Both Facebook and TikTok have blocked some hashtags being used to spread misinformation around the election — Twitter says it's ‘proactively monitoring’ them — Facebook and TikTok have blocked hashtags that were being used to spread misinformation and conspiracy theories about the presidential election.
Internal Facebook metric that assesses “violence and incitement trends” based on hashtags and search terms in posts increased by 45% since October 31 — As votes are being tallied across the country to determine the next US president, internal Facebook data shows that the company …
Sources: Facebook plans to temporarily add more “friction” to users' ability to share posts and other content and will also demote election misinfo in News Feed — New measures would slow down the flow of information and make election misinformation less visible, according to people with knowledge of the matter.
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Both Facebook and TikTok have blocked some hashtags being used to spread misinformation around the election — Facebook today began to block select hashtags that were being used to share misinformation related to the 2020 U.S. presidential election. — Now, searches for the hashtags #sharpiegate …
While performing machine translations, you may have situations where you wish to preserve specific sections of text from being translated, such as names, unique identifiers, or codes. We at the Amazon Translate team are excited to announce a tag modifications that allows you to specify what text should not be translated. This feature is available in both the real-time TranslateText API and asynchronous batch TextTranslation API. You can tag segments of text that you don’t want to translate in an HTML element. In this post, we walk through the step-by-step method to use this feature.
The following example shows you how to use the translate-text operation from the command line. This example is formatted for Unix, Linux, and macOS. For Windows, replace the backslash (\) Unix continuation character at the end of each line with a caret (^). At the command line, enter the following code:
You can specify any type of HTML element to do so, for example, paragraph <p>, text section <span>, or block section <div>. When you run the command, you get the following output:
In this example, we translate the following text from French to English:
Musée du Louvre, c’est ainsi que vous dites Musée du Louvre en français.
You don’t want to translate the first instance of “Musée du Louvre,” but you do want to translate the second instance to “Louvre Museum.” You can tag the first instance using a simple span tag:
The following screenshot shows the output on the Amazon Translate console.
The following screenshot shows the output translated to Arabic.
In this post, we showed you how to tag and specify text that should not be translated. For more information, see the Amazon Translate Developer Guide and Amazon Translate resources. If you’re new to Amazon Translate, try it out using our Free Tier, which offers 2 million characters per month for free for the first 12 months, starting from your first translation request.
Watson G. Srivathsan is the Sr. Product Manager for Amazon Translate, AWS’s natural language processing service. On weekends you will find him exploring the outdoors in the Pacific Northwest.
While performing machine translations, you may have situations where you wish to preserve specific sections of text from being translated, such as names, unique identifiers, or codes. We at the Amazon Translate team are excited to announce a tag modifications that allows you to specify what text should not be translated. This feature is available in both the real-time TranslateText API and asynchronous batch TextTranslation API. You can tag segments of text that you don’t want to translate in an HTML element. In this post, we walk through the step-by-step method to use this feature.
The following example shows you how to use the translate-text operation from the command line. This example is formatted for Unix, Linux, and macOS. For Windows, replace the backslash (\) Unix continuation character at the end of each line with a caret (^). At the command line, enter the following code:
You can specify any type of HTML element to do so, for example, paragraph <p>, text section <span>, or block section <div>. When you run the command, you get the following output:
In this example, we translate the following text from French to English:
Musée du Louvre, c’est ainsi que vous dites Musée du Louvre en français.
You don’t want to translate the first instance of “Musée du Louvre,” but you do want to translate the second instance to “Louvre Museum.” You can tag the first instance using a simple span tag:
The following screenshot shows the output on the Amazon Translate console.
The following screenshot shows the output translated to Arabic.
In this post, we showed you how to tag and specify text that should not be translated. For more information, see the Amazon Translate Developer Guide and Amazon Translate resources. If you’re new to Amazon Translate, try it out using our Free Tier, which offers 2 million characters per month for free for the first 12 months, starting from your first translation request.
Watson G. Srivathsan is the Sr. Product Manager for Amazon Translate, AWS’s natural language processing service. On weekends you will find him exploring the outdoors in the Pacific Northwest.
The pandemic has changed how people interact, how we receive information, and how we get help. It has shifted much of what used to happen in-person to online. Many of our customers are using machine learning (ML) technology to facilitate that transition, from new remote cloud contact centers, to chatbots, to more personalized engagements online. Scale and speed are important in the pandemic—whether it’s processing grant applications or limiting call wait times for customers. ML tools like Amazon Lex and Amazon Connect are just a few of the solutions helping to power this change with speed, scale, and accuracy. In this post, we explore companies who have quickly pivoted to take advantage of AI capabilities to engage more effectively online and deliver immediate impact.
GovChat is South Africa’s largest citizen engagement platform, connecting over 50 million citizens to 10,000 public representatives in the government. Information flowing to and from the government has gained a new level of urgency, and this connection between citizens and the government is critical in how we adjust and respond to the pandemic. GovChat exists to meet that demand—working directly with the South African government to facilitate the digitization of their COVID-19 social relief grants, help citizens find their closest COVID-19 testing facility, and enable educational institutions to reopen safely.
GovChat uses a chatbot powered by Amazon Lex, a managed AI service for building conversational interfaces into any application using voice and text. The chatbot, available on popular social media platforms such as WhatsApp and Facebook, provides seamless communication between the government and its citizens.
At the beginning of the pandemic, GovChat worked with the South African Social Security Agency to digitize, facilitate, and track applications for a COVID-19 social relief grant. The plan was to create a chatbot that could help citizens easily file and track their grant applications. GovChat needed to act quickly and provide an infrastructure that could rapidly scale to support the unprecedented demand for government aid. To provide speed of delivery and scalability while keeping costs down, GovChat turned to Amazon Lex for voice and text conversational interfaces and AWS Lambda, a serverless compute service. Within days, the chatbot was handling up to 14.2 million messages a day across social media platforms in South Africa regarding the social relief grant.
More recently, the South African Human Rights Commission (SAHRC) turned to GovChat to help gauge schools’ readiness to reopen safety. Parents, students, teachers, and community members can use their mobile devices to provide first-hand, real-time details of their school’s COVID-19 safety checks and readiness as contact learning is resumed, with special attention paid to children with disabilities. In GovChat’s engagements during the COVID-19 pandemic, they found that 28% of service requests at schools have been in relation to a disruption in access to water, which is critical for effective handwashing—a preventative component to fight the spread of the virus. With the real-time data provided by citizens via the chatbot, the government was able to better understand the challenges schools faced and identify areas of improvement. GovChat has processed over 250 million messages through their platform, playing an important role in enabling more effective and timely communications between citizens and their government.
Organizations of all kinds have also experienced a rapid increase in call volume to their call centers—from local government, to retail, to telecommunications, to healthcare providers. Organizations have also had to quickly shift to a remote work environment in response to the pandemic. Origin Energy, one of Australia’s largest integrated energy companies serving over 4 million customer accounts, launched an Amazon Connect contact center in March as part of their customer experience transformation. Amazon Connect is an omnichannel cloud contact center with AI/ML capabilities that understands context and can transcribe conversations.
This transition to Amazon Connect accelerated Origin’s move to remote working during the COVID-19 pandemic. This allowed their agents to continue to serve their customers, while also providing increased self-service and automation options such as bill payments, account maintenance, and plan renewals to customers. They deployed new AI/ML capabilities, including neural text-to-speech through Amazon Polly. Since the March 2020 launch, they’ve observed an increase in call quality scores, improved customer satisfaction, and agent productivity—all while managing up to 1,200 calls at a time. They’re now looking to further leverage natural language understanding with Amazon Lex and automated quality management with built-in speech-to-text and sentiment analysis from Contact Lens for Amazon Connect. Amazon Connect has supported Origin in their efforts to respond rapidly to opportunities and customer feedback as they focus on continually improving their customer experience with affordable, reliable, and sustainable energy.
Organizations are employing creative strategies to engage their customers and provide a more seamless experience. This is a two-way street; not only can organizations more effectively distribute key information, but—more importantly—they can listen. They can hear the evolving needs of their customers and adjust in real time to meet them.
To learn about another way AWS is working toward solutions from the COVID-19 pandemic, check out the blog article Introducing the COVID-19 Simulator and Machine Learning Toolkit for Predicting COVID-19 Spread.
Taha A. Kass-Hout, MD, MS, is director of machine learning and chief medical officer at Amazon Web Services (AWS). Taha received his medical training at Beth Israel Deaconess Medical Center, Harvard Medical School, and during his time there, was part of the BOAT clinical trial. He holds a doctor of medicine and master’s of science (bioinformatics) from the University of Texas Health Science Center at Houston.
Twitter says Trump's special treatment around offensive or misleading content rule violations will expire in January if he loses the presidency — - For now, violating posts get warning label, not removal — But ‘world leader’ newsworthiness rules wouldn't apply