People are so accustomed to presuming that fluent language comes from a thinking, feeling human that evidence to the contrary can be difficult to comprehend. Google’s Powerful Artificial Intelligence Spotlights a Human Cognitive Glitch ,How are people likely to navigate this relatively uncharted territory? Because of a persistent tendency to associate fluent expression with fluent thought, it is natural – but potentially misleading – to think that if an artificial intelligence model can express itself fluently, that means it also thinks and feels just like humans do.
As a result, it is perhaps unsurprising that a former Google engineer recently claimed that Google’s Powerful Artificial AI system LaMDA has a sense of self because it can eloquently generate text about its purported feelings. This event and the subsequent media coverage led to a number of rightly skeptical articles and posts about the claim that computational models of human language are sentient, meaning capable of thinking, feeling, and experiencing.
The question of what it would mean for an AI model to be sentient is actually quite complicated (see, for instance, our colleague’s take), and our goal in this article is not to settle it. But as language researchers, we can use our work in cognitive science and linguistics to explain why it is all too easy for humans to fall into the cognitive trap of assuming that an entity that can use language fluently is sentient, conscious, or intelligent. Text generated by models like Google’s Powerful Artificial LaMDA can be hard to distinguish from text written by humans. This impressive achievement is a result of a decadeslong program to build models that generate grammatical, meaningful language.
Early versions dating back to at least the 1950s, known as n-gram models, simply counted up occurrences of specific phrases and used them to guess what words were likely to occur in particular contexts. For instance, it’s easy to know that “peanut butter and jelly” is a more likely phrase than “peanut butter and pineapples.” If you have enough English text, you will see the phrase “peanut butter and jelly” again and again but might never see the phrase “peanut butter and pineapples.”
Today’s models, sets of data and rules that approximate human language, differ from these early attempts in several important ways. First, they are trained on essentially the entire internet. Second, they can learn relationships between words that are far apart, not just words that are neighbors. Third, they are tuned by a huge number of internal “knobs” – so many that it is hard for even the engineers who design them to understand why they generate one sequence of words rather than another. The models’ task, however, remains the same as in the 1950s: determine which word is likely to come next. Today, they are so good at this task that almost all sentences they generate seem fluid and grammatical.
Source: This news is originally published by scitechdaily