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Global content piracy on the rise

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As digital systems have become more prominent globally and accessible to most in the last couple of years, so has the rise of illegal web and unlicensed web streaming sites.In a report by MUSO, released in March 2019, global piracy numbers reached almost 190 billion pirate site visits in 2018.

It has been reported that over 189 billion visits were made to pirate sites in 2018. Television related piracy took up 49.38% of all activity related to piracy, with only 17.9% for access to film.  Music was ranked third most pirated medium in 2018, with a 15.87% share. Publishing and software were less affected with 11.49% and 6.16% respectively.

In addition, in a recent report from Irdeto, MultiChoice Group’s digital platform security partner, in March 2019, Ghana, Algeria, Angola, Cameroon, Cote d’Ivoire, Egypt, Kenya, Morocco, Nigeria, Senegal, South Africa and Tanzania were reported to be the highest consumers of pirated content in Africa.

Streaming piracy has now overtaken all other forms of threat to the revenue of legitimate service providers that have paid for content rights according to the wide consensus. It is estimated that almost 60 % of all piracy visits are to unlicensed web streaming sites, whereas public torrent networks now only equate to 13% of piracy visit activity.  The arrival of IP-based distribution has raised further concerns.  Recent data from Irdeto and its web analytics partner found an average of over 134 million visits per month and an average of almost 35 million unique visits per month to the top 10 live streaming linking sites.

Whilst there’s an urgent need to be vigilant and restrict the distribution of unlicensed content, there are also unintended consequences that most consumers are not aware off that are associated with pirated content.  Viewing illegal streams, visiting pirated sites and downloading pirated content exposes consumers.  Consumers are risking exposure to malware infection, cybercrime threats and other unintended consequences. The people behind the illegal streaming sites, pirated sites and distributors of illegal content are often criminals who will look to take advantage of unsuspecting consumers in any way they can to profit beyond pirated content.

Whilst it has also been suggested that there are links from large scale piracy to organized criminal groups who are involved in many other crimes, pirated content also impacts government’s tax revenue. The loss of income revenue by the creative industry, specifically film and TV producers, distributors and broadcasters amount to billions due to illegally downloading, purchase or streaming of content. As government cannot tax illegal purchases, a massive loss of tax revenue is incurred as a result of film & Television content piracy. A report last year from Digital TV Research predicted that lost revenue to online TV and movie piracy will nearly double to $52 million globally by 2022.

Society at large, creative industry, government, regulators, law enforcers and international policy enforcing agencies need to collaborate and work together to identify and takedown the distributors and personnel behind the pirated content.  They can effectively put a stop to the supply side by making it difficult for pirates to thrive and monetize pirated content, demand can be reduced by educating consumers and society at large about the dangers and unintended consequences of download, streaming and purchasing pirated content.

 

 

 

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How to Be More Confident on Camera as a Youtuber

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CONFIDENCE is very important. The more confident you appear, the more viewers will listen to you. (more…)

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Graduate who went viral for begging for a job with a placard says he’s received 50 offers since

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Isaac Kwame Addae, a young unemployed graduate who took to the streets with a placard, looking for a job, says he  has been offered opportunities by at least  50 different firms. (more…)

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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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I single-handedly popularized Shea Butter in the United States – Margaret Andega

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Ludwig Nii Jr urges information sharing in the creative space

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