What’s the Big Idea?

(this post’s image taken from Colossal, showing the work of Steve Lindsay)

Okay, okay, enough about me. This blog is supposed to be about “intelligence,” but what does that mean? What ideas am I actually researching and writing about? In this post, I’ll try to give you a broad overview of the areas that interest me. Going forward, I’ll focus on more narrow topics, so I can explore them more deeply.

What is Intelligence?

Perhaps the most common way we think of intelligence is “what human beings do.” Even though we talk about other forms of life (and even computer algorithms) being intelligent, we tend to talk about how close they come to achieving “human-level” performance, as if that’s the gold standard. More specifically, we look to the human brain as the idealized intelligent machine. This has always bothered me, for a number of reasons.

For one thing, you don’t need a brain to be smart. Plants are incredibly intelligent. They coordinate their life cycle with the seasons and weather, maneuver around obstacles, communicate with pheromones, and expertly manipulate other species into doing work for them. Even in human beings, we often overlook the ways our bodies shape our behavior, rather than our brains. Our bodies make delicate manipulation tasks effortless, carefully manage an array of vital resources, perform repairs, fight disease, and have a huge influence over our moods and desires. The brain helps with understanding, imagining, planning, and deciding, but just about everything else is deferred to the body.

For another thing, a human being isn’t so smart without a society. So much of what we think of as human intelligence is the accumulated knowledge, wisdom, and artifacts that are better thought of as human culture. Those things were all created by human beings (over a few hundred thousand years), but most people aren’t constantly inventing new ideas, they’re adopting ready-made solutions, often without full understanding. The remarkable thing is that people can share and remix ideas, which allows the limited intelligence of our brains to reach further. Without access to culture, we’re not much better off than other mammals. This is clear from “wild child” accounts, which show that an infant growing up in isolation will end up tragically stunted, traumatized, and unable to adapt to life in society.

So if intelligence is not human brains, what is it? Tentatively, I think of intelligence as “the ability to adapt effectively to environmental challenges.” In other words, intelligence is learning. It’s about observing reality and using prior examples to choose actions that will hopefully lead to the best outcome. Sometimes this is intentional (like when a person decides what to do) and sometimes it’s more of a blind process (like when natural selection preserves a behavior because it just happens to be adaptive).

My perspective is that all of life and culture is produced by a complex learning process. Or, more precisely, life is a system of learning processes that make learning processes that make learning processes. Our global society and ecosystem together make up one enormous web of intelligent agents, filling many roles and operating at many scales. This is not a new idea. There are whole branches of Complex System Theory devoted to the emergence and complexification of life. I’m reading about that prior work, and thinking about how it relates to my experience in algorithms, computer systems, and organizations.

Life as computation

One assumption that I’m making is that life can be thought of as a kind of computation. This may be hard for some to swallow, since computers as we know them today completely lack so many of life’s wondrous qualities. I’d argue that’s because of two essential differences. Firstly, life is solving a completely different problem than computers do. Organisms are born into the world with a body, and challenged to survive as best they can in an open-ended environment. In contrast, most computer systems are tools for people, designed to perform specific tasks on demand. Of course they behave quite differently. The second difference is sophistication. Our engineered computer systems are incredible, but life still puts them to shame in terms of complexity, nuance, and efficiency. That’s not surprising, given life’s multi-billion-year head start.

When I say life is computation, what I really mean is there is nothing supernatural about life. As strange, beautiful, creative, and unpredictable as life is, I assume that this is ultimately the result of physics. Very particular arrangements of molecules interact in reliable ways to reproduce, perpetuate, and refine patterns of behavior we see as intelligent, without the need for any outside influence. This assumption is mostly out of practicality. Science can’t explain magic, so to give science a chance at this problem means entertaining the idea that there is no magic.

In Computer Science, there’s a concept called the “universal computer,” first proposed by Alan Turing. In a thought experiment, he designed a very simple machine and showed that it could run any computer program you could imagine. More importantly, he showed that any machine that has a few key properties is equivalent to the one he designed, and can also perform any arbitrary computation. In other words, computers come in all shapes and sizes. Each one has unique performance characteristics, which make it better suited for solving some problems than others, but at least in principle any universal computer can run any possible program.

That’s why I find the model of computation so appealing for natural intelligence. In life, there are many different kinds of intelligent systems, built very differently, with different specializations, functions, and performance characteristics. But as long as there’s no magic, then there’s some mechanical process under the hood producing these behaviors, and that’s computation. More importantly, if we can describe all these systems using the same language, we can compare them directly with each other, and talk about how they compose to form larger, more complex systems. The language of computation is general and expressive enough that I think it can do the job.

What kinds of intelligence exist?

Our world is filled with an enormous diversity of intelligent systems, many of which have their own dedicated fields of research. There’s cell biology, evolutionary biology, ecology, neuroscience, psychology, sociology, computer science, and many more. Specialists study one intelligent system, in the context of a particular academic discipline, using the tools and language of that discipline. This narrow focus is very valuable, but it means our understanding of intelligence is siloed, and we tend to categorize intelligent systems based on what academic field studies them, rather than their computational properties.

To advance our understanding, we must learn to see past the obvious differences of these systems and the unique, messy ways they manifest in nature. Instead, we should focus on what they have in common. What properties are broadly shared by many kinds of intelligence? What questions can we ask about all intelligent systems, and when we look at the answers, how do we compare apples to apples? I’m not at all sure how to do this, but there are a few properties that already stand out to me as interesting places to look:

  • Substrates: What is the fabric this intelligence is built from? Molecules? Neurons? Transistors? People? What can that tell us about the strengths and weaknesses of that system? For instance, cells are molecular machines and thus constrained by the limitations of chemical processes like diffusion and catalysis. This severely limits the computational speed and physical size of cells, but it also provides a remarkably robust, efficient, and massively parallel form of computing that engineers can’t yet rival.
  • Visibility: What information is available to learn from? For instance, natural selection is almost completely blind. The only signal of success is when an organism reproduces. On the other hand, humans have rich sensory perceptions and a wealth of knowledge and experience, all of which factor into cognition. The level of visibility affects what kinds of patterns a system can recognize, and how quickly it can find a workable solution to a problem.
  • Purpose: What function does this intelligence serve? For most computer systems, a human decided the purpose in advance, then designed one particular solution to fulfill that need. On the other hand, many forms of intelligence are far more open-ended than that. Living things, human organizations, and even some forms of AI will strive to creatively fulfill their purpose, sometimes doing so in surprising ways. This can be very tricky, since a system’s “purpose” often isn’t clear, and can change over time.
  • Interface: Within a substrate, there are no clear boundaries. The genes for digesting lactose are spread ambiguously between you and your gut bacteria, for instance. But boundaries between substrates are much sharper, because the parts are made of different stuff, and are not naturally interoperable. At these edges, there are narrow interfaces between intelligent systems, sharing just enough information from one to another that they can work together. That gives us a natural window into what properties of a system matter most for fulfilling a particular function.

What I want to do here is to find useful ways of dividing up the world that might serve to integrate, compare, and contrast our notions of intelligence from different domains. I hope this will help to identify parallel examples, and to discover insights that can transfer from one domain to another. Life has evolved an incredible variety of learning processes, each optimized in different ways. Surely there are new algorithms and performance tricks waiting to be discovered. Perhaps we could even derive some general design principles for what learning tools work best under different constraints?

What does life compute?

So, if all of life is one big, complex computation, what is it actually computing? In one sense, the answer is simple: life constructs ecosystems of organisms, and optimizes them to reproduce and thrive. That’s a fine answer in the abstract, but our experience is much more specific than that. Life is typically a “yes and” sort of process. Any successful way of doing things tends to stick around, and in doing so it shapes everything that can come after. In other words, life on planet Earth isn’t just “thriving” in some generic sense, it’s found a very particular way of doing that which we must study if we want to understand, predict, and influence its direction.

To make this more concrete, think about modern American society. We use GDP as a (flawed) proxy for human thriving. We use capitalism and an ecosystem of corporations to redistribute resources and prioritize work so as to improve GDP. Those corporations are made out of people, who run the company, make the decisions, do the work, provide the services, buy the products, and use them in their daily lives. Those people have minds which are deeply embedded in cultural roles (citizen, employee, parent, etc.) and physical bodies, all of which have their own goals, limitations, preferences, and demands that tug the person in multiple directions at once. People are just one species, but we depend on countless others, which depend on each other and on the physical world, which is changing more rapidly than ever thanks to the power of human culture.

That system as a whole does things we want (like providing a comfortable standard of living for many) and things we don’t want (like instigating global climate change). It’s flawed, but we can’t start over from scratch, we can only try to steer it in a positive direction. That means understanding the structure of the system, and finding the points of high leverage, where a small nudge will have a big impact (and hopefully few side effects). To fight plastic pollution, should we invest in ocean clean-up, tell individuals to change their purchasing habits, tax corporations for plastic waste, or engineer plastic-eating bacteria? It’s hard to say what will work best, but if we can understand the major players, their incentives, and the ways they learn and adapt, we can make better educated guesses.

Describing and explaining all of life on Earth is impossible. It’s just too big, messy, complex, and rapidly changing, but that doesn’t bother me. The same is true of the source code for Google Search, but I worked with that system effectively for years. How? By using the engineering concept of a system architecture. If you know the major parts of a system, what they do, and how they fit together, you can say a lot about that system as a whole and navigate its subsystems with confidence. Of course, whatever model you make will be a huge oversimplification with many exceptions, but even a very rough model is profoundly useful.

This may be a pie-in-the-sky idea, but I’m fascinated by what a system architecture for life on Earth might look like. Could we really describe it all in one big picture? Could it be organized and subdivided in meaningful and useful ways, or is the real world just too messy? Could we use that model like an engineer does, to trace the steps leading to some behavior, identify the relevant subsystems, and make targeted interventions to change the system’s behavior?

More practically speaking, I’m interested in how intelligent systems compose with one another, even just two at a time. For instance, nascent research has shown great promise in using evolutionary algorithms to design architectures for deep learning. I hope that studying the relationship between mind and body might provide insights into how to integrate deep learning systems into other software, and how to balance the costs and benefits of intuitive thinking with other algorithms and heuristics. I’m also very interested in understanding the impact of social software and machine learning on society, and how to build software systems that conform to human values and ethics.

Conclusion

That’s a brief tour of the intellectual domain I want to work in. I’m well aware it’s an enormous territory, and I surely won’t get to it all. My plan is to take a broad but shallow pass over many examples of natural intelligence, and to go deep on my study of computer algorithms and machine learning. I hope this will allow me to take full advantage of my technical skills and spend time searching for practical innovations that show the value of this way of thinking. In the meantime, I can work towards a general theory of intelligence in the background. It’s fine if I never get there, it’s just good to have lofty aspirations.

That said, all of this will surely change as I make progress. I’m figuring this out as I go along, and making course corrections all the time. I’m still learning about prior work, and I’ll have to adapt my own work to complement it. I’m not sure what ideas will prove to be dead ends, or what surprising new questions and opportunities I’ll discover along the way. That’s a good thing. I want to keep an open mind about this work, and let it evolve into what it needs to be. Still, I hope these questions will be a fruitful place to start.

I also have more ideas I didn’t cover here. I’ve got lots to say about evolution, so much so that it deserves its own post. I have many thoughts and observations about the inner workings of the mind. I’d love to explore the consequences of these ideas on how we understand the human condition, the structure of society, and conventions for the ethical AI. Frankly, this is such a big domain, there are so many places I could go, and I intend to follow my passion and curiosity.

As always, I’m very interested in feedback. Does what I said make sense? Do you have questions? Do you disagree, or have other ideas to share? Got advice on how to make this research more productive? Did I touch on something you’d like me to explore in more depth in a future post? If so, please leave a comment. I’d love to hear from you.

Who is this guy?

(This post’s photo is of baby Nate on the farm)

This blog is about two things: me, and the ideas I’m passionate about. I’ll talk more about those ideas in the next post, but first I think I should introduce myself. I’m a 37 year old man. I live in suburban California with my wife and cat. My hobbies include hiking, cooking, and yoga. Until recently, I worked as a software engineer at Google, which was a pretty sweet gig. Then I decided to quit to pursue “intelligence research,” of all things. I think this was a big surprise to most of my friends, family, and coworkers, but looking back at my life, it almost feels inevitable to me. This is the culmination of many influences, over many years.

For one thing, I’ve always had a very deep interest in science. My parents were both highly educated and eager to teach me everything they could. My dad was a professor doing research in cell biology and the hormones that mediate human behavior. My mom was a vet technician, helping care for cats, dogs, and horses in a clinical setting. They taught me all about bodies and how they work, at both the micro and macro level. As a kid, dinner conversation might center on the latest veterinary medical mystery, or recent discoveries in genomics. When I was young, I started to devour books, shows, and movies about science fact and science fiction, and I never stopped. I’ve taken college classes and watched lecture series. My favorite topics have always been life, evolution, the mind, culture, and artificial intelligence.

So, I’m a life-long science enthusiast, with broad but sometimes shallow knowledge in biology and the behavioral sciences. To be clear I am not an authority on these topics—yet. That’s a big reason why I see academia as my next step. I want to verify what I think I know, find the primary sources, and learn to talk about these ideas with rigor and precision. That said, I don’t think you need formal training to have a good idea. Before I made the dramatic move of quitting my cushy job, I spent a lot of time convincing myself that I’m onto something real and exciting, even if it’s still a little fuzzy.

On the other hand, I am a trained authority on algorithms and complex software systems. I’ve been programming since I was 12, got a bachelor’s degree in Computer Science from Brown University in 2007, and in my 14 years at Google I worked my way up from junior engineer to trusted elder. All my life, I’ve wondered how life is like a computer. For most of that time, I struggled to get the ideas to line up at all. Living systems are profoundly different from computers the way we build them today. It’s taken decades of hands-on experience with natural intelligence and software systems for me to gradually see the similarities, and understand the limits of what that analogy can tell us.

I’ve always had a close relationship with nature and animals. I was raised on a working farm, with a few dozen different animals from horses to chickens to cats. I fed them, medicated them, and scooped their poop. I knew each animal’s name, but also their unique personality, preferences, and relationships. I learned the limits of their understanding and how to communicate with them (to the extent possible). I learned never to turn my back on a donkey. Most importantly, I learned that animals have rich inner lives, and I got to see first hand how similar and different those experiences can be from individual to individual, and from species to species.

My interest in human culture has been just as enduring. As a philosophical, progressive boy transplanted from New York to a down-to-earth, conservative town in rural Virginia, I simply didn’t fit in. I was bullied. Kicked. Called a f*ggot. A dear friend once told me with earnest compassion that I was going to hell, simply because I was raised Jewish. I was so confused. I struggled to understand who I was, what society expected of me, and where those ideas even came from in the first place. Why did I understand the world so differently from the folks around me, who mostly seemed smart, caring, and reasonable? Why couldn’t we even talk about certain topics without starting an argument? I became obsessed with such questions, and have pondered them ever since.

At Google, I got to learn about intentionally designed culture. I led teams, which meant building a personal relationship with each individual, and learning their strengths, weaknesses, and motivations. I would set a vision that everyone could rally behind, then tell a continually evolving story about our project and how we each fit into the grand plan. I used goals, metrics, meetings, reviews, and continuing education to optimize our performance. I cultivated inclusion, psychological safety, and a growth mindset. Or, at least, I tried to. One of the first things you learn as a leader is that you cannot “control” your team. A team is defined by its culture, which evolves organically and collaboratively. Each individual has only limited influence over the direction of the whole. It’s a fascinating dynamic, and it’s a miracle how well it works.

What fascinated me most about being a manager was the dual nature of the job. On one hand, a team is a very human thing. Personal, emotional, creative, unpredictable. It’s about caring for individuals and helping them do their best work. On the other hand, a team is a machine. It’s an algorithm, designed to solve a particular problem, implemented with people. Bigger and more complex problems require bigger and more complex teams, with well-defined sub-problems, zones of responsibility, and interfaces between adjacent teams. An org chart is a catalog of interpersonal relationships and it’s a system architecture diagram for a company, at the same time. I think this realization was the key that led me to see the messy organic systems of life as something compatible with the language of algorithms.

So, you see, I’ve touched on these themes over and over again throughout my life, building them up, tying them together, and seeing them from new perspectives. I’ve educated myself, sought out relevant experience, and chased my passions. I’ve reinvented myself repeatedly, circling in on a lifestyle where I can thrive and truly be myself. This next step is a big one, but it’s part of a very natural progression. Finally, I have the knowledge, skills, stability, and self-confidence to do this.

New Year, New Beginning

I’m trying something new. The last fourteen years of my life were spent as a software engineer and manager working on Google Search. That was mostly a very positive experience, but outside of work I had an unusual hobby: philosophy of mind. I was working on a few ideas that excite me, and I was starting to regret not having the time to pursue them properly. Then 2020 hit. It was a real kick in the pants, but it was the motivation I needed to change my life. I’ve decided to pursue my research full time, and see where it takes me.

This blog will talk about the philosophical ideas I’m exploring, my experience navigating a big career pivot, and personal reflections on my experiences and current events through the lens of behavioral science. In future posts, I’ll tell you more about myself and the problem space I want to explore. Before all that, though, let’s start with some basic context.

What am I trying to do? I want to develop some half-baked ideas of mine and get them out into the world. In order to do that, the first step is grad school. Contrary to popular belief in Silicon Valley, software engineers aren’t qualified for every job, and I know next to nothing about philosophical tradition or how to do serious academic research. I also need the right environment to support my work. I plan to do a lot of writing, for both technical and general audiences. I want to code up some computational models of biological systems to support my claims. Along the way, I might even find some interesting new insights about machine learning to bring back to the tech industry. We’ll see.

What am I going to research? I’ll go into much more depth in a future post, but the short answer is biological intelligence. I’m interested in what enables life to find creative solutions to problems and learn to thrive in virtually any conditions. My perspective is very heavily shaped by my software engineering background. I want to ask the question, “if life is a computational process, what does it compute?” This means exploring various kinds of natural intelligence (evolution, organisms, minds, societies, etc.) which today are studied by entirely separate fields, and trying to use the language of algorithms to describe how they work together as a system, make claims about their capabilities and limitations, and explore what that means for the human condition.

Since leaving Google in October, I’ve been very busy figuring out what comes next. I’ve been learning all about grad school, which is harder than I expected. It turns out, grad school is a diverse and immersive experience. It’s hard to describe, and outcomes vary wildly by person, discipline, and school. So I’ve been doing a lot of reading between the lines, reconciling perspectives, and wrestling with conflicting advice. I’ve also spent a lot of time putting a finer point on my ideas and deciding what specifically I want to do with them. That all might change when I get into the research, but having a clear idea up front will help me pick the right school and advisor.

For the near future, I’ll be pretty focused on researching departments, talking to faculty and students, sorting out prerequisites, and actually applying to programs. In the meantime, I’ll be doing lots of reading, to help pick up the essential jargon and ideas, and figure out how my work relates to what already exists. I’m on my own for now, but hopefully soon enough I’ll get into a grad program that can give me the community and structure I need to succeed.

These ideas have already had a big impact on me. They’ve changed how I see the world and my place in it, and they’ve turned out to be surprisingly relevant and useful in my day to day life. I hope they could do the same for you. I also hope my experience might be useful or motivating, especially since changing careers in mid life is a daunting idea for most people. If you’re interested in any of this, then follow along. I’m going to try to post about once a month for now. To make sure you never miss an update, please subscribe with your email address below:

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