Page 45 - Monocle Quarterly Journal Vol 3 Issue 2 Spring

to dismantle the possibility of language being innate, and many other linguists have followed suit, arguing against universal grammar in increasingly technically nuanced debates. That linguistic theory almost always orientates itself in relation to Chomsky’s universal grammar is, however, telling.
In fact, academic debate as to whether or not a universal grammar exists has reached fever pitch and, if one reads deeply into the arguments, they can sometimes appear to be quite petulant and almost personal in many regards. The reasons for this may not only reside in the fact that peoples’ careers would be somewhat undermined if the existence of universal grammar were to be definitively proven, but also because the existence of a universal grammar is viewed by many as somewhat akin to a linguistic argument for the uniqueness of human intelligence. The debate therefore appears to be much more about whether humans are special and distinct from other species that are not bestowed with the so-called gift of language.
Learning Language
In contemplation of one of the great unsolved problems of AI – that of natural language acquisition – it is fundamentally important to note that if a universal grammar were to exist, then no amount of data or neural network engineering or hardware or cloud-space would ever enable a machine to acquire language as humans do. The only way that a machine could ever acquire language would be to solve the problem in a so-called closed- form parametric solution. To be precise, if a universal grammar exists in humans, then neuroscientists would need to understand exactly how the human brain works in this specific regard, and exactly where in the brain this universal grammar actually resides. And this would then have to be replicated exactly in a machine. However, this presents something quite distinct and different from the goals of unsupervised deep learning neural networks, which seek to replicate the process of learning in general, rather than endowing machines with an architectural structure containing the specific, preordained rules and parameters that are only accessible to human beings and to no other species.
On the other hand, if we suppose that people do not have some kind of “language acquisition device” in their
brain, as Chomsky referred to it, and that a child learns language by hearing it, then it would seem logical that it would be possible for machines to similarly perfect human language, simply by analysing enough data. But despite the wealth of data that is currently at our disposal, there are a number of examples that show that natural language processing (NLP) remains a challenging area for AI. NLP research has certainly made great strides, not only teaching computers the words that exist in a language and the rules that govern their grammatical combination, but also teaching them when to use
However, these programs have not been without their
A TRUE TEST OF INTELLIGENCE
problems and have been criticised for misinterpreting
instructions ...
certain sentences. The first NLP computer program was created in the 1960s and was called Eliza. Although Eliza could hold a conversation with humans, she lacked any understanding of the exchange, using a pattern- recognition methodology to select responses from a pre-determined script. More recently, virtual assistants have advanced voice-recognition AI, performing tasks as directed by voice commands from users. However, these programs have not been without their problems and have been criticised for misinterpreting instructions and for requiring that commands be given with an unnatural degree of stiffness. These devices can certainly hear, interpret and respond to language – which in the most basic terms means they can communicate – but this communication is limited and is certainly nothing akin to what even young children are capable of.
In 2011, a chatbot called Cleverbot was able to fool 59.3% of the human participants at the Techniche festival at the Indian Institute of Technology Guwahati into thinking they were chatting, using text messages, with another human. The chatbot had been trained on millions of conversations with humans and worked by searching through these conversations to select the most fitting responses to the messages it received. This marked a key development for chatbots, with Cleverbot proving itself capable of communicating in a far more conversational manner than the NLP devices that had preceded it. But it still lacked any real understanding of
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