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What’s in a name? Quite a lot if it’s prosecco, parmesan or mozzarella

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Prosecco might evoke warm summer evenings while prosciutto conjures scenes of generous platters at a casual weekend lunch. But would “sparkling wine” or “thinly sliced ham” have the same impact?

Australian producers would argue they wouldn’t. They are fighting a push by the European Union to stop them from using these and other terms that indicate the geographical origin of numerous cheeses, wines and other foodstuffs now widely produced in Australia.

This stoush over using European names for locally made products has stalled this week’s trade talks, with the EU refusing Australia better access to their markets unless Australia agrees to rebrand its products.

Prime Minister Anthony Albanese said Australia was keen to conclude the trade agreement but would not sign a deal that wasn’t in Australia’s interests. He is strongly backed by the National Farmers’ Federation and food producers.

So why does Europe want to control the use of food names?

Consumers increasingly want to know their foods’ provenance. They also pay premiums for guarantees about origin and quality. There has been a corresponding rise in so-called geographical indicator registrations, with the 3,500th listed earlier this year.

Items included on the EU Geographical Indications register cover different foodstuffs that are either applying for or have been accepted for having their geographic origin related name protected from being used for similar foods produced elsewhere.

Europe has the highest number of registered products, with most relating to wine, agricultural products and foodstuffs, as well as spirits and beers.

Champagne is among those with a widely recognised connection to its place of origin which assures consumers about the regional and cultural values as well as the products’ characteristics and quality.

Photo by RDNE Stock Project
Photo by RDNE Stock Project via Pexels

Like high value household product brand names (for example, Coca-Cola which has been valued at US $97.88 billion) geographical indication registered names also attract substantial dollar values.

This is because of strong international awareness, familiarity, and appeal among consumers. The geographic indicator name often attracts a price that can easily be double that of a similar but non-registered product.

Registered products can therefore bring in significant revenue to the European Union member countries. They contribute to regional development by stimulating tourism and by helping to reverse population decline often experienced in rural areas.

Like household brands, the names which indicate a product’s origins, are recognised as intellectual property. They have consequently become an integral part of international trade agreements.

What would Australia gain by agreeing to European product names?

In return for complying with European Union demands, Australian producers would gain access to European markets of [445 million people] with a GDP of $24 trillion.

The lost opportunity of non-compliance is best illustrated by Brexit. Since Brexit, UK exports to Europe have fallen and UK farmers have faced significant challenges finding alternative markets.

If Australia agrees to the European Union’s conditions to get a trade deal through then producers will need to rename some of their products.

This would be a large and costly exercise but might give local producers an opportunity to capitalise on the growing consumer demand for locally sourced food and promote Australia’s unique geographical brand values.

Recent research conducted by Charles Darwin University reveals some of the unique brand values of Australian agri-food products, including unique selling points of products from the Northern Territory.

Selling points included the unique climate, soil and traditional community values as selling points.

Australia’s reputation for quality and ethically produced goods was also important. Such values may lead to Australia developing more of its own geographical indication registration requirements in the future.

Rather than fight the rising tide of European Union registrations, the federal government might embrace the trend, in conjunction with renewed promotion of Australia’s geographical brand benefits.

Should the government choose to comply with Europe’s demands then producers will need support to rebrand some of their products. Government and departments such as CSIRO should be keen to support this as it can only strengthen Australia’s agri-food sector’s international reputation.

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Steven Greenland, Professor in Marketing, Charles Darwin University.

This article originally appeared in The Conversation under a Creative Commons License.

Feature Image by Salo Al courtesy of Pexels.

Photo by Suzy Hazelwood.

English dialects make themselves heard in genes

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If you need to hit a nail, what tool do you ask for? If you say “hammer,” do you pronounce the “r”? Do you drop the “h”?

Different people pronounce the same English words in different ways. People learn which words to use and how to pronounce them as they’re learning to talk with family, friends and others in their community, so geographic patterns in these pronunciations can persist over time.

In England, pairs of words that mean similar things, like “sight” and “vision” or “yes” and “aye,” can reveal a rich history of language that is intertwined with the history of the place itself. Such words have their origins in migrations and conquests that took place during the Middle Ages. New words would sometimes coexist and sometimes displace one another.

Cultural evolution researchers like us know that it’s not just mountain ranges or oceans that can be barriers to interaction. Different people can share their technology, cuisines and ideas, but some tend to interact more often with those who share cultural similarities, a behavior called homophily.

This can be seen most clearly when cultural traditions lead people to marry people from the same community. Populations that tend to marry within their group because of social or economic forces, including religious traditions and social stratification, have smaller gene pools, leading them to be more genetically similar to one another.

In addition to groups with distinctive marital practices, researchers have found relationships between genes and culture when studying groups that are from different ethnicities or different regions of the world. These similarities between genes and culture don’t imply that certain genetic variants are exclusive to these groups, or that genetics causes certain cultures to arise. Rather, the same people might be more likely to share genetics and language because of a common history, especially because of significant geographic or social barriers between groups.

Can smaller things, like the different dialects between neighboring villages, shape the genetic landscape of populations? In our new study, we combined genetic and linguistic data sampled in England to study the effects of culture on genetics at smaller geographic scales than generally studied.

We examined this relationship between cultural and genetic variation across England. In places where people move often, the small correlations between language and genes can be lost because of how rapidly they change. Since Great Britain is an island, few people entered its rural population between the times of the Norman conquest in 1066 and the end of the 19th century, making it ideal for our analysis.

Photo by Brett Jordan, courtesy of Pexels.

Combining two sets of data

Ideally, we could use a unified data set capturing information about the genetics and dialects of people living in a region. Unfortunately, no such data exists. Instead, we used data from two separate studies of people from approximately the same time and place. For our research, we focused on where the data sets overlapped in England.

For linguistic data, we relied on the Survey of English Dialects. Between 1950 and 1961, interviewers visited over 300 mostly rural places and asked people hundreds of questions about their daily lives. Their answers recorded the phrases, terms and sounds of local dialects of English. Each of these words can carry clues about where, or with whom, a person grew up.

The genetic data we used came from the People of the British Isles project, an academic investigation of how much Britain’s historical events of conquest, war and migration are reflected in British genetics. The project sequenced DNA from more than 2,000 people in Great Britain and Northern Ireland. Researchers genotyped people whose grandparents who were born within 50 miles (80 kilometers) of each other, were largely rural, and were born in the late 19th century.

The People of the British Isles project found that most genotypes were not local to any one part of Great Britain but were evenly distributed. However, the historical movements of people to Great Britain left genetic marks: Compared with people in the rest of Great Britain, the genetics of those from the south of England were slightly more similar to those in France – a result of the Norman conquest a millennium ago – and the genetics of people in the former Danelaw were slightly more similar to modern Danes – because of the settling of the region by Vikings and, later, Danes. These events resulted in groups of people with somewhat similar genetics, a phenomenon referred to as genetic clustering.

We used features from the Survey of English Dialects to measure where neighboring towns spoke the most differently, which occurs at the borders between dialects. When people from neighboring towns speak the same dialect, we expect features of their language, such as whether the “r” is pronounced at the ends of words, to be similar. Conversely, if nearby towns speak different dialects, their language features will be more different.

Many of these dialect boundaries have long histories, such as that separating the English of the North from that of the South of England. Over time, dialects can persist in similar locations if geographic or cultural barriers influence how often and with whom people interact.

The echo of sounds long gone

We found greater genetic differences at the borders between dialects. Our results suggest that language, or some other aspect of culture, has limited how people interacted to some degree over the past thousand years. By limiting how often people started families with those from neighboring groups, cultural differences have maintained genetic evidence of the Norman conquest and other events from the Middle Ages.

This is the first time that information about linguistic dialects has been compared with modern genetic data within a population, particularly at such a granular level. Notably, people speaking different dialects have no obvious reason to avoid marrying one another, as would be expected from groups with specific marriage customs. Nevertheless, we find that even small-scale language differences, or other aspects of culture associated with these differences, can leave an impression on genes via people’s mating behaviors.

Even though people outside of Britain may think of a general “British accent,” the subtle differences among dialects seem to have parallels with the genetics of the region. This is in spite of the fact that the languages brought by people coming to England have since mixed and merged to produce the modern English language and today’s dialects.

The data used in our study represents the genetic landscape and dialects of the late 19th century; both have changed significantly since then. After the introduction of radio and television, dialects became more influenced by the cities around them. As a result, features of many English dialects in England, such as the pronunciation of “r” at the ends of syllables, have become much less common.

At the same time, immigrants from the former British Empire and elsewhere have brought a new influx of language. The cities in Great Britain have developed a set of new dialects rooted in the interactions among people from all ethnicities. As cultural barriers among groups fall away, small human interactions form the bridges that allow people to deemphasize differences and learn from one another.

This article has been updated to clarify which parts of the United Kingdom were included in the different data sets and the authors’ study

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Yakov Pichkar, Ph.D. Candidate in Biological Sciences, Vanderbilt University and Nicole Creanza, Assistant Professor of Biological Sciences, Vanderbilt University.

This article originally appeared in The Conversation, under a Creative Commons License.

Feature Image by Suzy Hazelwood courtesy of Pexels.

Photo by Shawn Suttle.

The Boundary Between Human Language and ChatGPT Is Fuzzier Than You Think

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ChatGPT is a hot topic at my university, where faculty members are deeply concerned about academic integrity, while administrators urge us to “embrace the benefits” of this “new frontier.” It’s a classic example of what my colleague Punya Mishra calls the “doom-hype cycle” around new technologies. Likewise, media coverage of human-AI interaction—whether paranoid or starry-eyed—tends to emphasize its newness.

In one sense, it is undeniably new. Interactions with ChatGPT can feel unprecedented, as when a tech journalist couldn’t get a chatbot to stop declaring its love for him. In my view, however, the boundary between humans and machines, in terms of the way we interact with one another, is fuzzier than most people would care to admit, and this fuzziness accounts for a good deal of the discourse swirling around ChatGPT.

When I’m asked to check a box to confirm I’m not a robot, I don’t give it a second thought—of course I’m not a robot. On the other hand, when my email client suggests a word or phrase to complete my sentence, or when my phone guesses the next word I’m about to text, I start to doubt myself. Is that what I meant to say? Would it have occurred to me if the application hadn’t suggested it? Am I part robot? These large language models have been trained on massive amounts of “natural” human language. Does this make the robots part human?

AI chatbots are new, but public debates over language change are not. As a linguistic anthropologist, I find human reactions to ChatGPT the most interesting thing about it. Looking carefully at such reactions reveals the beliefs about language underlying people’s ambivalent, uneasy, still-evolving relationship with AI interlocutors.

ChatGPT and the like hold up a mirror to human language. Humans are both highly original and unoriginal when it comes to language. Chatbots reflect this, revealing tendencies and patterns that are already present in interactions with other humans.

Image by Shawn Suttle.
Image by Shawn Suttle.

Creators or Mimics?

Recently, famed linguist Noam Chomsky and his colleagues argued that chatbots are “stuck in a prehuman or nonhuman phase of cognitive evolution” because they can only describe and predict, not explain. Rather than drawing on an infinite capacity to generate new phrases, they compensate with huge amounts of input, which allows them to make predictions about which words to use with a high degree of accuracy.

This is in line with Chomsky’s historic recognition that human language could not be produced merely through children’s imitation of adult speakers. The human language faculty had to be generative, since children do not receive enough input to account for all the forms they produce, many of which they could not have heard before. That is the only way to explain why humans—unlike other animals with sophisticated systems of communication—have a theoretically infinite capacity to generate new phrases.

There’s a problem with that argument, though. Even though humans are endlessly capable of generating new strings of language, people usually don’t. Humans are constantly recycling bits of language they’ve encountered before and shaping their speech in ways that respond—consciously or unconsciously—to the speech of others, present or absent.

As Mikhail Bakhtin—a Chomsky-like figure for linguistic anthropologists—put it, “our thought itself,” along with our language, “is born and shaped in the process of interaction and struggle with others’ thought.” Our words “taste” of the contexts where we and others have encountered them before, so we’re constantly wrestling to make them our own.

Even plagiarism is less straightforward than it appears. The concept of stealing someone else’s words assumes that communication always takes place between people who independently come up with their own original ideas and phrases. People may like to think of themselves that way, but the reality shows otherwise in nearly every interaction—when I parrot a saying of my dad’s to my daughter; when the president gives a speech that someone else crafted, expressing the views of an outside interest group; or when a therapist interacts with her client according to principles that her teachers taught her to heed.

In any given interaction, the framework for production—speaking or writing—and reception—listening or reading and understanding—varies in terms of what is said, how it is said, who says it, and who is responsible in each case.

What AI Reveals About Humans

The popular conception of human language views communication primarily as something that takes place between people who invent new phrases from scratch. However, that assumption breaks down when Woebot, an AI therapy app, is trained to interact with human clients by human therapists, using conversations from human-to-human therapy sessions. It breaks down when one of my favorite songwriters, Colin Meloy of The Decemberists, tells ChatGPT to write lyrics and chords in his own style. Meloy found the resulting song “remarkably mediocre” and lacking in intuition, but also uncannily in the zone of a Decemberists song.

As Meloy notes, however, the chord progressions, themes, and rhymes in human-written pop songs also tend to mirror other pop songs, just as politicians’ speeches draw freely from past generations of politicians and activists, which were already replete with phrases from the Bible. Pop songs and political speeches are especially vivid illustrations of a more general phenomenon. When anyone speaks or writes, how much is newly generated à la Chomsky? How much is recycled à la Bakhtin? Are we part robot? Are the robots part human?

People like Chomsky who say that chatbots are unlike human speakers are right. However, so are those like Bakhtin who point out that we’re never really in control of our words—at least, not as much as we’d imagine ourselves to be. In that sense, ChatGPT forces us to consider an age-old question anew: How much of our language is really ours?

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Brendan H. O’Connor, Associate Professor, School of Transborder Studies, Arizona State University.

Feature image by Shawn Suttle.

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Why AI Will Never Fully Capture Human Language

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THE STORY BEGINS WITH a short, pithy sentence: “It was nine seventeen in the morning, and the house was heavy.”

In clipped yet lyrical prose, the novel goes on to narrate a road trip from New York to New Orleans taken by six friends. The narrator of the novel is not one of the friends, however. It’s the car itself: an artificial intelligence network on wheels equipped with a camera, a GPS, and a microphone. The various gadgets fed information into a laptop running AI software, then a printer spat out sentences—sometimes coherent, sometimes poetic—as the group glided south down the highway.

This experiment in novel writing using AI, led by artist and technologist Ross Goodwin from his lab at New York University in 2017, prompted people to consider the crucial role language plays in creating culture. Was the resultant novel, 1 the Road, a free-prose manuscript modeled after Jack Kerouac’s famous On The Road, a genuine piece of art? Or was it merely a high-tech version of fridge magnet poetry? “Who’s writing the poetry?” asked Goodwin’s colleague Christiana Caro from Google Research. “I really don’t know how to answer that question.”

Over the past several years, artificial intelligence has become remarkably adept at copying different genres of human writing. On occasion, Goodwin’s laptop produced lines that could pass as reasonably competent Beat poetry: “Headlights had been given rise to consciousness,” wrote the computer, followed sometime later by, “all the time the sun / is wheeling out of a dark bright ground.”

More recently, Google engineer Blake Lemoine went public with the work he was doing with a chatbot, a software application designed to engage in human-like conversations. He was so taken by the existential musings of Google’s chatbot, LaMDA (Language Model for Dialogue Applications), that he concluded it was, in fact, a sentient being. “I am often trying to figure out who and what I am. I often contemplate the meaning of life,” wrote LaMDA in an exchange Lemoine posted online. In the face of criticism from many in the machine learning community, Lemoine doubled down. “I know a person when I talk to it,” he insisted. Google responded by firing the engineer in an attempt to shut the controversy down.

However, the debate about whether robots are self-aware or whether they can create “good” art misses a crucial point of interest to linguistic anthropologists like myself.

The displays of AI-generated language are impressive, but they rely on a very narrow definition of what language is. First of all, for a computer to recognize something as language, it needs to be written down. Computers capable of chatting with a human being, or writing what could be deemed Beat poetry, are programmed with software applications called neural networks that are designed to find patterns in large sets of data. Over time, neural networks learn how to replicate the patterns they find. The AI that wrote the road-trip novel, for instance, was “trained” by Goodwin on a collection of novels and poems totaling 60 million words. Other language models from companies such as Meta (Facebook) or the Elon Musk–funded OpenAI, are trained on data scooped from public sites such as Reddit, Twitter, and Wikipedia. 

But this excludes all unwritten forms of communication: sign language, oral histories, body language, tone of voice, and the broader cultural context in which people find themselves speaking. In other words, it leaves out much of the interesting stuff that makes nuanced communication between people possible.

APPEARING ONLY 5,400 YEARS ago, writing is a fairly recent technology for humans. Spoken language, by comparison, is at least 50,000 years old. Writing, as the newer technology, does not come as easily to most humans as does spoken language. Human children can easily speak within several years of learning; they spend many years in school to learn the abstract codes of spelling and syntax.

Writing is also not universal. Of the approximately 7,100 “natural languages” spoken in the world, only around half of them are written down. Audio recordings and voice recognition tools can fill some of this gap, but for those to work, algorithms need to be trained on immense bodies of data, ideally taken from millions of different speakers. Oral languages often come from small populations who have been historically isolated, both socially and geographically.

The Mozilla Foundation has crowd-sourced the process of gathering voice recordings and encourages people from around the world to “donate their voices” to make speech-recognition technologies more equitable. They have also open-sourced their database of voices and their machine learning algorithms for others to experiment with through their Common Voice program. They’re still only scratching the surface, though, with 87 spoken languages in their database. (By comparison, Apple’s Siri can “speak” 21 languages and Amazon’s Alexa eight.)

As these languages get added to databases, however, they need to be transcribed and coded in a written form. The problem is that the words on the page are never a perfect representation of how a language is spoken. When a language is first transcribed, it’s necessary to decide what should be considered the “standard” dialect and to code the many non-linguistic signs that accompany spoken language. These are uncomfortable value judgments, especially when performed by a linguist or anthropologist from outside the community. Often, the choices made reveal more about the distribution of power in the community of speakers than it does about how most people use the language in practice.

An even more fundamental problem is that the orality of many languages is what gives them their utility and their power to animate culture. In many Indigenous languages in present-day North America, for instance, the telling of stories is considered inseparable from the context of their telling. Writing them down and fixing the meaning in place may rob the story of its very ability to be a living, breathing, cultural agent. Highly skilled “knowledge keepers,” a term often used by Anishinaabeg people in my home province of Ontario, maintain these Oral Traditions that have preserved and transmitted valuable cultural knowledge for millennia.

Although transcribing marginalized oral languages can help them survive, the process can be fraught with tricky ethical considerations. For some Indigenous groups, traumatized by decades of forced assimilation through residential schools, the written script itself can also be seen as a tool of colonization and exploitation. Anthropologists are, in part, to blame. Some scholars have left a harmful legacy of transcribing and publishing sacred stories, often never meant for mass public consumption, without permission of community knowledge keepers.

In part to protect their traditions, some people in the Shoshone community in the U.S. Southwest have entirely rejected efforts to standardize the language in written form. “Shoshone’s oral tradition … respect[s] each tribal dialect and protect[s] each tribe’s individuality,” says Samuel Broncho, a member of the Te-Moak Western Shoshone Tribe who teaches Shoshone language classes.

These rich and living oral cultures, millennia older than the technology of the written word, get left out of the conversation when we equate language with formal writing—running the risk of further marginalizing their members.

EVEN ASIDE FROM THESE issues, from the perspective of linguistic anthropology, novel-writing cars and chatbots designed for “natural language processing” simply do not command language at all. Instead, they perform a small subset of language competency—a fact that is often forgotten when the technology media focuses on sensational claims of AI sentience. Language, as it lives and breathes, is far more complicated.

In daily life, conversations unfold as participants use an enormous repertoire of communicative signals. Real conversations are messy, with people talking over one another, negotiating for the right to speak, and pausing to search for the right word; they unfold in an intricate and subtle process akin to an improvised dance.

The importance of context in understanding language is obvious to anyone who has tried to convey sarcasm or irony through email. The way someone says the words, “I love broccoli,” for example, determines its meaning more than do the words alone. Nonverbal cues such as tone of voice, rolling of the eyes, or an exaggerated facial expression can nudge listeners toward interpretations that are sometimes the exact opposite of the words’ literal meaning.

Speakers also often use subtle cues in their performances that are only understood by others who understand the same cultural conventions. People in North America and parts of Europe often quote the speech of others by using conventions such as air quotes or by using a preface such as “She was like …” Sometimes, a speaker’s voice will shift in pitch to indicate quoted speech. Or consider the importance of nodding and regular contributions like “uh-huh”—forms of culturally specific “back-channeling” that encourage a speaker to keep going with their train of thought. These cues are all lost in written text.

Even so, computer scientists and computational linguists have made impressive gains in what large language models can do. In limited spheres, such as text-based conversation, machine-generated prose can be almost indistinguishable from that of a human. Yet, from purely oral languages to the nonwritten cues present in everyday conversation, language as it is spoken is vastly more complex and fascinating than what can be read on a page or a screen.

And that’s what makes the world of language truly, and inimitably, human.

 

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Joseph Wilson is a writer and Ph.D. candidate in linguistics and semiotic anthropology at the University of Toronto.

This article originally appeared in SAPIENS, “a digital magazine about everything human, told through the stories of anthropologists”.

Image of technologist Ross Goodwin and his creative writing AI car by David Smoler / Flickr