A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf. A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about. The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available. In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions. We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased. The approach also works on composite services. We implement it in the context of text translation and report interesting results.

For different language pairs, word-level neural machine translation (NMT) models with a fixed-size vocabulary suffer from the same problem of representing out-of-vocabulary (OOV) words. The common practice usually replaces all these rare or unknown words with a 〈UNK〉 token, which limits the translation performance to some extent. Most of recent work handled such a problem by splitting words into characters or other specially extracted subword units to enable open-vocabulary translation. Byte pair encoding (BPE) is one of the successful attempts that has been shown extremely competitive by providing effective subword segmentation for NMT systems. In this paper, we extend the BPE style segmentation to a general unsupervised framework with three statistical measures: frequency (FRQ), accessor variety (AV) and description length gain (DLG). We test our approach on two translation tasks: German to English and Chinese to English. The experimental results show that AV and DLG enhanced systems outperform the FRQ baseline in the frequency weighted schemes at different significant levels.

Google Docs is, at long last, getting a grammar-checking feature, which'll be able to identify mixed up words (like "affect" and "effect"), incorrect tenses, improper uses of commas and clauses, and more. To do all of that, Google says it'll be relying on machine translation -- the same technology it uses to translate between multiple languages. Except, instead of translating a sentence from, say, French to German, it sounds as though it'll be translating your imperfect writing into a grammatically correct passage. Details on what the grammar checking feature is capable of and exactly how its AI will work are limited right now. All we really know is that Google is already quite capable when it comes to machine translation -- two years ago, the company said its tech was approaching human levels of accuracy. So it makes sense that Google would lean on its already established tech when developing this feature.

Of course, not all languages can be easily translated. What you can do, however, is practice face-to-face communication skills. A study by the Harvard Business School and the University of Chicago finds that hand-shaking and other social interactions in a business setting promote "cooperative strategies and influences negotiation outcomes." The verbal and nonverbal communication that takes place during an in-person meeting is essential to the business relationship. Nothing would be possible without your employees: They are the core of your company and you rely on them to build partnerships and foster overall growth.

A newly-discovered glitch in Google Translate is causing the online tool to transform gibberish suggestions into doomsday warnings and prophesies about Jesus. The AI that powers Google Translate starts to produce the nonsensical warnings about the end of the world when asked to translate the phrase'dog dog dog dog dog dog dog dog dog' from Hawaiian to English. The nonsense sentence, when translated, throws up references to the doomsday clock and the second coming of Jesus Christ. Once the glitch was discovered, Google Translate fans quickly flooded social media with variations on the phrase, mocking the bizarre results thrown-up by the AI. Google Translate has been malfunctioning recently, spouting prophetic verses from gibberish.

A glitch with Google Translate has resulted in a series of mysterious messages and prophecies appearing when gibberish text is entered into the app. The translation service, which supports over 100 languages and serves over 500 million people each day, uses artificial intelligence to increase accuracy. But the technology has also caused an issue with some of the lesser-used languages. Typing in the word "dog" 18 times into Google Translate and selecting the input language as Maori results in the following message: "Doomsday Clock is three minutes at twelve We are experiencing characters and a dramatic developments in the world, which indicate that we are increasingly approaching the end times and Jesus' return." You can view your Google Maps History by visiting myactivity.google.com. In the search bar at the top of the page, you can filter by lots of different products, and Maps is one of them.

Tools and apps like Google Translate are getting better and better at translating one language into another. Alexander Waibel, professor of computer science at Carnegie Mellon University's Language Technologies Institute (@LTIatCMU), tells Here & Now's Jeremy Hobson how translation technology works, where there's still room to improve and what could be in store in the decades to come. "Over the years I think there's been a big trend on translation to go increasingly from rule-based, knowledge-based methods to learning methods. Systems have now really achieved a phenomenally good accuracy, and so I think, within our lifetime I'm fairly sure that we'll reach -- if we haven't already done so -- human-level performance, and/or exceeding it. "The current technology that really has taken the community by storm is of course neural machine translation.

When Google Translate converts news articles written in Spanish into English, phrases referring to women often become'he said' or'he wrote'. Software designed to warn people using Nikon cameras when the person they are photographing seems to be blinking tends to interpret Asians as always blinking. Word embedding, a popular algorithm used to process and analyse large amounts of natural-language data, characterizes European American names as pleasant and African American ones as unpleasant. These are just a few of the many examples uncovered so far of artificial intelligence (AI) applications systematically discriminating against specific populations. Biased decision-making is hardly unique to AI, but as many researchers have noted1, the growing scope of AI makes it particularly important to address.

We study open domain dialogue generation with dialogue acts designed to explain how people engage in social chat. To imitate human behavior, we propose managing the flow of human-machine interactions with the dialogue acts as policies. The policies and response generation are jointly learned from human-human conversations, and the former is further optimized with a reinforcement learning approach. With the dialogue acts, we achieve significant improvement over state-of-the-art methods on response quality for given contexts and dialogue length in both machine-machine simulation and human-machine conversation.

YOU'VE heard of it in movies or in passing conversations. Maybe your workplace uses it, or you're considering using it yourself. As technology continues to make ripples across the workplace, AI has become increasingly prevalent. Through AI, companies are able to analyse large amounts of data, which will allow them to better engage with customers. Today, AI is easily accessible.