Tech
African languages for AI: The project that’s gathering a huge new dataset
Artificial intelligence (AI) tools like ChatGPT, DeepSeek, Siri or Google Assistant are developed by the global north and trained in English, Chinese or European languages. In comparison, African languages are largely missing from the internet.
A team of African computer scientists, linguists, language specialists and others have been working on precisely this problem for two years already. The African Next Voices project recently released what’s thought to be the largest dataset of African languages for AI so far. We asked them about their project, with sites in Kenya, Nigeria and South Africa.
Why is language so important to AI?
Language is how we interact, ask for help, and hold meaning in community. We use it to organize complex thoughts and share ideas. It’s the medium we use to tell an AI what we want—and to judge whether it understood us.
We are seeing an upsurge of applications that rely on AI, from education to health to agriculture. These models are trained from large volumes of (mostly) linguistic (language) data. These are called large language models or LLMs but are found in only a few of the world’s languages.
Languages also carry culture, values and local wisdom. If AI doesn’t speak our languages, it can’t reliably understand our intent, and we can’t trust or verify its answers. In short: without language, AI can’t communicate with us—and we can’t communicate with it. Building AI in our languages is therefore the only way for AI to work for people.
If we limit whose language gets modeled, we risk missing out on the majority of human cultures, history and knowledge.
Why are African languages missing and what are the consequences for AI?
The development of language is intertwined with the histories of people. Many of those who experienced colonialism and empire have seen their own languages being marginalized and not developed to the same extent as colonial languages. African languages are not as often recorded, including on the internet.
So there isn’t enough high-quality, digitized text and speech to train and evaluate robust AI models. That scarcity is the result of decades of policy choices that privilege colonial languages in schools, media and government.
Language data is just one of the things that’s missing. Do we have dictionaries, terminologies, glossaries? Basic tools are few and many other issues raise the cost of building datasets. These include African language keyboards, fonts, spell-checkers, tokenizers (which break text into smaller pieces so a language model can understand it), orthographic variation (differences in how words are spelled across regions), tone marking and rich dialect diversity.
The result is AI that performs poorly and sometimes unsafely: mistranslations, poor transcription, and systems that barely understand African languages.
In practice this denies many Africans access—in their own languages—to global news, educational materials, health care information, and the productivity gains AI can deliver.
When a language isn’t in the data, its speakers aren’t in the product, and AI cannot be safe, useful or fair for them. They end up missing the necessary language technology tools that could support service delivery. This marginalizes millions of people and increases the technology divide.
What is your project doing about it—and how?
Our main objective is to collect speech data for automatic speech recognition (ASR). ASR is an important tool for languages that are largely spoken. This technology converts spoken language into written text.
The bigger ambition of our project is to explore how data for ASR is collected and how much of it is needed to create ASR tools. We aim to share our experiences across different geographic regions.
The data we collect is diverse by design: spontaneous and read speech; in various domains—everyday conversations, health care, financial inclusion and agriculture. We are collecting data from people of diverse ages, gender and educational backgrounds.
Every recording is collected with informed consent, fair compensation and clear data-rights terms. We transcribe with language-specific guidelines and a large range of other technical checks.
In Kenya, through Maseno Centre for Applied AI, we are collecting voice data for five languages. We’re capturing the three main language groups Nilotic (Dholuo, Maasai and Kalenjin) as well as Cushitic (Somali) and Bantu (Kikuyu).
Through Data Science Nigeria, we are collecting speech in five widely spoken languages—Bambara, Hausa, Igbo, Nigerian Pidgin and Yoruba. The dataset aims to accurately reflect authentic language use within these communities.
In South Africa, working through the Data Science for Social Impact lab and its collaborators, we have been recording seven South African languages. The aim is to reflect the country’s rich linguistic diversity: isiZulu, isiXhosa, Sesotho, Sepedi, Setswana, isiNdebele and Tshivenda.
Importantly, this work does not happen in isolation. We are building on the momentum and ideas from the Masakhane Research Foundation network, Lelapa AI, Mozilla Common Voice, EqualyzAI, and many other organizations and individuals who have been pioneering African language models, data and tooling.
Each project strengthens the others, and together they form a growing ecosystem committed to making African languages visible and usable in the age of AI.
How can this be put to use?
The data and models will be useful for captioning local-language media; voice assistants for agriculture and health; call-center and support in the languages. The data will also be archived for cultural preservation.
Larger, balanced, publicly available African language datasets will allow us to connect text and speech resources. Models will not just be experimental, but useful in chatbots, education tools and local service delivery. The opportunity is there to go beyond datasets into ecosystems of tools (spell-checkers, dictionaries, translation systems, summarization engines) that make African languages a living presence in digital spaces.
In short, we are pairing ethically collected, high-quality speech at scale with models. The aim is for people to be able to speak naturally, be understood accurately, and access AI in the languages they live their lives in.
What happens next for the project?
This project only collected voice data for certain languages. What of the remaining languages? What of other tools like machine translation or grammar checkers?
We will continue to work on multiple languages, ensuring that we build data and models that reflect how Africans use their languages. We prioritize building smaller language models that are both energy efficient and accurate for the African context.
The challenge now is integration: making these pieces work together so that African languages are not just represented in isolated demos, but in real-world platforms.
One of the lessons from this project, and others like it, is that collecting data is only step one. What matters is making sure that the data is benchmarked, reusable, and linked to communities of practice. For us, the “next” is to ensure that the ASR benchmarks we build can connect with other ongoing African efforts.
We also need to ensure sustainability: that students, researchers, and innovators have continued access to compute (computer resources and processing power), training materials and licensing frameworks (Like NOODL or Esethu). The long-term vision is to enable choice: so that a farmer, a teacher, or a local business can use AI in isiZulu, Hausa, or Kikuyu, not just in English or French.
If we succeed, built-in AI in African languages won’t just be catching up. It will be setting new standards for inclusive, responsible AI worldwide.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Tech
Left-Handed People Are More Competitive, Says Science
The very existence of left-handedness seems to defy Darwin. According to the theory of evolution by natural selection (in very simplified terms), a species should retain the characteristics necessary for survival and reproduction and discard those that are not very useful. And yet around 10 percent of people continue to develop greater dexterity in their left hand, a rate that has remained stable throughout history. Why do humans continue to retain this peculiar ability?
A study conducted by researchers at the University of Chieti-Pescara in Italy set out to confirm a hypothesis indicating that, while right-handed people have advantages in cooperative behaviors, left-handed people—particularly males, the study notes—have advantages in competitive behaviors, especially in one-on-one situations. This hypothesis is based on evolutionarily stable strategy (ESS), a concept from game theory applied to evolution.
This is how ESS explains why the proportion of left-handed people remains low but constant. If almost everyone in a population is right-handed, being left-handed offers a frequency-dependent advantage: Being in the minority, left-handers are less predictable in competitive interactions (e.g., a boxing match), which may translate into small advantages (left hook!). But if left-handedness became very common, that advantage would disappear because others would adapt to encountering left-handers with the same frequency. In evolutionary terms, a “stable equilibrium” is reached when the majority are right-handed and a minority are left-handed, because neither “strategy” can completely eliminate the other since their advantages change depending on how frequent each is in the population.
How can a study support this hypothesis? The Italian researchers conducted two experiments to see whether a dominant hand is linked to any specific personality type. The results were recently published in the academic journal Scientific Reports.
Righty vs. Lefty
In the first experiment, about 1,100 participants completed questionnaires designed to measure their handedness (their level of dexterity between one hand and the other) and various facets of competitiveness, such as their inclination to achieve personal goals or their aversion to anxiety-driven competition. The results showed that people who identified with greater left-handed laterality tended to show higher levels of personal development-oriented competitiveness and lower levels of anxious avoidance. That is, left-handers tended to be more inclined to engage in competitive situations than right-handers.
In addition, when strongly lateralized groups were compared (just pure southpaws, no ambidextrousness), left-handers scored higher on “hypercompetitiveness,” a trait that implies an intense desire to win, even at the expense of others.
In the second experiment, a subgroup of 48 participants (half right-handed and half left-handed, with equal proportions of men and women) took a pegboard test, a classic laboratory test that measures manual dexterity. Interestingly, no significant differences were observed here either between left-handers and right-handers or between laterality measures and competitiveness scores. This suggests that hand preference and competitiveness are not directly related to motor skills.
Give Them a Hand
According to the authors of the study, left-handedness is not simply a biological accident, but a characteristic that may offer advantages in competitive contexts and is therefore worth preserving. This supports, at least in part, the idea that the unequal distribution between right-handers and left-handers could be maintained by an evolutionary balance. While the right-handed majority favors social cooperation, the left-handed minority benefits in competitive contexts, where surprise plays a role.
But what about other personality types? Are left-handed people more extroverted or more emotionally unstable? The study cited here found no significant differences between left-handed and right-handed people in the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism). Nor was there any relationship between handedness and levels of depression or anxiety in this sample of people without a psychiatric diagnosis. This suggests that the advantage associated with left-handedness is more linked to competitiveness than to general differences in personality or mental health.
The study also examined differences by sex. Men, in general, scored higher on hyper-competitiveness and development-oriented competitiveness, while women showed a greater tendency to avoid competition due to anxiety. This suggests that the interaction between hand preference, competitive profile, and gender is complex and likely influenced by multiple biological and environmental factors that warrant further investigation.
This story originally appeared on WIRED en Español and has been translated from Spanish.
Tech
I Test Many Coffee Machines for a Living. This One Gets to Stay
Coffee is the original office biohack and the nation’s most popular productivity tool. As we lose sleep to the changeover to daylight saving time, the caffeine-addicted WIRED Reviews team is writing about our favorite coffee brewing routines and devices that’ll keep us alert and maybe even happy in the morning. Today, reviewer Matthew Korfhage expounds on his lasting love for drip coffee—and why the Ratio Four never leaves his counter. In the days after, we’ll add other Java.Base stories about other WIRED writers’ favorite brewing methods.
As with any vice worth having, a morning coffee routine can take on the character of religion. And like a lot of religion, it’s often born as much accident as moral conviction. My denomination is good, old-fashioned drip coffee. That’s what I drink first thing, before I even think about crafting a shot of espresso.
I’m WIRED’s lead coffee writer and I’ve developed a deep fondness for coffee’s many variations, from espresso to Aeropress to cold brew. But “coffee” to me, in my deepest soul, still means a steaming mug of unadulterated drip. Luckily, that’s also the coffee arena that has been transformed the most by technology in recent years. The drip coffee from the Ratio Four coffee maker (now quietly on its second generation) feels to me like coffee’s purest form, the liquid distillation of what my coffee beans smell like fresh off the grinder.
My love of filter coffee began as a teenager traveling and studying in India—perhaps my first glimpse of adult freedom. This is where I drank the first full cup of coffee I remember finishing. In Jaipur, filter coffee was an intense, jet-black gravity brew typically mixed with milk and sugar. I decided that if I was going to drink coffee, I would take it straight and learn to like it on its own terms. A newfound friend, tipping jaggery into his own brew, laughed at my insistence I didn’t want sweetened milk. I then downed a cup so thick and strong and caffeinated it made my hairs stand at perpendicular. If I’d made a mistake, I refused to admit it.
I carried this preference back to Oregon, drinking unadulteratedly black, terrible drip coffee at all-night diners and foul office breakrooms. Black coffee had become a morality clause, though it was hardly a matter of taste.
It wasn’t until years later that I discovered that drip coffee could actually be an indulgence every bit as refined as pinkies-up espresso.
Upping the Drip
In part, this was a problem of technology. Aside from a classic Moccamaster, it’s only very recently that home drip coffee makers have been able to produce a truly excellent cup. For years, I didn’t keep one at my home.
What woke me up to drip’s possibilities was a new wave of cafes in Portland, first third-wave coffee pioneer Stumptown Coffee and then especially Heart Coffee Roasters in Portland. Heart’s Norwegian owner-roaster, Wille Yli-Luoma, expounded to me at length about the aromatic purity of light-roast immersion coffee—the fruity aromatics of a first-crack Ethiopian that could smack of peach or nectarine or blueberry. Scandinavians had long prized this, he told me, and had evolved light-roast coffee into pure craft. America was finally catching up.
Still, I could never quite get that same flavor or clarity on a home brewer. Not until recently. To get the best version, I still had to walk up the street to Heart and get my coffee from the guy who roasted it. Or I had to spend way too long drizzling water over coffee in a conical filter. I rarely wanted to do this while still bleary from sleep, already late for work.
Tech
It’s Time to Wrangle Your Messy Wires With Our Handy Guide to Cable Management
There’s a reason we’re called WIRED. If there’s one thing most of today’s gadgets have in common, it’s that they typically need to be plugged in from time to time. But all those cables, cords, and wires can be tough to manage. They don’t have to end up in a tangled nest under your desk; you can bring order to the cable chaos.
As a gadget reviewer, I have more cords than most people, which is why I also have a regimented cable management strategy to keep everything orderly. Here are my tips and product recommendations for hiding those cords and power strips, and keeping your desktop tidy.
Jump To:
Planning and Prep
Start by surveying the scene, unplugging and untangling everything, and removing anything that doesn’t need to be there. You might be surprised to find a stray USB-B or Micro-USB you haven’t used in years in the mix. Before you get started on cable management, take a slightly damp microfiber cloth and wipe down all the surfaces and cables. Now, you can start planning routes and figuring out which cables it would make sense to bundle together.
Ideally, cables will be the exact required length, so if you have spares or you don’t mind snagging some new cables, it’s worth switching and getting as close as possible to exact lengths to reduce the excess cable you have to hide. If you have a standing desk, remember to take into account the cable length required for a standing position (trust me, dear reader, it’s no fun when you hit stand on the desk and it pulls your PC tower into the air by a DisplayPort cable that is now forever stuck in that port).
Cable Management
Tidying your tech often comes back to cable management, but there are several ways to keep those cords neatly out of sight. Many desks have channels, grommets, and power strip trays built-in, so have a quick look to make sure you’re using what’s available. Some monitor arms also have built-in cable management. You also likely have a bunch of cable ties in your junk drawer or toolbox, so gather them together.
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