October 2017

Lack of labeled training data for chatbots – our solution for the problem

One of the toughest problems in building bots is the scarcity of available training data. As all machine learning systems, chatbots and virtual customer assistants function best when they have been trained on good quality training data. In the beginning all bots have to be taught what things mean. In the context of understanding what customers want when they contact customer service, this means teaching bots what topics (i.e. intents) do certain questions refer to. As a simple example, people ask from a bank “I have not received my salary” or “Where is my transfer”. These are seemingly two different

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Nordic Data Science and Machine Learning Summit Recap

Yesterday our CTO Hendrik Luuk had the honours of presenting at the Nordic Data Science and Machine Learning Summit in Stockholm 🇸🇪 The conference was sold out, which gives a hint to the level of interest in the topic of machine learning in this region. Our talk was titled “Automating customer service chat via AI-based natural language understanding” and in a nutshell we gave an overview and key lessons from building up a customer service chat automation platform with built-in natural language understanding system from ground zero. The key takeaways of the talk were: Human-level language understanding is much more complicated than

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What I Learned From One Of The Most Interesting Panels On AI I Have Been To

Last week enjoyed an excellent panel discussion at Nordic Digital Business Summit with Alexander Khaytin from Yandex Data Factory, Ville Hulkko from Silo.AI and Olli Ventä from VTT. I have been on numerous panels but this one was one of the most enjoyable ones as the discussion was about where we stand with Artificial Intelligence and where are we headed. Couple of points that are worth sharing: There is a lot of AI hype. Everyone agreed on that. In some ways it is good as the topic rises to consciousness of a lot of people. But mostly it just raises

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