Lies, Noble and Otherwise

What was the first lie you remember telling? I remember mine. One day when I was in kindergarten, I noticed a tulip blooming in front of the school. I was completely enamored with it, so during recess, I snuck out to the front of the school and picked it. When I got caught, I denied taking it from the front, claiming I had found it elsewhere in the schoolyard. The lie was obvious to the teacher supervising the class as we played outside. At five, how could I have known that tulips are not a flower that commonly bloomed wild? I was caught in the lie, and had to spend the last ten minutes of recess inside - and what’s worse, I lost possession of that cheerful tulip I had so badly wanted. 

Learning to Lie

Many parents feel that when their children begin to lie, it is a sign of the loss of innocence of childhood. But should we really bemoan this new behavior? Under the surface, lying is evidence of a very important new ability: the ability to understand that others have minds, and that these minds have their own knowledge, beliefs, and intentions just like ours, that are separate from us. Children learn to lie when they understand two things: that another person can have different beliefs that they have, and that their words can influence the beliefs of others. Typically, learning that words can convey falsehood happens before children realize that other minds can have different beliefs, but language is still a one of the most important peculiarities that allows us to lie.

Although it’s common to consider this kind of deception as a uniquely (and detrimentally) human trait, lying is common in some other species, including birds who frighten others away by warning about a fake predator to have the first share of food, and primates who share many of our prevarication abilities.

However, this type of lying is not the only type of deception in the animal kingdom: there is a spectrum of lies. There are butterflies, moths, fish, birds and even lizards that evolved a distinctive physical pattern that has the appearance of large eyes, in the hopes that the pattern will frighten potential predators and keep them safe. Some animals have evolved to mimic the likeness of another plant or animal, either to blend in or to cause predators to confuse them for something dangerous.

These kinds of “lies” are not the result of intention, but instead the result of optimization. There are many examples in machine learning where this kind of result can come about. Is evolution the only mechanism that can generate this? Or are their other learning methods that could produce a similar result? Where can we draw the line between intentional lies and lies which come about by accident?

Noble Lies

Even since Plato, the social purpose of lying was an important part of the human way of life. Language gives us the ability to share our knowledge and experiences with others and a way to communicate about things that happened without others present. But it also lets us lie and influence the perceptions of others. Malicious gossip, whether true or not, is one of the easiest ways to exert power over someone else in the same social circle.

Although stories and gossip can be positive forces for a social community, malicious gossip and divisive stories can be a destructive force.

Ethically, however, the noble lie might be a worrying possibility in artificial intelligence, and it is a theme that is frequently found in science fiction stories. In Kubrick and Clark’s 2001: A Space Odessey, HAL’s reaction to lying gives us an insight into how important it might prove in AI: “He was only aware of the conflict that was slowly destroying his integrity - the conflict between truth, and concealment of truth.”

Lies come in many forms aside from self-preservation deception. What kinds of lies have been identified by scientists? Can artificial intelligence exhibit this fibbing behavior?

Paradoxes

Truth and falsehood give us some interesting paradoxes in logic. How does this influence how we think about and ultimately build artificial intelligence? Self-reference, is essential for these paradoxes and also essential for creating an understanding of the self. So far, paradoxes have been an especially challenging construct to add into machine learning.

Even some logical decision rules have brought challenges to early learning machines. The Perceptron, the first learning machine, was not able to learn an “exclusive or” operations - one that allows us to know that you might have a hamburger or pizza for dinner, but are quite unlikely to have both. We encounter these types of decisions often in everyday life. Although this problem has been solved by better machine learning structure, we explore whether there are other types of decisions like this that are impossible with the learning structures we now use, especially the challenging paradoxes and uncertainties.

Paradoxes bring many problems of their own: undecidable statements, ambiguity, and uncertainty. How might we approach them?

Next: Poise