(The image for this post is a photo I took of a scenic vista near my home in Burlington, VT. The foreground is a clifftop covered in a few inches of snow, with a fence a few feet from the edge. It overlooks bare trees, a school yard, and a white field around it. The skies are blue with fluffy white clouds and mountains in the distance.)
Recently, when people ask how my PhD is going, I say I’m about halfway through! Really, a PhD takes as long as it takes, but I’ve been at this for over two years now, and four to five years is typical. I’m almost done with classes, I’ve passed my qualifying exams, and I’m starting to sketch out my dissertation. So far, so good! Along the way, I’ve learned a lot about my area of research, academia, and myself. All this makes it seem like a good time to reflect on what I’ve done so far, and where I’ll go from here.
I think the hardest thing for me, in the past two years, was adjusting to life in academia. I worked in the tech industry for over a decade, and always felt I was “academia adjacent.” I was a technical expert with a college degree, and so was just about everyone I worked with. I didn’t do research myself, but I knew plenty of folks who did, currently or in the past. I never really considered graduate studies, but I heard a lot about it from people who had. It sounded tough, but rewarding. I wasn’t rigorous about it, but I tried to keep up with major trends and advances in computer science and AI. I was building systems that used some of that cutting edge technology, so I had to understand it, at least loosely! All in all, I felt pretty confident heading into grad school.
I don’t think anything I learned about academia was wrong, but it also didn’t really prepare me for the experience. The most painful part was discovering just how undignified and precarious the grad student life is. I was very pampered at Google, where engineers ran the show and gave themselves top status and many perks. As a grad student, I barely get paid, and my health insurance and benefits are inadequate. I don’t get most of the perks of being an employee (even though I work for the University) or of being a student (even though I take classes there). In fact, many systems and processes at UVM only take grad students into account as an afterthought, if at all, even though we are an R1 university (top tier in research!), and grad students do most of that work. Many of my peers struggle just to keep food on the table, while they do the most intellectually challenging work of their lives. Luckily my savings make life relatively comfortable, but it was a shock to switch into this lifestyle, to see how we treat the upcoming generation of scientists, and how unnecessarily difficult we make their lives. And that was before Trump started slashing funding. To be clear, UVM is all I know, but I don’t get the impression it’s a bad school, or anything. I think this is sadly typical for America.
I also had no idea what getting a PhD was really about. I knew I’d have to take classes and write a dissertation. I thought I understood the nature of science pretty well. But actually learning how to be a productive scientist has taken a lot of getting used to! The main difference from engineering is that nobody can tell you what to do. Sure, there are open problems that need to be solved, and projects that the professors here need help with. But a lot of being a good scientist is finding the particular ideas that motivate you, or the places where your particular skills will serve you best. To a large extent, you have to explore and see where you fit in. There’s also very little in the way of “best practices.” Science is, by definition, on the very edge of what we know how to do. My advisors teach me the established tools of the trade, but they would never tell me how to solve a problem, because they know we need new techniques and perspectives. More than anything, they want to coax me into finding my own way of investigating the world, rather than simply showing me how they did it. That’s much harder!
The other big challenge of academia is “the literature.” The driving force behind science for the past several decades has been “publish or perish.” Every single scientist is writing new papers all the time, and the sheer volume of text is astonishing. It’s also barely organized. The way you navigate it is just keywords, citations, and (most importantly!) word of mouth. Unfortunately, this makes finding what you need very tricky, until you become an insider. Contributing to science is also very challenging, because everything must be expressed in terms of the existing literature. This was very painful at first, since I had something to say, but I was struggling to express it. I was very ignorant of what came before, and most of what I found was so different, I couldn’t see how my idea would fit in! I’ve come to appreciate this more, though. The biggest challenge in science is communication. If I’ve got a big, complicated idea, how do I get people to understand it? How can I convince them to care? I have to start with what they know! Tedious as it is, this is the only way to narrow down my idea, state it precisely and concisely, justify it with evidence, and make it feel relevant and useful to other researchers.
This leads me to the knowledge itself. When I first set out, I wanted to write a book. I was frustrated by the common understanding of what intelligence is and how evolution works. I had another perspective that seemed better, I was baffled that nobody was talking about it, and I felt compelled to share it with the world! I still might do that, but since my first blog post three years ago I’ve read over 50 books (!!!), and that has changed my perspective. The big surprise is that people have written about these ideas. Many people, for many years! It’s just, these books aren’t very popular. Some of them are very technical or obscure. To get at the good ones, I needed to find the right keywords, references, and recommendations. Again, it’s hard to know unless you’re in the know. Frustrating. But at least I’m not crazy, and I’m not alone. In fact, these ideas have been coming up more often, with more justification, in more accessible places, all the time! Better stories of intelligence are getting out, just very slowly, as I suppose is the way in science.
Reading all those books and digging into science papers has been enlightening. It’s helped me understand the things I care about more precisely, taught me many different ways to study them and talk about them, and shown me exciting new evidence. It’s also shown me what I don’t like. I’ve actually gained a lot from reading works I hate. Often it’s because they’re talking about something I care about deeply, in ways very much like my own, but with some subtle yet all-important difference that gets under my skin. I get all worked up, and I know precisely why. There’s a specific detail I can point to that matters to me, and they got it wrong! That’s priceless. There’s no better way to find out what I need to take a stand on. All this reading has also helped me understand what aspects of my research passion have already been well covered, and which ones remain neglected. This is particularly important for directing my attention as a researcher.
I just went back to read the first blog post I wrote about what I wanted to research. It’s funny how much this has changed! It reminds me that it was being a manager at a software company—making intelligent systems that mix human and algorithmic components—that led to this perspective on what intelligence is. But the way I talked about laying out a system architecture for all intelligence on this planet seems outrageously ambitious! It’s still a lovely long-term goal, but I’ve had to narrow my focus dramatically for my PhD, and I think that’s a good thing. I should focus on what matters most, to make clear and strong claims that will have an impact. Certainly, when it comes to AI, evolution is a part of the story that is sorely neglected. It’s an area of research that’s less competitive, and one with plenty of untapped potential, I think. Evolution is also central to how I’ve come to understand people and society. Despite all our intelligence, sophistication, and technology, humans are also social apes, whose behavior is powerfully shaped by our bodies, our instincts, and the early days of our species. I find human intelligence only makes sense when I think of it as constrained by our evolutionary history from one end, and our evolved culture from the other. We live in that intersection.
I care a lot about evolution, and I have since I was very young! It’s fascinating to me, and poorly understood, especially in the general public. What we’ve learned about how life works in the past 25 years is astonishing, and I believe we need a major rewrite to the story of evolution. I’m not alone in this. There’s a growing movement to look at evolution in a new light, one where organisms play an active role in shaping their environments, each other, and their evolution. The main struggle is figuring out what the new story should be, how it differs from the old story, and what those differences mean. This is where I feel like I have an interesting role to play. The field of biology is working to flesh out this story, gradually, and with plenty of conflict. But they don’t really appreciate that giving organisms agency changes the computational properties of evolution as a process. It’s a different kind of search than we realize, more like a search for better ways of searching than a search for greater fitness, and it ought to be much more powerful because of that. Meanwhile, the field of evolutionary computation has been struggling to overcome major limitations, and discover algorithms that work more like nature does. But those researchers tend not to study biology, and have unwittingly become very stuck on the old “replicator” model of evolution (ie., Dawkins’ Selfish Gene), which I believe is holding the field back.
This leads me to my dissertation, or how I imagine it today. I’m sure this will change plenty as I develop the ideas, run more experiments, and see what they have to show me. But I think I have a great opportunity to fill this gap between fields. I want to build a model of evolution where the evolving organisms can observe the world, have some awareness of their fitness, and use that to influence their own process of evolution. I want the “rules” of evolution to come from them, rather than designing them myself and building them into the algorithm, as computer science researchers have always done in the past. My hope is this will produce evolutionary searches that are both more open-ended and more efficient. They will be less restricted by what I think the right answer should look like, and more able to find solutions I wouldn’t think of. They’ll be able to search more strategically, and adapt the search as they evolve, balancing the trade-off between seeking new opportunities and fine tuning the solutions they’ve already found. If I can do this, then I hope AI researchers, philosophers of evolution, and biologists might better appreciate the significance of this change in perspective, and be more likely embrace it in their own work.
I’m very excited to pursue this. I care a lot about the concept, I have very specific ideas of how to implement it, and enough theoretical knowledge from my studies that this result feels plausible. On the other hand, it’s also frustrating. There’s so much more I’d like to say than will fit into a PhD! What I’m planning isn’t even a complete model of how I think about single-celled organisms. This work is all inspired by proto-cells, ancestors even older bacteria. There’s yet another layer of complexity I’d like to add, just to account for the role of DNA in this story. And, going back to my original vision, it just builds up from there with bodies and brains, ecosystems and cultures. I may never get that far. In fact, I think I may focus purely on evolution, since there’s so much to say about it. But starting small is the only option, especially since I need results to convince other people to pay for this research. Sadly, the United States has very little funding to pursue science for its own sake.
PhD programs are generally about some young student helping a seasoned researcher with their work to gain experience. It’s unusual (but not unheard of!) for a grad student to come with their own project in mind. Unfortunately, that makes funding more difficult for me. My advisors appreciate my ideas and want to support me, but they have funding for their projects, not mine. We’ve had some success finding the intersection of their goals and my own, so we can advance both with one project. But this semester I have no research funding, and am working as a teaching assistant instead. That’s okay, but it means I have a bit less time for research. I’m applying for both a fellowship and a grant from NASA. If I get those, it would give me the freedom to pursue my research full time, and apply it to the design of antennas, since that’s the opportunity that arose. I’m cautiously optimistic about this one, in part because I have support from a researcher at JPL, who wants to see this project happen! But it’s never a sure thing, especially considering the massive budget cuts NASA has suffered recently.
I’m also thinking a lot about the future beyond my PhD. Originally, I set out to pursue my research and study machine learning, with the expectation that I could always just go back to the tech industry with some new skills, if this science thing doesn’t work out. I never imagined that the tech industry would change so dramatically in just a few years! The advent of LLMs has completely changed how we think of “AI,” and not for the better. I recently read The AI Con, which is a great book explaining the dangerous and unethical way this technology has been developed and marketed. Having read it, I no longer want to call my work “AI.” That has become a marketing term more than anything else, used to sell technology that devalues and replaces human creativity, craftsmanship, and labor. It’s become the overwhelming focus of a tech industry bent on extracting value from their customers, rather than serving them or benefiting society. This is not what my research is about, and I want no part in it. Hopefully the “AI” fad will pass, but I’m concerned the tech industry and software engineering work will never be the same, and I’m wary of which corporate projects I might join.
Becoming a professor is still on the table, but I’m very concerned about that route, too. The culture of academia was broken even before I got here. “Publish or perish” is a very bad incentive for researchers, and it has been leading to a crisis in academic publishing. The industry exploits researchers, students, and reviewers; the science has become “safer” but lest creative and diverse; and quantity gets prioritized over quality. The whole academic system is also badly unjust, with toxic power dynamics built-in. Grad students are overworked and underpaid, but in some ways professors have it harder. Their benefits are only marginally better, and the pace and quality of work they’re expected to churn out is often totally unrealistic! On top of this, the recent funding cuts mean that chasing grants is becoming an ever growing part of a professor’s job, that there’s more pressure to work on “high-value” research rather than pure science, and more of the money comes from industry partners with ulterior motives.
I wouldn’t take just any academic job. It would have to be the right opportunity, at the right kind of school. Similarly, I’m no longer interested in a “standard tech job” or a role as an “AI engineer” at any of the big companies you might recognize by name. I’ve found I really love evolutionary algorithms and parallel computing. I’d like to find a job doing that, if possible, but it’s a narrow specialty. This means I’m shopping around for very specific opportunities, and luckily I am finding some. The ALife and Diverse Intelligences communities have become important to me, and they’ve given me several leads and connections. I’m certainly not the only one chasing these sorts of dreams right now, and there are folks (in industry, academia, and government) who believe this will be important for the future. So, I’m cautiously optimistic, despite all the depressing challenges these days, but still unsure where this path is leading. I intend to play things by ear, as I have from the beginning.
In any case, it seems like I’ll be at this for another year or two or three. More and more, my focus will be research and the specific ideas I came here to pursue. I’ll keep reading books and posting reviews on Goodreads. I’ll keep writing blog posts. I imagine they’ll become more focused on evolutionary search processes, but I’ll try to mix things up with more posts about nature and I’m sure I’ll have more to say on “AI” as it continues to be such a large and growing part of our lives. Hopefully I’ll have more adventures to share with you, attending conferences and academic events around the globe. And I’ll keep looking for job opportunities, figuring out what my path forward looks like. I appreciate you coming along for the journey, I hope you’ll keep coming back to see what I’ve been thinking about, and that you’ll join the conversation in the comments section.
Thinking with Nate 