{"id":102085,"date":"2025-06-05T12:58:23","date_gmt":"2025-06-05T16:58:23","guid":{"rendered":"https:\/\/brocku.ca\/brock-news\/?p=102085"},"modified":"2025-11-13T11:07:06","modified_gmt":"2025-11-13T16:07:06","slug":"opinion-veena-dwivedi-discusses-limits-to-ais-ability-to-understand-like-a-human-brain","status":"publish","type":"post","link":"https:\/\/brocku.ca\/brock-news\/2025\/06\/opinion-veena-dwivedi-discusses-limits-to-ais-ability-to-understand-like-a-human-brain\/","title":{"rendered":"Opinion: Veena Dwivedi discusses limits to AI’s ability to ‘understand’ like a human brain"},"content":{"rendered":"

This piece written by Veena Dwivedi, Professor in the Department of Psychology and Centre for Neuroscience at Brock University, originally appeared in The Conversation<\/a>.<\/em><\/p>\n

As meaning-makers, we use spoken or\u00a0signed language\u00a0to understand our experiences in the world around us. The emergence of\u00a0generative artificial intelligence\u00a0such as\u00a0ChatGPT\u00a0(using\u00a0large language models) call into question the very notion of how to define \u201cmeaning.\u201d<\/p>\n

One popular characterization of AI tools is that they \u201cunderstand\u201d what they are doing. Nobel laureate and AI pioneer Geoffrey Hinton said: \u201cWhat\u2019s really surprised me is how good neural networks are at understanding natural language \u2014\u00a0that happened much faster than I thought\u2026. And I\u2019m still amazed that they really do understand what they\u2019re saying.\u201d<\/p>\n

Hinton repeated this claim in an interview with\u00a0Adam Smith, chief scientific officer for Nobel Prize Outreach. In it, Hinton stated that \u201cneural nets are much better at processing language\u00a0than anything ever produced by the\u00a0Chomskyan school of linguistics.\u201d<\/p>\n

Chomskyan linguistics refers to American linguist Noam Chomsky\u2019s theories about the nature of human language and its development. Chomsky proposes that there is a universal grammar innate in humans, which allows for the acquisition of any language from birth.<\/p>\n

I\u2019ve been researching how humans understand language since the 1990s, including\u00a0more than 20 years\u00a0of studies on the neuroscience of language. This has included measuring brainwave activity\u00a0as people read or listen to sentences. Given my experience, I have to respectfully disagree with the idea that AI can \u201cunderstand\u201d \u2014 despite the growing popularity of this belief.<\/p>\n

Generating text<\/h2>\n

First, it\u2019s unfortunate that most people conflate text on a screen with natural language. Written text is related to \u2014 but not the same thing as \u2014 language.<\/p>\n

For example, the same language can be represented by vastly different visual symbols. Look at Hindi and Urdu, for instance. At conversational levels, these are mutually intelligible and therefore considered the same language by linguists. However, they use entirely different writing scripts. The same is true for Serbian and Croatian. Written text is not the same thing as \u201clanguage.\u201d<\/p>\n

Next let\u2019s take a look at the claim that machine learning algorithms \u201cunderstand\u201d natural language. Linguistic communication mostly happens face-to-face, in a particular environmental context shared between the speaker and listener, alongside cues such as spoken tone and pitch, eye contact and facial and emotional expressions.<\/p>\n

The importance of context<\/h2>\n

There is a lot more to understanding what a person is saying than merely being able to comprehend their words. Even babies, who are not experts in language yet, can\u00a0comprehend context cues.<\/p>\n

Take, for example, the simple sentence: \u201cI\u2019m pregnant,\u201d and its interpretations in different contexts. If uttered by me, at my age, it\u2019s likely my husband would drop dead with disbelief. Compare that level of understanding and response to a teenager telling her boyfriend about an unplanned pregnancy, or a wife telling her husband the news after years of fertility treatments.<\/p>\n

In each case, the message recipient ascribes a different sort of meaning \u2014 and understanding \u2014 to the very same sentence.<\/p>\n

In my own\u00a0recent research, I have shown that even\u00a0an individual\u2019s emotional state\u00a0can alter brainwave patterns when processing the meaning of a sentence. Our brains (and thus our thoughts and mental processes) are\u00a0never without emotional context, as other neuroscientists have also\u00a0pointed out.<\/p>\n

So, while some computer code can respond to human language in the form of text, it does not come close to capturing what humans \u2014 and their brains \u2014 accomplish in their understanding.<\/p>\n

It\u2019s worth remembering that when workers in AI talk about neural networks, they mean computer algorithms, not the actual, biological\u00a0brain networks\u00a0that characterize brain structure and function. Imagine constantly confusing the word \u201cflight\u201d (as in birds migrating) versus \u201cflight\u201d (as in airline routes) \u2014 this could lead to some serious misunderstandings!<\/p>\n

Finally, let\u2019s examine the claim about neural networks processing language better than theories produced by Chomskyan linguistics. This field assumes that all human languages can be understood via\u00a0grammatical systems (in addition to context), and that these systems are related to some universal grammar.<\/p>\n

Chomsky conducted research\u00a0on syntactic theory as a paper-and-pencil theoretician. He did not conduct experiments on the psychological or neural bases of language comprehension. His ideas in linguistics are absolutely silent on the mechanisms underlying sentence processing and understanding.<\/p>\n

What the Chomskyan school of linguistics does do, however, is ask questions about how\u00a0human infants and toddlers can learn language with such ease, barring any neurobiological deficits or physical trauma.<\/p>\n

There are\u00a0at least 7,000 languages on the planet, and no one gets to pick where they are born. That means the human brain must be ready to comprehend and learn the language of their community at birth.<\/p>\n

From this fact about language development, Chomsky posited an (abstract) innate module for language learning \u2014 not processing. From a neurobiological standpoint, the brain has to be ready to understand language from birth.<\/p>\n

While there are plenty of examples of\u00a0language specialization in infants, the precise neural mechanisms are still unknown, but not unknowable. But objects of study become unknowable when scientific terms are misused or misapplied. And this is precisely the danger: conflating AI with human understanding can lead to dangerous consequences.<\/p>\n

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