The Languages AI Is Leaving Behind

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The Languages AI Is Leaving Behind

The generative-AI boom looks very different for non-English speakers.

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The Languages AI Is Leaving Behind​

The generative-AI boom looks very different for non-English speakers.

By Damon Beres

Illustration by The Atlantic

APRIL 19, 2024

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Generative AI is famously data-hungry. The technology requires huge troves of digital information—text, photos, video, audio—to “learn” how to produce convincingly humanlike material. The most powerful large language models have effectively “read” just about everything; when it comes to content mined from the open web, this means that AI is especially well versed in English and a handful of other languages, to the exclusion of thousands more that people speak around the world.

In a recent story for The Atlantic, my colleague Matteo Wong explored what this might mean for the future of communication. AI is positioned more and more as the portal through which billions of people might soon access the internet. Yet so far, the technology has developed in such a way that will reinforce the dominance of English while possibly degrading the experience of the web for those who primarily speak languages with less minable data. “AI models might also be void of cultural nuance and context, no matter how grammatically adept they become,” Matteo writes. “Such programs long translated ‘good morning’ to a variation of ‘someone has died’ in Yoruba,” David Adelani, a DeepMind research fellow at University College London told Matteo, “because the same Yoruba phrase can convey either meaning.”

But Matteo also explores how generative AI might be used as a tool to preserve languages. The grassroots efforts to create such applications move slowly. Meanwhile, tech giants charge ahead to deploy ever more powerful models on the web—crystallizing a status quo that doesn’t work for all.

— Damon Beres, senior editor

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Illustration by Matteo Giuseppe Pani. Source: Getty.

The AI Revolution Is Crushing Thousands of Languages

By Matteo Wong

Recently, Bonaventure Dossou learned of an alarming tendency in a popular AI model. The program described Fon—a language spoken by Dossou’s mother and millions of others in Benin and neighboring countries—as “a fictional language.”

This result, which I replicated, is not unusual. Dossou is accustomed to the feeling that his culture is unseen by technology that so easily serves other people. He grew up with no Wikipedia pages in Fon, and no translation programs to help him communicate with his mother in French, in which he is more fluent. “When we have a technology that treats something as simple and fundamental as our name as an error, it robs us of our personhood,” Dossou told me.

The rise of the internet, alongside decades of American hegemony, made English into a common tongue for business, politics, science, and entertainment. More than half of all websites are in English, yet more than 80 percent of people in the world don’t speak the language. Even basic aspects of digital life—searching with Google, talking to Siri, relying on autocorrect, simply typing on a smartphone—have long been closed off to much of the world. And now the generative-AI boom, despite promises to bridge languages and cultures, may only further entrench the dominance of English in life on and off the web.

Read the full article.

 

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The AI Revolution Is Crushing Thousands of Languages​

English is the internet’s primary tongue—a fact that may have unexpected consequences as generative AI becomes central to daily life.

By Matteo Wong

Illustration by Matteo Giuseppe Pani. Source: Getty.

APRIL 12, 2024

SAVE

Recently, Bonaventure Dossou learned of an alarming tendency in a popular AI model. The program described Fon—a language spoken by Dossou’s mother and millions of others in Benin and neighboring countries—as “a fictional language.”

This result, which I replicated, is not unusual. Dossou is accustomed to the feeling that his culture is unseen by technology that so easily serves other people. He grew up with no Wikipedia pages in Fon, and no translation programs to help him communicate with his mother in French, in which he is more fluent. “When we have a technology that treats something as simple and fundamental as our name as an error, it robs us of our personhood,” Dossou told me.

The rise of the internet, alongside decades of American hegemony, made English into a common tongue for business, politics, science, and entertainment. More than half of all websites are in English, yet more than 80 percent of people in the world don’t speak the language. Even basic aspects of digital life—searching with Google, talking to Siri, relying on autocorrect, simply typing on a smartphone—have long been closed off to much of the world. And now the generative-AI boom, despite promises to bridge languages and cultures, may only further entrench the dominance of English in life on and off the web.

Scale is central to this technology. Compared with previous generations, today’s AI requires orders of magnitude more computing power and training data, all to create the humanlike language that has bedazzled so many users of ChatGPT and other programs. Much of the information that generative AI “learns” from is simply scraped from the open web. For that reason, the preponderance of English-language text online could mean that generative AI works best in English, cementing a cultural bias in a technology that has been marketed for its potential to “ benefit humanity as a whole.” Some other languages are also well positioned for the generative-AI age, but only a handful: Nearly 90 percent of websites are written in just 10 languages (English, Russian, Spanish, German, French, Japanese, Turkish, Portuguese, Italian, and Persian).

Some 7,000 languages are spoken in the world. Google Translate supports 133 of them. Chatbots from OpenAI, Google, and Anthropic are still more constrained. “There’s a sharp cliff in performance,” Sara Hooker, a computer scientist and the head of Cohere for AI, a nonprofit research arm of the tech company Cohere, told me. “Most of the highest-performance [language] models serve eight to 10 languages. After that, there’s almost a vacuum.” As chatbots, translation devices, and voice assistants become a crucial way to navigate the web, that rising tide of generative AI could wash out thousands of Indigenous and low-resource languages such as Fon—languages that lack sufficient text with which to train AI models.

“Many people ignore those languages, both from a linguistic standpoint and from a computational standpoint,” Ife Adebara, an AI researcher and a computational linguist at the University of British Columbia, told me. Younger generations will have less and less incentive to learn their forebears’ tongues. And this is not just a matter of replicating existing issues with the web: If generative AI indeed becomes the portal through which the internet is accessed, then billions of people may in fact be worse off than they are today.

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Adebara and Dossou, who is now a computer scientist at Canada’s McGill University, work with Masakhane, a collective of researchers building AI tools for African languages. Masakhane, in turn, is part of a growing, global effort racing against the clock to create software for, and hopefully save, languages that are poorly represented on the web. In recent decades, “there has been enormous progress in modeling low-resource languages,” Alexandra Birch, a machine-translation researcher at the University of Edinburgh, told me.

In a promising development that speaks to generative AI’s capacity to surprise, computer scientists have discovered that some AI programs can pinpoint aspects of communication that transcend a specific language. Perhaps the technology could be used to make the web more aware of less common tongues. A program trained on languages for which a decent amount of data are available—English, French, or Russian, say—will then perform better in a lower-resourced language, such as Fon or Punjabi. “Every language is going to have something like a subject or a verb,” Antonios Anastasopoulos, a computer scientist at George Mason University, told me. “So even if these manifest themselves in very different ways, you can learn something from all of the other languages.” Birch likened this to how a child who grows up speaking English and German can move seamlessly between the two, even if they haven’t studied direct translations between the languages—not moving from word to word, but grasping something more fundamental about communication.

Read: The end of foreign-language education

But this discovery alone may not be enough to turn the tide. Building AI models for low-resource languages is painstaking and time-intensive. Cohere recently released a large language model that has state-of-the-art performance for 101 languages, of which more than half are low-resource. That leaves about 6,900 languages to go, and this effort alone required 3,000 people working across 119 countries. To create training data, researchers frequently work with native speakers who answer questions, transcribe recordings, or annotate existing text, which can be slow and expensive. Adebara spent years curating a 42-gigabyte training data set for 517 African languages, the largest and most comprehensive to date. Her data set is 0.4 percent of the size of the largest publicly available English training data set. OpenAI’s proprietary databases—the ones used to train products such as ChatGPT—are likely far larger.

 

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Much of the limited text readily available in low-resource languages is of poor quality—itself badly translated—or limited use. For years, the main sources of text for many such low-resource languages in Africa were translations of the Bible or missionary websites, such as those from Jehovah’s Witnesses. And crucial examples for fine-tuning AI, which has to be intentionally created and curated—data used to make a chatbot helpful, human-sounding, not racist, and so on—are even rarer. Funding, computing resources, and language-specific expertise are frequently just as hard to come by. Language models can struggle to comprehend non-Latin scripts or, because of limited training examples, to properly separate words in low-resource-language sentences—not to mention those without a writing system.

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The trouble is that, while developing tools for these languages is slow going, generative AI is rapidly overtaking the web. Synthetic content is flooding search engines and social media like a kind of gray goo, all in hopes of making a quick buck.

Most websites make money through advertisements and subscriptions, which rely on attracting clicks and attention. Already, an enormous portion of the web consists of content with limited literary or informational merit—an endless ocean of junk that exists only because it might be clicked on. What better way to expand one’s audience than to translate content into another language with whatever AI program comes up on a Google search?

Read: Prepare for the textpocalypse

Those translation programs, already of sometimes questionable accuracy, are especially bad with low-resourced languages. Sure enough, researchers published preliminary findings earlier this year that online content in such languages was more likely to have been (poorly) translated from another source, and that the original material was itself more likely to be geared toward maximizing clicks, compared with websites in English or other higher-resource languages. Training on large amounts of this flawed material will make products such as ChatGPT, Gemini, and Claude even worse for low-resource languages, akin to asking someone to prepare a fresh salad with nothing more than a pound of ground beef. “You are already training the model on incorrect data, and the model itself tends to produce even more incorrect data,” Mehak Dhaliwal, a computer scientist at UC Santa Barbara and one of the study’s authors, told me—potentially exposing speakers of low-resource languages to misinformation. And those outputs, spewed across the web and likely used to train future language models, could create a feedback loop of degrading performance for thousands of languages.

Imagine “you want to do a task, and you want a machine to do it for you,” David Adelani, a DeepMind research fellow at University College London, told me. “If you express this in your own language and the technology doesn’t understand, you will not be able to do this. A lot of things that simplify lives for people in economically rich countries, you will not be able to do.” All of the web’s existing linguistic barriers will rise: You won’t be able to use AI to tutor your child, draft work memos, summarize books, conduct research, manage a calendar, book a vacation, fill out tax forms, surf the web, and so on. Even when AI models are able to process low-resource languages, the programs require more memory and computational power to do so, and thus become significantly more expensive to run—meaning worse results at higher costs.

AI models might also be void of cultural nuance and context, no matter how grammatically adept they become. Such programs long translated “good morning” to a variation of “someone has died” in Yoruba, Adelani said, because the same Yoruba phrase can convey either meaning. Text translated from English has been used to generate training data for Indonesian, Vietnamese, and other languages spoken by hundreds of millions of people in Southeast Asia. As Holy Lovenia, a researcher at AI Singapore, the country’s program for AI research, told me, the resulting models know much more about hamburgers and Big Ben than local cuisines and landmarks.

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It may already be too late to save some languages. As AI and the internet make English and other higher-resource languages more and more convenient for young people, Indigenous and less widely spoken tongues could vanish. If you are reading this, there is a good chance that much of your life is already lived online; that will become true for more people around the world as time goes on and technology spreads. For the machine to function, the user must speak its language.

By default, less common languages may simply seem irrelevant to AI, the web, and, in turn, everyday people—eventually leading to abandonment. “If nothing is done about this, it could take a couple of years before many languages go into extinction,” Adebara said. She is already witnessing languages she studied as an undergraduate dwindle in their usage. “When people see that their languages have no orthography, no books, no technology, it gives them the impression that their languages are not valuable.”

Read: AI is exposing who really has power in Silicon Valley

Her own work, including a language model that can read and write in hundreds of African languages, aims to change that. When she shows speakers of African languages her software, they tell her, “‘I saw my language in the technology you built; I wasn’t expecting to see it there,’” Adebara said. “‘I didn’t know that some technology would be able to understand some part of my language,’ and they feel really excited. That makes me also feel excited.”

Several experts told me that the path forward for AI and low-resource languages lies not only in technical innovation, but in just these sorts of conversations: not indiscriminately telling the world it needs ChatGPT, but asking native speakers what the technology can do for them. They might benefit from better voice recognition in a local dialect, or a program that can read and digitize non-Roman script, rather than the all-powerful chatbots being sold by tech titans. Rather than relying on Meta or OpenAI, Dossou told me, he hopes to build “a platform that is appropriate and proper to African languages and Africans, not trying to generalize as Big Tech does.” Such efforts could help give low-resource languages a presence on the internet where there was almost none before, for future generations to use and learn from.

Today, there is a Fon Wikipedia, although its 1,300 or so articles are about two-thousandths of the total on its English counterpart. Dossou has worked on AI software that does recognize names in African languages. He translated hundreds of proverbs between French and Fon manually, then created a survey for people to tell him common Fon sentences and phrases. The resulting French-Fon translator he built has helped him better communicate with his mother—and his mother’s feedback on those translations has helped improve the AI program. “I would have needed a machine-translation tool to be able to communicate with her,” he said. Now he is beginning to understand her without machine assistance. A person and their community, rather than the internet or a piece of software, should decide their native language—and Dossou is realizing that his is Fon, rather than French.

Matteo Wong is an associate editor at The Atlantic.

 

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Intron Health gets backing for its speech-recognition tool that recognizes African accents | TechCrunch

Intron Health has created what it claims to be Africa’s largest clinical database, which comprises 3.5 million audio clips (16,000 hours) from over 18,000 contributors, mainly healthcare practitioners, representing 29 countries and 288 accents.

techcrunch.com

Intron Health gets backing for its speech recognition tool that recognizes African accents​

Annie Njanja



4:04 AM PDT • July 25, 2024



Comment

Image Credits: Intron Health

Voice recognition is getting integrated in nearly all facets of modern living, but there remains a big gap: speakers of minority languages, and those with thick accents or speech disorders like stuttering are typically less able to use speech recognition tools that control applications, transcribe or automate tasks, among other functions.

Tobi Olatunji, founder and CEO of clinical speech recognition startup Intron Health, wants to bridge this gap. He claims that Intron is Africa’s largest clinical database, with its algorithm trained on 3.5 million audio clips (16,000 hours) from over 18,000 contributors, mainly healthcare practitioners, representing 29 countries and 288 accents. Olatunji says that drawing most of its contributors from the healthcare sector ensures that medical terms are pronounced and captured correctly for his target markets.


“Because we’ve already trained on many African accents, it’s very likely that the baseline performance of their access will be much better than any other service they use,” he said, adding that data from Ghana, Uganda and South Africa is growing, and that the startup is confident about deploying the model there.

Olatunji’s interest in health-tech stems from two strands of his experience. First, he got training and practiced as a medical doctor in Nigeria, where he saw first-hand the inefficiencies of the systems in that market, including how much paperwork needed to be filled out, and how hard it was to track all of it.

“When I was a doctor in Nigeria a couple years ago, even during medical school and even now, I get irritated easily doing a repetitive task that is not deserving of human efforts,” he said. “An easy example is we had to write a patient’s name on every lab order you do. And just something that’s simple, let’s say I’m seeing the patients, and they need to get some prescriptions, they need to get some labs. I have to manually write out every order for them. It’s just frustrating for me to have to repeat the patient name over and over on each form, the age, the date, and all that… I’m always asking, how can we do things better? How can we make life easier for doctors? Can we take some tasks away and offload them to another system so that the doctor can spend their time doing things that are very valuable?”

Those questions propelled him to the next phase of his life. Olatunji moved to the U.S. to pursue a initially a masters degree in medical informatics from the University of San Francisco and then another in computer science at Georgia Tech.

He then cut his teeth at a number of tech companies. As a clinical natural language programming (NLP) scientist and researcher at Enlitic, a San Francisco Bay Area company, he built models to automate the extraction of information from radiology text reports. He also served Amazon Web Services as a machine learning scientist. At both Enlitic and Amazon, he focused on natural language processing for healthcare, shaping systems that enable hospitals to run better.



Throughout those experiences, he started to form ideas around how what was being developed and used in the U.S. could be used to improve healthcare in Nigeria and other emerging markets like it.



The original aim of Intron Health, launched in 2020, was to digitize hospital operations in Africa through an Electronic Medical Record (EMR) System. But take-up was challenging: it turned out physicians preferred writing to typing, said Olatunji.

That led him to explore how to improve that more basic problem: how to make physicans’ basic data entry, writing, work better. At first the company looked at third-party solutions for automating tasks such as note taking, and embedding existing speech to text technologies into his EMR program.

There were a lot of issues, however, because of constant mis-transcription. It became clear to Olatunji that thick African accents and the pronunciation of complicated medical terms and names made the adoption of existing foreign transcription tools impractical.

This marked the genesis of Intron Health’s speech recognition technology, which can recognize African accents, and can also be integrated in existing EMRs. The tool has to date been adopted in 30 hospitals across five markets, including Kenya and Nigeria.

There have been some immediate positive outcomes. In one case, Olatunji said, Intron Health has helped reduce the waiting time for radiology results at one of West Africa’s largest hospitals from 48 hours to 20 minutes. Such efficiencies are critical in healthcare provision, especially in Africa, where the doctor to patient ratio remains one of the lowest in the world.


“Hospitals have already spent so much on equipment and technology…Ensuring that they apply these tech is important. We’re able to provide value to help them improve the adoption of the EMR system,” he said.

Looking ahead, the startup is exploring new growth frontiers backed by a $1.6 million pre-seed round, led by Microtraction, with participation from Plug and Play Ventures, Jaza Rift Ventures, Octopus Ventures, Africa Health Ventures, OpenseedVC, Pi Campus, Alumni Angel, Baker Bridge Capital and several angel investors.



In terms of technology, Intron Health is working to perfect noise cancelation, as well as ensuring that the platform works well even in low bandwidths. This is in addition to enabling the transcription of multi-speaker conversations, and integrating text-to-speech capabilities.

The plan, Olatunji says, is to add intelligence systems or decision support tools for tasks such as prescription or lab tests. These tools, he adds, can help reduce doctor errors, and ensure adequate patient care besides speeding up their work.

Intron Health is among the growing number of generative AI startups in the medical space, including Microsoft’s DAX Express, which are reducing administrative tasks for clinicians by generating notes within seconds. The emergence and adoption of these technologies come as the global speech and voice recognition market is projected to be valued at $84.97 billion by 2032, following a CAGR of 23.7% from 2024, according to Fortune Business Insights.



Beyond building voice technologies, Intron is also playing a pivotal role in speech research in Africa, having recently partnered with Google Research, the Bill & Melinda Gates Foundation, and Digital Square at PATH to evaluate popular Large Language Models (LLMs) such as OpenAI’s GPT-4o, Google’s Gemini, and Anthropic’s Claude across 15 countries, to identify strengths, weaknesses, and risks of bias or harm in LLMs. This is all in a bid to ensure that culturally attuned models are available for African clinics and hospitals.