AI Challenges Are Being Met but... Who Broke Ground Here?

[fa icon="calendar'] Dec 13, 2018 3:39:54 PM / by Ana Moreno posted in Query Rewriting, NLU, Deep Linguistic Analysis

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While AI is one of the most important trends nowadays, there are still challenges to overcome. Apart from common technical issues such as a lack of quality data, there is much  beyond its abilities for an AI to effectively understand and react when it comes to human-machine interaction.

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How to Make Your Bot Conversational in Three Steps

[fa icon="calendar'] Nov 6, 2018 6:00:00 PM / by Ana Moreno posted in NLG, NLU, Sentiment Analysis

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Users speak the way they choose, not as you wish. Developers are building tens of bots and almost all of them bump into the same problem: they are not properly working. Most of the bots don’t understand users and the problem lies in their training. That’s the simple truth every bot developer must face sooner or later. The question is: are they facing it the right way?

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Improving Rasa's results by 30% with artificial training data: Part II

[fa icon="calendar'] Jun 5, 2018 5:23:25 PM / by Bitext posted in NLU, RASA

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Increasing bot accuracy has never been so easy. How? Generating artificial training data, not manually, but using auto-generated query variations. We have benchmarked Rasa and other platforms, and their accuracy comes up to a 93% thanks to Bitext artificial training data tech.

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Google I/O: Multiple Actions or Double Intent?

[fa icon="calendar'] May 24, 2018 5:40:07 PM / by Leticia Martín-Fuertes posted in NLU, Deep Linguistic Analysis

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In the past weeks a lot has been said about last Google I/O's presentation of Duplex, an assistant powered by AI that can make phone calls and talk to humans to make arrangements for you. Some people are so impressed by the achievement that they are already pointing out the ethical consequences of not being able to tell apart a human from a machine, and some are playing it down, highlighting that we have only seen a demo.

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Building bots? You can now stop fearing day 1 of production

[fa icon="calendar'] Apr 30, 2018 5:44:39 PM / by Leticia Martín-Fuertes posted in Chatbots, NLU, NLP

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What happens after you build a chatbot for a client? I mean, when the architecture is set up, the NLU module has been built, the conversation flows are designed, and everything is ready. All is left is going into production, where you will have thousands of users' interactions that will train your bot and make it super smart, right?

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