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This company is constructing AI for African languages

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This company is constructing AI for African languages

Inside a co-working space within the Rosebank neighborhood of Johannesburg, Jade Abbott popped open a tab on her computer and prompted ChatGPT to count from 1 to 10 in isiZulu, a language spoken by greater than 10 million people in her native South Africa. The outcomes were “mixed and hilarious,” says Abbott, a pc scientist and researcher. 

Then she typed in a number of sentences in isiZulu and asked the chatbot to translate them into English. Once more, the answers? Not even close. Although there have been efforts to incorporate certain languages in AI models even when there shouldn’t be much data available for training, to Abbott, these results show that the technology “really still isn’t capturing our languages.”  

Abbott’s experience mirrors the situation faced by Africans who don’t speak English. Many language models like ChatGPT don’t perform well for languages with smaller numbers of speakers, especially African ones. But a brand new enterprise called Lelapa AI, a collaboration between Abbott and a biomedical engineer named Pelonomi Moiloa, is attempting to use machine learning to create tools that specifically work for Africans.

Vulavula, a brand new AI tool that Lelapa released today, converts voice to text and detects names of individuals and places in written text (which may very well be useful for summarizing a document or looking for someone online). It will possibly currently discover 4 languages spoken in South Africa—isiZulu, Afrikaans, Sesotho, and English—and the team is working to incorporate other languages from across Africa. 

The tool will be used by itself or integrated into existing AI tools like ChatGPT and online conversational chatbots. The hope is that Vulavula, which implies “speak” in Xitsonga, will make accessible those tools that do not currently support African languages.

The shortage of AI tools that work for African languages and recognize African names and places excludes African people from economic opportunities, says Moiloa, CEO and cofounder of Lelapa AI. For her, working to construct Africa-centric AI solutions is a technique to help others in Africa harness the immense potential advantages of AI technologies. “We are attempting to unravel real problems and put power back into the hands of our people,” she says.  

“We cannot wait for them”   

There are literally thousands of languages on this planet, 1,000 to 2,000 of them in Africa alone: it’s estimated that the continent accounts for one-third of the world’s languages. But though native speakers of English make up just 5% of the worldwide population, the language dominates the net—and has now come to dominate AI tools, too.  

Some efforts to correct this imbalance exist already. OpenAI’s GPT-4 has included minor languages like Icelandic. In February 2020, Google Translate began supporting five recent languages spoken by about 75 million people. However the translations are shallow, the tool often gets African languages unsuitable, and it’s still a great distance from an accurate digital representation of African languages, African AI researchers say.

Earlier this 12 months, for instance, the Ethiopian computer scientist Asmelash Teka Hadgu ran the identical experiments that Abbott ran with ChatGPT at a premier African AI conference in Kigali, Rwanda. When he asked the chatbot questions in his mother tongue of Tigrinya, the answers he got were gibberish. “It generated words that do not make any sense,” says Hadgu, who cofounded Lesan, a Berlin-based AI startup that’s developing translation tools for Ethiopian languages. 

Lelapa AI and Lesan are only two of the startups developing speech recognition tools for African languages. In February, Lelapa AI raised $2.5 million in seed funding, and the corporate plans for the subsequent funding round in 2025. But African entrepreneurs say they face major hurdles, including lack of funding, limited access to investors, and difficulties in training AI to learn diverse African languages. “AI receives the least funding amongst African tech startups,” says Abake Adenle, the founding father of AJALA, a  London-based startup that gives voice automation for African languages.  

The AI startups working to construct products that support African languages often get ignored by investors, says Hadgu, owing to the small size of the potential market, an absence of political support, and poor web infrastructure. Nonetheless, Hadgu says small African startups including Lesan, GhanaNLP, and Lelapa AI are playing a vital role: “Big tech corporations don’t give focus to our languages,” he says, “but we cannot wait for them.”  

A model for African AI  

Lelapa AI is attempting to create a brand new paradigm for AI models in Africa, says Vukosi Marivate, an information scientist on the corporate’s AI team. As a substitute of tapping into the web alone to gather data to coach its model, like corporations within the West, Lelapa AI works each online and offline with linguists and native communities to collect data, annotate it, and discover use cases where the tool may be problematic. 

Bonaventure Dossou, a researcher at Lelapa AI specializing in natural-language processing (NLP), says that working with linguists enables them to develop a model that’s context-specific and culturally relevant. “Embedding cultural sensitivity and linguistic perspectives makes the technological system higher,” says Dossou. For instance, the Lelapa AI team built sentiment and tone evaluation algorithms tailored to specific languages. 

Marivate and his colleagues at Lelapa AI envision a future wherein AI technologies work for and represent Africans. In 2019, Marivate and Abbott established Masakhane, a grassroots initiative that goals to advertise NLP research in African languages. The initiative now has hundreds of volunteers, coders, and researchers working together to construct Africa-centric NLP models. 

It matters that Vulavula and other AI tools are built by Africans for Africans, says Moiloa: “We’re the custodians of our languages. We needs to be the builders of technologies that work for our languages.”           

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