Frequently asked questions

There are some questions that family and friends ask me repeatedly. Because of my profession and interests, they are often in the vein of "what do you do for work?" and "what do you think about artificial intelligence?" I tend to have difficulties articulating good answers that are somewhat understandable on the spot. Partly for my own sake, and for those who might be interested, I've therefore decided to write down my responses to these frequently asked questions (FAQs) in a more thoughtful manner. My answers here will be phrased so that very little to no background in science is necessary to understand them. Note that there will be several words which will have to be defined in order to explain the concepts without background knowledge, and for the most important terms and concepts I go on to define them.

The responses included in this FAQ so far are the following:

• What is AI? (2026-10-03)
• What do I do for work? (2026-10-03)
• Do I think AI is conscious? (To be written)
• Do I think AI will make humans extinct? (To be written)

Note the date stamp attached to the posts, since my answers on these matters may change over time - particularly in fields like AI that change rapidly. I have, however, attempted to not make my responses depend too heavily on the current status of the fields.

What is AI?

The opposite of artificial is natural and the simplest and broadest definition of artificial intelligence is therefore "any form of intelligence that is not natural". Our brains and those of other species are natural in the sense that they have evolved in nature through natural selection, whereas intelligent systems that have been engineered by us are not. There is a natural follow-up question as to the definition of intelligence, which is notoriously difficult to pinpoint. Instead of defining intelligence, I tend to go with the tactic employed by U.S. Supreme Court Justice Potter Stewart in 1964 in his response to the definition of pornography: "I know it when I see it". That'll do.

The idea that artificial systems, like computers, could become intelligent was first conceived, or at least popularized, by Alan Turing in the 1950s. By the 1950s, the field of computer science was in its infancy, but the earliest computers saw increasing capabilities. Today, the term artificial intelligence has been skewed from its original meaning, and is either tied to algorithms that seem to possess some sort of humanity, like the system embedding in ChatGPT, but is also often tied to any computer system that seemingly learns or adapts. I will go on to explain these concepts further.

Before we even start thinking about what artificial intelligence might be, there are some terms that need to be cleared up. Firstly, we must understand what an algorithm is. An algorithm can be thought of as a recipe or equivalently a set of instructions. If we wish to sum up all numbers up to 100, we could write an algorithm that keeps a tally of the sum, starting at 0, and iteratively counting up until it reached 100, adding each number to the sum. This can be written in instructions that executes on the computer chip, and reports back the answer. All computer systems are built from a set of algorithms written in a formalized language called a programming language. At first, a programming language looks very strange and foreign, but just like any other language it is learnable through practice. For someone who has coded for several years, the computer language reads like a native tongue.

To someone who isn't too familiar with mathematics or computer science, it might seem odd that an algorithm can learn. What does that even mean? How can a recipe learn? Part of the confusion is because we tend to think of learning as something tied to living things, like humans or dogs, while in mathematics and computer science we see learning as a way to "adjust a model to align with an objective". That last part requires some explanation. First, what is a model? A model, like a model airplane or a model train, is a simplified representation of the real thing. In mathematics, we create models that are simple but supposed to represent something very complex, often to make predictions. For example, we can create a model of the weather, to try to forecast it. Similarly, we can make models of disease spread, to try to forecast it, alongside various modelled interventions. Fundamentally, however, a model is just a set of equations. Numbers and variables on a sheet of paper that represent something. Models are everywhere - without them the world is too complex to understand. There is a famous saying that "some models are useful, but all models are wrong" - and that is true. No model exactly represents something, but it may be accurate enough for whatever we want to use it for.

Models may seem alien to some, so here is an example. Say you want to create a model that represents the price of a house. An extremely simple model could simply take the number of rooms; the more rooms, the more expensive it is. The model would look something like:

Price of house = number of rooms × 50,000

which means that the price of a house is 50,000 times the number of rooms. A plot of this model can be seen below.

This model wouldn't be nearly correct - there are many more things that go into the price of a house, but it is not entirely wrong either. The price of a house tends to increase with the number of rooms.

Creating a good mathematical model is challenging and requires a lot of expertise about whatever is being modelled. For example, to create a weather model that is somewhat accurate, knowledge about the physics of wind patterns, heat dispersion and water streams is required. However, in the 20th century there was a revelation: what if we could make the computers learn the model themselves? The key behind this revelation was an algorithm that allowed the model to use observations (data) to adjust so that it fits with observations. Starting with a poor model, the method could be employed to gradually improve the model. It should be noted that there is no magic involved: the method for adjusting the model can be written down fairly succinctly and isn't too hard to grasp, although I will not attempt to explain it here. In brief however, it entails penalizing and adjusting a model when it's wrong, and adjusting its parameters to make it slightly better - and doing so over and over again. In the example with houses, we could image having a large bank of data; examples that include various features of the house, like location, the year it was build, area, and how many rooms it has, alongside their prices. The algorithm could then utilize the features of the houses, to create a model that accurately predicts their prices. Such a model would likely be much better than the one we created above, and we did not have to create the model ourselves through tedious trial and error.

There are lots of methods that now apply this general framework: start with a poor model, and adjust it using data until it's better. This is generally called "machine learning", and adjusting the model is usually called "training" or "fitting" the model. A particular type of model is inspired by the structure and inner workings of our brain. Our brain consists of specialized cells called neurons which are connected in large networks. The neurons specialize in propagating electrical signals, and such signals are passed around in this network and eventually out onto the nerve endings in our muscles, making us move, walk and talk. These signals also make us able to think and somehow make us conscious, although we are uncertain precisely why or how (more on this in the section "is AI conscious?"). In the 1950s, it was realized that this general "neural network" framework could be simulated in a computer by setting up a simplified model of neurons, and this model could be combined with the "learning algorithm" described previously. The "neural network"-paradigm is still the foundation of the most powerful learning systems today, including large language models. We should remember, that just like the learning algorithm, neural networks are not magic. The underlying mathematical principles can be written down on a single piece of paper and are part of most undergraduate degrees in engineering, math or physics.

Around the 50s to 70s is when "machine learning" and "artificial intelligence" had their first wave of interest - and an astounding part of the fundamental theory was developed already back then. Visionaries of the time imagined how the neural networks could eventually be used to create intelligences that were human-like, giving rise to heaps of science-fiction movies and novels. However, these systems require powerful computers and lots of data to learn efficiently, and it wasn't until the 2000s and 2010s that they became powerful enough to outperform the classical methods. The period from 1980 to 2000 is called the "machine learning winter", and hopes that these systems would ever be viable and scalable were low. Circa 2010 was when a researcher realized that the hardware that had been developed for computer games, the so-called GPU, was especially well suited for the type of calculations that were needed to accelerate the learning of neural networks, which led to rapid success in continued development.

The 2010s saw a revolution in these learning systems. They were extremely hyped in academia, and everyone and their uncle was doing research on machine learning and artificial intelligence, and it was during this period that I got interested in the field! Towards the final years of the 2010s (2017-2019), there were several advancements in using these methods for translation, which paved the way for large language models and chatbots. These times also saw leaps of advancement in the ability to create new images, and lots more.

But if the machines are the ones doing the learning, what are humans who work with artificial intelligence doing? What does research look like? Well, there are many answers to this question, but generally, there are three main steps to creating a machine learning model: first, setting up the "architecture" of the model. The architecture can be thought of as the scaffold, template or general outline of the model, and is extremely important for the ability of the model to learn. As mentioned earlier, neural networks was a very potent way structure for making computers learn, and today researchers are putting artificial neurons together in networks with advanced structures that further facilitate learning. Remember that all of these neurons are just lines of code in a computer, are any structure that is created only modifies the underlying mathematics. However, it is helpful to think of these neurons as actual objects with substance, and one can imagine how these networks seemingly twist and turn and connect in various ways. Then, gathering adequate and high-quality data that will be used in the learning algorithm is equally important. A nice quote in this domain is "shit in, shit out" - meaning that if the data that goes into the model is of low quality, the model, too, will be of low quality. Lastly, the learning algorithm itself can be adjusted to more effectively update the model using the data. There are continuous developments in all of these domains, and part of my research is in developing new architectures and methods (see "What do I do for work?").

When it comes to the celebrated language models which are in the limelight today, the data does not correspond to real estate features and prices, but instead to text. Fortunately, we have lots of text - the internet is full of it (in fact, I'm contributing to the opus by writing this). We don't have to go into the architecture of these models, but you can imagine that they are immensely complicated and constantly subject to improvements. The learning process is also very complicated nowadays, but its backbone is based on something called "masking". As I mentioned earlier, all a model does is predict, and in terms of language, the prediction is made as follows: take a sentence, remove some words or letters, and make the model predict those words or letters. For example a masked sentence could be: "My ____ is Erik Hartman" - and the right answer would be to fill the blank space with "name". It is amazing that such a simple idea gives rise to the sophisticated language models we have today, that are able to solve problems many humans can't solve.

But how can this work? How can these models seem to become "intelligent" just by learning to fill in characters or words? Well, imagine that you are reading a murder mystery novel, and right when you are nearing the end and the murderer is about to be revealed, the name of the murderer has been masked out. Then imagine that you are now tasked to fill it in. Doing so requires you to figure out who the murderer actually was, which could be a terribly challenging task! So this simple exercise of predicting what word should be put at a certain spot turns out to involve solving a murder mystery, which would require some sort of intelligence or complex pattern recognition. It's a tricky problem in disguise. As such, intelligence is seemingly emerging through the simple learning process of masking and prediction.

Now that we've talked about artificial intelligence models that are created and trained to mimic language, it is important to remember that this is only a small part of the plethora of models that exist. Other models are those which steer self-driving cars, or predict stock prices. My research, for example, does not deal with language, driving, or stock - it deals with biology. Instead of words and letters, I create artificial intelligence systems that "learn" about genes, proteins, organisms, and chemistry. The goal of my research is to make these models help us design new drugs, and to make them help us unveil and understand parts of biology we couldn't understand without their help. You see, the human brain is limited in a number of ways. We do not have a very long memory, and we tend to only be able to keep a few things in our head at the same time. Models do not have those limitations, and can therefore pick up patterns that we can't.

Artificial intelligence systems and machine learning raise some fundamental philosophical questions. Naturally, they make us rethink what intelligence and consciousness is, questioning our place in the universe. If intelligence can arise by fitting a very complex model to data, what is so special about being human? Can a model - which fundamentally is a set of equations - be conscious? (It should be noted that a large model may have as many as 1 trillion parameters, and there isn't enough paper in the world to write down the equations it is made of, but it is an equation, nonetheless.) I deal with some of these questions in the section "Is AI conscious?"

This little tour of artificial intelligence hopefully makes the field less mystical and a bit clearer. I think the most important thing to remember is that although making machines learn seems like magic, it isn't. The mathematical toolkit for the field has been around for 100 years. The only thing that seems magical is how well these methods actually work! It should also be noted that the development of artificial intelligence models, particularly language models, is subject to tremendous engineering feats in efforts to make them better. The potential capital involved in this market is massive, and the companies are drafting some of the smartest people in the world in effort to take a share in the market. If anything is magical, it is human ingenuity and motivation.

What do I do for work?

One of the problems I have when trying to make others understand what I do for work is that my field of study is not in one of the classical categories. If I were to be a mathematician, a biologist, a medical doctor, or a philosopher, I could just say that and be understood since these categories carry enough context and cultural stereotypes. Unfortunately, I work in the field of computational biology, which is fairly new and unfamiliar to most - we don't have a subject in school for it yet, and people's eyes tend to turn somewhat glassy when I mention my profession.

The easiest way to describe what a computational scientist does is to divide it up into method and application. The application of my research pertains to biology, but the methods I use are mathematical. I do not wear a lab coat or work with pipettes. My research is done purely in my head, on paper, on the computer, or on a blackboard. Eventually, the ideas and predictions I make are tested in the lab, but luckily I can leave the dirty work to my colleagues. To be a computational biologist you have to be native in two separate languages, that of mathematics and logic on one hand, but also that of biology and chemistry on the other. However, it turns out that there is a deep connection between nature and mathematics, and many mathematical methods have been inspired by biology, and likewise, many biological problems have analogies in mathematics.

While I wrote that computational biology is a newcomer to the sciences, that is not exactly true. Scientists have mixed mathematics and biology for centuries; particular early examples include mathematical modeling of population and disease dynamics by Bernoulli and Malthus (17-18th century). Additionally, many classical biologists who predated the advent of computers would, in my mind, have been computational biologists if computers were available. One such case, I believe, is Charles Darwin, who was a theoretician more than anything and who likely would have been a computational scientist, had he lived today. Additionally, there were several cases of mathematicians in the early 20th century becoming interested in biology towards the end of their lives. I'd like to think that this is the mathematicians equivalent of becoming religious when the end is neigh.

Being a computational biologist makes you think in a certain way. You're constantly translating between the natural and the abstract, and eventually the two sort of melt together. The result of which is a single framework that fits both biology and mathematics, and does so very neatly. I'm certain that the surge the field is seeing today will lead to many great discoveries, partly due to simply merging the abstract and the natural. In the past, medical and biological discoveries were largely made through serendipity and luck. You'd look through the microscope and happen to find something, as Leeuwenhoek did when finding bacteria - or you place a petri dish next to your bedside table and accidentally discover penicillin, like Alexander Fleming did. Computational biology is emerging as a way to create large mathematical frameworks encompassing the complex biological world, which is our most promising way of systematically discover new drugs, moving away from serendipity and understanding biology as a whole.

So my work lies in this soup of mathematics and biology, but additionally, my PhD has two separate tracks. One track deals with a specific biological problem whereas the other generally deals with new cool advances in mathematics that tickle my brain in the right way. But first, let's get into my first track.

Our body consists of a host of molecules. Arguably the most important category of molecules is called proteins. Proteins are the functional molecules of biology - they are what make cells do things as opposed to being static. They are the building blocks of life. If our genes is a recipe book, the proteins are the cake. Naturally, lots of research focuses on trying to map the function of proteins, as doing so would help us understand and manipulate biology, to, for example, treat diseases. However, my work does not deal with proteins directly - instead it deals with the product that is generated when proteins break down. Our body is constantly breaking down millions of protein molecules as part of a normal recycling process. In fact, we have special biological scissor-proteins that are specialized in breaking down other proteins. Whenever something happens in our body, if we get ill or if we get hurt for example, our body needs to exchange what proteins we have in our cells in order to deal with the disease or damage. These scissor proteins break down the old to give space for the new. Mobilize the forces, so to speak.

Imagine that a protein is a piece of paper, and breaking it down means tearing the paper apart. Eventually you'll have tiny bits of paper, and the protein equivalents of these small pieces are called "peptides" (recognize the suffix "id" as indicating that it is small). The presence of these tiny peptides have been ignored throughout history, and they were simply considered to be byproducts generated randomly during protein breakdown; however, it turns out that some of these degradation products have functions in themselves! This is a fascinating finding which has great implications not only for humans, but for all biological life. It tells us something fundamental about biology: that function exists not only at the protein level, but that more function emerges when the protein breaks down. Similarly to those pants that have sippers at the knees so that the bottom half can be removed to make shorts, proteins have functions when split in twain.

Particularly interesting examples of these peptides are those that play a part in our immune system. It turns out that some of these peptides kill bacteria and modulate inflammatory processes in the body. Not only that, bacteria themselves utilize peptides to fight each other and us, their host. This new world of peptides is extremely tricky to study, but computational methods can help us uncover their secret world. This research is a direct descendant of my dad's work, which focused on finding precisely these antimicrobial peptides. My research, focuses on generalizing this idea to all life on earth, and beyond just the immune system. I hypothesize that these peptides exist throughout the tree of life, and that they hold many functions for all sorts of biological processes. This ambitious program, which seeks to uncover a (potentially) fundamental aspect of biology, is maybe what I will spend my life working on.

The second track of my PhD pertains simply to creating mathematical and computational methods I find interesting. I do have a passion for mathematics, and these project challenge me, forcing me to continuously learn about new mathematical concepts. Often, this involves developing methods in the field of machine learning and artificial intelligence that apply to biology. Instead of training these systems to understand natural language, I focus on the language of biology. For example, instead of making these models generate images I train models that have specific architectures that allow them to generate molecules. Another example of recent work involves creating a model that can predict how patients with sepsis will progress in their disease. As I explained above, the core underlying mechanism of artificial intelligence is based on creating models that learn through examples. Once they are trained, biological artificial intelligence models can predict things - like which genes or proteins are associated with disease.

Do I think AI is conscious?

To be written

Do I think AI will make humans extinct?

To be written