An Intuitive Explanation of Solomonoff Induction

This is the completed article that Luke wrote the first half of. My thanks go to the following for reading, editing, and commenting; Luke Muehlhauser, Louie Helm, Benjamin Noble, and Francelle Wax.

People disagree about things. Some say that television makes you dumber; other say it makes you smarter. Some scientists believe life must exist elsewhere in the universe; others believe it must not. Some say that complicated financial derivatives are essential to a modern competitive economy; others think a nation’s economy will do better without them. It’s hard to know what is true.

And it’s hard to know how to figure out what is true. Some argue that you should assume the things you are most certain about and then deduce all other beliefs from your original beliefs. Others think you should accept at face value the most intuitive explanations of personal experience. Still others think you should generally agree with the scientific consensus until it is disproved.

Wouldn’t it be nice if determining what is true was like baking a cake? What if there was a recipe for finding out what is true? All you’d have to do is follow the written directions exactly, and after the last instruction you’d inevitably find yourself with some sweet, tasty truth!

In this tutorial, we’ll explain the closest thing we’ve found so far to a recipe for finding truth: Solomonoff induction.

There are some qualifications to make. To describe just one: roughly speaking, you don’t have time to follow the recipe. To find the truth to even a simple question using this recipe would require you to follow one step after another until long after the heat death of the universe, and you can’t do that.

But we can find shortcuts. Suppose you know that the exact recipe for baking a cake asks you to count out one molecule of H2O at a time until you have exactly 0.5 cups of water. If you did that, you might not finish the cake before the heat death of the universe. But you could approximate that part of the recipe by measuring out something very close to 0.5 cups of water, and you’d probably still end up with a pretty good cake.

Similarly, once we know the exact recipe for finding truth, we can try to approximate it in a way that allows us to finish all the steps sometime before the sun burns out.

This tutorial explains that best-we’ve-got-so-far recipe for finding truth, Solomonoff induction. Don’t worry, we won’t be using any equations, just qualitative descriptions.

Like Eliezer Yudkowsky’s Intuitive Explanation of Bayes’ Theorem and Luke Muehlhauser’s Crash Course in the Neuroscience of Human Motivation, this tutorial is long. You may not have time to read it; that’s fine. But if you do read it, we recommend that you read it in sections.



1. Algorithms — We’re looking for an algorithm to determine truth.

2. Induction — By “determine truth”, we mean induction.

3. Occam’s Razor — How we judge between many inductive hypotheses.

4. Probability — Probability is what we usually use in induction.

5. The Problem of Priors — Probabilities change with evidence, but where do they start?

The Solution

6. Binary Sequences — Everything can be encoded as binary.

7. All Algorithms — Hypotheses are algorithms. Turing machines describe these.

8. Solomonoff’s Lightsaber — Putting it all together.

9. Formalized Science — From intuition to precision.

10. Approximations — Ongoing work towards practicality.

11. Unresolved Details — Problems, philosophical and mathematical.


At an early age you learned a set of precisely-defined steps — a ‘recipe’ or, more formally, an algorithm — that you could use to find the largest number in a list of numbers like this:

21, 18, 4, 19, 55, 12, 30

The algorithm you learned probably looked something like this:

  1. Look at the first item. Note that it is the largest you’ve seen on this list so far. If this is the only item on the list, output it as the largest number on the list. Otherwise, proceed to step 2.

  2. Look at the next item. If it is larger than the largest item noted so far, note it as the largest you’ve seen in this list so far. Proceed to step 3.

  3. If you have not reached the end of the list, return to step 2. Otherwise, output the last noted item as the largest number in the list.

Other algorithms could be used to solve the same problem. For example, you could work your way from right to left instead of from left to right. But the point is that if you follow this algorithm exactly, and you have enough time to complete the task, you can’t fail to solve the problem. You can’t get confused about what one of the steps means or what the next step is. Every instruction tells you exactly what to do next, all the way through to the answer.

You probably learned other algorithms, too, like how to find the greatest common divisor of any two integers (see image on right).

But not just any set of instructions is a precisely-defined algorithm. Sometimes, instructions are unclear or incomplete. Consider the following instructions based on an article about the scientific method:

  1. Make an observation.

  2. Form a hypothesis that explains the observation.

  3. Conduct an experiment that will test the hypothesis.

  4. If the experimental results disconfirm the hypothesis, return to step #2 and form a hypothesis not yet used. If the experimental results confirm the hypothesis, provisionally accept the hypothesis.

This is not an algorithm.

First, many of the terms are not clearly defined. What counts as an observation? What counts as a hypothesis? What would a hypothesis need to be like in order to ‘explain’ the observation? What counts as an experiment that will ‘test’ the hypothesis? What does it mean for experimental results to ‘confirm’ or ‘disconfirm’ a hypothesis?

Second, the instructions may be incomplete. What do we do if we reach step 4 and the experimental results neither ‘confirm’ nor ‘disconfirm’ the hypothesis under consideration, but instead are in some sense ‘neutral’ toward the hypothesis? These instructions don’t tell us what to do in that case.

An algorithm is a well-defined procedure that takes some value or values as input and, after a finite series of steps, generates some value or values as output.

For example, the ‘find the largest number’ algorithm above could take the input {21, 18, 4, 19, 55, 12, 30} and would, after 13 steps, produce the following output: {55}. Or it could take the input {34} and, after 1 step, produce the output: {34}.

An algorithm is so well written, that we can construct machines that follow them. Today, the machines that follow algorithms are mostly computers. This is why all computer science students take a class in algorithms. If we construct our algorithm for truth, then we can make a computer program that finds truth—an Artificial Intelligence.


Let’s clarify what we mean. In movies, scientists will reveal “truth machines”. Input a statement, and the truth machine will tell you whether it is true or false. This is not what Solomonoff induction does. Instead, Solomonoff induction is our ultimate “induction machine”.

Whether we are a detective trying to catch a thief, a scientist trying to discover a new physical law, or a businessman attempting to understand a recent change in demand, we are all in the process of collecting information and trying to infer the underlying causes.

-Shane Legg

The problem of induction is this: We have a set of observations (or data), and we want to find the underlying causes of those observations. That is, we want to find hypotheses that explain our data. We’d like to know which hypothesis is correct, so we can use that knowledge to predict future events. Our algorithm for truth will not listen to questions and answer yes or no. Our algorithm will take in data (observations) and output the rule by which the data was created. That is, it will give us the explanation of the observations; the causes.

Suppose your data concern a large set of stock market changes and other events in the world. You’d like to know the processes responsible for the stock market price changes, because then you can predict what the stock market will do in the future, and make some money.

Or, suppose you are a parent. You come home from work to find a chair propped against the refrigerator, with the cookie jar atop the fridge a bit emptier than before. You like cookies, and you don’t want them to disappear, so you start thinking. One hypothesis that leaps to mind is that your young daughter used the chair to reach the cookies. However, many other hypotheses explain the data. Perhaps a very short thief broke into your home and stole some cookies. Perhaps your daughter put the chair in front of the fridge because the fridge door is broken and no longer stays shut, and you forgot that your friend ate a few cookies when he visited last night. Perhaps you moved the chair and ate the cookies yourself while sleepwalking the night before.

All these hypotheses are possible, but intuitively it seems like some hypotheses are more likely than others. If you’ve seen your daughter access the cookies this way before but have never been burgled, then the ‘daughter hypothesis’ seems more plausible. If some expensive things from your bedroom and living room are missing and there is hateful graffiti on your door at the eye level of a very short person, then the ‘short thief’ hypothesis becomes more plausible than before. If you suddenly remember that your friend ate a few cookies and broke the fridge door last night, the ‘broken fridge door’ hypothesis gains credibility. If you’ve never been burgled and your daughter is out of town and you have a habit of moving and eating things while sleepwalking, the ‘sleepwalking’ hypothesis becomes less bizarre.

So the weight you give to each hypothesis depends greatly on your prior knowledge. But what if you had just been hit on the head and lost all past memories, and for some reason the most urgent thing you wanted to do was to solve the mystery of the chair and cookies? Then how would you weigh the likelihood of the available hypotheses?

When you have very little data but want to compare hypotheses anyway, Occam’s Razor comes to the rescue.

Occam’s Razor

Consider a different inductive problem. A computer program outputs the following sequence of numbers:

1, 3, 5, 7

Which number comes next? If you guess correctly, you’ll win $500.

In order to predict the next number in the sequence, you make a hypothesis about the process the computer is using to generate these numbers. One obvious hypothesis is that it is simply listing all the odd numbers in ascending order from 1. If that’s true, you should guess that “9” will be the next number.

But perhaps the computer is using a different algorithm to generate the numbers. Suppose that n is the step in the sequence, so that n=1 when it generated ‘1’, n=2 when it generated ‘3’, and so on. Maybe the computer used this equation to calculate each number in the sequence:

2n − 1 + (n − 1)(n − 2)(n − 3)(n − 4)

If so, the next number in the sequence will be 33. (Go ahead, check the calculations.)

But doesn’t the first hypothesis seem more likely?

The principle behind this intuition, which goes back to William of Occam, could be stated:

Among all hypotheses consistent with the observations, the simplest is the most likely.

The principle is called Occam’s razor because it ‘shaves away’ unnecessary assumptions.

For example, think about the case of the missing cookies again. In most cases, the ‘daughter’ hypothesis seems to make fewer unnecessary assumptions than the ‘short thief’ hypothesis does. You already know you have a daughter that likes cookies and knows how to move chairs to reach cookies. But in order for the short thief hypothesis to be plausible, you have to assume that (1) a thief found a way to break in, that (2) the thief wanted inexpensive cookies from your home, that (3) the thief was, unusually, too short to reach the top of the fridge without the help of a chair, and (4) many other unnecessary assumptions.

Occam’s razor sounds right, but can it be made more precise, and can it be justified? How do we find all consistent hypotheses, and how do we judge their simplicity? We will return to those questions later. Before then, we’ll describe the area of mathematics that usually deals with reasoning: probability.


You’re a soldier in combat, crouching in a trench. You know for sure there is just one enemy soldier left on the battlefield, about 400 yards away. You also know that if the remaining enemy is a regular army troop, there’s only a small chance he could hit you with one shot from that distance. But if the remaining enemy is a sniper, then there’s a very good chance he can hit you with one shot from that distance. But snipers are rare, so it’s probably just a regular army troop.

You peek your head out of the trench, trying to get a better look.

Bam! A bullet glances off your helmet and you duck down again.

“Okay,” you think. “I know snipers are rare, but that guy just hit me with a bullet from 400 yards away. I suppose it might still be a regular army troop, but there’s a seriously good chance it’s a sniper, since he hit me from that far away.”

After another minute, you dare to take another look, and peek your head out of the trench again.

Bam! Another bullet glances off your helmet! You duck down again.

“Whoa,” you think. “It’s definitely a sniper. No matter how rare snipers are, there’s no way that guy just hit me twice in a row from that distance if he’s a regular army troop. He’s gotta be a sniper. I’d better call for support.”

This is an example of reasoning under uncertainty, of updating uncertain beliefs in response to evidence. We do it all the time.

You start with some prior beliefs, and all of them are uncertain. You are 99.99% certain the Earth revolves around the sun, 90% confident your best friend will attend your birthday party, and 40% sure that the song you’re listening to on the radio was played by The Turtles.

Then, you encounter new evidence—new observations—and you update your beliefs in response.

Suppose you start out 85% confident that the one remaining enemy soldier is not a sniper. That leaves only 15% credence to the hypothesis that he is a sniper. But then, a bullet glances off your helmet — an event far more likely if the enemy soldier is a sniper than if he is not. So now you’re only 40% confident he’s a non-sniper, and 60% confident he is a sniper. Another bullet glances off your helmet, and you update again. Now you’re only 2% confident he’s a non-sniper, and 98% confident he is a sniper.

Probability theory is the mathematics of reasoning with uncertainty. The keystone of this subject is called Bayes’ Theorem. It tells you how likely something is given some other knowledge. Understanding this simple theorem is more useful and important for most people than Solomonoff induction. If you haven’t learned it already, you may want to read either tutorial #1, tutorial #2, tutorial #3, or tutorial #4 on Bayes’ Theorem. The exact math of Bayes’ Theorem is not required for this tutorial. We’ll just describe its results qualitatively.

Bayes’ Theorem can tell us how likely a hypothesis is, given evidence (or data, or observations). This is helpful because we want to know which model of the world is correct so that we can successfully predict the future. It calculates this probability based on the prior probability of the hypothesis alone, the probability of the evidence alone, and the probability of the evidence given the hypothesis. Now we just plug the numbers in.

Of course, it’s not easy to “just plug the numbers in.” You aren’t an all-knowing god. You don’t know exactly how likely it is that the enemy soldier would hit your helmet if he’s a sniper, compared to how likely that is if he’s not a sniper. But you can do your best. With enough evidence, it will become overwhelmingly clear which hypothesis is correct.

But guesses are not well-suited to an exact algorithm, and so our quest to find an algorithm for truth-finding must continue. For now, we turn to the problem of choosing priors.

The Problem of Priors

In the example above where you’re a soldier in combat, I gave you your starting probabilities: 85% confidence that the enemy soldier was a sniper, and 15% confidence he was not. But what if you don’t know your “priors”? What then?

Most situations in real life are complex, so that your “priors” (as used in Bayes’ Theorem) are actually probabilities that have been updated several times with past evidence. You had an idea that snipers were rare because you saw many soldiers, but only a few of them were snipers. Or you read a reliable report saying that snipers were rare. But what would our ideal reasoning computer do before it knew anything? What would the probabilities be set to before we turned it on? How can we determine the probability of a hypothesis before seeing any data?

The general answer is Occam’s razor; simpler hypotheses are more likely. But this isn’t rigorous. It’s usually difficult to find a measure of complexity, even for mathematical hypotheses. Is a normal curve simpler than an exponential curve? Bayesian probability theory doesn’t have anything to say about choosing priors. Thus, many standard “prior distributions” have been developed. Generally, they distribute probability equally across hypotheses. Of course this is a good approach if all the hypotheses are equally likely. But as we saw above, it seems that some hypotheses are more complex than others, and this makes them less likely than the other hypotheses. So when distributing your probability across several hypotheses, you shouldn’t necessarily distribute it evenly. There’s also a growing body of work around an idea called the Maximum Entropy Principle. This principle helps you choose a prior that makes the least assumptions given the constraints of the problem. But this principle can’t be used to handle all possible types of hypotheses, only ones for which “entropy” can be mathematically evaluated.

We need a method that everyone can agree provides the correct priors in all situations. This helps us perform induction correctly. It also helps everyone be more honest. Since priors partly determine what people believe, they can sometimes choose priors that help “prove” what they want to prove. This can happen intentionally or unintentionally. It can also happen in formal situations, such as an academic paper defending a proposed program.

To solve the problem of priors once and for all, we’d like to have an acceptable, universal prior distribution, so that there’s no vagueness in the process of induction. We need a recipe, an algorithm, for selecting our priors. For that, we turn to the subject of binary sequences.

Binary Sequences

At this point, we have collected a lot of background material. We know about algorithms, and we know we need an algorithm that does induction. We know that induction also uses Occam’s razor and probability. We know that one of the problems in probability is selecting priors. Now we’re ready to formalize it.

To start, we need a language in which we can express all problems, all data, all hypotheses. Binary is the name for representing information using only the characters ‘0’ and ‘1’. In a sense, binary is the simplest possible alphabet. A two-character alphabet is the smallest alphabet that can communicate a difference. If we had an alphabet of just one character, our “sentences” would be uniform. With two, we can begin to encode information. Each 0 or 1 in a binary sequence (e. g. 01001011) can be considered the answer to a yes-or-no question.

In the above example about sorting numbers, it’s easy to convert it to binary just by writing a computer program to follow the algorithm. All programming languages are based on binary. This also applies to anything you’ve ever experienced using a computer. From the greatest movie you’ve ever seen to emotional instant messaging conversations, all of it was encoded in binary.

In principle, all information can be represented in binary sequences. If this seems like an extreme claim, consider the following...

All your experiences, whether from your eyes, ears, other senses or even memories and muscle movements, occur between neurons (your nerve cells and brain cells). And it was discovered that neurons communicate using a digital signal called the action potential. Because it is digital, a neuron either sends the signal, or it doesn’t. There is no half-sending the action potential. This can be translated directly into binary. An action potential is a 1, no action potential is a 0. All your sensations, thoughts, and actions can be encoded as a binary sequence over time. A really long sequence.

Or, if neuron communication turns out to be more complicated than that, we can look to a deeper level. All events in the universe follow the laws of physics. We’re not quite done discovering the true laws of physics; there are some inconsistencies and unexplained phenomena. But the currently proposed laws are incredibly accurate. And they can be represented as a single binary sequence.

You might be thinking, “But I see and do multiple things simultaneously, and in the universe there are trillions of stars all burning at the same time. How can parallel events turn into a single sequence of binary?”

This is a perfectly reasonable question. It turns out that, at least formally, this poses no problem at all. The machinery we will use to deal with binary sequences can turn multiple sequences into one just by dovetailing them together and adjusting how it processes them so the results are the same. Because it is easiest to deal with a single sequence, we do this in the formal recipe. Any good implementation of Solomonoff induction will use multiple sequences just to be faster.

A picture of your daughter can be represented as a sequence of ones and zeros. But a picture is not your daughter. A video of all your daughter’s actions can also be represented as a sequence of ones and zeros. But a video isn’t your daughter, either; we can’t necessarily tell if she’s thinking about cookies, or poetry. The position of all the subatomic particles that make up your daughter as she lives her entire life can be represented as a sequence of binary. And that really is your daughter.

Having a common and simple language can sometimes be the key to progress. The ancient Greek mathematician Archimedes discovered many specific results of calculus, but could not generalize the methods because he did not have the language of calculus. After this language was developed in the late 1600s, hundreds of mathematicians were able to produce new results in the field. Now, calculus forms an important base of our modern civilization.

Being able to do everything in the language of binary sequences simplifies things greatly, and gives us great power. Now we don’t have to deal with complex concepts like “daughter” and “soldier.” It’s all still there in the data, only as a large sequence of 0s and 1s. We can treat it all the same.

All Algorithms

Now that we have a simple way to deal with all types of data, let’s look at hypotheses. Recall that we’re looking for a way to assign prior probabilities to hypotheses. (And then, when we encounter new data, we’ll use Bayes’ Theorem to update the probabilities we assign to those hypotheses). To be complete, and guarantee we find the real explanation for our data, we have to consider all possible hypotheses. But how could we ever find all possible explanations for our data? We could sit in a room for days, making a list of all the ways the cookies could be missing, and still not think of the possibility that our wife took some to work.

It turns out that mathematical abstraction can save the day. By using a well-tested model, and the language of binary, we can find all hypotheses.

This piece of the puzzle was discovered in 1936 by a man named Alan Turing. He created a simple, formal model of computers called “Turing machines” before anyone had ever built a computer.

In Turing’s model, each machine’s language is—you guessed it—binary. There is one binary sequence for the input, a second binary sequence that constantly gets worked on and re-written, and a third binary sequence for output. (This description is called a three-tape Turing machine, and is easiest to think about for Solomonoff induction. The normal description of Turing machines includes only one tape, but it turns out that they are equivalent.)

The rules that determine how the machine reacts to and changes the bits on these tapes are very simple. An example is shown on the diagram above. Basically, every Turing machine has a finite number of “states”, each of which is a little rule. These rules seem bland and boring at first, but in a few paragraphs, you’ll find out why they’re so exciting. First, the machine will start out in a certain state, with some binary on the input tape, all zeros on the work tape, and all zeros on the output tape. The rules for that first state will tell it to look at the input tape and the work tape. Depending on what binary number is on those two tapes, the rules will say to perform certain actions. It will say to;

  1. feed the input tape (or not)

  2. write 0 or 1 to the work tape

  3. move the work tape left or right

  4. write 0 or 1 to the output tape

  5. feed the output tape (or not).

After that, the rules will say which state to move to next, and the process will repeat. Remember that the rules for these states are fixed; they could be written out on pieces of paper. All that changes are the tapes, and what rule the machine is currently following. The basic mathematics behind this is fairly simple to understand, and can be found in books on computational theory.

This model is incredibly powerful. Given the right rules, Turing machines can

  • calculate the square of a number,

  • run a spreadsheet program,

  • compress large files,

  • estimate the probability of rain tomorrow,

  • control the flight of an airplane,

  • play chess better than a human,

  • and much, much more.

You may have noticed that this sounds like a list of what regular computers do. And you would be correct; the model of Turing machines came before, and served as an invaluable guide to, the invention of electronic computers. Everything they do is within the model of Turing machines.

Even more exciting is the fact that all attempts to formalize the intuitive idea of “algorithm” or “process” have been proven to be at most equally as powerful as Turing machines. If a system has this property, it is called Turing complete. For example, math equations using algebra have a huge range of algorithms they can express. Multiplying is an algorithm, finding the hypotenuse of a right triangle is an algorithm, and the quadratic formula is an algorithm. Turing machines can run all these algorithms, and more. That is, Turing machines can be used to calculate out all algebraic algorithms, but there are some algorithms Turing machines can run that can’t be represented by algebra. This means algebra is not Turing complete. For another example, mathematicians often invent “games” where sequences of symbols can be rewritten using certain rules. (They will then try to prove things about what sequences these rules can create.) But no matter how creative their rules, every one of them can be simulated on a Turing machine. That is, for every set of re-writing rules, there is a binary sequence you can give a Turing machine so that the machine will rewrite the sequences in the same way.

Remember how limited the states of a Turing machine are; every machine has only a finite number of states with “if” rules like in the figure above. But somehow, using these and the tape as memory, they can simulate every set of rules, every algorithm ever thought up. Even the distinctively different theory of quantum computers is at most Turing complete. In the 80 years since Turing’s paper, no superior systems have been found. The idea that Turing machines truly capture the idea of “algorithm” is called the Church-Turing thesis.

So the model of Turing machines covers regular computers, but that is not all. As mentioned above, the current laws of physics can be represented as a binary sequence. That is, the laws of physics are an algorithm that can be fed into a Turing machine to compute the past, present and future of the universe. This includes stars burning, the climate of the earth, the action of cells, and even the actions and thoughts of humans. Most of the power here is in the laws of physics themselves. What Turing discovered is that these can be computed by a mathematically simple machine.

As if Turing’s model wasn’t amazing enough, he went on to prove that one specific set of these rules could simulate all other sets of rules.

The computer with this special rule set is called a universal Turing machine. We simulate another chosen machine by giving the universal machine a compiler binary sequence. A compiler is a short program that translates between computer languages or, in this case, between machines. Sometimes a compiler doesn’t exist. For example, you couldn’t translate Super Mario Brothers onto a computer that only plays tic-tac-toe. But there will always be a compiler to translate onto a universal Turing machine. We place this compiler in front of the input which would have been given to the chosen machine. From one perspective, we are just giving the universal machine a single, longer sequence. But from another perspective, the universal machine is using the compiler to set itself up to simulate the chosen machine. While the universal machine (using its own, fixed rules) is processing the compiler, it will write various things on the work tape. By the time it has passed the compiler and gets to the original input sequence, the work tape will have something written on it to help simulate the chosen machine. While processing the input, it will still follow its own, fixed rules, only the binary on the work tape will guide it down a different “path” through those rules than if we had only given it the original input.

For example, say that we want to calculate the square of 42 (or in binary, 101010). Assume we know the rule set for the Turing machine which squares numbers when given the number in binary. Given all the specifics, there is an algorithmic way to find the “compiler” sequence based on these rules. Let’s say that the compiler is 1011000. Then, in order to compute the square of 42 on the universal Turing machine, we simply give it the input 1011000101010, which is just the compiler 1011000 next to the number 42 as 101010. If we want to calculate the square of 43, we just change the second part to 101011 (which is 101010 + 1). The compiler sequence doesn’t change, because it is a property of the machine we want to simulate, e. g. the squaring machine, but not of the input to that simulated machine, e. g. 42.

In summary: algorithms are represented by Turing machines, and Turing machines are represented by inputs to the universal Turing machine. Therefore, algorithms are represented by inputs to the universal Turing machine.

In Solomonoff induction, the assumption we make about our data is that it was generated by some algorithm. That is, the hypothesis that explains the data is an algorithm. Therefore, a universal Turing machine can output the data, as long as you give the machine the correct hypothesis as input. Therefore, the set of all possible inputs to our universal Turing machine is the set of all possible hypotheses. This includes the hypothesis that the data is a list of the odd numbers, the hypothesis that the enemy soldier is a sniper, and the hypothesis that your daughter ate the cookies. This is the power of formalization and mathematics.

Solomonoff’s Lightsaber

Now we can find all the hypotheses that would predict the data we have observed. This is much more powerful than the informal statement of Occam’s razor. Because of its precision and completeness, this process has been jokingly dubbed “Solomonoff’s Lightsaber”. Given our data, we find potential hypotheses to explain it by running every hypothesis, one at a time, through the universal Turing machine. If the output matches our data, we keep it. Otherwise, we throw it away.

By the way, this is where Solomonoff induction becomes incomputable. It would take an infinite amount of time to check every algorithm. And even more problematic, some of the algorithms will run forever without producing output—and we can’t prove they will never stop running. This is known as the halting problem, and it is a deep fact of the theory of computation. It’s the sheer number of algorithms, and these pesky non-halting algorithms that stop us from actually running Solomonoff induction.

The actual process above might seem a little underwhelming. We just check every single hypothesis? Really? Isn’t that a little mindless and inefficient? This will certainly not be how the first true AI operates. But don’t forget that before this, nobody had any idea how to do ideal induction, even in principle. Developing fundamental theories, like quantum mechanics, might seem abstract and wasteful. But history has proven that it doesn’t take long before such theories and models change the world, as quantum mechanics did with modern electronics. In the future, men and women will develop ways to approximate Solomonoff induction in a second. Perhaps they will develop methods to eliminate large numbers of hypotheses all at once. Maybe hypotheses will be broken into distinct classes. Or maybe they’ll use methods to statistically converge toward the right hypotheses.

So now, at least in theory, we have the whole list of hypotheses that might be the true cause behind our observations. These hypotheses, since they are algorithms, look like binary sequences. For example, the first few might be 01001101, 0011010110000110100100110, and 1000111110111111000111010010100001. That is, for each of these three, when you give them to the universal Turing machine as input, the output is our data. Which of these three do you think is more likely to be the true hypothesis that generated our data in the first place?

We have a list, but we’re trying to come up with a probability, not just a list of possible explanations. So how do we decide what the probability is of each of these hypotheses? Imagine that the true algorithm is produced in a most unbiased way: by flipping a coin. For each bit of the hypothesis, we flip a coin. Heads will be 0, and tails will be 1. In the example above, 01001101, the coin landed heads, tails, heads, heads, tails, and so on. Because each flip of the coin has a 50% probability, each bit contributes ½ to the final probability.

Therefore an algorithm that is one bit longer is half as likely to be the true algorithm. Notice that this intuitively fits Occam’s razor; a hypothesis that is 8 bits long is much more likely than a hypothesis that is 34 bits long. Why bother with extra bits? We’d need evidence to show that they were necessary.

So, why not just take the shortest hypothesis, and call that the truth? Because all of the hypotheses predict the data we have so far, and in the future we might get data to rule out the shortest one. We keep all consistent hypotheses, but weigh the shorter ones with higher probability. So in our eight-bit example, the probability of 01001101 being the true algorithm is ½^8, or 1256. It’s important to say that this isn’t an absolute probability in the normal sense. It hasn’t been normalized—that is, the probabilities haven’t been adjusted so that they add up to 1. This is computationally much more difficult, and might not be necessary in the final implementation of Solomonoff induction. These probabilities can still be used to compare how likely different hypotheses are.

To find the probability of the evidence alone, all we have to do is add up the probability of all these hypotheses consistent with the evidence. Since any of these hypotheses could be the true one that generates the data, and they’re mutually exclusive, adding them together doesn’t double count any probability.

Formalized Science

Let’s go back to the process above that describes the scientific method. We’ll see that Solomonoff induction is this process made into an algorithm.

1. Make an observation.

Our observation is our binary sequence of data. Only binary sequences are data, and all binary sequences can qualify as data.

2. Form a hypothesis that explains the observation.

We use a universal Turing machine to find all possible hypotheses, no fuzziness included. The hypothesis “explains” the observation if the output of the machine matches the data exactly.

3. Conduct an experiment that will test the hypothesis.

The only “experiment” is to observe the data sequence for longer, and run the universal machine for longer. The hypotheses whose output continues to match the data are the ones that pass the test.

4. If the experimental results disconfirm the hypothesis, return to step #2 and form a hypothesis not yet used. If the experimental results confirm the hypothesis, provisionally accept the hypothesis.

This step tells us to repeat the “experiment” with each binary sequence in our matching collection. However, instead of “provisionally” accepting the hypothesis, we accept all matching hypotheses with a probability weight according to its length.

Now we’ve found the truth, as best as it can be found. We’ve excluded no possibilities. There’s no place for a scientist to be biased, and we don’t need to depend on our creativity to come up with the right hypothesis or experiment. We know how to measure how complex a hypothesis is. Our only problem left is to efficiently run it.


As mentioned before, we actually can’t run all the hypotheses to see which ones match. There are infinitely many, and some of them will never halt. So, just like our cake recipe, we need some very helpful approximations that still deliver very close to true outputs. Technically, all prediction methods are approximations of Solomonoff induction, because Solomonoff induction tries all possibilities. But most methods use a very small set of hypotheses, and many don’t use good methods for estimating their probabilities. How can we more directly approximate our recipe? At present, there aren’t any outstandingly fast and accurate approximations to Solomonoff induction. If there were, we would be well on our way to true AI. Below are some ideas that have been published.

In Universal Artificial Intelligence, Marcus Hutter provides a full formal approximation of Solomonoff induction which he calls AIXI-tl. This model is optimal in a technical and subtle way. Also it can always return an answer in a finite amount of time, but this time is usually extremely long and doubles with every bit of data. It would still take longer than the life of the universe before we got an answer for most questions. How can we do even better?

One general method would be to use a Turing machine that you know will always halt. Because of the halting problem, this means that you won’t be testing some halting algorithms that might be the correct hypotheses. But you could still conceivably find a large set of hypotheses that all halted.

Another popular method of approximating any intractable algorithm is to use randomness. This is called a Monte Carlo method. We can’t test all hypotheses, so we have to select a subset. But we don’t want our selection process to bias the result. Therefore we could randomly generate a bunch of hypotheses to test. We could use an evolutionary algorithm, where we test this seed set of hypotheses, and keep the ones that generate data closest to ours. Then we would vary these hypotheses, run them again through the Turing machine, keep the closest fits, and continue this process until the hypotheses actually predicted our data exactly.

The mathematician Jürgen Schmidhuber proposes a different probability weighing which also gives high probability to hypotheses that can be quickly computed. He demonstrates how this is “near-optimal”. This is vastly faster, but risks making another assumption, that faster algorithms are inherently more likely.

Unresolved Details

Solomonoff induction is an active area of research in modern mathematics. While it is universal in a broad and impressive sense, some choices can still be made in the mathematical definition. Many people also have philosophical concerns or objections to the claim that Solomonoff induction is ideal and universal.

The first question many mathematicians ask is, “Which universal Turing machine?” I have written this tutorial as if there is only one, but there are in fact infinitely many sets of rules that can simulate all other sets of rules. Just as the length of a program will depend on which programming language you write it in, the length of the hypothesis as a binary sequence will depend on which universal Turing machine you use. This means the probabilities will be different. The change, however, is very limited. There are theorems that well-define this effect and it is generally agreed not to be a concern. Specifically, going between two universal machines cannot increase the hypothesis length any more than the length of the compiler from one machine to the other. This length is fixed, independent of the hypothesis, so the more data you use, the less this difference matters.

Another concern is that the true hypothesis may be incomputable. There are known definitions of binary sequences which make sense, but which no Turing machine can output. Solomonoff induction would converge to this sequence, but would never predict it exactly. It is also generally agreed that nothing could ever predict this sequence, because all known predictors are equivalent to Turing machines. If this is a problem, it is similar to the problem of a finite universe; there is nothing that can be done about it.

Lastly, many people, mathematicians included, reject the ideas behind the model, such as that the universe can be represented as a binary sequence. This often delves into complex philosophical arguments, and often revolves around consciousness.

Many more details can be found the more one studies. Many mathematicians work on modified versions and extensions where the computer learns how to act as well as predict. However these open areas are resolved, Solomonoff induction has provided an invaluable model and perspective for research into solving the problem of how to find truth. (Also see Open Problems Related to Solomonoff Induction.)

Fundamental theories have an effect of unifying previously separate ideas. After Turing discovered the basics of computability theory, it was only a matter of time before these ideas made their way across philosophy and mathematics. Statisticians could only find exact probabilities in simplified situations; now they know how to find them in all situations. Scientists wondered how to really know which hypothesis was simpler; now they can find a number. Philosophers wondered how induction could be justified. Solomonoff induction gives them an answer.

So, what’s next? To build our AI truth-machine, or even to find the precise truth to a single real-world problem, we need considerable work on approximating our recipe. Obviously a scientist cannot presently download a program to tell him the complexity of his hypothesis regarding deep-sea life behavior. But now that we know how to find truth in principle, mathematicians and computer scientists will work on finding it in practice.

(How does this fit with Bayes’ theorem? A followup.)