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Starsight Energy is Africa’s fastest growing renewable energy company

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Emmanuel Ayifa Baah Managing Director for Starsight Energy Ghana

Starsight Energy, a leading African commercial & industrial solar power supplier with a significant presence in Ghana, has been ranked the fastest growing solar and renewable energy company in Africa by the Financial Times (FT).

According to the FT, in its inaugural ranking of Africa’s fastest-growing companies in 2022, Starsight is the sixth fastest growing company in Africa and the first energy and renewable energy on the list. The rankings show that Starsight made tremendous progress since its inception in 2015. The company’s revenues in 2020 stood at US$8.886million as compared to its revenues in 2017 which was at US$0.832million.

Currently, its absolute growth is 1154.403 percent and a compound annual growth rate of 132.351 percent. Staff strength of Starsight stood at 42 in 2017 but increased to 108 in 2020, hence making it to the fastest growing company rankings by FT.

With more than 51MW of solar power installed in West Africa as at the end of 2021, the pan-African renewable energy company operates in three regions in Africa: West, East and South Africa with a clear plan to be in other regions.

Managing Director for Starsight Energy Ghana, Emmanuel Ayifa Baah in an interview with the B&FT described the feat as humbling.

“It is humbling but at the same time it is a recognition of the hard work the team has put in place over the period. It is just natural that you get rewarded when you do solve a problem. It is humbling to know that what you are doing to help companies Go Green whiles saving them on electricity cost & carbon emissions has been recognised on a global scale.

When you have a business operating the previous year only in Nigeria and just a year plus down the line, you have operations in Ghana, Kenya, Rwanda, Tanzania, and then  into South Africa, obviously that is a major leap from one location to all of these locations,” Mr. Ayifa Baah said.

Operations in Ghana

Delving deeper into the company’s operations in Ghana, Mr. Ayifa Baah explained that there is some 10MW of solar power under deployment for commercial and industrial institutions with over 50percent installed. “The bulk of our installations are in Accra but we have installations in Kumasi, and some parts of the Eastern Region,” he said.

Mr. Ayifa Baah, striking an optimistic tone, said by the end of 2022, Starsight Energy Ghana is seeking to have more than 10MW installed for Commercial & Industrial clients.

The call to adopt solar as a more sustainable power option

With the recent request for increment in electricity tariffs as a result of  a general increase in generation cost of utility companies, Mr. Baah urged consumers, especially commercial and industrial users of power, to look at solar as a more sustainable option. “When you go solar, you save money and protect the environment at the same time,” he said, adding that at Starsight, their lease-to-own package lead to consumers saving at least 15 to 45 percent of their monthly electricity bills.

“We run a lease-to-own model. We build the solar plant at our cost, we maintain it at our cost, you just pay for using the plant and while you are paying, you are acquiring it. Our contracts start from five years and if you want a contract for over 10 years, we can, we are very flexible with our options.

You do not incur any additional cost; you are still paying your monthly utility at a reduced amount and yet get to own the plant within a number of years. Our target starts from 15 percent reduction in your tariff all the way to 45 percent per kilowatts hour savings on electricity cost,” Mr. Ayifa Baah added.

About Starsight Energy

Founded in 2015, Starsight is the leading African Commercial & Industrial (C&I) energy-as-aservice provider. It has deployed approximately 63 MW of generation assets and 25 MWh of storage at over 500 sites in all six African countries. The Company’s team of highly experienced professionals provides power solutions to a diverse clientele, including banks, gas stations, schools, and large commercial agricultural companies.

Starsight’s proprietary smart technology optimizes energy consumption enabling customers to significantly reduce energy costs and boost profitability without incurring any upfront expense. Starsight delivers an end-to-end service, starting with assessing a client’s energy needs to the installation and maintenance of a renewable energy solution. This is then followed by ongoing technical support.

Starsight’s long-term relationships with its blue-chip clients reflect the quality of its in-house engineering, procurement, and construction services, along with the reliability of its operations, maintenance, and customer service. Together these provide customers with the peace of mind that all their power-related issues are in trustworthy hands. Starsight has consistently maintained the highest ESG credentials and continues to reduce its customers’ carbon footprint and harmful diesel emissions.

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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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