Instead of perfect information, there is a host of unknown possibilities, ranging from missing information to deliberate deception.

Take a self-driving car for example — you can set the goal to get from A to B in an efficient and safe manner that follows all laws. But what happens if the traffic gets worse than expected, maybe because of an accident ahead? Sudden bad weather? Random events like a ball bouncing in the street, or a piece of trash flying straight into the car’s camera?

A self-driving car needs to use a variety of sensors, including sonar-like ones and cameras, to detect where it is and what is around it. These sensors are never perfect as the data from the sensors always includes some errors and inaccuracies called “noise”. It is very common then that one sensor indicates that the road ahead turns left, but another sensor indicates the opposite direction. This needs to be resolved without always stopping the car in case of even a slightest amount of noise.

One of the reasons why modern AI methods actually work in real-world problems - as opposed to most of the earlier “good old-fashioned" methods in the 1960-1980s - is their ability to deal with uncertainty.

Note

The history of AI has seen various competing paradigms for handling uncertain and imprecise information. For example, you may have heard of fuzzy logic. Fuzzy logic was for a while a contender for the best approach to handle uncertain and imprecise information and used in many customer-applications such as washing machines where the machine could detect the dirtiness (a matter of degrees, not only dirty or clean) and adjust the program accordingly.

However, probability has turned out to be the best approach for reasoning under uncertainty, and almost all current AI applications are based, in at least some degree, on probabilities.

We are perhaps most familiar with applications of probability in games: what are the chances of getting three of a kind in poker (about one in 46), what are the chances of winning in the lottery (very small), and so on. However, far more importantly, probability can also be used to quantify and compare risks in everyday life: what are the chances of crashing your car if you exceed the speed limit, what are the chances that the interest rates on your mortgage will go up by five percentage points within the next five years, or what are the chances that AI will automate particular tasks such as detecting fractured bones in X-ray images or waiting tables in a restaurant.

Note

The most important lesson about probability that we’d like you to take away is not probability calculus. Instead, it is the ability to think of uncertainty as a thing that can be quantified at least in principle. This means that we can talk about uncertainty as if it were a number: numbers can be compared (“is this thing more probable than that thing”), and they can often be measured. The numbers in probability will sometimes be somewhat subjective, but we can nevertheless critically evaluate them, and our numbers can sometimes be found to be right or wrong. In other words, the lesson is that uncertainty is not something that is beyond the scope of rational thinking and discussion.

The fact that uncertainty can be quantified is of paramount importance, for example, in decision concerning vaccination or other public policies. Before entering the market, any vaccine is clinically tested, so that its benefits and risks have been quantified. The risks are never known to the minutest detail, but their magnitude is usually known to sufficient degree that it can be argued whether the benefits outweigh the risks.

Note

If we think of uncertainty as something that can't be quantified or measured, the uncertainty aspect may become an obstacle for rational discussion. We may for example argue that since we don’t know exactly whether a vaccine may cause a harmful side-effect, it is too dangerous to use. However, this may lead us to ignore a life-threatening disease that the vaccine will eradicate. In most cases, the benefits and risks are known to sufficient precision to clearly see that one is more significant than the other.

The above lesson is useful in many everyday scenarios and professionally: for example, medical doctors, judges in a court of law, investors have to process uncertain information and make rational decisions based on them. Since this is an AI course, we will discuss how probability can be used to automate uncertain reasoning. The examples we will use include medical diagnosis (although it is usually not a task that we’d wish to automate), and identifying fraudulent email messages (“spam”).

Probably the easiest way to represent uncertainty is through odds. They make it particularly easy to update beliefs when more information becomes available (we will return to this in the next section).

Before we proceed any further, we should make sure you are comfortable with doing basic manipulations on ratios (or fractions). As you probably recall, fractions are numbers like 3/4 or 21/365. We will need to multiply and divide such things, so it's good to refresh these operations if you feel unsure about them. A compact presentation for those who just need a quick reminder is Wikibooks: Multiplying Fractions. Another fun animated presentation of the basic operations is Math is Fun: Using Rational Numbers. Feel free to consult your favourite source if necessary.

By odds, we mean for example 3:1 (three to one), which means that we expect that for every three cases of an outcome, for example winning a bet, there is one case of the opposite outcome, not winning the bet. The other way to express the same would be to say that the chances of winning are 3/4 (three in four). These are called natural frequencies since they involve only whole numbers. With whole numbers, it is easy to imagine, for example, four people out of whom, three have brown eyes. Or four days out of which it rains on three (if you’re in Helsinki).

Note

Three out of four is of course the same as 75%. (Mathematicians prefer to use fractions like 0.75 instead of percentages.) It has been found that people get confused and make mistakes more easily when dealing with fractions and percentages than with natural frequencies or odds. This is why we use natural frequencies and odds whenever convenient.

An important thing to notice is that while expressed as two numbers, 3 and 1 for example, the odds can actually be thought of as a single fraction or a ratio, for example 3/1 (three divided by one, which is clearly just 3). Thus the odds 3:1 is the same as the odds 6:2 or 30:10 since these are also equal to 3/1. Likewise, the odds 1:5 can be thought of as 1/5 = 0.2, which is the same as the odds 2:10 or 10:50. But be careful: the odds 1:5, even if it can be expressed as the number 0.2, is different from 20% probability (or probability 0.2 using the mathematicians' notation). For odds that are greater than one, such as 5:1, it is easy to remember that we are not dealing with probabilities because no probability can be greater than 1 (or greater than 100%), but for odds that are less than one such as 1:5, the danger of confusion lurks around the corner. The correspondence between odds and probabilities is further demonstrated in the following exercise.

Next section

II. The Bayes Rule

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