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[Assistant] I thought about the dark room in the first gnome with all the children looking into bowls of oil. Were they looking for us right now? The thought was creepy. I tried to sit up and winced again. Stay still, Bost ordered. Really, Carter, you should learn to fall like a cat. I'll work on that, I promised. How are you even alive? Is it that nine lives thing?


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[Assistant] Oh, that's just a silly legend. I'm immortal. But the scorpions...


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[Assistant] Sadie scrunched in closer, shivering and drawing Bast's raincoat around her shoulders. We saw them overwhelm you. Bast made a purring sound. Dear Sadie, you do care.


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[Assistant] I must say I've worked for many children of the pharaohs, but you too. She looked genuinely touched. Well, I'm sorry if I worried you.


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[Assistant] It's true, the scorpions reduced my power to almost nothing. I held them off as long as I could.


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[Assistant] Then I had just enough energy to revert to Muffin's form and slip into the Duat. I thought you weren't good at portals, I said. Well, first off, Carter, there are many ways in and out of the Duat. It has many different regions and layers. The Abyss, the River of Night, the Land of the Dead, the Land of Demons. Sounds lovely, Sadie muttered.


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[Assistant] They pass through the Duat to connect one part of the mortal world to another, and yes, I'm not good at those, but I am a creator of the Duat.


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[Assistant] If I'm on my own, slipping into the nearest lair for a quick escape is relatively easy. And if they'd killed you? I asked. I mean, killed Muffin? That would have banished me deep into the Duarte.


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[Assistant] It would have been rather like putting my feet in concrete and dropping me into the middle of the sea. It would have taken years, perhaps centuries.


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[Assistant] How long were you in the first gnome before they decided to kill you? Uhm, about 24 hours? Bast whistled. They've gotten friendlier.


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[Assistant] They used to blast godlings to dust in the first few minutes. We're not- Wait, what did you call us? Sadie answered, sounding as if in a trance. Godlings. That's what we are, aren't we? That's why Zia was so frightened of us. Why Dejardin wants to kill us. Bast padded Sadie's knee. You always were bright, dear. Hold on, I said.


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[Assistant] You mean hosts for gods? That's not possible. I think I'd know if... Then I thought about the voice in my head, warning me to hide when I met Iskandar. I thought about all the things I was suddenly able to do.


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[Assistant] Like fight with a sword and summon a magical shell of armor. Those were not things I'd covered in home school. Carter, Sadie said. When the Rosetta stones shattered, it let out five gods, right? Dad joined with Osiris. Amos told us that. Set, I don't know. He got away somehow. But you and I, the amulets protected us. I clutched the eye of Horus around my neck. Dad said they would. If we had stayed out of the room, as Dad told us to.


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[Assistant] Sadie recalled. But we were there. Walking.


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[Assistant] We wanted to help him, we practically asked for power, Carter. Bost nodded. That makes all the difference, an invitation. And since then, Sadie looked at me tentatively, almost daring me to make fun of her. I've had this feeling, like a voice inside me.


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[Assistant] If Sadie hadn't said something maybe I could have denied what was happening a little longer. But I thought about what Amos had said about our


[User] Please transcribe this text

[Assistant] It's very quick, and if you've watched our pipeline videos, which I'll link below, the process is very similar.


[User] Please transcribe this text

[Assistant] This time though, we're going to be using transfer learning and doing some training ourselves rather than just loading a model and using it as is. So to learn more about transfer learning, head to the what is transfer learning video and we'll link that below as well.


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[Assistant] But for now, let's look at this code. To start, we pick which model we want to start with. In this case, we're going to use the famous, the original BERT as the foundation for our training today. But what is this monstrosity line, this TF auto model for sequence classification? What does that mean?


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[Assistant] Well, the TF stands for TensorFlow, and the rest means take a language model and stick a sequence classification head onto it if it doesn't have one already.


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[Assistant] So this line of code loads BERT, which is a general purpose language model. It loads at weights, architecture and all, and then adds a new sequence classification head onto it with randomly initialized weights. So this method needs to know two things. Firstly, it needs to know the name of the model you wanted to load the architecture and weights for.


[User] Please transcribe this text

[Assistant] And secondly, it needs to know how many classes your problem has, because that will determine the size, the number of neurons in the output head.


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[Assistant] Well, if you're familiar with Keras, you've probably seen this already. But if not, this is one of the core methods in Keras that you're gonna see again and again. You always need to compile your model before you train it. And compile needs to know two things.


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[Assistant] Firstly, it needs to know the loss function, which is what you're trying to optimize. So here we import the sparse categorical cross entropy loss function.


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[Assistant] So that's a mouthful, but it's the standard loss function for any neural network that's doing a classification task. It basically encourages the network to output large values for the right class and low values for the wrong classes.


[User] Please transcribe this text

[Assistant] Note that you can specify the loss function as a string, like we did with the optimizer, but there's a risk there, there's a very common trap people fall into, which is that by default, this loss assumes the output is probabilities after a softmax layer.


[User] Please transcribe this text

[Assistant] But what our model has actually output is the values before the softmax, often called the logits, sometimes logits, no one's quite sure how to pronounce that one.


[User] Please transcribe this text

[Assistant] But you've probably seen these before in the video about pipelines. So if you get this wrong, your model won't train and it'll be very annoying to figure out why.


[User] Please transcribe this text

[Assistant] In future videos, we're going to see how to use the model's internal loss computations so that you don't have to specify the loss yourself and you don't have to worry about these details. But for now, remember to set from logits to true.


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[Assistant] The second thing compile needs to know is the optimizer you want. In our case, we use Adam, which is sort of the standard optimizer for deep learning these days.


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[Assistant] The one thing you might want to change is the learning rate. And to do that, we'll need to import the actual optimizer rather than just calling it by string.


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[Assistant] Again, if you've used Keras before, this is all going to be very familiar to you. But if not, let's very quickly look at what we're doing here.


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[Assistant] Fit is pretty much the central method for Keras models. It tells the model to train on the data we're passing in.


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[Assistant] So here we pass the datasets we made in the previous section. The dataset contains both our inputs and our labels. So we don't need to specify separate labels when we're calling fit.


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[Assistant] These few lines of code are really all it takes to apply the power of a massive pre-trained language problem, a massive pre-trained language model, excuse me, to your NLP problem.


[User] Please transcribe this text

[Assistant] Could we do better than this? I mean, we certainly could. Uh, with a few more advanced Keras features, like a tuned, scheduled learning rate, we can get an even lower loss and an even more accurate, more useful model. And what do we do with our model after we train it? So all of this is going to be covered in the videos that are coming up, so stay tuned.


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[Assistant] So as an introduction, I'd like to get started by telling you a bit about how I arrived at this topic of study.


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[Assistant] This was generally a really popular move with students and, uh, there were pop culture topics like television shows or comics.


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[Assistant] I really enjoyed most of these themes myself, but I also found that some of them were a little bit intense.


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[Assistant] So for instance, this one that had a James Bond theme, although honestly that's kind of clever for a security class, uh, or this one modeled after Star Trek.


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[Assistant] Or especially this one. This was a very intense Matrix theme. Wait, it gets worse.


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[Assistant] So these were all real course websites while I was at Brown. And it's not that I had any particular issue with The Matrix or with Star Trek, but


[User] Please transcribe this text

[Assistant] It was that despite the fun environment, I had noticed many of my friends in those courses ended up dropping out of the courses or out of the major. And this seemed especially true for my women classmates.


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[Assistant] One that I knew didn't have anything to do with the actual material of the course, but it was still there. Years later, when I started my PhD,


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[Assistant] Ambient belonging is a term that describes the feeling of fitting in with a culture or a community that's passively elicited by the surrounding environment.


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[Assistant] Belongingness describes the psychological need to be accepted as a member of a group.


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[Assistant] My hypothesis was that these should have different effects on potential students.


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[Assistant] So that would mean hypothetically producing results figures that look something like this.


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[Assistant] I expected there'd be very little difference in the two bars for men, that's the pair on the left, who are, you know, pretty comparable height. But for women, that's the pair on the right, that the stereotypical interface would have a negative impact.


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[Assistant] Also note here that rather than potentially negatively impacting a whole cohort of actual Stanford students, I ran the experiment using crowd workers of undergraduate age in the United States. And what I found was striking.


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[Assistant] So starting with these course related measures, women exposed to the stereotypical website suffered.


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[Assistant] As you can see, for each measure, the rightmost, uh, dark bar that corresponds to women who saw the stereotypical website is statistically significantly lower than all the others in the figure.


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[Assistant] Moreover, women who saw that stereotypical website were also negatively impacted on longer-term measures, the ones that ostensibly had little to do with the course. They said they were less interested in computer science as a field, less confident in their technical abilities, and they expected to be much more stereotyped in the discipline.


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[Assistant] So I was able to conclude that gender bias could negatively impact people's sense of belonging through a user interface.


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[Assistant] You can consider for instance the effect of an image like this one of Paul Ryan's interns when he was Speaker of the House.


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[Assistant] Broadly, these are systems that shape and are shaped by users. They interface directly with the user, and in turn, they're often shaped by user behaviors.


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[Assistant] And in my work, I close that loop by studying the system and the user.


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[Assistant] The first, what kind of content do socio-technical systems produce? And the second,


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[Assistant] How does that content impact users towards building systems that do better?


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[Assistant] Now, traditionally in a software system, we're used to asking the first question. We know what it means for a system to work and we conduct tests and use monitoring to verify that it does actually function as expected.


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[Assistant] Sociotechnical systems, meanwhile, because they're in a loop with users, the users are literally part of the system.


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[Assistant] It's not enough that a platform just functions. To evaluate them effectively, we also need to ask first what those outputs look like at scale and second, what influence they have on users.


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[Assistant] In the example we started with comparing two websites, we can see that content that's created by a single person, like a course instructor, can communicate bias and undermine belonging. But this isn't an individual scale problem.


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[Assistant] And we rely on that algorithmic content for basic social functions like connection to family and friends, gathering information and civic engagement.


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[Assistant] And as of 2012, which is unfortunately the most recent Pew data on search engine use, over 90% of online adults use search engines. That's a number we can somehow imagine is even higher now.


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[Assistant] But these systems do have significant biases. We've started to see concern about bias along the lines of gender, race, political affiliation, and more.


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[Assistant] Studying these systems, going beyond criticism to precisely measure the content that they produce and to quantify the impact they have on people remains a challenge.


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[Assistant] First, these systems are dynamic. They change constantly, which can pose significant technical challenges in trying to study them.


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[Assistant] And second, their byproducts are ephemeral. A user's experience of their newsfeed or search engine results disappears without a trace after that interaction. There's no way to examine it retrospectively.


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[Assistant] They are embedded. These systems are in a loop with users and users are interacting with dozens of such systems simultaneously in a single browser or on a single device throughout their daily lives. And finally, they're personal. Each user's view of the system is unique.


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[Assistant] And I'll finally describe the project, the final project in my dissertation, building infrastructure for intervention audits.


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[Assistant] In the field of advertising, prior work has shown that representing marginalized people in college brochures can make a university seem more appealing to students. The same has been found to be true for employees at a company and also for customers of a business. These kinds of cues are important for marginalized people. They indicate that an organization might be more welcoming to them.


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[Assistant] But importantly, the effects of visual representation are not restricted to those groups alone. Visual diversity signals inclusivity to everyone.


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[Assistant] The US Bureau of Labor Statistics tracks demographic breakdowns of several hundred common occupations. Doctors, lawyers, real estate agents, and so on.


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[Assistant] So based on that BLS list, I set out to collect image search results from Google using as the query term, the names of those occupations.


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[Assistant] This is where studying algorithmic content becomes technically challenging.


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[Assistant] My pipeline for automatically collecting and processing image search results begins with a scraper to query Google with each of those query terms and capture the resulting top 100 images to avoid potential personalization from repeated searches.


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[Assistant] Above this line are cases where search results overrepresented women, and below where search results underrepresented women.


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[Assistant] There are of course some cases like among librarians where we see that people of color are a very small fraction of librarians, but Google actually over represents them in search.


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[Assistant] Next, to quantify the degree of underrepresentation in both cases, I fit generalized linear models to the data predicting Google representation from BLS data.


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[Assistant] So looking at these figures visually, we can see that there's generally an under-representation of women and people of color relative to the workforce, even more so in the case of race than of gender.


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[Assistant] For an occupation with 22% people of color, the model predicts that images will show 16% people of color.


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[Assistant] And in both cases these differences are statistically significant, even more strongly in the case of race than of gender.


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[Assistant] So going back to that question, do search results reflect real world diversity? In this dataset, we find that no.


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[Assistant] Algorithm audits are the process of providing repeated inputs and measuring outputs of an opaque system to draw inferences about its inner workings.


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[Assistant] Auditing without the algorithm part is a powerful tool that's been used in the social sciences for decades to identify discrimination in things like employers' hiring practices or loaners' lending practices.


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[Assistant] So having answered that first question using an algorithm audit, in this project, I also closed the loop by connecting a second study answering this second question.


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[Assistant] First, to do this I selected a set of ten occupations, five for race and five for gender, in which marginalized people are heavily underrepresented.


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[Assistant] For each of the ten occupations, I created three synthetic search results pages, one with images predominantly from the dominant group, one with images predominantly from the marginalized group, and another with equal representation.


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[Assistant] After that exposure, I then asked each participant questions about three measures of interest on a seven-point Likert scale. First, I asked about their perception of how inclusive the occupation is.


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[Assistant] So what did we find? First, let me explain how to read the figures I'm about to show you.


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[Assistant] Changed as the proportion of women in those synthetic search results increased.


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[Assistant] We find that greater representation of marginalized groups makes occupations seem more inclusive across the board.


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[Assistant] Reading all three lines from left to right across the figure, we can see that participants' expectations of inclusivity in this case generally increased the more women they were shown.


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[Assistant] In particular, showing medium or high levels of people of color representation had a positive effect on perceived inclusivity relative to low levels of racial representation. We also validate these findings statistically.


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[Assistant] You'll also remember that this is consistent with prior literature on visual diversity, making organizations seem more inclusive.


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[Assistant] Next, let's look at results in the same format for the second measure, participants reported interest in an occupation. Here we see somewhat different results.


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[Assistant] So in this case, search results with more women had if anything a negative effect on our outcome measure. What might explain that?


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[Assistant] So importantly, in addition to exposing participants to different conditions and collecting their responses, I also collected demographic characteristics like race and gender for each participant.


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[Assistant] So first, let's examine sense of belonging as women's representation was increased, separating participants by gender.


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[Assistant] What this means is that we can actually compensate for pre-existing differences in gendered perceptions of belonging by increasing visual representation of women.


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[Assistant] Across the board, white participants reported a higher sense of belonging than people of color, and no amount of racial representation in those search results could change that. And again, we confirm this finding statistically.


[User] Please transcribe this text

[Assistant] So to conclude, these findings confirm that visual representation in algorithmic content can change people's expectations of the world and of their place in it. But as we saw, there are also some important nuances to keep in mind. First, more isn't always better.


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[Assistant] Greater levels of marginalized people's representation can actually have deterring effects because of underlying social issues.


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[Assistant] So as I've just shown you, I use a combination of technical and behavioral science tools to answer these two questions. What content socio-technical systems produce and how that impacts users.


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[Assistant] Instead, empirically, they consume the entire page of results at a glance. The whole page is essentially a single object. In my research, I've given a name to this idea. I call this search media.


[User] Please transcribe this text

[Assistant] That positions Google in a role similar to that of a newspaper publisher, presenting users with a front page for every query.


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[Assistant] In the political context, one important kind of representation that matters is the representation of political perspectives and the potential for bias is along partisan lines.


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[Assistant] That's every candidate running for either chamber, the House or the Senate in any state that year.


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[Assistant] To collect a data set, I built infrastructure to perform queries with those politicians' names, collecting the first page of search results.


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[Assistant] Scraping data at this scale entails several technical challenges.


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[Assistant] Another is that a single machine conducting each of these 3000 queries wouldn't actually finish in the course of one day. And I wanted to collect these scrapes daily.


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[Assistant] Finally, I used an established metric to label each of these URLs with a partisanship score. That's a measure from very liberal to very conservative that describes the ratio of liberals to conservatives who normally engage with that website.


[User] Please transcribe this text

[Assistant] From what sources does Google create its search media for these political queries?


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[Assistant] For instance, if Google was biased against conservative news, we might expect there to be very few sources on the right side of this figure.


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[Assistant] What we found is that search media drew extensively from conservative sources, regardless of query.


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[Assistant] So at least by these metrics, we can conclude that there isn't some widespread bias against conservative content in search media.


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[Assistant] Now you'll remember I said I collected this data daily for six months leading up to the election. I was interested in whether there were any temporal changes over the course of the election.


[User] Please transcribe this text

[Assistant] And I did find evidence that political events affecting individual political candidates changed their search media.


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[Assistant] So a page of more partisan search media would be a data point higher up on this figure, whereas a more moderate page would be lower.


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[Assistant] Election winners have much more moderate search media than do losers.


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[Assistant] Incumbent candidates have much more moderate search media than do challengers or those running for open seats.


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[Assistant] Whereas incumbents who are looking to win a more general election where citizens from both parties will be voting should take care to come across as more moderate.


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[Assistant] So recently I've gathered a team of leading algorithm audit researchers from four universities and we've written an authoritative piece on algorithm auditing.


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[Assistant] Research like mine closes that loop, connecting the socio and technical parts of these systems, using a combination of computational and behavioral science strategies.


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[Assistant] But the overhead to doing this work as we've seen is significant. So for each website we want to study, we need to build new custom infrastructure.


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[Assistant] And in order to control the stimuli that users see, we have to run experiments in highly controlled artificial experimental settings.


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[Assistant] So in order to enable researchers to do this work and to do it more sustainably, in addition to creating guidelines and best practices, we also need more effective tools.


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[Assistant] This need for tools brings me to the final project in my PhD, creating infrastructure for intervention audits.


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[Assistant] There are many examples of one-off infrastructure for passive audits, as we've seen in this talk. And these can be really valuable, but they generally only collect data from one website at a time. New infrastructure has to be built for every new audit.


[User] Please transcribe this text

[Assistant] And these data sources also don't allow for experiments and can be really expensive to get access to.


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[Assistant] Plus, researchers can't follow up with participants through surveys or other kinds of additional data collection.


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[Assistant] So going back to those challenges, ideally what we want to do is allow researchers to collect data themselves in real time.


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[Assistant] Choosing exactly what data to collect from user browsing across all websites.


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[Assistant] On the cross-site browsing experience for a group of consenting compensated participants.


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[Assistant] The front end is user-facing with an admin interface for researchers. It handles onboarding and offboarding, collects survey responses, gives users privacy controls for their data, and allows researchers to manage participants.


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[Assistant] The extension handles data collection, collecting the trail of links that participants visit online.


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[Assistant] The backend then combines and securely stores these two data sources where researchers can access them later for analysis.


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[Assistant] Finally, after their participation is over, they uninstall the extension, complete an exit survey, receive payment, and can potentially be contacted with follow ups in the future.


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[Assistant] And finally, during the experiment in management phase, researchers can do things like randomly assign participants to different experimental conditions and collect and analyze the resulting data.


[User] Please transcribe this text

[Assistant] First, in terms of exposure, what headlines do people see but not visit? Browser history logs only record sites that people do visit.


[User] Please transcribe this text

[Assistant] In contrast, Intervenor can collect data on each page of a user's browsing and study things like selective avoidance by examining all of the links that people see but don't click on. Next, Intervenor is user-centered.


[User] Please transcribe this text

[Assistant] In addition to log data, we might want to deploy surveys or interviews to understand how people's beliefs change over time in relation to the content they consume.


[User] Please transcribe this text

[Assistant] But if we wanted to study the set of all ads, actual users are shown. Current algorithm auditing techniques fall short.


[User] Please transcribe this text

[Assistant] For instance, we can look at things like the distribution of gender and skin tone in those ads and ask whether they're representative of diversity.


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[Assistant] Second, Intervenor will deploy to a group of real users, collecting all the ads they're shown across all the websites they visit and allow us to study those repeated exposures in the context of those users' own demographics.


[User] Please transcribe this text

[Assistant] Practices like these are necessary for making that research sustainable by creating trusting relationships with participants who we can work with again in the future.


[User] Please transcribe this text

[Assistant] So to summarize, over the course of my PhD, I've worked on studying representation and bias in socio-technical systems, including the four projects I've talked about today.


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[Assistant] We can build socio-technical systems that have positive impacts on ourselves and on our communities.


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[Assistant] Thank you so much for your time and attention. Uhm, just quickly before opening for questions, I'd like to take a moment to make a few acknowledgements.


[User] Please transcribe this text

[Assistant] And finally, doing a PhD is certainly more than just a professional undertaking. I am so supremely grateful to the friends and family who have supported me throughout.


[User] Please transcribe this text

[Assistant] Yeah, definitely. So the process there was, uh, first we took the set of actual images that we had produced, um, by scraping in the first part.


[User] Please transcribe this text

[Assistant] So basically we were trying to get something as close to that first set of search results that people would see, while also including the right number of marginalized people for that, uh, for that experimental condition. And so sometimes this meant going further down on the page to find enough marginalized people to put in the search results because there really weren't very many.


[User] Please transcribe this text

[Assistant] We were looking for the low condition to have approximately 10%, the medium or equal representation condition to have 50-50 and the high to be 90% marginalized people.


[User] Please transcribe this text

[Assistant] So that doesn't necessarily mean affirming the representation of particular marginalized people.


[User] Please transcribe this text

[Assistant] The same way that we would ideally want to say, you know, let's take each individual's own, uh, identity category and over represent that particular thing.


[User] Please transcribe this text

[Assistant] I think that that's kind of like the next important step. In this case we actually didn't have enough, um, images of different types of marginalized people in order to be able to do that.


[User] Please transcribe this text

[Assistant] We also couldn't be sure exactly how, you know, from some set of images for each individual participant who they would say they identified with and who they wouldn't. But I think that that's kind of the ultimate goal is to understand each person has


[User] Please transcribe this text

[Assistant] Uhm, these are definitely simplifying assumptions that we're making in order to sort of discretize the problem and make it approachable as a first step as more and more of this work is being done.


[User] Please transcribe this text

[Assistant] First of all, to examine identity in more nuanced and more multi-dimensional ways, but also I think to do follow-up work that specifically focuses only on marginalized people's experiences, while anchoring it to this sort of like broader literature that we've sort of started to establish, you know, about like everyone, because unfortunately in science, you know, the understanding of who is the default person,


[User] Please transcribe this text

[Assistant] I mean, even while I was there, I was arguing on the core staffs that I was part of, that like, you know, we should at least be thinking about this dimension, and I hope, knowing the students at Brown, I'm sure they are, but, uh, the department as a whole hasn't really changed anything that I know of as of now.


[User] Please transcribe this text

[Assistant] And whether the, uh, whether what really we're finding is that Google is biased towards conservative perspectives, uhm, the figure that I showed you there was, uh, double counting was looking at not the set of unique sources, but like how many times they came up. And so that's not adjusted for something like the number of political candidates from the different groups.


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[Assistant] So I can't conclude that necessarily. If we look at, I have similarly the figure, you can see this in the paper, the figure of, uh, unique counts of sources, and we find roughly the same total weight on like the more left side of that figure and the right side of that figure. It's like about even in weight. I think actually what they're trying to do is to


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[Assistant] Which could be its own kind of bias, like I don't condone balance as the right answer, but it's, uh, I think that's what's going on.


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[Assistant] And we haven't measured that in this case, although I expect it shouldn't be that persistent. Uhm, there has been some previous work that followed up with people a few weeks later and did actually find some effect. So, you know, perhaps even on the scale of weeks. But the other point you make is around repeated exposure.


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[Assistant] And so that's kind of one of the goals of building intervenors to be able to do that kind of longitudinal follow up with the same people and also to be able to record the content that they're actually seeing in a sort of ecologically valid way in their browser over the course of all of their web browsing.


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[Assistant] Dan, you got sass up from the ass up. Oh shit, the air's on.


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[Assistant] Uhm, good morning. What should we do today? Uh, we should probably get Nyotengu. Yeah, I don't know. Let's watch the girls participating in an activity in owner mode.


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[Assistant] I'm gonna watch the girls participate in a- take a photo of beach volleyball. Wait a second. Oh shit! I didn't mean to! No! This does nothing to further my and Nyotangu's relationship! Her hair's really fucked up.


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[Assistant] Alright, cool, nice, great, great, great. I like it, I like it, I like it.


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[Assistant] Owner mode's boring. I don't get to play any games. And there's my girl! Do it. Fuckin' YoTango. God fucking dammit! What does it take? What does it take?


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[Assistant] That's a bunch of bull hockey. Oh man. That's a bunch of fart knocking. Hmm. That's a bunch of butt smacking. Sushi! She's gotta like sushi! You'd think. I mean, come the fuck on!


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[Assistant] Alright, let's play beach flags or so- I don't fuc- It's just a waste of time as far as I'm concerned, cause there's no goddamn fuckin' You know what, I'm gonna build my relationship some more with- I'm gonna relax, poolside.


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[Assistant] That's a pretty sweet shot. I know. Check this shit out.


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[Assistant] Get a shot of the tie in her ponytail. Just a really cool tie. Nice one. Dude. Oh my god. The negative space? And the fact that you can't see her face? Damn. Boom. Good shot of this place. Oh, so hot.


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[Assistant] Woah, dude! You didn't give her two gifts, did you? Yeah, I sent her one last night. Well then. I know where this is going. Let's see. See, this one was for- Gifts I pr- Oh, I didn't give it to her yet. Okay. I'm gonna open it. Yay!


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[Assistant] So she gave me sushi! And then also sent me back the tea set, but that's okay! That's okay, that's alright! I'm making progress.


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[Assistant] Ah, dude, alright, I'm gonna send her this fuckin' Zack present, cuz fuck the presents I get from Zack. He sucks at giving me presents. I'm gonna send a gift to Nyotangu, and she's gonna fuckin' like it.


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[Assistant] Let's have some fun today, you know. It's a great place to relax. That's all I wanna do. I just wanna have fun.


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[Assistant] Play beach volleyball. Satisfaction and rewards change. Is it satisfaction? Is that what she wants? I can't get no satisfaction. I'm gonna, yeah, I'm gonna give her a gift.


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[Assistant] Oh no, I feel like I got so far and then I just fell right off the cliff.


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[Assistant] The sushi's gone bad. The sushi's gone bad, fuck. Well, maybe that's why she didn't like it. Yeah. Just to get there.


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[Assistant] Oh, it's delicious, it smells so good. I gotta learn about her. What if she likes...


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[Assistant] You like it, baby? She'll like it. Is that what you want? She'll like it. I know she will. It's up her alley.


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[Assistant] Her alley is dark, and I don't feel safe there. Her alley is dark and full of terrors. I'm gonna play Beach Flags with Moji, because fuck all these other girls, all I want is Nyotangu.


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[Assistant] I did this. This was my win. Your tan lines are weird.


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[Assistant] Whatever dude. Okay. I'm not worried about my tan lines. I'm worried about my tan lines if they affect my relationship with Nyotangu.


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[Assistant] Whoa, a laptop? I could've re-gifted that! Oh fuck, dude. Holy shit. I'm indifferent. Come on. I dislike the crab.


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[Assistant] Oh shit dude. Harsh. I don't care. I don't care. I got my girl right here.


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[Assistant] She's still fucking, fucking... You know what? You know what? You know what? I'm just going for the kill. Do it. Hello. I like your beauty mark. Oh, it's difficult. Oh, oh, oh, oh, oh, oh.


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[Assistant] I think one time she saw it, but, you know, it's romantic if she-


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[Assistant] Well it's romantic when you, when you make like a little doll out of her hair. Like, I need enough to make the doll. And it's a secret, I don't want her to know, cause it's a surprise, I want to surprise her with it.


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[Assistant] Fifteen. A godly birthday party.


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[Assistant] They took Carter to a different dormitory, so I don't know how he slept, but I couldn't get a wink.


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[Assistant] But the girl's dormitory just wasn't as posh as Amos' mansion. The stone walls sweated moisture.


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[Assistant] Creepy pictures of Egyptian monsters danced across the ceiling in the torchlight. I got a floating cot to sleep in.


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[Assistant] And the other girls in training, initiates, Zia had called them, were much younger than me. So when the old dorm matron told them to go to sleep straight away, they actually obeyed. The matron waved her hand and the torches went out. She shut the door behind her, and I could hear the sound of locks clicking. Lovely. Imprisoned in a nursery school dungeon.


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[Assistant] I stared into the dark until I heard the other girls snoring. A single thought kept bothering me. An urge I just couldn't shake.


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[Assistant] Finally, I crept out of bed and tugged on my boots. I felt my way to the door. I tugged at the handle. Locked, as I suspected. I was tempted to kick it.


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[Assistant] Apparently there wasn't much nightlife in the first no.


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[Assistant] I sneaked through the city back the way we'd come and saw nothing but an occasional cobra slithering across the floor.


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[Assistant] After the last couple of days, that didn't even faze me.


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[Assistant] I thought about trying to find Carter, but I wasn't sure where they'd taken him, and honestly, I wanted to do this on my own.


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[Assistant] After our last argument in New York, I wasn't sure how I felt about my brother.


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[Assistant] The idea that he could be jealous of my life while he got to travel the world with dad, please. And he had the nerve to call my life normal.


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[Assistant] Alright, I had a few mates at school, like Liz and Emma, but my life was hardly easy.


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[Assistant] If Carter made a social faux pas or met people he didn't like, he could just move on. I had to stay put.


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[Assistant] I couldn't answer simple questions like, where are your parents? Or, what does your family do? Or even, where are you from? Without exposing just how odd my situation was. I was always the different girl. The mixed race girl. The American who wasn't American.


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[Assistant] The girl whose mother had died. The girl with the absent father. The girl who made trouble in class. The girl who couldn't concentrate on her lessons.


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[Assistant] After a while, one learns that blending in simply doesn't work.


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[Assistant] Absolutely. Headmaster says I'll have to call your parents, young lady. I say, good luck. Carter didn't know anything about my life. But enough of that.


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[Assistant] The point was, I decided to do this particular bit of exploring alone, and after a few wrong turns,


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[Assistant] I certainly didn't want to meet Monsieur Evil again, or creepy old Lord Salamander, but I did want to see those images, memories, Zia had called them. I pushed open the bronze doors. Inside, the hall seemed deserted.


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[Assistant] No balls of fire floated around the ceiling. No glowing hieroglyphs.


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[Assistant] But images still shimmered between the columns, washing the hall with strange multicoloured light. I took a few nervous steps.


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[Assistant] I wanted another look at the age of the gods.


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[Assistant] On our first trip through the hall, something about those images had shaken me.


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[Assistant] I knew Carter thought I'd gone into a dangerous trance, and Zia had warned that the scenes would melt my brain.


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[Assistant] I felt a connection to those images, like there was an answer within, a vital piece of information I needed.


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[Assistant] I stepped off the carpet and approached the curtain of golden light. I saw sand dunes shifting in the wind.


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[Assistant] Storm clouds brewing, crocodiles sliding down the Nile. I saw a vast hall full of revelers. I touched the image. And I was in the Palace of the Gods.


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[Assistant] Huge beings swirled around me, changing shape from human to animal to pure energy.


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[Assistant] On a throne in the center of the room sat a muscular African man in rich black robes.


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[Assistant] He had a handsome face and warm brown eyes. His hands looked strong enough to crush rocks.


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[Assistant] We walked through a hall of jackal-headed statues and I could swear their eyes watched us as we passed.


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[Assistant] With dozens of stalls selling weird items like boomerang wands, animated clay dolls, parrots, cobras, papyrus scrolls, and hundreds of different glittering amulets.


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[Assistant] Next we crossed a path of stones over a dark river teeming with fish. I thought they were perch until I saw their vicious teeth. Are those piranhas? I asked. Tiger fish from the Nile. Zia said. Like piranhas, except these can weigh up to 16 pounds. I watched my step more closely after that.


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[Assistant] Seated pharaohs were chiseled into the walls and the doorway was shaped like a coiled serpent. What's in there? Sadie asked. We peeked inside and saw rows of children, maybe two dozen in all, about six to ten years old or so, sitting cross-legged on cushions.


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[Assistant] They were hunched over brass bowls, peering intently into some sort of liquid and speaking under their breath.


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[Assistant] At first I thought it was a classroom, but there was no sign of a teacher, and the chamber was lit only by a few candles.


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[Assistant] Judging by the number of empty seats, the room was meant to hold twice as many kids.


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[Assistant] The first gnome must keep in contact with our brethren all over the world. We use our youngest as operators, I suppose you would say. So, you've got bases like this all over the world? Most are much smaller, but yes. I remembered what Amos had told us about the gnomes. Egypt is the first gnome. New York is the 21st. What's the last one, the 360th? That would be Antarctica.


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[Assistant] Zia said, a punishment assignment.


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[Assistant] Nothing there but a couple of cold magicians and some magic penguins. Magic penguins? Don't ask. Sadie pointed to the children inside. How does it work? They see images in the water? It's oil, Zia said, but yes. So few, Sadie said. Are these the only initiates in the whole city? In the whole world, Zia corrected.


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[Assistant] There were more before. She stopped herself. Before what? I asked. Nothing. Zia said darkly.


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[Assistant] Initiates do our scrying because young minds are most receptive.


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[Assistant] Magicians begin training no later than the age of 10, with a few dangerous exceptions. You mean us, I said.


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[Assistant] She glanced at me apprehensively, and I knew she was still thinking about what the bird spirit had called me. A good king.


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[Assistant] It seems so unreal, like our family name in that Blood of the Pharaoh scroll. How could I be related to some ancient kings?


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[Assistant] And even if I was, I certainly wasn't a king. I had no kingdom. I didn't even have my single suitcase anymore. They'll be waiting for you, Zia said. Come alone. We walked so far, my feet began to ache. Finally, we arrived at a crossroads.


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[Assistant] On the right was a massive set of bronze doors with fires blazing on either side. On the left, a 20 foot tall sphinx carved into the wall.


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[Assistant] A doorway nestled between its paws, but it was bricked in and covered in cobwebs. That looks like the Sphinx at Giza, I said. That's because we are directly under the real Sphinx, Zia said. That tunnel leads straight up to it, or it used to before it was sealed. But I did some quick calculations in my head. The Sphinx is like 20 miles from the Cairo airport.


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[Assistant] Roughly. No way we've walked that far.


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[Assistant] Ziya actually smiled and I couldn't help noticing how pretty her eyes were. Distance changes in magic places, Carter. Surely you've learned that by now. Sadie cleared her throat. So why is the tunnel closed then? The Sphinx was too popular with archaeologists, Ziya said. They kept digging around. Finally, in the 1980s,


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[Assistant] That they've only discovered thirty percent of the ancient ruins in Egypt. In truth, they've only discovered one tenth, and not even the interesting tenth. What about King Tut's tomb? I protested. That boy, King? Zia rolled her eyes. Boring. You should see some of the good tombs. I felt a little hurt.


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[Assistant] Dad had named me after Howard Carter, the guy who discovered King Tut's tomb, so I'd always felt a personal attachment to it. If that wasn't a good tomb, I wondered what was. Zia turned to face the bronze doors. This is the Hall of Ages. She placed her palm against the seal, which bore the symbol of the House of Life. The hieroglyphs began to glow, and the doors swung open.


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[Assistant] Zia turned to us, her expression deadly serious. You are about to meet the chief lector. Behave yourselves, unless you wish to be turned into insects.


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[Assistant] 14. A French guy almost kills us.


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[Assistant] The last couple of days I'd seen a lot of crazy things, but the Hall of Ages took the prize.


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[Assistant] Double rows of stone pillars held up a ceiling so high you could have parked a blimp under it with no trouble.


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[Assistant] A shimmering blue carpet that looked like water ran down the center of the hall, which was so long, I couldn't see the end even though it was brightly lit.


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[Assistant] Balls of fire floated around like helium basketballs, changing color whenever they bumped into one another.


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[Assistant] Millions of tiny hieroglyphic symbols also drifted through the air.


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[Assistant] Randomly combining into words and then breaking apart. I grabbed a pair of glowing red legs. They walked across my palm before jumping off and dissolving. But the weirdest things were the displays. I don't know what else to call them. Between the columns on either side of us.


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[Assistant] And then I was trying to beat the competitor's time.


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[Assistant] It was high summer when Woodcarver's army left for the north. The preparations have been frantic.


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[Assistant] With Vendacious driving himself and everyone else to the point of exhaustion, there had been thirty cannons to make.


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[Assistant] Scrupulo cast 70 tubes, before getting 30 that would fire reliably.


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[Assistant] There have been canoneers to train and safe methods of firing to discover. There have been wagons to build and kerhogs to buy.


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[Assistant] Surely word of the preparations had long ago filtered north. Woodcarvers was a port city. They could not close down the commerce that moved through it.


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[Assistant] Vendacious warned them of this in more than one inner council meeting. Steele knew they were coming. The trick was in keeping the flencerists uncertain as to numbers and timing and exact purpose. We have one great advantage over the enemy he said.


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[Assistant] We have agents in his highest counsels. We know what he knows of us.


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[Assistant] They couldn't disguise the obvious from the spies, but the details were a different matter.


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[Assistant] The army departed along inland routes, a dozen wagons here, a few squads there.


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[Assistant] In all, there were a thousand packs in the expedition, but they would never be together till they reached Deep Forest.


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[Assistant] It would have been easier to take the first part of the trip by sea, but the flencerists had spotters hidden high in the fjordlands. Any ship movement, even deep in woodcarver territory, would be known in the north. So they traveled on forest paths, through areas that Vendatius had cleared of enemy agents.


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[Assistant] At first, the going was very easy, at least for those with the wagons. Joanna rode in one of the rear ones with wood carver and data set.


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[Assistant] Even I'm beginning to treat the thing like an oracle, thought Joanna. Too bad it couldn't really predict the future.


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[Assistant] The weather was as beautiful as Joanna had ever seen it on Tide's World, an endless afternoon.


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[Assistant] It was strange that such unending fairness should make her so nervous, but she couldn't help it.


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[Assistant] This was so much like her first time on this world when everything had gone wrong.


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[Assistant] During the first day-arounds of the journey while they were still in home territory, Woodcarver pointed out every peek that came into view and tried to translate its name into Samdorsk for her.


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[Assistant] After 600 years, the Queen knew her land well.


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[Assistant] Each page was from a different year and showed her special snow patches as they had appeared on the same day of the summer.


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[Assistant] Riffling through the leaves, it was almost like a crude piece of animation. Joanna could see the patches moving, growing over a period of decades, then retreating.


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[Assistant] Most packs don't live long enough to feel it, said Woodcarver. But to me, the patches that last all summer are like living things. See how they move?


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[Assistant] They're like wolves, held off from our lands by our fire that is the sun. They circle about, grow. Sometimes they link together, and a new glacier starts toward the sea. Joanna had laughed a little nervously. Are they winning? For the last four centuries, no. The summers have often been hot and windy.


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[Assistant] In the long run, I don't know. And it doesn't matter quite so much to me anymore.


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[Assistant] She rocked her two little puppies for a moment and laughed gently.


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[Assistant] Peregrine's little ones are not even thinking yet, and I'm already losing my long view. Joanna reached out to stroke her neck. But they are your puppies too.


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[Assistant] I know, most of my pups have been with other packs, but these are the first that I have kept to be me.


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[Assistant] Her blind one nuzzled at one of the puppies. It wriggled and made a sound that warbled at the top of Joanna's hearing. Joanna held the other on her lap.


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[Assistant] Tyne pups looked more like baby sea moles than dogs.


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[Assistant] Their necks were so long compared to their bodies, and they seemed to develop much more slowly than the puppies she and Jeffrey had raised.


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[Assistant] Even now they seem to have trouble focusing. She moved her fingers slowly back and forth in front of one puppy's head. Its efforts to track were comical.


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[Assistant] And after 60 days, woodcarver's pups couldn't really walk. The queen wore two special jackets with carrying pouches on the sides. Most of the waking day her little one stayed there, suckling through the fur on her tummy.


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[Assistant] In some ways, Woodcarver treated her offspring as a human wood. She was very nervous when they were taken from her sight.


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[Assistant] She like to cuddle them and play little games of coordination with them.


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[Assistant] I nibble the one whose paw was last touched. Peregrine is worthy of me. These two are already thinking a little, see?


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[Assistant] She pointed to the puppy that had convulsed into a ball, avoiding most of her surprise tickle.


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[Assistant] In other ways, Tainish parenting was alien, almost scary.


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[Assistant] Neither Woodcarver nor Peregrine ever talked to their pups in audible tones, but their ultrasonic thoughts seem to be constantly probing the little ones.


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[Assistant] Some of it was so simple and regular that it set sympathetic vibrations through the walls of the little wagon.


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[Assistant] The wood buzzed under Joanna's hands. It was like a mother humming a lullaby, but she could see it had another purpose. The little creatures responded to the sounds, twitching in complicated rhythms.


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[Assistant] The troops standing turns as sentry lines. Even during the traveling part of the day, they stopped numerous times to clear the trail or await the return of a patrol or simply to rest. And one such, Joanna sat with Peregrine in the shade of a tree that looked like pine but smelled of honey.


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[Assistant] Pilgrim played with his young ones helping them to stand up and walk a few steps.


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[Assistant] She could tell by the buzzing in her head that he was thinking at the pups.


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[Assistant] And suddenly they seemed more like marionettes than children to her.


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[Assistant] Brothers? Sisters? What do you call siblings born to the other pack? With woodcarver's pups.


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[Assistant] Even more than woodcarver. The Pilgrim had tried to learn human customs.


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[Assistant] He was by far the most flexible pack she knew. After all, if you can accommodate a murderer in your own mind you must be flexible. But Pilgrim was visibly startled by her question. The buzzing in her head stopped abruptly.


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[Assistant] Yes, I see how natural that would seem to you. To us it would be a kind of perversion.


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[Assistant] No, worse than that, since perversions are at least fun for some people some of the time. But if a pup were raised a singleton, or even a duo, it would be making an animal of what could be a sturdy member.


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[Assistant] You mean that pups never have a life of their own?


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[Assistant] Peregrine cocked his hands and scrunched close to the ground. One of him continued to nose around the puppies, but Joanna had his attention. He loved to puzzle over human exotica.


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[Assistant] Well, sometimes there is a tragedy. An orphan pup left to itself. Often there is no cure for it. The creature becomes too independent to meld with any pack.


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[Assistant] In any case, it is a very lonely, empty life. I have personal memories of just how unpleasant.


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[Assistant] You're missing a lot. I know you've watched children's stories on data set. It's sad you can never be young and foolish.


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[Assistant] Hey, I never said that. I've been young and foolish lots. It's my way of life. And most packs are that way when they have several young members by different parents.


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[Assistant] As they talked, one of Peregrine's pups had struggled to the edge of the blanket they sat on.


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[Assistant] Wow, I can smell the flowers with him. I bet we'll be seeing through each other's eyes well before we get to Flensers hidden island.


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[Assistant] The pup backed up, and the two did a little dance on the blanket. Peregrine's heads bombed in time with the movement.


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[Assistant] They are such bright little ones. He grinned. Oh, we are not so different from you, Joanna.


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[Assistant] I know humans are proud of their young ones. Both Woodcarver and I wonder what ours will become. She is so brilliant, and I am, well, a bit mad.


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[Assistant] Will these two make me a scientific genius? Will woodcarvers turn her into an adventurer?


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[Assistant] Boycarver's a great brood-kinner, but even she's not sure what our new souls will be like. Oh, I can't wait to be six again.


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[Assistant] It had taken Scriber and Pilgrim and Joanna only three days to sail from Flensers domain to the harbor at Woodcarver's.


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[Assistant] It would take this army almost 30 days to walk back to where Joanna's adventure began.


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[Assistant] On the map it had looked a torturous path, wiggling this way and that through the fjordland. Yet the first ten days were amazingly easy. The weather stayed dry and warm.


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[Assistant] It was like the day of the ambush, stretched out forever and ever. A dry wind summer, Woodcarver called it.


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[Assistant] In summer there should be occasional storms, at least cloudiness. Instead the sun circled endlessly above the forest canopy. And when they broke into the open,


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[Assistant] Never for long and then only when Vendatius was sure that it was safe. The sky was clear and almost cloudless.


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[Assistant] In fact, there was already uneasiness about the weather. At noon, it could get downright hot. The wind was constant, drying.


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[Assistant] The forest itself was drying out. They must be careful with fires.


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[Assistant] Scrupulo was a council member and the Queen's chief engineer.


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[Assistant] Since his experiment with the cannon, he had insisted on the title Commander of Cannoneers. To Joanna, the engineer had always seemed curt and impatient.


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[Assistant] His members were almost always moving and with jerky abruptness. He spent almost as much time with the data set as did the Queen or Peregrine Wickraxgar.


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[Assistant] His fur was scored with dozens of powder burns. He claimed that nearby gun thunder cleared the mind, but most everybody else agreed it made you daft.


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[Assistant] During rest stops, Scroop was a familiar figure, strutting up and down the line, wranging his canineers. He claimed even the shortest stop was an opportunity for training, since in real combat speed would be essential.


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[Assistant] He had designed special epaulettes based on Nigerian Gunner's earmuffs. They didn't cover the low sound ears at all, but instead the forehead and shoulder tympana of the trigger member.


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[Assistant] Actually tying the muffs down was a mind-numbing thing to do, but for the moments right around firing, it was worth it. Scrupulo wore his own muffs all the time, but unsnugged.


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[Assistant] They look like silly little things sticking out from his head and shoulders. He obviously thought the effect was raffish, and in fact his gunner crews also made a big thing of wearing the gear at all times.


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[Assistant] After a while, even Joanna could see that the drill was paying off. At least, they could swing the gun tubes around at an instant's notice, stuff them with fake powder and ball, and shout the tiny-ish equivalent of BANG!


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[Assistant] The army carried much more gunpowder than food. The packs were to live off the forest. Joanna had little experience with camping in an atmosphere. Were forests usually this rich?


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[Assistant] It was certainly nothing like the urban forests of Straum where you needed a special license to walk off marked paths. And most of the wildlife were mechanical imitations of Nigerian originals.


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[Assistant] This place was wilder than even the stories of Nijora.


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[Assistant] After all, that world had been well settled before it fell to medievalism. The tines had never been civilized, had never spread cities across continents.


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[Assistant] Pilgrim guessed there were fewer than 30 million packs in all the world.


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[Assistant] That was rarely practical here, but they got almost as much pleasure from chasing the unwary into ambushes.


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[Assistant] Joanna didn't like it. Was this a medieval perversion or a peculiarly tiny one?


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[Assistant] If allowed the time, the troops didn't use their bows and knives.


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[Assistant] The pleasure of the hunt included slashing at throats and bellies with teeth and claws.


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[Assistant] Not that the forest creatures were without defenses. For millions of years threat and counter threat had evolved here. Almost every animal could generate ultrasonic screeching that totally drowned the thought of any nearby pack.


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[Assistant] There were parts of the forest that seemed silent to Joanna, but through which the army drove at a cautious gallop.


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[Assistant] 25 days out, the army was stuck trying to get across the biggest valley yet.


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[Assistant] In the middle, mostly hidden by the forest, a river flowed down to the western sea.


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[Assistant] The walls of these valleys were like nothing Joanna had seen in the parks of Straum.


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[Assistant] If you took a cross section at right angles to the river, the walls made a U shape. They were cliff-like steep at the high edges, then became slopes and finally a gentle plain where the river ran.


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[Assistant] That's how the ice gouges it, explained Woodcarver. There are places further up where I've actually watched it happen. And she showed Joanna explanations in the data set.


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[Assistant] That was happening more and more. Pilgrim, and woodcarver, and sometimes even scrupulose, seem to know more of a child's modern education than Joanna.


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[Assistant] They had already been across a number of smaller valleys. Getting down the steep parts was always tedious, but so far the paths have been good. Vendatius took them to the edge of this latest valley.


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[Assistant] Woodcarver and staff stood under the forest cover just short of the drop-off. Some meters back, Joanna sat surrounded by Peregrine Wick Rackscar.


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[Assistant] The trees at this elevation reminded Joanna a little of pines. The leaves were narrow and sharp and lasted all year. But the bark was blistered white and the wood itself was pale blonde. Strangest of all were the flowers. They sprouted purple and violet from the exposed roots of the trees.


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[Assistant] Tines's world had no analog of honeybees, but there was constant motion among the flowers as thumb-sized mammals climbed from plant to plant.


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[Assistant] There are thousands of them, but they seem to have no interest in anything except the flowers and the sweetness that oozed from them.


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[Assistant] She leaned back among the flowers and admired the view, while the Queen gobbled with Vandacious. How many kilometers could you see from here?


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[Assistant] The air was as clear as she had ever known it on Time's World.


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[Assistant] East and west, the valley seemed to stretch forever. The river was a silver thread where it occasionally showed through the forest of the valley floor.


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[Assistant] Pilgrim nudged her with a nose and nodded toward the queen.


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[Assistant] Woodcarver was pointing this way and that over the drop off. Argument is in the air. You want a translation? Yeaah. Woodcarver doesn't like this path.


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[Assistant] Pilgrim's voice changed to the tone the Queen used when speaking Sabdorsk.


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[Assistant] The path is completely exposed. Anyone on the other side can sit and count our every wagon, even from miles away. A mile is a fat kilometer.


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[Assistant] Vendacious whipped his heads around in that indignant way of his. He gobbled something that Joanna knew was angry. Pilgrim chuckled and changed his voice to imitate the security chiefs. Your majesty, my scouts have scoured the valley and far wall. There is no threat.


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[Assistant] You've done miracles, I know, but do you seriously claim to have covered that entire north face? That's five miles away, and I know from my youth that there are dozens of cavelets. You have those memories yourself.


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[Assistant] That stopped him, said Pilgrim, laughing. Come on, just translate. She was quite capable of interpreting body language and tone by now. Sometimes even the tiniest chords made sense. Hmph, okay.


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[Assistant] The queen hiked her baby packs around and sat down. Her tone became conciliatory.


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[Assistant] If this weather weren't so clear, or if there were night times, we might try it, but, you remember the old path?


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[Assistant] 20 miles inland from here, that should be overgrown by now. And the road coming back is gobble hiss from Vendatious Angry. I tell you, this is safe. We'll lose days on the other path. If we arrive late at Flenzer's, all my work will be for nothing. You must go forward here.


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[Assistant] I understand your anxiety, pack of my blood, but we go forward where I say. If that is intolerable to you, I will regretfully accept your resignation.


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[Assistant] Parts of Vendacious walked in quick circles, stopping for angry instants to stare at Woodcarver. Finally, all his necks drooped. Uhm, my apologies, your majesty.


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[Assistant] As long as you find me of use, I beg to continue in your service.


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[Assistant] Forgiven, I want your independent advice, Vendacious. It has been miraculously good. Vendacious smiled weakly. I didn't think the jerk had it in him, Pilgrim said, near Joanna's ear.


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[Assistant] It took two day-arounds to reach the old path. As Woodcarver had predicted, it was overgrown.


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[Assistant] More, in places there was no sign of the path at all. It would take days to get down the valley side this way.


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[Assistant] If Woodcarver had any misgivings about the decision, she didn't mention them to Joanna.


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[Assistant] The queen was 600 years old. She talked often enough about the inflexibility of age.


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[Assistant] Now Joanna was getting a clear example of what that meant.


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[Assistant] When they came to a washout, trees were cut down and a bridge constructed on the spot. It took a day to get by each such spot, but progress was agonizingly slow even where the path was still in place. No one rode in the carts now.


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[Assistant] The edge of the path had worn away and the cartwheels sometimes turned on nothingness.


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[Assistant] On Joanna's right, she could look down at tree crowns that were a few meters from her feet.


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[Assistant] They ran into the wolves six days along the detour when they had almost reached the valley floor. Wolves.


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[Assistant] That's what Pilgrim called them anyway. What Joanna saw looked like gerbils.


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[Assistant] They had just completed a kilometer stretch of easy going.


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[Assistant] Even under the trees they could feel the wind, dry and warm and moving ceaselessly down the valley.


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[Assistant] The last patches of snow between the trees were being sucked to nothingness. And there was a haze of smoke beyond the north wall of the valley.


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[Assistant] Joana was walking alongside Woodcarver's cart. Pilgrim was about 10 meters behind, chatting occasionally with them. The Queen herself had been very quiet these last days.


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[Assistant] Suddenly there was a screech of tiniish alarm from above them. A second later, Vandacious shouted from a hundred meters ahead. Through gaps in the trees, Joanna could see troopers on the next switchback above them, unlimbering crossbows, firing into the hillside above them.


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[Assistant] The sunlight came dappled through the forest cover, bringing plenty of light


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[Assistant] But in splotches that broke and moved as the soldiers hustled about. Chaos, but


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[Assistant] There were things up there that weren't tines, small, brown or gray. They flitted through the shadows and the splotches of light. They swept up the hillside, coming upon the soldiers from the opposite direction that they were shooting.


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[Assistant] Turn around! Turn around! Joanna screamed, but her voice was lost in the turmoil. Besides, who there could understand her?


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[Assistant] Olive Woodcarver was peering up at the battle. She grabbed Joanna's sleeve. You see something up there? Where?


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[Assistant] Joanna stuttered in explanation, but now Pilgrim had seen something too. His gobbled shouting came loud over the battle.


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[Assistant] He raced back up the trail to where Scrupulo was trying to get a cannon unlimbered. Joanna, help me!


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[Assistant] Woodcarver hesitated then said, yes, it may be that bad. Help with the cannon, Joanna.


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[Assistant] It was only 50 meters to the gun cart, but uphill. She ran. Something heavy smashed into the path just behind her. Part of a soldier.


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[Assistant] Wic Raksgar was standing heads together just a few meters from Skrupalo. He was armed in every adult member, mouth knives and steel tines. He waved Joanna down next to him.


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[Assistant] We, we run on a nest of, of wolves. His speech was awkward, slurred. It must be between here and path above. A lump like a little castle tower. Gotta kill nest. Can you see it?


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[Assistant] Pilgrim didn't answer. His members were twitching, his mouth knives waving randomly. She leaped away from the flashing metal. He had already caught himself. Sound attack!


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[Assistant] Others, woodcarver on her cart, huddled in heaps, with scarcely a head showing.


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[Assistant] Just beyond the nearest uphill trees, she could see a grey tide. The wolves. Each furry lump looked innocent enough. Altogether.


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[Assistant] Joanna froze for an instant, watching them tear out the throat of a trooper's member.


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[Assistant] Joanna was the only sane person left, and all it would mean is, she would know she was dying. Kill the nest.


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[Assistant] On the gun cart beside her, only one of Scrupulo was left, old whitehead.


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[Assistant] Daffy as ever, it had pulled down its gunner's moffs and was nosing around under the gun tube. Kill the nest. Maybe not so daffy after all. Joanna jumped up on the wagon.


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[Assistant] It rolled back toward the drop off banging against a tree. She scarcely noticed.


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[Assistant] But with just his one pair of jaws he couldn't handle it.


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[Assistant] Without the rest of the pack, it had neither hands nor brains. It looked up at her, its eyes wide and desperate.


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[Assistant] She grabbed the other end of the bag and the two of them got the powder into the barrel.


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[Assistant] Whitehead dived back into the equipment, nosing around for a cannonball. Smarter than a dog, and trained. Between them, maybe they had a chance.


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[Assistant] Just half a meter beneath her feet, the wolves were running by. One or two she could have fought off herself, but there were dozens down there, worrying and tearing at random members.


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[Assistant] Three of Pilgrim were standing around Scarbot and the pups, but their defense was unthinking, slashing. The pack had dropped its mouth knives and tines.


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[Assistant] She and Whitehead got the round down the barrel. Whitehead whipped back to the rear, began playing with the little wick lighter the gunners used.


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[Assistant] Whitehead looked hurt for an instant. The complaint wasn't completely clear to him. He had dropped the standoff wand, but still held the lighter. He flicked on the flame and circled determinedly back, tried to worm past Joanna's legs. She pushed him back again and looked uphill. The dark thing, that must be the nest.


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[Assistant] She tilted the gun tube on its mounting and sighted down the top. Her face ended up just centimeters from the persistent whitehead and his flame.


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[Assistant] His muffed head darted forward and the flame touched the fire hole.


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[Assistant] The blast almost knocked Joanna off the cart. For a moment she could think of nothing but the pain that stabbed into her ears. She rolled to a sitting position, coughing in the smoke.


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[Assistant] She couldn't hear anything beyond a high pitched ringing that went on and on.


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[Assistant] She just sat dazed for a few seconds, mystified by the blood, trying to imagine how she had ever ended up here.


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[Assistant] A voice somewhere in the back of her head was screaming, no time, no time!


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[Assistant] She forced herself to her knees and looked around, memories coming back painfully slow.


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[Assistant] There was splintered trees uphill of them. The blonde wood glinted among the leaves. Beyond them, where the nest had been, she saw a splash of fresh turned earth. They had killed it, but the fighting continued.


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[Assistant] There were still wolves on the path, but now they were the ones running in all directions.


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[Assistant] As she watched, dozens of them catapulted off the edge of the trail into the trees and rocks below. And the tines were actually fighting now.


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[Assistant] Pilgrim had picked up his knives. The blades and his muzzles drift red as he slashed.


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[Assistant] Something gray and bleeding flew over the edge of the cart and landed by Joanna's leg.


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[Assistant] The wolf couldn't have been more than 20 centimeters long, its hair dirty gray brown. It really did look like a pet.


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[Assistant] But the tiny jaws clicked at her ankles with murderous intent. Joanna dropped a cannonball on it.


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[Assistant] During the next three days, while woodcarver's people struggled to bring their equipment and themselves back together, Joanna learned quite a bit about the wolves.


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[Assistant] What she and Scrupulo's Whitehead did with the cannon had stopped the attack cold. Without doubt, knocking out the nest had saved a lot of lives and the expedition itself.


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[Assistant] The wolf nests didn't care about high intelligence. Woodcarver claimed that a nest might have thousands of members. Certainly the one they'd tripped over was huge. Such a mob couldn't be as smart as a human. In terms of raw reasoning power, it probably wasn't much brighter than a single pack member. On the other hand, it could be a lot more flexible.


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[Assistant] Pilgrim had legends of nests with almost packish intelligence, of foresters who made treaties with nearby nests for protection in return for food.


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[Assistant] As long as the high powered noises in the nest lived, the worker wolves could coordinate almost like time members. But kill the nest and the creature fell apart like some cheap centered topology network.


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[Assistant] Certainly this nest had done a number on Woodcarver's army. It had waited quietly until the troopers were within its inner loudness. Then, outlying wolves had used synchronized mimicry to create sonic ghosts, tricking the packs into turning from the nest and shooting uselessly into the trees. And when the ambush actually began,


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[Assistant] The nest had screamed concentrated confusion down on the tines.


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[Assistant] That attack had been a far more powerful thing than the stink noise they'd encountered in other parts of the forest.


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[Assistant] To the tines, the stinkers had been painfully loud and sometimes even frightening.


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[Assistant] But not the mind destroying chaos of the wolf nest attack.


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[Assistant] More than one hundred packs had been knocked out in the ambush. Some, mostly packs with pups, and huddled. Others, like Scrupulo, had been blasted apart.


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[Assistant] In the hours following the attack many of these fragments straggled back and reassembled. The resulting tines were shaken but unharmed.


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[Assistant] Intact soldiers hunted up and down the forested cliffs for injured members of their comrades.


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[Assistant] There were places along the drop-off that were more than 20 meters deep.


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[Assistant] Where their fall wasn't cushioned by tree boughs, members landed on naked rock.


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[Assistant] Five dead ones were eventually found, and another twenty seriously injured. Two carts had fallen.


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[Assistant] They were kindling and their kerhogs were too badly injured to survive.


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[Assistant] By great good luck, the gunshot had not started a forest fire.


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[Assistant] Three times the sun made its vast tilted course around the sky.


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[Assistant] Woodcarver's army recovered in a camp in the depths of the valley forest by the river.


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[Assistant] Vendatious had posted lookouts with signalling mirrors on the northern valley wall.


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[Assistant] This place was about as safe as any they could find so far north. It was certainly one of the most beautiful. It didn't have the view of the high forest, but there was the sound of the river nearby. So loud it drowned the sighing of the dry wind.


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[Assistant] The lowland trees didn't have root flowers, but they were still different from what Joanna had known.


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[Assistant] There was no underbrush, just a soft bluish moss that Pilgrim claimed was actually part of the trees. It stretched like mown parkland to the edge of the river.


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[Assistant] On the last day of their arrest, the Queen called a meeting of all the packs, not at guard or lookout. It was the largest collection of tines Joanna had seen in one place since her family was killed.


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[Assistant] Only these packs weren't fighting. As far as Joanna could see across the bluish moss, there were packs, each at least eight meters from its nearest neighbor.


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[Assistant] For an absurd instant, she was reminded of Settlers Park at Overby.


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[Assistant] Families picnicking on the grass, each with its own traditional blanket and food lockers.


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[Assistant] But these families were each a pack, and this was a military formation.


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[Assistant] The rose were gently curving arcs, all facing toward the queen.


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[Assistant] Peregrine Wickrackscar was ten meters behind her in shadow. Being Queen's consort didn't count for anything official.


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[Assistant] On woodcarver's left lay the living casualties of the ambush, members with bandages and splints. In some ways, such visible damage wasn't the most horrifying. There were also what Pilgrim called the walking wounded.


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[Assistant] These were singletons and duos and trios that were all that was left of whole packs. Some of these tried to maintain a posture of attention.


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[Assistant] But others mooned about occasionally breaking into the Queen's speech with aimless words.


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[Assistant] It was like Scriber Jacarama Fen all over again. But most of these would live.


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[Assistant] Some were already melding, trying to make new individuals. Some of these might even work out as Peregrine Wickrackscar had done.


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[Assistant] For most, it would be a long time before they were fully people again.


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[Assistant] Joanna sat with Scrupulo in the first rank of troopers before the Queen.


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[Assistant] The commander of cannoneers stood at tiniest parade rest. Rumps on the ground, chest high, most heads facing front. Scroop had come through it without serious damage.


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[Assistant] His white head and a few more scorch marks and one of the other members had sprained a shoulder falling off the path.


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[Assistant] He wore his flying cannoneer muffs as flamboyantly as always, but there was something subdued about him. Maybe it was just a military formation and getting a medal for heroism. The Queen was wearing her special jackets. Each head looked out at a different section of her audience.


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[Assistant] Joanna still couldn't understand Tynish and would certainly never speak it without mechanical assistance.


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[Assistant] But the sounds were mostly within her range of hearing. Low frequencies carry a lot better than high ones. Even without memory aids and grammar generators, she was learning a little. She could recognize emotional tone easily and things like the raucous arc arc arc that passed for applause around here.


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[Assistant] As for individual words, well, they were more like chords, single syllables that had meaning.


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[Assistant] Nowadays, if she listened really carefully, and Pilgrim wore it nearby to give a running translation, she could even recognize some of those.


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[Assistant] Just now, for instance, Woodcarver was saying good things about her audience. Approving arc arcs came from all directions. They sounded like a bunch of seamels. One of the Queen's heads dipped into a bowl coming up with a small carvin' doodad in its mouth.


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[Assistant] She spoke a pack's name, a multi-chord, tum-titty-tum, that if Joanna heard often enough, she might be able to repeat as Jack-o-Rama-fan, or even see meaning in as Wick-Rack-Scar.


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[Assistant] From the front of the audience, a single member trotted toward the Queen. It stopped, practically nose to nose with the Queen's nearest member.


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[Assistant] Woodcarver said something about bravery, and then two of her fastened the wooden brooch to the member's jacket. It turned smartly and returned to its pack.


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[Assistant] Woodcarver picked out another decoration and called on another pack. Joanna leaned over towards Scrupulo. What's going on?


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[Assistant] She said wonderingly, why are single members getting medals and how can they stand to get so near another pack?


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[Assistant] Scrupulo had been standing more stiffly at attention than most backs and was pretty much ignoring her. Now he turned one head in her direction. Shh. He started to turn back, but she grabbed him by one of his jackets. Foolish one, he finally replied.


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[Assistant] The award is for the whole pack. One member is extended to accept. More would be madness.


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[Assistant] One after another, three more packs extended a member to take their decorations. Some were full of precision like human soldiers in stories.


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[Assistant] Others started out smartly, then became timid and confused as they approached Woodcarver. Finally, Joanna said, S-scrupulo, when do we get ours?


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[Assistant] This time he didn't even look at her. All his heads faced rigidly toward the queen.


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[Assistant] His bodies were almost shaking with the intensity of their brace. He's scared witless.


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[Assistant] And suddenly Joanna guessed why. Apparently Woodcarver had no problem maintaining her mind with one outside member nearby. But the reverse would not be true.


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[Assistant] Sending one of yourself into another pack meant losing some consciousness and placing trust in that other pack. Looking at it that way.


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[Assistant] Well, it reminded Joanna of the historical novels she used to play.


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[Assistant] On Nijora, during the Dark Age, ladies traditionally gave their sword to their queen when granted audience, and then knelt. It was a way to swear loyalty.


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[Assistant] Same thing here. Except that looking at Scrupulo, Joanna realized that even as a matter of form, the ceremony might be damn frightening.


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[Assistant] Three more metals bestowed and then Woodcarver gobbled the cords that were Scrupulo's name.


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[Assistant] The commander of canineers went absolutely rigid, made faint whistling noises through his mouths.


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[Assistant] Johanna Olsendot, said Woodcarver, then more Tynish, something about coming forward.


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[Assistant] Joanna stood up, but not one of Scrupulo moved.


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[Assistant] The queen made a human laugh. She was holding two polished brooches.


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[Assistant] I'll explain all in Samdorsk later, Joanna. Just come forward with one of scrupulo. Scrupulo?


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[Assistant] Suddenly they were the center of attention with thousands of eyes watching. There was no more arcing or background chatter.


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[Assistant] Joana hadn't felt so exposed since she played first colonist in her school's landing play.


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[Assistant] For all his jaunty, cannoneer muffs and standoffish manners, Groupolo was terrified. But for him it wasn't stage fright. I can't tear me apart so soon. I can't.


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[Assistant] There was Murmur Goblin in the ranks behind them. Scrupulo's own cannoneers. By all the powers, would they hold this against him? Welcome to the middle ages, stupid people.


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[Assistant] That one part of me alone could never have done it.


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[Assistant] Right, and neither could I. But together we killed a wolf nest.


[User] Please transcribe this text

[Assistant] Scrupulo stared at her a second, eyes wavering. Yes, we really did. He came to his feet, frisked his heads, so the cannoneer muffs flapped.


[User] Please transcribe this text

[Assistant] Yes! And he moved his white headed one closer to her.


[User] Please transcribe this text

[Assistant] Joanna straightened. She and Whitehead walked out into the open space. Four meters. Six.


[User] Please transcribe this text

[Assistant] She kept the fingertips of one hand lightly on his neck. When they were about 12 meters from the rest of Scrupulo, Whitehead's pace faltered. He looked sideways up at Joanna, then continued more slowly.


[User] Please transcribe this text

[Assistant] Joanna didn't remember much of the ceremony, so much of her attention was on Whitehead. Woodcarver said something long and unintelligible.


[User] Please transcribe this text

[Assistant] Somehow they both ended up with intricately carving decorations on their collars and were headed back toward the rest of Scrupulo. Then she was aware of the crowd once more.