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GreenKey Creates NLP Tool for Hedge Funds

AI

Focus Studio, the new application from GreenKey, will provide users with natural language processing workflows specific to hedge fund management.

Bank sales teams often turn to natural language processing (NLP) to find client insights — such as OTC quotes and trades — within emails, direct messages, and phone calls and manage increasing amounts of conversational data. This type of computational power is theoretically possible cross-linguistically as well, which has interesting implications for the language services industry. Specifically, the types of text processed might require unique needs to generate trade ideas. Addressing a specific need for hedge funds, GreenKey, creator of natural language processing (NLP) workflows for sales and trading, has released its latest version of the “Focus Studio” application.

Users of Focus Studio can customize NLP to go through various files and deliver highlighted insights as daily reports or power real-time automation, such as chatbots. This latest version of Focus Studio now includes NLP models designed specifically for hedge funds to help them cope with the amount of unstructured text they process.

Based in Chicago with offices in New York and London, GreenKey is the creator of a patented speech recognition (ASR) and NLP platform that recognizes complex jargon across real-time audio and text sources and transforms them into actionable insights. GreenKey converts disparate communications streams into structured data tools that help banks, trading firms, and emergency services operators automate complex workflows.

GreenKey trains the new NLP models on real sell-side human analysts to capture their insights and include the ability to rapidly customize those models through a quick annotation process. Traders will select from the base models called “trusted curators” and can even ask their favorite sell-side research analyst to create and contribute one. The custom model collection can be fed thousands of documents and will identify trending topics, intents, entities, and can even provide innovative raw sentiment scores such as “word disfluency.” The pre-trained models also include in-depth product knowledge across global fixed income, credit, equities, FX, and commodity markets.

“NLP is already changing the way sales and trading occurs on the sell-side, enabling a wave of automation and insight generation across various workflows,” said GreenKey Founder and CEO Anthony Tassone. “Now the buy-side can begin to leverage NLP to automate and scale their analysis, while retaining the ‘trusted curator’ role of the sell-side research provider and analyst.”

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GlobalLink AI Portal Delivers Over 1 Billion Words Per Month

AI

Surpassing one billion words per month, TransPerfect’s GlobalLink AI Portal solution has doubled in usage this year, as TransPerfect signs on several new clients.

TransPerfect, one of the world’s largest providers of language and technology solutions for global business, announced this week that the adoption of its GlobalLink AI Portal machine translation (MT) solution for corporate clients has surpassed the one billion words per month milestone and continues to grow.

With the addition of new clients like Cushman & Wakefield, HARMAN International, and Cummins, GlobalLink AI Portal usage has more than doubled this year. Serving over 5,000 global organizations, GlobalLink Product Suite simplifies management of multilingual content. TransPerfect has seen long-term clients who use the service achieve an increase of 15% in quality scores over the previous year, demonstrating the AI solution’s capacity to continuously improve in a secure environment. The increased quality allows clients to reduce the scope and scale of necessary post-edits by streamlining time and cost.

Among TransPerfect’s numerous language and technology solutions, GlobalLink AI Portal focuses on real-time self-service MT and supports more than 40 different languages and 30 different file formats. The solution makes neural MT more accessible to corporate clients looking to integrate the technology into their business workflows. Offering a hybrid approach, TransPerfect combines AI and human translation to help clients achieve an optimal position on the quality-cost translation matrix for the content’s end use.

Furthermore, GlobalLink AI Portal offers unique security features that include the use of certified collocation facilities, encryption, secure HTTPS access, optional deactivation of data storage, single sign-on (SSO) integration, and user permissions and hierarchies.

TransPerfect President and CEO Phil Shawe said that “Efficiency and security are two of the pillars on which our company has operated for over 25 years. I’m happy to see the marketplace’s rapid adoption of our GlobalLink AI solution, but I’m even more pleased to know that we’ve delivered this technology in a way that both drives productivity and respects privacy.”

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MultiLingual creates go-to news and resources for language industry professionals.

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Anuvadak Platform Translates India’s MyGov COVID Site

AI, Localization

The expansion of COVID-19 information now to include 10 of India’s 22 official languages will ensure millions of Indian internet users access to vital information. Reverie Language Technologies hopes this is just the beginning for Anuvadak, its website localization AI.

In the effort boost access to the internet among India’s multitude of languages, Reverie Language Technologies has leveraged its machine translation AI Anuvadak to automatically publish the MyGov COVID-19 page in ten Indian languages.

Launched as a platform to publish websites in Indian languages, Anuvadak accelerates the process of localizing content to better serve the needs of 536 million Indian-language internet users. Along with translating language using neural machine translation, the platform can also automatically update websites, manage workflows, and optimize SEO search results using built-in web analytics.

“It is a platform that accelerates the process of creating, launching, and optimizing your website in multiple languages,” said Reverie Language Technologies CEO and Co-founder Arvind Pani in a recent interview. “The platform enables you to connect with customers in their language with faster go-to-market and effortless content management. Anuvadak can scale down the website localization time by 40% and can save as much as 60% of the localization and content management costs.”

After winning the QPrize in 2011, Reverie Language Technologies became the first company to offer language computing solutions for all 22 official Indian languages. However, despite India’s claim to the world’s second-largest English-speaking population, only around 10% of the Indian population speak English. Accordingly, the vast number of internet resources serve a minority of Indian internet users.

As COVID-19 cases continue to rise, access to information one’s native language is still vital globally, and many around the world are calling for efforts to deliver information in a timely manner. The increased language capacity on India’s MyGov website will play a major role in disseminating life-saving information as epidemiologists gather new information about the virus. Furthermore, an internet with broader localization strategies will ensure Indian internet-users with more equal access to opportunities in business, education, and cultural exchange.

“We are focused on building products to address all user engagement aspects, be it input, search, voice, translation, or localization,” said Pani. “We plan to empower more number of the rapidly rising Indian-language users with our language by enabling large businesses and governments to connect with more people in regional languages.”

Although a great effort is still needed to deliver access to information through the internet and technology to India’s diverse language speakers, Anuvadak will contribute to the broader effort to serve Indian internet users.

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The GPT-3 AI Language Model Might Be on Your Newsfeed

AI

OpenAI’s GPT-3 was released privately for beta testing in July. The AI language model has already generated tens of thousands of views and dozens of subscribers on one beta test blog from many unwitting readers.

When OpenAI released its GPT-3 AI language model for beta testing last month, the San Francisco-based AI research and deployment company was aware that issues might arise. After all, following the racist, homophobic, misogynistic language generated by the GPT-2, the GPT-3 could easily fall into similar patterns.

To prepare for such outcomes, OpenAI decided to launch the language model in beta to limit its capacity to stray into problematic territory. Although the company released the beta primarily to university and industry researchers, one computer science major at University of California, Berkeley reached out to a PHD candidate to request access to GPT-3.

Once the graduate student agreed to collaborate, Liam Porr wrote a script for him to run. The script gave GPT-3 a headline and introduction for a blog post and ordered it to generate multiple completed versions. Porr then created Adolos, a blog he would use to test his hypothesis that the AI could convince an audience that the blog was written by a human.

Porr did as little as creating a title and introduction, choosing a photo, and copy-pasted from one of the outputs with little to no editing. After two weeks, the blog had over 26,000 visitors and 60 subscribers, with one post even making it to the number one spot on Hacker News. Furthermore, while a few readers suspected the posts had been written by GPT-3, many of those comments were subsequently down-voted by community members.

One of the tricks Porr discovered, which would allow the algorithm to function at a more convincingly human level, was choosing the right subject matter. Although the GPT-3 language model is far more vast than the GPT-2, it still struggles to produce language in a rational, logical way. Indeed, even OpenAI’s first use of the model was to write a poem about previous board member Elon Musk.

Focusing on subject matter that utilizes more emotional, creative language, Porr settled on productivity and self-help. He then searched through Medium and Hacker News articles to emulate titles related to those subjects and let the AI loose.

After conducting this two-week experiment, Porr wrote a post on his blog — without the help of GPT-3 — discussing his findings and the implications of OpenAI’s newest language model. With the promising efficiency of GPT-3, the model could have a major impact on the future of online media, according to Porr. The experiment comes during global discussions around the ethics of AI.

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OpenAI Creates New Language-Generating AI

AI

In its latest development of language-generating AI, OpenAI has created GPT-3, its most massive language model to date. The AI represents some of the best and worst parts of language.

Speech-related AI is breaking new ground. OpenAI has developed a new AI software called Generative Pre-Trained Transformer 3, or more commonly GPT-3. The new language-generating AI represents the San Francisco-based AI laboratory latest development in its mission to steer the creation of intelligent machines.

Relying on a statistical model, the algorithm has better capacity to perform human-like speech patterns if exposed to more text. In order to train a large enough statistical language model, OpenAI sourced from the biggest set of text ever amassed, including a mixture of books, Wikipedia articles, and billions of pages of text from the internet.

GPT-3’s size is one of the factors that differentiate it from its predecessors. With that in mind, OpenAI provided the GPT-3 with 175 billion parameters — the weights of the connections between the network’s nodes, and a proxy for the model’s complexity —in relation to the GPT-2’s 1.5 billion parameters and GPT-1’s 117 million parameters.

Based on a graph published in The Economist, the GPT-3’s massive number of parameters makes distinguishing its AI-generated news articles from human-generated ones nearly equivalent to a guess at random. Furthermore, GPT-3 can even write poetry, such as this verse about Elon Musk:

“The sec said, ‘Musk,/your tweets are a blight./They really could cost you your job,/if you don’t stop/all this tweeting at night.’/…Then Musk cried, ‘Why?/The tweets I wrote are not mean,/I don’t use all-caps/and I’m sure that my tweets are clean.’/’But your tweets can move markets/and that’s why we’re sore./You may be a genius/and a billionaire,/but that doesn’t give you the right to be a bore!’”

This new development fulfills several tech predictions in recent years and signals promising advances in the field of AI language modeling, but not without flaws that have perpetually marred both AI imaging and AI text generation. Despite GPT-3’s grammatically fluent text, the statistical word-matching does not reflect understanding of the world.

Melanie Mitchell, a computer scientist at the Santa Fe Institute, said that the text generated by GPT-3 “doesn’t have any internal model of the world — or any world — and so it can’t do reasoning that requires such a model.”

The result has led it into similar pitfalls discovered in the GPT-1 and 2, namely the AI’s inability to distinguish language promoting racism, anti-Semitism, misogyny, homophobia, or any other oppressive language that it finds in its sources. OpenAI even added a filter to the GPT-2 to disguise the problem of mimicking bigotry by limiting the model’s ability to talk about sensitive subjects. However, the issue still poses a risk to GPT-3, which has already reproduced prejudiced text.

To work around the problem, OpenAI has added a filter to a newer model of the GPT-3, but the fix may just be a band-aid at this point. Still, with such rapid development of new language models, the GPT-3 will likely soon be replaced by a version with an even larger scale and maybe some power of discernment.

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Journalist at MultiLingual Magazine | + posts

Jonathan Pyner is a poet, freelance writer, and translator. He has worked as an educator for nearly a decade in the US and Taiwan, and he recently completed a master’s of fine arts in creative writing.

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Translated to provide EU Parliament with real-time speech translation AI

AI, Business News

Debates will be transcribed and translated by a new state-of-the-art MT system that keeps humans in the loop

blue and white flags on poleTranslated has been selected by the European Parliament to automatically transcribe and translate parliamentary multilingual debates in real-time, covering the 24 official languages used by the institution. The service will be provided by new software available both through fully-localized web and mobile applications, and live streaming APIs for third-party developers. It it purported to be the first human-in-the-loop speech machine translation (MT) system, and should leverage context and user feedback to adapt the output in less than one second.

The product will be developed in collaboration with two companies that have already worked with Translated in building products for professional translators: Fondazione Bruno Kessler (FBK), a world-leading research center in MT and automatic speech recognition (ASR); and PerVoice, an ASR world-leading provider. Within the next 12 months, the consortium will release a prototype to be tested by the European Parliament. This solution will be considered alongside solutions provided by two other groups, following rules put forth in “Live Speech to Text and Machine Translation Tool for 24 Languages.” The best-performing tool will be confirmed as the official one for the following two years.

The new product is not a simple concatenation of ASR and MT, but a new, fully-integrated system in which the MT algorithms are tolerant of ASR errors. This approach will not only help deliver more contextualized translations, but it will also open up the opportunity to improve the quality of the output while the plenary session is happening. This is possible thanks to the human correction feedback that the tool allows by both the end-users and a team of professional translators.

“For this project, we are bringing together ten years of research in machine translation and speech recognition,” says Simone Perone, Translated’s vice president of product management. Some of the new AI models that will be used have already been put to work successfully in products such as ModernMT (an MT that improves from corrections and adapts to the context), Matecat (a computer-assisted translation tool that makes post-editing easy), and Matesub (the first subtitling tool offering suggestions during the transcription, now in beta and due to be released in September 2020).

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Appen (ASX:APX) Targeted By Hackers

AI, Business News, Machine Learning

Appen Attacked by Hackers

Investors received multiple alerts about a security breach at Appen.

Appen Limited (ASX:APX), a multilingual AI enablement and machine learning company headquartered in Sydney, issued a report on July 30th (Australian time) that malicious actors had hacked a third-party provider and stolen access to its systems. Appen is listed on the Australian stock market, ASX.

The third-party system was being used on a trial basis and Appen ceased using it immediately. The company believes that the attack was random and not targeted specifically to its repositories, since other companies were also victims of similar incidents. Even though it was determined that the hackers gained access to Appen’s Annotation Platform, which contained customer and crowd names, company names, email addresses, encrypted passwords, IP addresses, and historical login and log off times, and some phone numbers, the IT security team asserts that the incident is limited in nature and not material.

According to the release issued to the Australian Stock Exchange, Appen — which analyzes data for eight of the largest ten technology companies in the world — has not suffered any interruption to its operations and has reported the incident to legal authorities.

The statement affirms that customer AI training data, the core business of the company, is stored separately and there is no evidence that it was stolen. The unauthorized access was detected as soon as it occurred, and Appen took the necessary steps to secure its systems. Relevant clients were contacted and had their passwords and security tokens reset. A cyber forensics firm was hired to assist in the investigation.

According to Dow Jones, Appen shares were up 1.2% at AUD 36.11. After a previous filing announcement before the breach, shares had been up as much as 3%.

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AI Data Training Company Appen Sees Increasing Rise in Stock Price

AI

This week, Appen LTD, a global leader in machine learning and artificial intelligence (AI) data, reported a 6.9% increase in share value — the most in 11 weeks. Appen is of note to the localization industry primarily as a seller of training data that can be used in applications such as machine translation.

The Motley Fool reported last month that Appen has seen a steady increase in share prices since March. The news comes as a boon to the company’s shareholders, who saw a significant drop in prices in mid-February as countries began responding to COVID-19. In spite of this year’s challenges, the company is in the middle of a strong upswing.

Indeed, during one of the price peaks, the founder and the CEO of the company cashed in their shares for a combined $61 million. Since March, the price has increased 99% from its $17.14 low to a high of $34.17. Following a year of immense success in 2019, with a 47% increase in revenue to $536 million, Appen has proven its resiliency during the pandemic.

With over 1 million contractors in over 130 countries and 180 languages, the company studies human speech and interactions with each other and with AI to collect training data that teaches AI models and machine learning algorithms to (theoretically) make good decisions. Its worldwide crowd of contributors paired with its innovative data collection platform ensures premium localization of text, images, audio, video, and sensory content, which has built it a strong reputation in a variety of industries.

Appen claims to be the data industry’s “most advanced AI-assisted data annotation platform.” Working with tech companies such as Apple, Google, Microsoft, and Adobe, Appen sells data sets to assist with machine translation, proofing tools, automatic speech recognition, computer vision, semantic search, text-to-speech, virtual assistants and chatbots.

 

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NBA, Microsoft May Alter Global Sports Consumption

AI, Machine Learning, Personalization and Design

Marketing games for international venues, celebrating the Lunar New Year with Chinese characters on jerseys, or recruiting a global class of players, the National Basketball Association is no stranger to localizing its content. But deepening AI and machine learning technologies promise an approach to its global fan base like never before.

Spalding basketball in courtThe NBA announced in April that it has made plans to enter a multi-year partnership with Microsoft to create a more personalized, localized experience for its international fan base. But while fans around the world already enjoy watching NBA broadcasts in 47 languages broadcast in 215 countries, the partnership promises fundamental innovations to modernize fan interaction using artificial intelligence and machine learning technology.

One of the ways the alliance will change NBA content is by creating a direct-to-consumer platform on Microsoft Azure, Microsoft’s cloud computing service, which will provide data analytics, computing, storage, and networking that they anticipate will allow them to personalize fan experience “through state-of-the-art machine learning, cognitive search and advanced data analytics solutions,” according to the report.

Analyzing metrics around fan behavior is nothing new for the NBA, but with such data at its fingertips, the NBA-fan relationship may flourish like never before. NBA senior vice-president of direct to consumer Chris Benyarko said about the benefits of machine learning AI curating fan experience, “Instead of the fan having to pick and choose and turn them on or off one by one, the platform is now starting to behave like a game producer, automatically selecting and presenting the game in a different way.”

Deb Cupp, Corporate Vice President of Enterprise and Commercial Industries at Microsoft, said, “The AI eventually learns that I like to learn about stats, so it’s going to start presenting me more information about stats as I go into the game… It’s this experience where instead of just watching a game, it actually has the opportunity to interact in a way that matters to me as that fan.”

With fans in all different time zones who experience the game and consume content in a variety of ways, the NBA will be able to gather a global array of data to bring in new fans and retain old ones. What that will mean for sports remains to be seen, but the machine learning will likely provide invaluable information on fan behavior worldwide and grant the NBA a chance to solidify its place as a premier global sport.

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Journalist at MultiLingual Magazine | + posts

Jonathan Pyner is a poet, freelance writer, and translator. He has worked as an educator for nearly a decade in the US and Taiwan, and he recently completed a master’s of fine arts in creative writing.

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