200-year-old math helps us understand AI

Pillows in saucepans

Søren Hauberg has found mathematical methods that provide us with a glimpse inside the black box. His approach is to examine the potential errors that can occur when a large data set is compressed. The artificial intelligence system has to compress data in order to filter out irrelevant information.

“Data comprises not only the information we need, but also errors in measurement and other irrelevant information we refer to as noise – and all of this noise is removed through compression. In other words, a type of data filtering takes place where the wheat is separated from the chaff,” says Søren Hauberg.

However, unexpected correlations in the data can occur during the compression process, which leads to the artificial intelligence finding incorrect patterns and then outputting the wrong result.
Søren Hauberg explains the whole mess with a home-moving analogy:

“Imagine you’re moving home and have to pack your whole house into boxes. In order to make the best possible use of your boxes, you put your pillow inside a saucepan. If someone who doesn’t know anything about how we live were to draw conclusions from that box, they might well believe that we keep our pillows in the kitchen or saucepans in the bedroom. Yet the two things have nothing to do with each other – and there is no correlation between them. Packing them into the box like that was simply the smart thing to do. The same is true for data compression. There are lots of ways that artificial intelligence can compress data, and if the technology then tries to establish what the underlying patterns are in the data that has been ‘packed away’, there’s a risk it draws the wrong conclusions.”

200-year-old mathematics

In his research, Søren Hauberg has therefore sought out mathematical formulas that correct for the errors that can occur in data sets during compression.

“As part of our basic research, we’ve found a systematic solution that allows us to theoretically walk backwards so we can keep track of which patterns are grounded in reality and which ones have been fabricated by the compression process. When we are able to separate these, we humans can gain a better understanding of how artificial intelligence works – while also being reassured that the artificial intelligence isn’t listening to false patterns.”

The mathematical formulas utilized by Søren Hauberg and his colleagues are hardly brand new – they were in fact developed in the 19th century for use in cartography.

“When they tried to draw maps, they were seeking to transfer information from a three-dimensional sphere to a two-dimensional surface. This created a number of distortions: for example, the land masses are not in accurate proportion to one another meaning that Greenland appears to be much bigger than Africa. The mathematical formulas that correct for these distortions can also be used in our research examining the black boxes of artificial intelligence,” says Søren Hauberg.

May prevent ChatGPT’s hallucinations

The researchers have now made sufficient progress that they are able to look inside the black boxes of artificial intelligence models that use data compression.

“These models are typically used in research when researchers try to find out whether there are any underlying patterns in the data they are working with. The prevention of incorrect conclusion is directly relevant to the working processes of academia,” says Søren Hauberg.

He adds that their work is still unable to correct errors that are found in artificial intelligence systems such as ChatGPT. However, he notes that the researchers’ work has the potential to do so in future.

“We’d love to be able to explain why a chatbot like ChatGPT hallucinates. We can’t do that yet, but perhaps we will be able to in a couple of years’ time,” says the professor, who received a new EU grant worth EUR 2 million in early 2024 to support his continued research into black boxes.