a stack of hardback books about graduate school

So that book I'm writing! (Read the book announcement!)

Currently, I have over 80,000 draft words and notes (including the sample chapters I submitted with the proposal). This is a milestone because the manuscript goal is 80,000 words.

However, these 80,000 words are first draft words, not submission-ready words. They are only loosely lumped into chapters, not fully organized or structured. They are my reading notes, my thoughts and ideas, paragraphs that don't flow together yet, comments about statistics I need to look up, facts I need to verify, and references I need to track down. In short, it's all the raw content that will be able to be shaped into a book.

For those curious about mechanics, I have logged the majority of these words from my phone (I mostly use voice typing). I sit at the kitchen table taking notes while the kids eat breakfast. I add a few words from the floor in our playroom while the kids build with Legos. A couple hundred words a day adds up fast.

In my research for the book, I've read existing books on getting through graduate school, such as:

  • Jennifer Calarco: A Field Guide to Graduate School
  • Amanda I. Seligman: Is Graduate School Really For You?
  • Peter J Feibelman: A PhD Is Not Enough! A Guide to Survival in Science

I'm reading books about non-academic career paths, including:

  • Susan Basalla and Maggie Debelius: So What Are You Going to Do with That? Finding Careers Outside Academia
  • Christopher L. Caterine: Leaving Academia: A Practical Guide

I'm reading books about issues in graduate education, such as:

  • Julie R. Posselt: Inside Graduate Admissions
  • Leonard Cassuto: The Graduate School Mess: What Caused It and How We Can Fix It
  • Kathleen Fitzpatrick: Generous Thinking: A Radical Approach to Saving the University

I've also been reading nonfiction books that aren't specifically about graduate school, but are nonetheless highly relevant to thriving and making the most of your education and your life, such as:

  • Daniel H. Pink: Drive: The Surprising Truth About What Motivates Us
  • Bill Burnett and Dave Evans: Designing Your Life: How To Build a Well-Lived, Joyful Life (Read my review!)
  • Adam Grant: Originals: How Non-Conformists Move the World

I have more to read and research, of course—there's always more to read and research.

But as another student in graduate school told me once, at some point, you have to stop reading and start creating. You have to turn everything you know into something.

That's the point I'm at now. And honestly? Revision is the fun part.

Revision is where the magic happens. Revision is when I make the words flow. It's when ideas become coherent. It's when I hunt down that quote from that book I read two years ago that would be perfect to mention in this section, add references, rearrange content, and generally improve the coherency and structure of my words.

Revision is not a one-time process. It's not write, revise, done. It's write, rewrite, reword, rearrange, revise, repeat. Revision is what I'll be doing on the book for the next six months.

Unfortunately, revision is harder to do on my phone. I need more office time with a proper keyboard and monitor. So I've switched from having a daily book word count to having a daily book time count. This will be easier and easier to schedule as the weather warms up—I'll send everyone else outside while I get my quiet writing time in!

Making progress. I'll keep you updated!

* This post first appeared on The Deliberate Owl.


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silhouette of a person with arms outstretched on wintery day, in front of bare-limbed trees and a dim sunset sky

Some personal news: I have a book deal!

I'm writing a pragmatic, up-to-date guide to thriving in graduate school while keeping a healthy personal life, filled with sensible suggestions, concrete exercises, and detailed resource lists.

Tentatively titled #PhDone: How to Get Through Grad School Without Leaving the Rest of Your Life Behind, it'll be published by Columbia University Press in spring 2023 (tentatively—titles and dates will be finalized later!). I'm represented by Joe Perry.

From my proposal:

Every year, more than 500,000 people start graduate programs. Although more than half of these students are women, there's no book out there explaining how to balance breastfeeding with benchwork, or childcare with conference travel. Grad students today are on average 33 years old ... so why aren't we talking about managing marriage and a thesis, saving for retirement, or the fact that nearly 57% of students are also employed outside of school? Not only that, but of the 50,000 students who complete PhDs each year, a shrinking number collect coveted tenure-track positions ... even though everyone's still being trained as if they're all professors-to-be.

There's a serious mismatch between the advice about grad school that's currently available and our present reality. It's time to fix that.

I'm excited about this book. It's the book I wish I'd been able to read when I started grad school.

A long game

This project is years in the making. I spent months crafting a book proposal. I submitted to agents for a year before landing on the right fit. Then it took us over a year to find the right publisher.

Many people would have become discouraged even part of the way through this process. Some may have given up entirely. Others may have switched to self-publishing, thinking the speed of getting their work out and the upfront costs would be worth it—and for some, it would be.

But I went in knowing that publishing is a long game. Getting your writing out into the world takes time: to submit, resubmit, get reviews, revise, revise again. I don't want to be my own publisher; I want to write and have a team working with me on editing, publishing, marketing, etc.

Next steps for the book

Now that the book's been picked up by Columbia University Press, I have a deadline—which is exciting! I like knowing when my deadline is. That way, I can plan backwards and ensure I'm working enough up front, incrementally, so that I never run into crunch time. And yes, I've already made a spreadsheet to track my progress and keep tabs on book-related tasks.

While the full book timeline is approximate at this stage, the next steps are:

  • I write the book. I have a couple chapters drafted already, with outlines and notes for the rest. That's an interesting thing about nonfiction books—they're generally sold on proposal and not from a finished manuscript.
  • My editor at CUP reads it. I revise as needed.
  • Once the manuscript is finished, time to print is less than a year. In that time, the publishing team works their magic: formatting, cover design, cover copy, production, sales and distribution work, etc. We ramp up marketing for the book.
  • Then you can buy it!

I'll post updates along the way!

* This post first appeared on The Deliberate Owl.


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river rocks partly submerged in still water

Revise and resubmit the paper... again??

One morning, in my second year of grad school, I opened my email to find a note from Sidney, a professor I'd worked for a while back:

"Reopening this old thread ... Someone requested the paper ... looking through it again I thought it was a damn good paper. We should definitely resubmit. What do you think?"

What did I think? After rejections from several journals two years prior, and over 30 revisions (I lost count), thinking about that particular unpublished paper made me feel tired. I'd finally given up on it as a lost cause. Its fate was to forever be one of those learning experiences that was probably valuable, but ultimately showed no tangible result and felt like a waste of time.

I tried ignoring the email while I drank a cup of tea and tended to the rest of my inbox. It nagged.

Really? A damn good paper? Maybe revising it again and resubmitting wouldn't be so bad. Sure, every round of reviewers had their own ideas of where any given paper should go and would nitpick different things, but we had already fixed so many minor errors and clarified potential points of confusion… I liked the idea of having something to show for all my effort on the paper so far. Or was this line of reasoning an instance of the sunk costs fallacy? (That is: I'd put so much work in so far, I should put in more work instead of cutting my losses.)

The First Draft

I had gotten a year-long job as a research intern straight out of college. I'd enjoyed my undergrad research experiences and liked the idea of getting more experience while applying to grad school. So I joined Sidney's lab. I shadowed his grad students, worked on odd bits of many different projects, ran participants through experimental studies, and learned about research at the intersection of psychology and computer science.

One day, Sidney handed me the project that became the damn good paper. He and a colleague had an algorithm and some software that their labs had used to track human body motion in a couple studies. He wanted to verify that the software worked as intended—i.e., that it tracked body motion from video in a way comparable to some other sensors. So, the plan was to collect some clean data with a camera and those other sensors. Compare the output. Run the software on a couple existing datasets that had captured body motion in video and with other sensors. Write it up, cite the paper whenever he used the software in future projects, open source the software so other folks could use it, too.

Sidney was a powerhouse writer. This was my first proper academic paper. He gave me the reins of the project and said he'd check in later.

In retrospect, having supervised a number of undergrad research assistants during my PhD, the project was a classic "give it to the student who wants experience, I don't have time for it, but like the idea of it being finished someday" project. (I have a growing list of these…) It was a good bet on Sidney's part—I took ownership. I wanted to learn how to put together a good paper.

I collected the data. We talked over an outline, and I started writing. We went back and forth on drafts a dozen times. Sidney picked a journal to submit to and sent me a couple cover letters as examples. When we got reviews back, he explained how they weren't so bad (they looked bad), and gave me some example revision response letters for when I revised the paper and drafted a reply.

But it took a while. The reviewers weren't happy. Ultimately, they rejected it. The next journal was a desk reject. And so on. Eventually, when I left Sidney's lab and started my PhD program at MIT, I left the paper, too.

The Words Aren't Right Yet

Thanks to Twitter, around the time I got Sidney's email asking about resubmission, I found myself reading the blog of science fiction and fantasy author Kameron Hurley. I enjoy her books in large part because of the gruesome realism about life and survival: characters who make it to the end of a book alive are the ones who are winning.

In one post, Kameron Hurley wrote about her experiences as a professional copywriter. She wrote words for other people for a living. She talked about a manager trying to "gently" give her feedback from a client, to which she replied: Don't mince words. Give it to me. If the words are wrong, write them until they are the right words. It was literally her job to make the words right for that client. If they weren't right, they needed revision. She needed the client's hard-hitting feedback.

Her attitude toward writing was inspirational. Her post reminded me that the words on the page aren't me. They're just one attempt at communicating an idea through the imperfect and difficult medium of language. If that communication attempt fails, we are given the opportunity to try again. As Hurley put it, "You write until the words are the right ones."

If we care about communicating our ideas, then the revision process can be a conversation. The goal is to make the writing better. The goal is to improve the presentation of ideas. The goal is to make the words right.

Writing isn't a one-time action. It's not like baking a cake—mix the ingredients, pop it in the oven, and it's done. Writing is a process. Editing is part of that process.

Reviewer feedback, like any other feedback, is aimed at making the writing better—and like any other feedback, it may need to be taken with a grain of salt. There are myriad ways to present ideas. People encounter ideas from where they are at; they may need different amounts of detail or supporting information to understand your words. And that's okay. Learning to judge your audience is a skill that takes practice, too.

Revision and Resubmission

I revised the paper for what felt like the millionth time. This time, though, it wasn't as bad as I had feared. In fact, the two years that had passed had lent me much-needed distance from the paper. As I re-read the reviewer comments from our last rejection, all the comments felt addressable. I could see where the words weren't right.

My co-authors commented and gave feedback. I revised the paper more. We submitted it to a new journal. Major revisions. We resubmitted. Major revisions. We resubmitted. Finally, the words were almost right: Minor revisions. And then it was published.

It's not the paper I'm most proud of, but it is a paper that taught me more than most. When I look at work I have in progress now—like a paper that's now on its 15th+ version, second journal, fifth year of work—I try to remember that academic publishing is often a long process. I try to remember that if the words aren't right yet, then with more time, effort, practice, and feedback, I can get a little closer to making the words right. Even a paper I'd initially given up on could be vanquished.

This article originally appeared on the Resilience in Academic Writing Blog, March 29, 2020


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a child puts her arm around a fluffy red and blue robot and grins

Relational Robots

My latest research in Cynthia Breazeal's Personal Robots Group has been on relational technology.

By relational, I mean technology that is designed to build and maintain long—term, social—emotional relationships with users. It's technology that's not just social—it's more than a digital assistant. It doesn't just answer your questions, tell jokes on command, or play music and adjust the lights. It collects data about you over time. It uses that data to personalize its behavior and responses to help you achieve long term goals. It probably interacts using human social cues so as to be more understandable and relatable—and furthermore, in areas such as education and health, positive relationships (such as teacher—student or doctor—patient) are correlated with better outcomes. It might know your name. It might try to cheer you up if it detects that you're looking sad. It might refer to what you've done together in the past or talk about future activities with you.

Relational technology is new. Some digital assistants and personal home robots on the market have some features of relational technology, but not all the features. Relational technology is still a research idea more than a commercial product. Which means right now, before it's on the market, is the exact right time to talk about how we ought to design relational technology.

As part of my dissertation, I performed a three-month study with 49 kids aged 4-7 years. All the kids played conversation and storytelling games with a social robot. Half the kids played with a version of the robot that was relational, using all the features of relational technology to build and maintain a relationship and personalize to individual kids. It talked about its relationship with the child and disclosed personal information about itself; referenced shared experiences (such as stories told together); used the child's name; mirrored the child's affective expressions, posture, speaking rate, and volume; selected stories to tell based on appropriate syntactic difficulty and similarity of story content to the child's stories; and used appropriate backchanneling actions (such as smiles, nods, saying "uh huh!"). The other half of the kids played with a not-relational robot that was just as friendly and expressive, but without the special relational stuff.

Besides finding some fascinating links between children's relationships with the robot, their perception of it as a social-relational agent, their mirroring of the robot's behaviors, and their language learning, I also found some surprises. One surprise was that we found gender differences in how kids interacted with the robot. In general, boys and girls treated the relational and not-relational robots differently.

Boys and girls treated the robots differently

Girls responded positively to the relational robot and less positively to the not-relational robot. This was the pattern I expected to see, since the relational robot was doing a lot more social stuff to build a relationship. I'd hypothesized that kids would like the relational robot more, feel closer to it, and treat it more socially. And that's what girls did. Girls generally rated the relational robot as more of a social-relational agent than the not-relational robot. They liked it more and felt closer to it. Girls often mirrored the relational robot's language more (we often mirror people more when we feel rapport with them), disclosed more information (we share more with people we're closer to), showed more positive emotions, and reported feeling more comfortable with the robot. They also showed stronger correlations between their scores on various relationship assessments and their vocabulary learning and vocabulary word use, suggesting that they learned more when they had a stronger relationship.

graph showing on the left, that kids in the not-relational condition didn't have as strong a correlation while in the relational condition, there was a stronger correlation - but that this varied by gender

Children who rated the robot as more of a social-relational agent also scored higher on the vocabulary posttest—but this trend was stronger for girls than for boys.

Boys showed the opposite pattern. Contrary to my hypotheses, boys tended to like the relational robot less than the not-relational one. They felt less close to it, mirrored it less, disclosed less, showed more negative emotions, showed weaker correlations between their relationship and learning (but they did still learn—it just wasn't as strongly related to their relationship), and so forth. Boys also liked both robots less than girls did. This was the first time we'd seen this gender difference, even after observing 300+ kids in 8+ prior studies. What was going on here? Why did the boys in this study react so differently to the relational and not—relational robots?

I dug into the literature to learn more about gender differences. There's actually quite a bit of psychology research looking at how young girls and boys approach social relationships differently (e.g., see Joyce Benenson's awesome book Warriors and Worriers: The Survival of the Sexes). For example, girls tend to be more focused on individual relationships and tend to have fewer, closer friends. They tend to care about exchanging personal information and learning about others' relationships and status. Girls are often more likely to try to avoid conflict, more egalitarian than boys, and more competent at social problem solving.

Boys, on the other hand, often care more about being part of their peer group. They tend to be friends with a lot of other boys and are often less exclusive in their friendships. They frequently care more about understanding their skills relative to the skills other boys have, and care less about exchanging personal information or explicitly talking about their relationships.

Of course, these are broad generalizations about girls versus boys that may not apply to any particular individual child. But as generalizations, they were often consistent with the patterns I saw in my data. For example, the relational robot used a lot of behaviors that are more typical of girls than of boys, like explicitly sharing information about itself and talking about its relationship with the child. The not-relational robot used fewer actions like these. Plus, both robots may have talked and acted more like a girl than a boy, because its speech and behavior were designed by a woman (me), and its voice was recorded by a woman (shifted to a higher pitch to sound like a kid). We also had only women experimenters running the study, something that has varied more in prior studies.

I looked at kids' pronoun usage to see how they referred to the relational versus not-relational robot. There wasn't a big difference among girls; most of them used "he/his." Boys, however, were somewhat more likely to use "she/her." So one reason boys might've reacted less positively to it because they saw it as more of a girl, and they preferred to play with other boys.

We need to do follow-up work to examine whether any of these gender-related differences were actually causal factors. For example, would we see the same patterns if we explicitly introduced the robot as a boy versus as a girl, included more behaviors typically associated with boys, or had female versus male experimenters introduce the robot?

a child sits at a table that has a fluffy robot sitting on it

Designing Relational Technology

These data have interesting implications for how we design relational technology. First, the mere fact that we observed gender differences means we should probably start paying more attention to how we design the robot's gender and gendered behaviors. In our current culture and society, there are a range of behaviors that are generally associated with masculine versus feminine, male versus female, boys versus girls. Which means that if the robot acts like a stereotypical girl, even if you don't explicitly say that it is a girl, kids are probably going to treat it like a girl. Perhaps this might change if children are concurrently taught about gender and gender stereotypes, but there are a lot of open questions here and more research is needed.

One issue is that you may not need much stereotypical behavior in a robot to see an effect—back in 2009, Mikey Siegel performed a study in the Personal Robots group that compared two voices for a humanoid robot. Study participants conversed with the robot, and then the robot solicited a donation. Just changing the voice from male to female affected how persuasive, credible, trustworthy, and engaging people found the robot. Men, for example, were more likely to donate money to the female robot, while women showed little preference. Most participants rated the opposite sex robot as more credible, trustworthy, and engaging.

As I mentioned earlier, this was the first time we'd seen these gender differences in our studies with kids. Why now, but not in earlier work? Is the finding repeatable? A few other researchers have seen similar gender patterns in their work with virtual agents...but it's not clear yet why we see differences in some studies but not others.

What gender should technology have—if any?

Gendering technological entities isn't new. People frequently assign gender to relatively neutral technologies, like robot vacuum cleaners and robot dogs—not to mention their cars! In our studies, I've rarely seen kids not ascribe gender to our fluffy robots (and it has varied by study and by robot what gender they typically pick). Which raises the question of what gender a robot should be—if any? Should a robot use particular gender labels or exhibit particular gender cues?

This is largely a moral question. We may be able to design a robot that acts like a girl, or a boy, or some androgynous combination of both, and calls itself male, female, nonbinary, or any number of other things. We could study whether a girl robot, a boy robot, or some other robot might work better when helping kids with different kinds of activities. We could try personalizing the robot's gender or gendered behaviors to individual children's preferences for playmates.

But the moral question, regardless of whatever answers we might find regarding what works better in different scenarios or what kids prefer, is how we ought to design gender in robots. I don't have a solid recommendation on that—the answer depends on what you value. We don't all value the same things, and what we value may change in different situations. We also don't know yet whether children's preferences or biases are necessarily at odds with how we might think we should design gender in robots! (Again: More research needed!)

Personalizing robots, beyond gender

A robot's gender may not be a key factor for some kids. They may react more to whether the robot is introverted versus extraverted, or really into rockets versus horses. We could personalize other attributes of the robot, like aspects of personality (such as extraversion, openness, conscientiousness), the robot's "age" (e.g., is it more of a novice than the child or more advanced?), whether it uses humor in conversation, and any number of other things. Furthermore, I'd expect different kids to treat the same robot differently, and to frequently prefer different robot behaviors or personalities. After all, not all kids get along with all other kids!

However, there's not a lot of research yet exploring how to personalize an agent's personality, its styles of speech and behavior, or its gendered expressions of emotions and ideas to individuals. There's room to explore. We could draw on stereotypes and generalizations from psychological research and our own experiences about which kids other kids like playing with, how different kids (girls, boys, extroverts, etc.) express themselves and form friendships, or what kinds of stories and play boys or girls prefer (e.g., Joyce Benenson talks in her book Warriors and Worriers about how boys are more likely to include fighting enemies in their play, while girls are more likely to include nurturing activities).

We need to be careful, too, to consider whether making relational robots that provide more of what the child is comfortable with, more of what the child responds to best, more of the same, might in some cases be detrimental. Yes, a particular child may love stories about dinosaurs, battles, and knights in shining armor, but they may need to hear stories about friendship, gardening, and mammals in order to grow, learn, and develop. Children do need to be exposed to different ideas, different viewpoints, and different personalities with whom they must connect and resolve conflicts. Maybe the robots shouldn't only cater to what a child likes best, but also to what invites them out of their comfort zone and promotes growth. Maybe a robot's assigned gender should not reinforce current cultural stereotypes.

Dealing with gender stereotypes

A related question is whether, given gender stereotypes, we could make a robot appear more neutral if we tried. While we know a lot about what behaviors are typically considered feminine or masculine, it's harder to say what would make a robot come across as neither a boy nor a girl. Some evidence suggests that girls and women are culturally "allowed" to display a wider range of behaviors and still be considered female than are boys, who are subject to stronger cultural rules about what "counts" as appropriate masculine behavior (Joyce Benenson talks about this in her book that I mentioned earlier). So this might mean that there's a narrower range of behaviors a robot could use to be perceived as more masculine... and raises more questions about gender labels and behavior. What's needed to "override" stereotypes? And is that different for boys versus girls?

One thing we could do is give the robot a backstory about its gender or lack thereof. The story the robot tells about itself could help change children's construal of the robot. But would a robot explicitly telling kids that it's nonbinary, or that it's a boy or a girl, or that it's just a robot and doesn't have a gender be enough to change how kids interact with it? The robot's behaviors—such as how it talks, what it talks about, and what emotions it expresses—may "override" the backstory. Or not. Or only for some kids. Or only for some combinations of gendered behaviors and robot claims about its gender. These are all open empirical questions that should be explored while keeping in mind the main moral question regarding what robots we ought to be designing in the first place.

Another thing to explore in relation to gender is the robot's use of relational behaviors. In my study, I saw the robot's relational behaviors made a bigger difference for girls than for boys. Adding in all that relational stuff made girls a lot more likely to engage positively with the robot.

This isn't a totally new finding—earlier work from a number of researchers on kids' interactions with virtual agents and robots has found similar gender patterns. Girls frequently reacted more strongly to the psychological, social, and relational attributes of technological systems. Girls' affiliation, rapport, and relationship with an agent often affected their perception of the agent and their performance on tasks done with the agent more than for boys. This suggests that making a robot more social and relational might engage girls more, and lead to greater rapport, imitation, and learning. Of course, that might not work for all girls... and the next questions to ask are how we can also engage those girls, and what features the robot ought to have to better engage boys, too. How do we tune the robot's social-relational behavior to engage different people?

More to relationships than gender

There's also a lot more going on in any individual or in any relationship than gender! Things like shared interests and shared experiences build rapport and affiliation, regardless of the gender of those involved. When building and maintaining kids' engagement and attention during learning activities with the robots over time, there's a lot more than the robot's personality or gender that matters. Here's a few I think are especially helpful:

  • Personalization to individuals, e.g., choosing stories to tell with an appropriate linguistic/syntactic level,
  • Referencing shared experiences like stories told together and facts the child had shared, such as the child's favorite color,
  • Sharing backstory and setting expectations about the robot's history, capabilities, and limitations through conversation,
  • Using playful and creative story and learning activities, and
  • The robot's design from the ground up as a social agent—i.e., considering how to make the robot's facial expressions, movement, dialogue, and other behaviors understandable to humans.

Bottom line: People relate socially and relationally to technology

When it comes to the design of relational technology, the bottom line is that people seem to use the same social-relational mechanisms to understand and relate to technology that they use when dealing with other people. People react to social cues and personality. People assume gender (whether male, female, or some culturally acceptable third gender or in-between). People engage in dialogue and build shared narratives. The more social and relational the technology is, the more people treat it socially and relationally.

This means, when designing relational technology, we need to be aware of how people interact socially and how people typically develop relationships with other people, since that'll tell us a lot about how people might treat a social, relational robot. This will also vary across cultures with different cultural norms. We need to consider the ethical implications of our design decisions, whether that's considering how a robot's behavior might be perpetuating undesirable gender stereotypes, challenging them in positive ways, or whether it's mitigating risks around emotional interaction, attachment, and social manipulation. Do our interactions with gendered robots change to how we interact with people of different genders? (Some of these ethical concerns will be the topic of a later post. Stay tuned!).

We need to explicitly study people's social interactions and relationships with these technologies, like we've been doing in the Personal Robots group, because these technologies are not people, and there are going to be differences in how we respond to them—and this may influence how we interact with other people. Relational technologies have a unique power because they are social and relational. They can engage us and help us in new ways, and they can help us to interact with other people. In order to design them in effective, supportive, and ethical ways, we need to understand the myriad factors that affect our relationships with them—like children's gender.

This article originally appeared on the MIT Media Lab website, August, 2019


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close up of a hand wearing a silver and black MIT ring

I've officially finished at MIT!

I've even got a ring to prove it. And some cool letters to tack on to my name.

I defended my dissertation in December. I finished all the revisions my committee had requested in February. The official graduation was in June.

My grad school admissions essay started off with the line, "I'm going to grad school because it'll be fun." I was right. At the MIT Media Lab, I worked on fluffy robots that helped young kids learn language skills through storytelling and play, an endeavor that included forays into art, robot voice acting, philosophy and ethics, psychology, child development, cognitive science, programming, electronics, statistics, and not a small amount of writing. It was a wonderful opportunity to both dabble and dive deeply.

Reaching this milestone wouldn't have been possible without the help and support of a great many people, most of whom I hope I remembered to add to the acknowledgements section of my dissertation. (If I forgot someone, there is written in the final document a promise of compensatory cupcakes.) You can find a pdf copy of my dissertation here.

As for what's next...


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