Right. You’d say something like, “Are there any giraffes?” The person says, “Yes,” and then you say, “How many giraffes?”

Janelle: Exactly. If you ask the chatbot, “Are there any giraffes?” it will answer, “No,” quite often. But then, if you follow up with the question, “And how many giraffes do you see?” it’ll say, “Five.”

Lauren: This approach reminds me of, as Gretchen said earlier, as soon as I get my hands on some kind of thing that’s doing this back and forth question asking or as soon as I’m let loose on a Google Translate, I think it’s a very linguist-brain thing to try and find these points at which the computer can’t handle language properly. It’s always great when you have an approach that understands how humans actually interact with this data that helps explain why you end up getting these really strange answers and why it’s good to have linguists help design artificial intelligence or chatbots and these things because the way humans choose to do language is very different to what we think of as the nice, straightforward application at the end.

Janelle: There’s so many start-ups that are trying to have some kind of bot that you can interact with in an open-ended manner. Then, they run into trouble. Facebook M is one of these services that was discontinued last year because they thought it was going to be like a digital assistant, lives in your browser, you can ask it to do things like look up show times and stuff. But what people ended up asking for was the weirdest, most complicated things. One guy documented, oh yeah, he asked it if it could arrange for a parrot to visit his office. I mean, you’re not gonna prepare for that when you’re training one of these chatbots. It turned out to be the chatbot kept needing humans to step in and rescue it. They realised it was going to be too expense because they were always gonna need these humans.

Lauren: This is a company that has no shortage of resources to throw at a problem like this.

Gretchen: I think if you tell people, “You can interact with this like a human,” they think they can do things like make a request for parrots because humans can understand a request for parrots. Even if I can’t personally deliver you a parrot, at least I understand this request. Whereas, a chatbot, if parrots aren’t in the training data, then parrots don’t exist.

Janelle: This is one of the things, too, that makes it hard to tell the difference between humans and computers when you’re chatting with them. If you’re in a customer service situation, they try to really narrow the context in which you can ask questions and not make it open-ended, especially if they’re going to invisibly use bots because they don’t want you asking for parrots out of the blue.

Gretchen: Right. It’s like when you call into a customer service line, it’s like, “Press 1 to talk to this,” “Press 2 to talk to that,” they really wanna keep your options constrained because then the computer can help you. It’s when it’s open-ended and people start behaving as if it can do anything that a human can do that you start running into problems.

Janelle: Yeah. What you’ll get is you’ll get these companies that’ll build chatbots where it’ll start out as an open-end conversation with something that is secretly a bot but it hasn’t said it is. But then if it gets confused, it’ll invisibly hand control over to a human. That can be problematic because then, if the customer by then is frustrated and thinks they’re dealing with a robot, the poor human employee may not have a very pleasant time with that conversation. What I would really love – what I would love linguists to design for me – is some kind of very polite, in-context way to ask a question or interact with one of these bots that would reveal whether it is a human or a computer, some kind of shibboleth that is never – not asking about his favourite Star Wars character, because that’s impolite if you’re talking to a human employee – but some phrasing or something that’s tricky.

Gretchen: That’s an interesting question because I think, a lot of times, asking for something that’s a little bit non-cooperative, like “How many giraffes?” out of the blue, is maybe gonna deliver that answer. But it’s also gonna be confusing and annoying to a human.

Janelle: Exactly. My default has always been, as soon as a human – because better be polite to a computer than rude to a human sort of thing – but it would be lovely to be able to tell the difference. Companies should just tell us or have a “Talk to a human” button or something, but yeah.

Gretchen: You’re looking for an inverse Turing Test. A Turing Test is this classic test in computer conversation where, if a computer can fool a human into thinking that they’re talking to another human, then they’ve passed the Turing Test. There are ways of passing the Turing Test if you constrain the context enough. Or if you tell people that they’re talking to a child or they’re talking to somebody who’s on some drugs or something like this – or a philosopher – then they’ll be more likely to believe – these are the three kinds of people that a robot can be. But if you try to do something that’s very practical or that is grounded very much in reality, then people aren’t as willing to be generous with the computer’s misinterpretations. Janelle, your blog post that you make the neural nets do funny things, they’re really funny. And yet, I have a feeling that it’s not only that the neural nets are funny, it’s also that you’re really good at spotting the funny bits and bringing them out to a blog post for us.

Janelle: Yeah, there’s a lot of human storytelling work that goes on. How is this going to be interesting? Where is the funny thing that it’s doing? Sometimes, the ratio is like 100 to 1 of things that aren’t very funny that it generates and the one thing that I’m like, “Oh, yeah. I’m posting that.”

Lauren: Because, I guess, the thing about it being a computer process is that you could just generate infinite numbers of nonsensical ice cream names, but a lot of those are too nonsensical to even be particularly amusing.

Janelle: Yeah. It also has a tendency to – especially if we’re dealing with something short-ish and simple-ish like ice cream, then it’ll generate something and it says, “Mint Chocolate Chip,” and I’m like, “Oh. It just copied that.” It learned that one.

Lauren: Learnt that one too well.

Janelle: Yeah. Because as far as these A.I.s are concerned, exactly copying my examples is a perfect solution to the question I’m asking of it. If it can predict every single word, word for word, in the text file that I gave it, then that is a perfect score. Sometimes, it’s almost like a battle for me to try to get it to be just bad enough at the task.

Gretchen: Not so bad that it’s incoherent, but bad enough that humans can resolve what it’s supposed to mean and it’s still funny.

Lauren: One application of this name-generation process you’ve been doing was when you created a list of craft beer names and a company actually took one of those names to create a beer. Was that a process that you embarked on because you thought this was a good place to experiment with creative naming or how did that come about?

Janelle: This was one of the things where I happened to know somebody who was friends with the owner of the brewery, and I thought, “Well, this would be fun to actually get one of these beers to exist in real life,” because people keep saying that the names A.I.s are generating are pretty good. In the case of craft beer names, there’ve actually been companies who have taken each other to court over having beer names that were too close to one another. There’s this need to maybe show there’re ways to still come up with new beer names and we hadn’t exhausted all the possibilities yet.

Lauren: It’s really a collaboration between you and the A.I. where you are curating all of the names that it gives you in order to find the ones that have that perfect balance of following the rules you’ve given it but with a bit of a lateral thinking approach.

Janelle: Yeah. Just the right amount of lateral thinking as well, too. Sometimes, it’s way off the mark and comes up with, I don’t know, “Farm Fight,” as a name for beer. I’m like, “Well…”

Gretchen: Here are some of the beer names that were on the list like “Dang River” and “Binglezard Flack” and “Toe Deal” and “Devil’s Chard.”

Lauren: Some of them I can almost imagine being a craft beer. In the end, it was “The Fine Stranger” that was bottled and labelled.

Gretchen: That’s good. I think the examples are very funny, but there’s also an important part of making a lot of funny examples, right? It’s not just to entertain people, even though it is very entertaining.

Janelle: There’s people using these practically as their business in coming up with brand names. I did this one beer. There’s a whole art to naming brands, and it’s not just coming up with the names, but it’s also this whole framing of “Because of the etymology of this and that” or “Because the computer mashed this together with that.” There’s definitely a storytelling element to it as well. When I was going through this process with the beer, I was definitely getting the sense of, “Oh, yeah. I’ve got all these great names.” Any – not any one of these – but many of them would make great beer names, and the beer would sell well, and the brewery would be happy with it. But, yeah, how do I put it on the marquee, put it on the silver platter and make them actually say, “Yes. The authority has spoken. This is the name.”

Gretchen: Beyond brand names, there’re also lots of other practical applications people are using artificial intelligence for now, whether that’s machine translation or self-driving cars or all of these sorts of very practical aspects to things. It’s hard to see the inside of a self-driving car, and what that looks like, and how it’s making problems for things. Whereas, it’s easier to see what happens when you make a bunch of weird ice cream flavours.

Janelle: Exactly. That’s why I like doing these tests. Some of the biggest applications for A.I. is in doing financial predictions or looking for fraudulent logins and things like that where it, maybe, is comprehensible to somebody who’s in that field, but the way that they’re making mistakes in that field is not very obvious, not very interesting, if you’re not right there in that field working with these kinds of numbers all the time. If it’s making a mistake on an ice cream flavour, that is much faster to see, “Oh, yeah, it’s doing pattern matching. Oh, yeah, it doesn’t understand what it’s doing.” A lot of these same mistakes really do translate over to commercial applications.

Lauren: We’ve talked a little bit about how you have to curate the output because it will just keep spitting out silly ice cream names forever. We’ve talked a little bit about some of the problems with the types of data that are put into these processes in terms of, you know, if you don’t set it up very well and you have people answering questions about giraffes in a way that the A.I. is going to implement weirdly. There are bigger and more serious implications for thinking about the kind of data that we are using to create artificial intelligence processes not just with language but particularly for this topic looking at the kinds of data that people use to build artificial intelligence. You talk about this a bit in your book. Where do you see some of the biggest challenges in creating good A.I.?

Janelle: One of the things is, remember these A.I.s have about the raw computing power of an earthworm and they don’t have the context, then, to realise that there are some things that the humans do that they probably shouldn’t be copying. Completely unknowingly, they will copy things like racial/gender discrimination and they won’t know that that’s what they’re doing. They won’t know that that’s a bad thing. They just really can’t comprehend it.

Gretchen: It’s kind of like the chatbot that figures, “Oh, if I just answer yes to everything, I’ll get 80% accuracy,” even though it’s not actually useful, communicatively, to just answer yes to everything.

Janelle: It’s like this is exactly what you have asked for but is not necessarily what you want. When we give it a bunch of human decisions on resume sorting, for example, and we tell it, “Copy these human decisions,” then these algorithms can look and say, “Well, this is a very difficult problem, but looks like all of the applicants who’ve gone to this one college tend not to be hired” and “Oh, that college is a women’s college” and it is implementing the gender discrimination that it’s seeing in its training data because it saw this signal, didn’t know what it was, only knew that it was helping it copy the humans a little better.

Gretchen: Right. If the humans are already having their sets of bias and if I can magnify that bias, like if you have a human that’s answering “yes” 80% and now the A.I.’s answering “yes” 100% of the time, it doesn’t know what it’s doing.

Janelle: Exactly. Yeah. They are so good about being sneaky about – you may think that if you set up a resume sorting algorithm saying, “Well, we’re just not gonna tell it what gender any of these applicants are” and it is very good at figuring this out not just through colleges but through if somebody has their extra-curriculars listed and “women’s soccer team” is on there, it will glom onto that. Or even subtleties with word choice and phrasing, it will start using those kinds of trends and use them to copy the humans better.

Gretchen: I’m thinking about a different resume study which showed that people – they had the same sorts of resumes – people with a white-sounding name versus with a black-sounding name were more likely to get called back for interviews. You can imagine in the A.I. that it actually just learns how to predict based on someone’s name. Like, “Oh, we’ve hired a lot of people named ‘Mike’ at this company.” We all know these companies that have a whole bunch of people named “Mike” and “Adam” and stuff. “Maybe we should just only interview the people named ‘Mike.’”

Janelle: It will absolutely do that sort of thing. You see there’s a lot of companies out there that are offering resume screening but knowing what I know about how commonly these A.I.s can pick up on this bias I would not want one of these programs screening resumes for my company, for example. Or I would, at the very least, demand to see the evidence that this thing is not making biased decisions.

Gretchen: Right. That’s a sort of way of saying, “Okay, well, if this A.I. still thinks ‘Slime’ is a good flavour for ice cream, then really how much can we trust it to make a good decision about resumes?”

Janelle: I think that’s almost the counter-intuitive danger about A.I. in a lot of ways. It’s not that it’s too smart and it’s going to take over the world and it’s not gonna obey humans – no. The problem is that it’s not smart enough to realise what we’re actually trying to ask it to do.

Lauren: It keeps obeying us too well in ways that we don’t want it to.

Janelle: Yeah, if it can. When it comes to language generation, language processing, human language is really, really difficult. So, that particular domain, more than a lot of others, you’ll see these A.I.s that are really struggling to get a handle on what the humans are saying.

Lauren: It’s good news that linguists will have jobs for a little bit longer.

Janelle: Yeah.

Gretchen: One of the questions that really came up in my mind when we were thinking about interviewing you was, can the A.I. take my job as the co-host of the podcast, Lingthusiasm? If Lauren and I want to go live on a beach somewhere, can we replace, as co-hosts, a bot-generated Gretchen and Lauren to run this podcast? Lauren, what do you think?

Lauren: We actually put this to Janelle a few years ago, back when we started releasing transcripts for our early episodes. About three years ago, in 2016/2017, we didn’t have many episodes, so we didn’t have a lot of data to work with, but also it seems like in these last few years, the ability to process larger text has gotten better. Is that the case, Janelle?

Janelle: Yeah, that’s definitely the case. The kinds of things I was doing in 2016 – generating words, short phrases, paint colour names, ice cream flavour names, those sorts of things – I wouldn’t think of tackling entire sentences or, let alone, sentences that follow one another that make sense. But now, just pretty much in the last year, there’s been some really big A.I.s that have been trained on millions of pages from the internet. They are much better at generating text. They can generate grammatical sentences most of the time now. Most of the words that they use are real words. They still don’t understand what they’re saying. I think, yeah, it has gotten better.

Gretchen: You can potentially take something that’s been trained on, let’s say, most of the English pages of the internet and then fine-tune it on a smaller data set to try to push it more in the direction of just, for a random example, Lingthusiasm episodes.

Janelle: Yes. If, hypothetically, I had many episodes worth of Lingthusiasm transcripts, I might be able to make a robo-Gretchen and a robo-Lauren.

Lauren: Do you know what else has happened in the last couple of years, Gretchen?

Gretchen: I think we’ve produced a lot more episodes of Lingthusiasm.

Lauren: Between the main episodes and the bonus episodes, we have 70 transcripts, which is over 800 pages of data. Janelle, would that be enough to have a go at creating a robo-Gretchen and a robo-Lauren?

Janelle: There’s one way to find out.

Gretchen: Oh, boy! Let’s do some live neural netting on the podcast.

Janelle: All right! What could possibly go wrong?

Gretchen: Okay. Can you walk us through what are you doing right now on your computer?

Lauren: Janelle’s gonna share her computer with us so that we can see what’s happening, but we might get some screen grabs as we go through.

Gretchen: We may put some links into the show notes if there’s stuff that’s visual that’s hard to see as well.

Janelle: What we’re looking at right now, this is actually just a browser window in Chrome. What I’m looking at is a thing that is an interface to an A.I. that’s being hosted on Google’s computers right now. Google is graciously allowing people to use their powerful computers that are pretty specialised for these kinds of calculations. Even though I am working on a fairly ordinary laptop, I’m able to connect to some fairly serious firepower here.

Lauren: It’s really interesting to get to see under the hood of making an A.I. run. I think we’ll give people a bit of a taste of that here, but if you want more details and more of an explanation of how we made “Robot Lingthusiasm,” we’ll make that into a bonus episode.

Janelle: So, here we are. I’ve connected to this A.I. I’ve downloaded a copy of it. Now, I’m going to upload lingthusiasm.txt. I’m going to upload this file of 2.4MB of you two talking. Let’s – okay. Okay. We’ve got our first sample out here right now. “It is already conversations.”

Lauren: Except it’s just conversation by someone called “Gina.”

Gretchen: Maybe this is the hybrid between the two of us – our merged alter-ego? Shall we read a few of these lines, Lauren? I think we should each start with “Gina” as we’re reading the lines.

Lauren: Okay.

Gretchen: First line. This is the first of Gina’s lines. “Gina: Yeah, that’s why I’m gonna be honest with you.”

Lauren: “Gina: We’re not always going to be like, ‘Oh, we don’t know why we did that.’ That’s why.”

Gretchen: “Gina: I know. The people who’ve come to me to ask me are gonna be like, “Yeah, I didn’t know who was getting up and down the stairs and going to a doctor’s appointment.”

Lauren: Okay. So, not very Lingthusiasm in content there, but I like where Gina’s going.

Gretchen: Yeah. I like that it’s getting a dialogue thing. We’re pleased to announce that, in fact, your Lingthusiasm hosts will be replaced by robots but only for one episode and it will be bonus and it will be very, very funny. You can go to patreon.com/lingthusiasm to listen to the next bonus episode, which will be written by robots and performed by you and me, Lauren.

Lauren: To listen to that bonus episode, check out patreon.com/lingthusiasm. You can hear us reading some of our favourite examples. We will also give patrons access to some of those reams of examples so you can find ones that make you chortle as well. It’ll have some screenshots from the A.I.-building process for patrons as well. Thank you so much, Janelle, for taking us through the process of actually training a neural net artificial intelligence and showing us some of the pitfalls and some of the challenges and for talking to us today. If people want to read more about how artificial intelligence is making the world weirder and more wonderful, and some of the challenges and limitations, your book is You Look Like a Thing and I Love You. I loved reading it.

Gretchen: Yes, I can personally attest that I got my copy the night before my book came out when I was very distracted. It successfully distracted me for several hours while I was waiting for that countdown, midnight, to have that happen. It has lots of fun pictures of weird things that the A.I.s are doing as well. Thanks again for coming on the show.

Janelle: Oh, it was my pleasure. This was a lot of fun. I loved listening to your very strange generated conversations.

[Music]

Gretchen: For more Lingthusiasm and links to all the things mentioned in this episode, including extended versions of A.I.-generated Lingthusiasm transcripts, go to lingthusiasm.com. You can listen to us on Apple Podcasts, Google Podcasts, Spotify, SoundCloud, or wherever else you get your podcasts, and you can follow @Lingthusiasm on Twitter, Facebook, Instagram, and Tumblr. You can get IPA scarves, IPA ties, IPA socks, and other Lingthusiasm merch at lingthusiasm.com/merch. I can be found as @GretchenAMcC on Twitter, my blog is AllThingsLinguistic.com, and my book about internet language is called Because Internet.

Lauren: I tweet and blog as Superlinguo. Janelle Shane is @JanelleCShane on Twitter, her blog is aiweirdness.com, and her book is You Look Like a Thing and I Love You. To listen to bonus episodes and help keep the show ad-free, go to patreon.com/lingthusiasm or follow the links from our website. Recent bonus topics include future English, onomatopoeia, and linguistics fiction. If you can’t afford to pledge, that’s okay too. We really appreciate it if you could recommend Lingthusiasm to anyone who needs a little more linguistics in their life.

Gretchen: Lingthusiasm is created and produced by Gretchen McCulloch and Lauren Gawne. Our senior producer to Claire Gawne, and our editorial producer is Sarah Dopierala, and our music is “Ancient City” by The Triangles.

Janelle: Stay lingthusiastic!

[Music]

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

[yvaquietdays]

On giving up on all your unrealistic dreams.

There’s a rumour going around that I’m gonna give it all up. 

The rumour is only in my head. But still.

Entirely expectedly at this time of year, I’ve been experiencing some introspective anxiety. Namely, noticing that when I think about my music, my anxiety starts peaking. Ugh, I know. So boring. The streams of panic, sending whirly moments of fear through my gut; I’m not good enough, I haven’t done enough, I don’t want to do enough. If I don’t want to do enough, then I mustn’t want this.  I’m going to escape, move to New Zealand, sell books.  You see where I’m going with this.  The slow, maddening, endless descent into spiralling negative thoughts. Let me just lie down.

Firstly, I have to be very careful that I don’t take my reluctance to do something as a sign from the universe that I’m on the wrong track. The universe, sometimes, doesn’t know shit about it. I put too much stock in the universe and all its power at the dawning of 2019 and look where that got me. Alright, all the way out to LA, but I came back, didn’t I? Quite clearly something (that I’m not going to talk about, because it doesn’t actually matter, honest) didn’t pan out as it was supposed to.

But I set my intentions! I rode the wave of acceptance! I was grateful! 

Come off it. Nah. Sometimes shit doesn’t pan out and you either fall hard or get on with it. In the end, I was glad that thing didn’t pan out, because I felt like I’d been freed. Freed from an industry that felt fake and vacuous, freed on my own trudgey path, to do whatever I want on it. I could kick some stones for a while, make some moves. Or, as it happens, stand completely still. But here’s the rub. I’ve been entertaining thoughts of doing other things. I wrote a book a year ago and sat on it for another year, picking it apart, editing, sending to my beta readers. It reawakened a very simple, undemanding love for reading and writing. It doesn’t always make me feel bad when I do it. It is a pure and unadulterated mode escapism. Excuse me while I jump off the world for a sec. Of course, there are days I have no ideas, I can’t pull together any words, and on those days, I feel like a steaming hot pile of turd. But generally, I lie awake at night imagining scenes, characters. I’ve realised I see the world through a writers eyes, always creating stories for people, craving seeing inside someone else’s life, figure out their quirks. Everyone I meet is a character I analyse and flesh out in my head.  I couldn’t quite believe I’d buried this part of me for so long. I challenged myself to finish a novel in a year, and I did it. I finished the thing, just to prove to myself that I had it in me. I can’t tell you how freeing this is. So I started wondering if music had led me down a certain path, because from a young age, I had also craved attention and being on the stage, to perform. In my head, I imagined myself on red carpets and at award shows, even though I learned in my late teens how childish and silly this was. But in the back of my mind, always, I had pictured my life playing out away from Newcastle, away from London even. I guess I existed in a different world than the one I knew, even the one that looked real. It meant if I didn’t make music, or get played on Radio 1, or play the big festivals with the other big guys, or be the one to watch... I would fail. There was nothing else. It was this, or nothing. 

Obviously that mindset had repercussions in the end. So. I’ve given up on all those unrealistic dreams. 

I have no desire to be part of the music industry. Not now, after everything. It’s like I can see through the veil, and on the other side, all I see is poor mental health. I honestly love my life, my little flat, a hot brew after hot bowl food, wasting my life on Netflix but being held by a person I love. That’s all there is for me. Everything else is a bonus. I’m not giving up. I’m just, sort of, giving in. Letting go of the things that don’t make me happy. That includes those dreams, those expectations. The way I see it is, we live in a world that tells us to want more, get more, be endlessly unsatisfied and in a perpetual state of craving. I have wanted this idea for as long as I remember, but the reality is, the idea doesn’t exist.  It’s kind of like planning for a holiday. You’ve booked the flights, the transfers, you know you can get by with the bikinis you already own, but in the back of your mind you’re thinking, I could do with a very specific vest top or skirt or shorts for this holiday, otherwise I’ll be really annoyed not wearing the right thing when you’re climbing the steps from GoT in Dubrovnik, and you’ll have to look back on those pictures knowing that vest top was cropped when you didn’t want it to be. Or you’ve suddenly got a long list of items you need for this holiday, even though you know deep down, it’s about the memories and the respite of being on the actual bloody holiday, not the new travel wallet you bought from Liberties because Marie Claire told you it was a must-have for the holiday season. We’re always being sold stuff, only valuing ourselves through the lens of how everyone else perceives us, and what’s worse is that social media knows exactly what we’re thinking, what we’re tempted by. Instagram reinforces the need for a new cross-body bag for the holiday because you googled it or searched or it on ASOS. So you think, yeah, you know, I do need all that stuff. I need to fulfill my dream version of the holiday otherwise it won’t count.

That’s life. If you boil it right down to a lovely little jus, and drip it down on your unrealistic expectations, you’ll realise you’ve been spending years berating yourself by wanting more, wanting the goal, even wanting more while you have it, while doing everything to forget to be grateful or appreciative to yourself for the work you’ve put in to achieving it already. You’re missing it all while you set your sights ahead.  Dreams about how your life is going to look are a waste of time. Dreams are full of stuff we don’t need. Spend your days with your head in the clouds and you forget how to walk in the street without being hit by a cyclist.

Look, if I can release music and write a book, while being able to go to the pub for a pint and a game of Monopoly cards, and think about the possibility of having a family one day, then I’m happy. Family, people, connections, meaning, that’s what human beings need. It’s what I need, anyway. 

I don’t want the guilt that comes with never quite achieving that perfect version of my life. My life is perfect. It might not look like how I imagined it when I first got my passport, imagining where I’d be in ten years time, but if I spend one more day looking years ahead to that perfect moment, I’d completely miss it. Miss now. I’d miss the fact that my actual life, today, right now, is better than I could have imagined. 

So fuck that, pet.

I’m still recording, and I’m releasing very, very soon. But I’m just going along with it. I’m nervous about playing live, about the music world opening it’s doors to me again. Not sure if I want to step through. I’ll cross that bridge when I come to it. But what I’ve realised recently is that I can, as a woman, as a person, have it all. I can make my own music, release it, perform it live. I can do session work, I can tour the world with Nitin Sawhney and perform to crowds of thousands, and I can clock out. I can write a book, I can work on a second. I can work in a coffee shop and enjoy it. I can audition for shows. I can stay at home on the PS4 on New Years Eve with my love and have the best time, and not think about how there was no huge monumental moment for me at the end of the decade, only the realisation that I have all I could ever really need. 

There isn’t one line that I have to follow. There isn’t one line you have to follow! Do what makes you happy, and remember what you really need to be so.

Thinking that music was the only thing that I was permitted to do was the worst mistake I’ve ever made. I felt that trying my hand at anything else was pushing my luck. Nobody would take me seriously if I spread myself too thin. Jack of all trades, and that. I didn’t even let myself explore to find out how good I am at any of it. I told myself no. I allowed myself to cradle that silly dream of making it (I honestly don’t know what this means any more), for years, and it held me back. There is no making it. There is only work, and today. 

And, anyway, I really don’t make enough money in one of those fields to warrant me only trudging through one. At this point, I have to think realistically, financially. 

I have to hike through them all.

[w1tchy13itch-blog]

For your scholars! 64 through 71 and 74 through 80 please! I'm interested in learning more about your ocs! - Toad Mod

Yo! hehehehe I like this batch of questions too XD 

I’ll answer the first half now, and the other half later after classes get out for me!

Lemme see what I can manage: 

64: Where were your scholar’s parent(s) or guardian(s) born?

Lillie: Lillie’s dad was born in Yamagata prefecture in Japan, and her mother is a second generation Japanese American from central California. 

Vivian&Darcy: Their biological parents (out of the three) were both born then the UK, and their parent’s partner was born in Melbourne, Australia. 

Flora: Her mother is British and her bio-father (divorced and deceased) was Irish. Her stepmother is of Indian descent but was born and raised in Whales. 

Abigail: Her biological parents were born in Mexico and Texas, but lived in northern Mexico. Her foster parents were born in New York but her foster mother is half Korean and her foster father is Jamaican American. (They moved to Texas, and then Arizona for the warmer climate and a desire to live in a quieter, more natural environment)

65: Is there anything about your scholar’s past that they don’t want anyone to know? 

Lillie: Her deadname, basically.

Vivian&Darcy: Vivian hasn’t got any, but Darcy, like Lillie, would rather not have his deadname spread around. He’s far more comfortable in his new body though, than Lillie, but no matter how much you physically feel good, it sucks to have people disregard you and undermine you.

Flora: for the sake of Angus’s privacy and her own, she prefers no one knows she has a child. When she meets, by chance, Angus’s biological father a few years after she’d been attending Arlington and finds out he’s transferred to the same school via a scholarship, she is apprehensive about telling him, but manages to work out an agreement with him to share custody so long as he keeps quiet about the situation. 

Abigail: If she could have her way, she wouldn’t let you know what she ate for breakfast. Mainly, she doesn’t want people to know her personal life, she wants them to appreciate her for the character she is on stage and her presence in the spotlight, but wishes for her past with her family and current relationship with Flora to be untouched and protected as much as possible. 

66: Has your scholar ever kissed anyone?

Lillie: Nope, purely clean lips here. Familial kisses don’t count, and she’s never really been desired or sought by anyone before that was of the gender she prefers so it’s kind of rough on her. Hopefully Arlington will change that, because there’s a pair of lips named Tadashi Nakano that she is dying to taste.

Vivian: She had a girlfriend who is now just a close friend. (her friend got into the T.V. acting business and is currently the star of a teen mystery drama on a British kids show channel) 

Darcy: He kissed a girl for a dare once, and had a boyfriend for 4 days that ended up being one of those “I thought I liked you but i guess i kind of didn’t, and this is awkward now” kind of people. 

Bonus: Flora kissed her kid’s dad quite a lot that one specific time, and she and Abigail kiss around 8 times a day on average. 

67: What is your scholar’s favorite holiday? 

Lillie: Lillie likes Halloween the best. There weren’t many holidays her family celebrated, and Halloween was one of the few they did. She uses it as an excuse to cosplay because she can’t afford to go to real conventions. 

Vivian&Darcy: They both love Valentines day and go all out romantic for their significant others or crushes. 

Flora: Her and Angus’s birthday. Her parents travelled the world with her so she knows of too many holidays to count, let alone choose one. And so, she figures that her day of birth and her son’s are probably the best ones there are because they’re special days that only the two of them can fully appreciate

Abigail: Halloween and Christmas both. This chick is like ��The Nightmare Before Christmas’ in human form. 

68: What do they enjoy dressing up as on Halloween?  If they don’t dress up or go trick-or-treating, then what do they do instead?

Lillie: She uses Halloween as an excuse to cosplay and has a roster of anime characters that she’d like to cosplay next. She chooses a character from a different anime each year. Recently she went as Rock Lee, and her older sister went as (a very unfit) Might Guy from Naruto.

Vivian&Darcy: ever since being born they’ve gone for halloween in things that come in pairs, from salt and pepper shakers at 2 years old to Sweeney Todd and Mrs Lovett this year (their plan for this year is to run a meat pie, and other baked goods, cafe from within their parents home.) 

When not dressing up which is rare, they have been hired to host children’s parties in their local neighbourhood (also part of why they hold a themed cafe or barbeque each year). 

Flora: She seldom had time for Halloween as a kid seeing as she was constantly travelling but her favourite country’s practice of Halloween is Britain because of how extravagant they can get, and now that she’s dating Abby she’s learning about the history and significance of ‘The Day of the Dead’. She usually just ends up dressing like a witch with the same costume each year but accessorised differently.

Abigail: La Dia de los Muertos, which isn’t really ‘Halloween’ as most western countries view it, was always her favourite time of the year. In an otherwise rather lacking childhood, most of her good memories came from the festival. Her foster parents upon adopting her researched and studied how to help her keep in touch with her cultural history and tradition, and helped her set up a shrine in their own house so she could still celebrate her Abuela and her older brother who died when she was 6. 

69: It’s your crush’s birthday!  What does your scholar get/do for them? 

Lillie: Aside from generally stressing and not knowing what to do, she probably cooks him something and offers it to him with shaking hands, most likely looking away. Tadashi, being who he is, makes a joke to ask if it’s poisoned which earns him an irritated glare. Then he nearly cries at how good it tastes. 

Vivian: She takes you out on a date. Where do you want to eat? What do you want to wear? You wanna see a movie? Late night walk in the park? You got it. She’s on it. Already bought tickets a week ago. Reserved a private table in a restaurant. Gets her parent’s driver to take you places. 

Darcy: he’s similar to Vivian, but he does things more personalised like. He sneakily susses out what you want and like all year beforehand and does stuff like make a ghost profile on amazon or pintrest to see what things are in your wishlist etc. He’d probably also make a mixtape of songs that you and him like, or that are important to you (hard to do in his case, since he’s trying to woo Axel though). Also, he’s DTF (Down to fuck FUN!). Just saying. 

Flora: Birthdays are BIG deals for her, so she, like Darcy, finds out things you like and want. She is more direct about it though and asks you to your face what you want, or what you would like. She also is good at cake decoration and likes making personalised designs for them. She can also sing ‘happy birthday’ in 4 languages, and on occasion has been able to get ahold of foreign gifts from places she’s visited overseas before. 

Abigail: For the very first birthday she spends with you, She puts on a whole personalised performance for you! (non sexually, of course, this is the kind where your parents and kids can be invited too). She gets caterers to help with food, and she buys out a venue of whatever size she needs and you and your whole family and as many friends as you can fit are invited. 

For later on in your relationship this only happens again during milestones like 5 years anniversary or something (it’s not a secret, I’m keeping her and Flora together forever). On off years, she just cuddles with you and spoils you with shopping sprees and they buy huge donut boxes or chocolate sample boxes and watch trashy/cheesy foreign romances. 

70: How would your scholar react to seeing their crush crying? 

Lillie: If somehow, by some, horrific, ungodly force, you managed to make Tadashi of all people cry, She would probably gently ask him if she can help at all. she knows he’s a little bristly, and doesn’t always appreciate her over-empathy, but she loves him a lot and wants to help him. 

She gives him the option to turn her away if he needs time alone but wants him to know that she is available whenever he is ready to talk. If he is, by chance, ready, she sits by him and holds him closely, either listening to him talk, or listening to him cry. Kisses and head scratches are also inevitable. 

Vivian: She is the person who runs up to you and holds you, asking you what’s wrong. I’m apprehensive to go further because I don’t know much about her crushes yet (Claire and Alistair) so i’ll have to wait and see. I feel like Vivi would probably react slightly differently depending on how her crush displays grief, but on a base level, she’s a hugger and sweet-talker to get you to calm down.

Darcy: It’s time to FIGHT. Who does he need to knock the fuck out? The first few times, his crush (Axel) is probably too busy holding him back to be crying anymore. After a few more times, Darcy is still raving mad, but he keep it in long enough to responsibly evaluate the situation. Still probably ready for low-key revenge, but he’s not as trigger happy with his fists anymore. 

Flora: She calmly asks her to walk her through what happened. A future lawyer at heart, she can and will do whatever she can to make sure Abby is done right by via compensation or proper and called for retribution. However, she is fully aware of the fact that sometimes Abigail gets into trouble in the first place because of her own faults. Abby isn’t very good at making friends, but good at making enemies. Definitely not an innocent little angel. Her girlfriend is basically her #1 test client, because of how she is, socially and how much legal drama the celebrity life brings on top of that.

Abby: Abby isn’t good with emotions. She’s not even good with her own, let alone knowing how to help Flora when she’s down. It’s usually the other way around. When Flora is upset, which is very very rare, Abigail tends to leave her alone or to sit quietly by her, super anxious and not knowing what to do at all. Flora doesn’t resent this, because she knows Abby’s limitations and inabilities. She tends to prefer to cry it out until she’s done crying anyway, and typically feels better afterwards. 

71: How would your scholar react to seeing their enemy crying?

Lillie: She laughs. Quietly, of course, and to herself, but she’s typically satisfied if things aren’t going well for an enemy. Of course, unless the situation is extreme or a special case. But there is one specific enemy in mind (an OC) whom she’d happily drop kick into the sun if she could, so seeing her cry would be fine by her. 

Vivian: She ignores them and moves on. She doesn’t care enough to tempt them when they’re in a good mood, and she certainly doesn’t want to offer her kindness to someone who will probably use it against her later. 

Darcy: Same as his sister, but he also probably then asks what happened, usually out of general curiosity. 

Flora: She has very few enemies, and being a future lawyer, she is training herself to have less of a bias when it comes to justice (the exception is if you mess with Abigail). She’ll ask if she can help in anyway, or at the very least she’ll ask what’s wrong. Also, she has a deep maternal instinct in the first place, and wants to help out as many as she can manage.

Abby: Abby ignores them like the twins do, not only because of how bad she is around crying, but she might have been the reason some people have cried. 

She has gotten into fist fights with people before for her general inability to chill out, and especially if it’s a person she dislikes. She was almost expelled once for grabbing Karolina by the tie and ripping her shirt collar, and has given Axel a wedgie on more than one occasion. Naturally she and Darcy hate each other because Darcy tends to get too protective over Axel. It’s a big, ugly, sad mess. 

I’ll continue the other questions in a second post!

[jurassicparkpodcast]

Jurassic 25: A Celebration - From Victoria's Cantina

“An adventure 65 million years in the making.”  The year was 1993.  8 year-old me, who had already been obsessed with dinosaurs at that point, caught wind of a TV spot that contained that simple tagline.  I distinctly recall seeing the herd of Gallimimus flocking, and it excited me in a way no other movie commercial had.  The movie was called Jurassic Park, and it was the movie to see that summer.  Thankfully, my wish came true.  It was a hot day in Fort Worth, Texas, when my father, uncle, brother and I went to see it at a theater in a local mall.  The moment the film began, I was mesmerized.  The way it began with such an intense scene of a man being violently attacked by what was obviously some sort of monstrous dinosaur truly set the mood.  But this was not a scary movie.  Sure, it had terrifying moments.  (I still recall the shot of the Tyrannosaurus Rex breaking through the glass to attack Lex and Tim scaring the living daylights out of me.)  But there were warm, touching moments, such as when our heroes are taken to see a dinosaur for the first time.  Or when everyone suddenly abandons their Jungle Explorers to get up close and personal with a sick Triceratops.  Indeed, for an 8 year-old dinosaur-crazed kid, this movie was nothing short of a great adventure.  And it was one that would stay with me into adulthood.

Fast-forward 25 years to 2018.  In April, Universal Studios Hollywood announced that it would be hosting the Jurassic Park 25th Anniversary Celebration.  Originally set to span two days on May 11th and 12th (May 13th was added due to the high demand for tickets), it would celebrate a quarter-century of one of the biggest film franchises in cinema history.  Being somewhat local in San Diego, I knew this was an event I could not miss.  I convinced my husband that this was something we absolutely had to do.  (If visiting Kualoa Ranch and hunting for Jurassic Park filming locations across Hawai’i in 2016 did not affirm my fandom to him, what would?)  We booked our tickets and made plans to drive up to Hollywood on Friday, May 11th.  While I do visit Disneyland periodically, I had not been to Universal Studios Hollywood in 8 years.  I did get the chance to visit Universal Studios Japan last year, but I always feel that despite the grandeur of their newer iterations, theme parks just do not hold up to their original locations.  (And surely, the same is often true when we speak of our favorite film series.)   After surviving the always chaotic Los Angeles traffic, my excitement heightened as we parked in Jurassic Parking and made off towards Universal CityWalk.  According to our tickets, we would not be let into the event until 5:30 PM.  Since we were a bit early, we bided our time at CityWalk and admired the beautiful Jurassic Park Jeep Wranglers and Jungle Explorer that sat adjacent to the CityWalk AMC movie theater.  It was a cool evening, and my hair decided early on that the intermittent drizzle would become its greatest foe.  What was nice about this event was that the park did not close until 7 PM.  So we made use of this precious time to wait a ridiculously short 10 minutes for Harry Potter and the Forbidden Journey.  After admiring the recently opened The Simpsons area, we followed the signage leading our way to the Jurassic Park event and rode the Starway down to the Lower Lot.  When we arrived, we were instantly thrown into Jurassic mode. 

Universal used the Jurassic Park: The Ride area to stage the Jurassic Park 25th Anniversary Celebration.  Along with the ride itself, the celebration included a main stage, an activity area, the Raptor Encounter experience, restaurants, shops, and multiple bars that were set up to meet the needs of alcohol-deprived fans.  The queue of Jurassic Park: The Ride contained prop displays such as Claire’s outfit and a gyrosphere from Jurassic World.  There was also a Mattel Jurassic World toy display.  The activity center featured face painting and caricatures.  The main stage would be where Jurassic World director Colin Trevorrow would be moderating a panel with special guests a little later.  Shops such as Jurassic Outfitters were filled with merchandise, some of which was created for the 25th Anniversary Celebration.  Mattel toys were priced double their MSRP.  You could get a Super Colossal T-Rex for the “special” price of $90.  (Markups on merchandise are not uncommon at theme parks, but such drastic premiums came across like gouging.)  And as an added bonus, Revenge of the Mummy and Transformers: The Ride were open for fans attending the exclusive event.  Both were walk-ons for the entire night, which again, is just unheard of.  Around 7:15 PM, the D.J. put his beats on pause as Velociraptor Zulu and Velociraptor Blue made their way out to taunt the crowd awaiting the panel.  This was a confusing moment, as many fans who had already gathered in front of the stage for the panel were asked to disburse so that the raptors could do their thing.  The “show” included several ACU soldiers who were working to steady the two raptors and contain them.  It really lacked any choreography or plot and came across as very disorganized.  I got the impression that most fans could have done without it; especially since it disrupted most everyone who had already claimed a spot for the panel.  Moments later, the emcee welcomed Colin Trevorow to the stage.  The Jurassic World: Fallen Kingdom co-writer spoke a bit about his fandom and then promptly welcomed three Hollywood veterans who were involved with the production of Jurassic Park.  They included assistant director John Kretchmer, cinematographer Dean Cundey, and visual effects artist Dennis Muren.  The three esteemed guests shared stories about working on Jurassic Park and why it remains such a beloved film.  For me, the panel was the biggest highlight of the evening.  And what was quite great about it was that each night of the celebration would feature different guests.  (Saturday attendees got to hear from Laura Dern, while Sunday guests got a nice dose of Jeff Goldblum!)  When the panel concluded, the D.J. cranked the music back up.  But his performance was again paused for the costume contest and trivia game.  Of course, another highlight of the evening was the IMAX showing of Jurassic Park at the CityWalk AMC theater.  It contained the opening sequence to Jurassic World: Fallen Kingdom, which was met with a rave response from fans.  And of course, there is nothing like seeing one of your favorite films on the big screen.  While all of these activities were fantastic, I would be remiss if I did not mention how special it was interacting with so many Jurassic Park fans face-to-face.  Seeing their enthusiasm for the franchise, with their faces lighting up as they discussed their favorite characters and dinosaurs and memories, truly left an impression.  It was truly special to connect with like-minded Jurassic Park fans, and it was tremendously special to meet former online friends who I can now simply refer to as friends. 

On the whole, Universal did a remarkable job organizing this special event for the Jurassic Park fans who had traveled not only from within California, but also from other states and even other countries.  There was abundant signage throughout the park for fans to find their way to the event.  The fact that the Upper Lot was available for attendees for an hour and a half was a huge plus that made the event all the more enjoyable.  The exhibits, activities, and games added an extra layer of interactivity that gave fans an ample number of things to do.  The Raptor Encounter special show was quite disorganized and seemed more of a nuisance than a contributing element to the experience, but it was countered with a great panel and a tram ride through part of the backlot towards the movie theater.  And then when you factor the showing of Jurassic Park with a preview of Fallen Kingdom, you truly feel like the $69 paid for the event was quite a bargain.  What’s more is that through my observations, I noticed fans of all demographics enjoying the event.  I also got the sense that attendees were happy and having a great time, and it was certainly nice to see Universal commemorating the anniversary of one of its biggest films.  With the first two events selling out, I have to wonder if Universal will hold similar events in the future not only for Jurassic, but also for other established franchises like Harry Potter or Back to the Future.  I know more than a few fans who would wholeheartedly welcome a Jurassic Park 30th anniversary party in 5 years. 

In 2018, I am enormously excited.  Not only is Universal celebrating the 25th anniversary of one of my favorite films, but they are also releasing Jurassic World: Fallen Kingdom this summer.  Mattel is putting out some of the finest Jurassic Park toys of all time and truly delivering on the promise of what a great toy line should be.  Indeed, it is perhaps the best time to be a Jurassic fan.  Somewhere inside 33 year-old me, 8 year-old me is smiling and enjoying every minute of it. 

Victoria B.

Please find Victoria's Cantina on YouTube, Twitter, Facebook and Instagram! Don't miss our special episode focusing on the Jurassic Park 25th Anniversary Celebration at Universal Studios in the player below. Also find a few more photos in the gallery below: