Language Learning


Memrise raises $15.5M as its AI-based language-learning app passes 35M users

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Memrise, a UK startup whose eponymous language-learning app employs machine learning and localised content to adapt to users' needs as they progress through their lessons, has raised another $15.5 million in funding to expand its product. The funding comes after a period of strong growth: Memrise has now passed 35 million users globally across its 20 language courses, and it tipped into profitability in Q1 of this year. Ed Cooke, who co-founded the app with Ben Whately and Greg Detre, told TechCrunch that this places it as the second-most popular language app globally in terms of both users and revenues. This round, a Series B, was led by Octopus Ventures and Korelya Capital, along with participation from existing investors Avalon Ventures and Balderton Capital. Memrise is not disclosing its valuation -- it has raised a relatively modest $22 million to date -- but Cooke (who is also the CEO) said the plan will be to use the funding to expand its AI platform and add in more features for users.


How SingularityNET is Advancing Unsupervised Language Learning

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For many AI services, it is critical to be able to comprehend human language and even converse in it with human users. So far, advances in natural language processing (NLP) powered with "sub-symbolic" machine learning based on deep neural networks allows us to solve multiple tasks like machine translation, classification, and emotion recognition. However, using these approaches requires enormous amount of training. Additionally, there are increasing legal restrictions in particular applications due to recent regulations, making current solutions unviable. The ultimate goal for these industry initiatives is to allow humans and AI to interact fluently in a common language.


Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game

arXiv.org Artificial Intelligence

Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.



AI-powered language learning promises to fast-track fluency

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A linguistics company is using AI to shorten the time it takes to learn a new language. It takes about 200 hours, using traditional methods, to gain basic proficiency in a new language. This AI-powered platform claims it can teach from beginner to fluency in just a few months – through once-daily 20 minute lessons. Learning a new language is hard. Some people seem to pick up new dialects with ease, but for the rest of us it's a trudge through rote memorization.


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A linguistics company is using AI to shorten the time it takes to learn a new language. It takes about 200 hours, using traditional methods, to gain basic proficiency in a new language. This AI-powered platform claims it can teach from beginner to fluency in just a few months – through once-daily 20 minute lessons. Learning a new language is hard. Some people seem to pick up new dialects with ease, but for the rest of us it's a trudge through rote memorization.


Machines Are Developing Language Skills Inside Virtual Worlds

MIT Technology Review

Machines are learning to process simple commands by exploring 3-D virtual worlds. Devices like Amazon's Alexa and Google Home have brought voice-controlled technology into the mainstream, but these still only deal with simple commands. Making machines smart enough to handle a real conversation remains a very tough challenge. And it may be difficult to achieve without some grounding in the way the physical world works. Attempts to solve this problem by hard-coding relationships between words and objects and actions requires endless rules, making a machine unable to adapt to new situations.


[R] From DeepMind: Grounded Language Learning in a Simulated 3D World • r/MachineLearning

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Abstract: We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions.


Automatic sign language translators turn signing into text

New Scientist

Machine translation systems that convert sign language into text and back again are helping people who are deaf or have difficulty hearing to communicate with those who cannot sign. KinTrans, a start-up based in Dallas, Texas, is trialling its technology in a bank and government offices in the United Arab Emirates, and plans to install it in more places over the next couple of months. SignAll, a company based in Budapest, Hungary, will begin its own trials next year. KinTrans uses a 3D camera to track the movement of a person's hands as they sign words. A sign language user can approach a bank teller and sign to the KinTrans camera that they'd like assistance, for example.


From machine learning to Python language skills: 6 tech skill sets that fetch maximum salary

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Technology is gradually taking over workplaces and that is one of the reasons why'human workers' are becoming redundant. As a result of which, time and again we see reports about companies laying off employees. While imagining the repercussions of having automation in other industries is palpable, what does it mean for the technology industry? Adoption of newer technologies is believed to be one of the key reasons for the job cuts and if tech workers are not trained in advanced skills, the future for human workforce appears all the bleaker. India would lose about 69,000 jobs until 2021 due to the adoption of IoT technology, according to a research conducted by a consulting firm.