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Habana Labs Announces Best-Performing AI Inference Processor 

Habana Labs Announces Best-Performing AI Inference Processor  | Language Tech Market News | Scoop.it

Habana Labs, Ltd. (www.habana.ai), is sampling its first AI processor to select customers. A PCIe card based on its Goya HL-1000 processor delivers 15,000 images/second throughput on the ResNet-50 inference benchmark, with 1.3 milliseconds latency, while consuming only 100 watts of power.  Habana Labs' AI processors offer one to three orders of magnitude better performance than solutions commonly deployed in data centers today.

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AI Bot Turns Words into Vaguely-recognizable Pictures

AI Bot Turns Words into Vaguely-recognizable Pictures | Language Tech Market News | Scoop.it

Cristobal Valenzuela, who builds machine learning tools, created a site to demonstrate how AI can parse words and try to convey their meaning in a visual way like “a visual chatbot in reverse.” The algorithm tries to generate a picture from what you tell it. The concept is based on research from a paper published last year called AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

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Using #NMT to Ensure Low-Latency Speech Translation

Using #NMT to Ensure Low-Latency Speech Translation | Language Tech Market News | Scoop.it

Researchers from the Karlsruhe Institute of Technology (KIT), in Germany, "In this work, we aim to remedy the problem of partial sentence translation in NMT," the researchers wrote. "Ideally, we want a model that is able to generate appropriate translations for incomplete sentences, without any compromise during other translation use cases."  Their adaptation of NMT achieved high-quality translations at low latency, minimizing the number of corrected words by 45 percent. In the future, their study could have meaningful practical implications, helping to develop better tools for real-time speech translation.

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@scriptbook Analyses Film Scenarios to Predict Boxoffice Success

@scriptbook Analyses Film Scenarios to Predict Boxoffice Success | Language Tech Market News | Scoop.it

QcriptBook (BE): The system works like this: ScriptBook users upload a PDF file of a screenplay into the system. About five minutes later they receive a detailed analysis of the project that, among other things: predicts the MPAA rating, analyzes its characters, detecting the protagonists and antagonists; assesses the emotions of each character; predicts the target audience, including gender and race; and, most importantly, makes box office predictions.

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@Explosion.ai Automates Annotation to Accelerate Machine Learning

@Explosion.ai Automates Annotation to Accelerate Machine Learning | Language Tech Market News | Scoop.it

Explosion AI (Germany) provides Prodigy, software which automates some parts of annotation. It can extrapolate a corpus of relevant terms from a few seed words and helps data scientists quickly confirm the targeted language using a Tinder-like graphical interface. Co-founder Ines Montani has demonstrated the efficiency of Prodigy in annotating insulting language to help moderate online behavior, for example on social media or ecommerce feedback comments, but the tools have been used to build applications analyzing text in financial services, she says.
 “The bottleneck is training data. Companies are amassing data, hoping they can do something with it. While machine learning might provide some good applications, you still have to document and label the data to use it for training machine learning models,” Montani says.

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Microsoft is Trying to Build Literate Machines

Microsoft is Trying to Build Literate Machines | Language Tech Market News | Scoop.it

Adam Trischler (MS scientist in Canada): "What I’m most excited about is seeing a literate machine as a kind of librarian, or a tutor, who could guide, like, a human student or just somebody who has an interest in something through new books, new materials, new ideas, like stoking their natural curiosity and feeding them new information as the student would ask questions."

LT-Innovate's insight:

Long interview touching on many language+AI issues. Good example of where a transcript should have been edited (by a machine?) to cut out speech detritus ("you know", "well"). Some transcripts (eg legal cases) need to be word complete, others need to be summarised/edited. Basic idea: the virtual assistant as a friendly reader.

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Why Deep Learning Needs Standards for Industrialization

To scale Deep Learning development into a practice that is predictable, reliable and efficient will require standardization. The intent of standardization is to maximize participation of many independent parties. It is a common language or a coordination mechanism for parties to accelerate progress. Accelerated progress is necessary for Deep Learning to become not just confined to research labs but to be industrialized and available to many.
LT-Innovate's insight:

At least starts the conversation on this difficult topic.

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@Reuters Tracer Uses #AI to Revolutionize News Collection for Story Production

@Reuters Tracer Uses #AI to Revolutionize News Collection for Story Production | Language Tech Market News | Scoop.it
The new system is called Reuters Tracer. It uses Twitter as a kind of global sensor that records news events as they are happening. The system then uses various kinds of data mining and machine learning to pick out the most relevant events, determine their topic, rank their priority, and write a headline and a summary. The news is then distributed around the company’s global news wire.
LT-Innovate's insight:

Complex process compacted into a function on a platform by means of ML. 

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Machine Learning Used by Over Half of Top Insurers 

Machine Learning Used by Over Half of Top Insurers  | Language Tech Market News | Scoop.it

More than half of insurance firms around the world have implemented machine learning technology into their business, says Earnix, a provider of analytics solutions for the financial services industry, who announced the results of a global survey of more 193 insurance executives today (May 31). Results revealed that over half (54%) of the executives surveyed said that their organisation was using Machine Learning for predictive analytical modelling. Of those deploying the technology, 70% said they were using it for risk modelling, followed by demand models (45%) and fraud detection (36%).

LT-Innovate's insight:

Possibly an indication of market for certain insurance-centric LT solutions

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LexisNexis on the Future of Legal AI

LexisNexis on the Future of Legal AI | Language Tech Market News | Scoop.it

Interview on Lexis Answers, the company's R&D: Future lab projects:

  • Deep learning capabilities through artificial neural networks
    Advanced interfaces, such as cognitive assistants or chatbots that can communicate both via voice and text input/output.
  • Entity extraction and relationship mapping for improved data analytics and visualization
  • Personalization, where the technology “learns” the needs of it users, anticipates their needs based on their past behaviors, remembers where they left off in their legal research, and delivers more customized results based on their role within the organization.
  • Smarter decisioning tools such as prescriptive analytics that can proactively make recommendations on what legal strategies to employ, based on outcomes of similar cases.
  • Greater man/machine interaction, such as asking the user questions if the system does not have enough information to make a decision or recommendation; proactively conducting legal or Internet research to find relevant information that can support or refute the user’s case; or even draft case arguments based on inputted fact patters or case concepts, using verbiage taken from the outcomes of similar, successful cases.
LT-Innovate's insight:

Quite appropriately, this reads like a summary of language technology development hotspots.

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Conceptual Captions: A New Dataset and Challenge for Image Captioning

Conceptual Captions: A New Dataset and Challenge for Image Captioning | Language Tech Market News | Scoop.it

We introduce Conceptual Captions, a new dataset consisting of ~3.3 million image/caption pairs that are created by automatically extracting and filtering image caption annotations from billions of web pages. Introduced in a paper presented at ACL 2018, Conceptual Captions represents an order of magnitude increase of captioned images over the human-curated MS-COCO dataset. As measured by human raters, the machine-curated Conceptual Captions has an accuracy of ~90%. Furthermore, because images in Conceptual Captions are pulled from across the web, it represents a wider variety of image-caption styles than previous datasets, allowing for better training of image captioning models.

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#NLP Architect by Intel Lab: Release 0.2

#NLP Architect by Intel Lab: Release 0.2 | Language Tech Market News | Scoop.it
New version of NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding http://nlp_architect.nervanasys.com/
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'Deep-Speare' Emulates William S Using ML

'Deep-Speare' Emulates William S Using ML | Language Tech Market News | Scoop.it

A deep learning model was trained on William Shakespeare's sonnets. The results were mixed.

While Deep-Speare could generate poems that scored high in rhyme and meter (even higher than human poets, according to the experts), the poems overall lacked readability and emotion.

“Despite excellent form,” the authors conclude, “the output of our model can easily be distinguished from human-written poetry due to its lower emotional impact and readability

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FacebookTeaches Bots to Navigate NY Using Natural Language Naming

FacebookTeaches Bots to Navigate NY Using Natural Language Naming | Language Tech Market News | Scoop.it

The idea is that this could be a more efficient way to teach AI systems like this to communicate effectively, rather than by training them on pure-text data sets. And in its experiments, Facebook's AI research team found that its bot guide was more accurate than humans performing the same navigation task.

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@Bertin IT Runs Deep Learning Workshop on Conversational Analytics

@Bertin IT Runs Deep Learning Workshop on Conversational Analytics | Language Tech Market News | Scoop.it

Bertin IT tiendra un atelier le mardi 10 avril à 14h en salle 7, animé par notre experte en la matière, Ariane Nabeth-Halber, Directeur Speech Solutions : Comment le Deep Learning réinvente le Speech Analytics ? Les récents progrès en intelligence artificielle et tout particulièrement en deep learning (apprentissage profond) révolutionnent nombre de disciplines et d’applications. La reconnaissance vocale et le traitement du langage en font partie de façon notable.

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Storytelling Interface Tool to Add Vocal Comment and Cluster Family Photo Albums

Storytelling Interface Tool to Add Vocal Comment and Cluster Family Photo Albums | Language Tech Market News | Scoop.it

The tabletop app allows users to swipe and select pictures spread across their tablet while they talk about the memories associated with those pictures. The tool will record and attach the audio to the grouping and uses natural language processing to cluster the photos. The Univ of Toronto are demoing the current prototype Frame of Mind: Using Storytelling for Speech-Based Clustering of Family Pictures at the ACM’s international conference on intelligent user interfaces, being held this week in Tokyo. 

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AWS to Enable Access to Google‘s #ML Stack?

AWS to Enable Access to Google‘s #ML Stack? | Language Tech Market News | Scoop.it

AWS will introduce a service called Ironman that‘s meant to simplify AI tasks involving lots of different kinds of data, said on Monday. It also plans to make it easier for people to use a Google-created technology called TensorFlow, an open-source framework for deep learning — that companies can use for image recognition, speech recognition, language translation and other processes. 

LT-Innovate's insight:

The gradual commoditisation of AI

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Ohio Project to Automatically Learn Unresourced Languages for Disaster Relief Situations

Ohio Project to Automatically Learn Unresourced Languages for Disaster Relief Situations | Language Tech Market News | Scoop.it

Low Resource Languages for Emergent Incidents (LORELEI) is a DARPA project to develop technology for languages about which translators and linguists know nothing.  An Ohio University team is using the Ohio Supercomputer Center’s Owens Cluster to develop a grammar acquisition algorithm to discover the rules of lesser-known languages, learning the grammars without supervision so disaster relief teams can react quickly.

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Speech Detection & Automatic Subtitle Synchronization

Speech Detection & Automatic Subtitle Synchronization | Language Tech Market News | Scoop.it

The project I present here is a bit simpler than that but still very useful. Synchronizing audio and subtitles is a tedious and boring task since you have to identify when the human speech happens and modify each subtitle to fill that temporal space. But, what if a machine could do that for us?  I describe how I have trained a Neural Network to detect human speech on videos and use that information to synchronize their subtitles.

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