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Paul Wekem, CPP Writes: Africa Security Landscape

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Economic hardship, urbanization and climate change is likely to restructure Africa’s security landscape by 2023 couple with natural disaster that is: flooding resulting from high sea levels and torrential rainfall and a surge in migration. The impact of these tendencies will contrast over time. Economic hardship, unemployment, crime and corruption on the continent will trigger youth agitation and demonstration.

The unescapable evolution of Africa’s security landscape will clearly change the security environment rendering it volatile and complex. The combined consequence of growing populace and desertification in the Sahel will activate a heighten tensions rendering the inhabitants prone to vehemence extremism. The diverse pathways and potential impacts of this evolution could inform the design and implementation of relevant policies to further strengthen the corridors of security.

The 10-month civil war in Ethiopia which has exiled about 2 million people from the fragile environment, has led to massive human rights violations, and famine in the northern Tigray region. Studies has shown that the Gulf of Guinea remains the hotspot for piracy, drug trafficking and other transnational organized crime remains an enduring concern in Central, West and East Africa.

Cyber criminals continue to operate across the region by taken an advantage over weak infrastructures.

On the other hand, U.S. adversaries are expanding their footprint on the continent, building naval bases, selling surveillance technology and drones, and dispatching mercenaries to conflict zones.

Russian legionnaires are operating in Central African Republic and have been asked by the Malian government to render services in the Sahel. Russia has signed more than 20 bilateral military cooperation agreements with African states since 2015. Africa’s future is threatened by violent extremist in the Sahel and Al-Shabaab affiliates in Somalia, fueled by developed countries to their advantage.

China has a base in Djibouti and has funded about 44 commercial ports across the region and continue to expand its economic and military presence on the continent. Today China is Africa’s largest trading partner.

 

The limitations of African militaries in response to these threats coupled with over reliance on developed countries have been all too evident. Their weakness in Mali and the CAR was probably inevitable in view of their economic and geographic fundamentals. While retrospectively it is always easy to identify political weaknesses, prior to its collapse Mali was not on any of the three independently maintained lists of fragile states. It was one of the better-conducted democracies.

In contrast, the limitations of the military in Nigeria and Kenya are not fundamentally economic. In both countries the military tasks required to respond to terrorist attacks were relatively modest. In Nigeria, about 200 schoolgirls were abducted by a small rebel group and taken to a forest area, yet they could not immediately be traced by the Nigerian military. In Kenya, a shopping mall in Nairobi was overrun by terrorists, and coastal villages were brutally attacked. When the military was called in to the Nairobi shopping mall, they took the opportunity to loot the shops.

 

We need to recognize the opportunity of Africa and invest in the continent in support of African aspirations for both democratic governance, repositioning of our defense and response strategies  to deal with the growing insecurity  in the region.

Written By Security Analyst, Paul Wekem, CPP.

 

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Meta’s AI AI machine translation research helps break language barriers

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Today, Meta announced that it has built and open sourced ‘No Language Left Behind’ NLLB-200, a single AI model that is the first to translate across 200 different languages, including 55 African languages with state-of-the-art results. Meta is using the modelling techniques and learnings from the project to improve and extend translations on Facebook, Instagram, and Wikipedia.

 

In an effort to develop high-quality machine translation capabilities for most of the world’s low-resource languages, this single AI model was designed with a focus on African languages. They are challenging from a machine translation perspective. AI models require lots and lots of data to help them learn, and there’s not a lot of human translated training data for these languages. For example, there’s more than 20M people who speak and write in Luganda but examples of this written language are extremely difficult to find on the internet.

 

We worked with professional translators for each of these languages to develop a reliable benchmark which can automatically assess translation quality for many low-resource languages. We also work with professional translators to do human evaluation too, meaning people who speak the languages natively evaluate what the AI produced. The reality is that a handful of languages dominate the web, so only a fraction of the world can access content and contribute to the web in their own language. We want to change this by creating more inclusive machine translations systems – ones that unlock access to the web for the more than 4B people around the world that are currently excluded because they do not speak one of the few languages content is available in.

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“It’s impressive how much AI is improving all of our services. We just open-sourced an AI model we built that can translate across 200 different languages — many of which aren’t supported by current translation systems. We call this project No Language Left Behind, and the AI modelling techniques we used are helping make high quality translations for languages spoken by billions of people around the world. To give a sense of the scale, the 200-language model has over 50 billion parameters, and we trained it using our new Research SuperCluster, which is one of the world’s fastest AI supercomputers. The advances here will enable more than 25 billion translations every day across our apps. Communicating across languages is one superpower that AI provides, but as we keep advancing our AI work it’s improving everything we do — from showing the most interesting content on Facebook and Instagram, to recommending more relevant ads, to keeping our services safe for everyone,” said Meta CEO Mark Zuckerberg in a post on his Facebook profile.

 

Language is our culture, identity, and lifeline to the world. However, as high-quality translation tools don’t exist for hundreds of languages, billions of people today can’t access digital content or participate fully in conversations and communities online in their preferred or native languages. This is especially true for hundreds of millions of people who speak the many languages of Africa.

 

“Africa is a continent with very high linguistic diversity, and language barriers exist day to day. We are pleased to announce that 55 African languages will be included in this machine translation research, making it a major breakthrough for our continent,” Balkissa Ide Siddo, Public Policy Director for Africa said while speaking about the launch of the AI model. “In the future, imagine visiting your favourite Facebook group, coming across a post in Igbo or Luganda, and being able to understand it in your own language with just a click of a button – that’s where we hope research like this leads us. Highly accurate translations in more languages could also help to spot harmful content and misinformation, protect election integrity, and curb instances of online sexual exploitation and human trafficking.”

 

While commenting on accessibility and inclusion in the pursuit of building an equitable metaverse, Ide Siddo added “At Meta, we are working today to ensure that as many people as possible will be able to access the new educational, social and economic opportunities that the next evolution of the internet will bring to future technology and an everyday living experience tomorrow.”

 

To confirm that the translations are high quality, Meta also created a new evaluation dataset, FLORES-200, and measured NLLB-200’s performance in each language. Results revealed that NLLB-200 exceeds the previous state of the art by an average of 44 percent.

 

Meta is also open-sourcing the NLLB-200 model and publishing a slew of research tools to enable other researchers to extend this work to more languages and build more inclusive technologies. Meta AI is also providing up to $200,000 of grants to non-profit organizations for real world applications for NLLB-200.

 

There are versions of Wikipedia in more than 300 languages, but most have far fewer articles than the 6+ million available in English. Following Meta’s partnership with the Wikimedia Foundation, the non-profit organization that hosts Wikipedia and other free knowledge projects, modelling  techniques and learnings from the NLLB research are now also being applied to translation systems used by Wikipedia editors. Using the Wikimedia Foundation’s Content Translation Tool, articles can now be easily translated in more than 20 low-resource languages (those that don’t have extensive datasets to train AI systems), including 10 that previously were not supported by any machine translation tools on the platform.

 

To explore a demo of NLLB-200 showing how the model can translate stories from around the world, visit here. You can also read the research paper here.

 

 

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