Artificial intelligence (AI) is surpassing human performance in a growing number of domains. However, there is limited evidence of its economic effects. Using data from a digital platform, we study a key application of AI: machine translation. We find that the introduction of a machine translation system has significant increased international trade on this platform, increasing exports by 17.5%. Furthermore, heterogeneous treatment effects are all consistent with a substantial reduction in translation-related search costs. Our results provide causal evidence that language barriers significantly hinder trade and that AI has already begun to improve economic efficiency in at least one domain.

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.

For more than 200 years, the synthesis of organic molecules has remained one of the most important tasks in organic chemistry. The work of chemists has scientific and commercial implications that range from the production of Aspirin to that of Nylon. Yet, little has been done to change age-old practices dramatically and allow a new era of productivity based on pioneering artificial intelligence (AI) science and technologies. The challenge for organic chemists in fields such as chemistry, materials science, oil and gas, and life sciences is that there are hundreds of thousands of reactions and, while it is manageable to remember a few dozen in a narrow specialist's field, it's impossible to be an expert generalist. To address this, we asked ourselves, can we use deep learning and artificial intelligence to predict reactions of organic compounds?

Translating between human languages is something which artificial intelligence – specifically machine learning – has proven to be very competent at. So much so that the CEO of one of the world's largest employers of human translators has warned that many of them should be facing up to the stark reality of losing their job to a machine. One Hour Translation CEO Ofer Shoshan told me that within one to three years, neural machine technology (NMT) translators will carry out more than 50% of the work handled by the $40 billion market. His words stand in stark contrast to the often-repeated maxim that, in the near future at least, artificial intelligence will primarily augment, rather than replace, human professionals. Shoshan told me that the quality of machine translation has improved by leaps and bounds in recent years, to the point where half a million human translators and 21,000 agencies could soon find themselves out of work.

Besides the Dutch-English and Indonesian-English translation systems we offer through our client apps and connectors, we have built neural machine systems for the Turkish-English, Latvian-English and Spanish-English language pairs. Whether you are a professional translator, translation company or a business user, we can put artificial intelligence and deep learning to work for you. If you already have parallel texts (texts with source language & translation) we can use these as a basis for building a customised system for you. If you don't have such material we can usually build a useful baseline system from freely available public resources. Your own neural machine translation system can then be placed on a secure server "in the cloud" or on a dedicated server located in your offices and accessible only on your corporate network.

Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks. These language models are almost universally autoregressive in nature, generating sentences one token at a time from left to right. This paper studies the influence of token generation order on model quality via a novel two-pass language model that produces partially-filled sentence "templates" and then fills in missing tokens. We compare various strategies for structuring these two passes and observe a surprisingly large variation in model quality. We find the most effective strategy generates function words in the first pass followed by content words in the second. We believe these experimental results justify a more extensive investigation of generation order for neural language models.

Although the recent flurry of blogs (I count myself in this), emails and media coverage on the topic could leave you thinking otherwise, data security is much more than just GDPR. It's about placing customer and data privacy at the centre of everything you do. It may sound simple, but in a world that also includes legislation such as the Electronic Communications Privacy Act (ECPA) and the China Data Protection Regulation (CDPR), and more specifically in financial services MiFID II and PSD2, complying with different regulations while keeping data privacy front of mind is a complex environment for any digital business to navigate. Especially for brands that engage with content hungry customers across multiple countries and languages. While my primary focus is our financial services industry, since May the number of retailers and IT organizations I speak to has grown.

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

Evolution shows that social mimicry is a major component of our survival mechanism, pushing us to belong to groups. We're all subject to the chameleon effect, this tendency to unconsciously mirror what "others" do, and one of its most apparent manifestations is language and accents. Members of the same social group tend to mimic the speech patterns of others, leading to the rise of different regional accents within the same language. In the Southern United States, for example, English has developed in contact with Spanish, leading speakers on the two sides of the borders to pick up dialectal elements Like in Puerto Rico, the mix was so deep that "Spanglish" appeared. On both sides of the pond, in the United States and Britain, people speak English, yet with very distinctive accents that, in some cases, could be mutually unintelligible.

Natural Language Processing; it's Artificial Intelligence that learns words and patterns of words so that it can respond to human searches and questions. Siri and Alexa are examples of this technology. And this technology is continually improving. As more and more conversations are held with these machines, they continue to learn and respond more accurately. Machines are also in use for translations.