Although chatbots have become quite popular in recent years, there is still room for improvement. A well-trained chatbot must correctly react to any query sent by a user creating a successful human-like conversation. Is that happening? We don’t think so.

Round-the-clock service, cost reduction and delivering a better customer experience are, among others, the main benefits of chatbots for customer service automation. If you are thinking of setting up a conversational agent to take care of your customers, it’s all-important for you to know not only the bright side, but also the dark one. You just can't spin up a basic chatbot and expect it to work well.

Two concepts, one mission: to make machines understand humans. Natural Language Processing (NLP) and Machine Learning (ML) are all the rage right now, but people tend to mix them up. In this post, there will be a distinction between these two different but complementary terms in the field of Artificial Intelligence.

On 24 and 25 April 2019, at the old Customs House in Hamburg, Bitext was one of 54 companies backed by the European Innovation Council (EIC) pilot that had the opportunity to pitch and present their business cases and to engage in one-on-one business meetings with representatives from some of the leading Corporates in Europe. The multi-corporate event was promoted by the EIC pilot together with comdirect.

By 2021, organizations that bypass privacy requirements and are caught lacking in privacy protection will pay 100% more in compliance costs than competitors that adhere to best practice. (Gartner- Top 10 Strategic Technology Trends for 2019: A Gartner Trend Insight Report).

Applying anonymization techniques to your data can bring some considerable benefits and liberate you from certain obligations set out in GDPR or California Consumer Privacy Act.  Do you know for example that if you want to anonymize new data collected from your website, then you’ll either need to obtain consent to collect personal data (like cookies, IP addresses and device ID) and then apply anonymization techniques, or only collect anonymous data from the start? This is why any business is concerned about anonymization.

A ‘word embeddings’ approach has been widely adopted for machine learning processes. While an extensive research has been carried out during these years to analyze all theoretical underpinnings of algorithms such as word2vec, GloVe or fastText, it is surprising that little has been done, in turn, to solve some of the more complex linguistic issues raised when getting down to business.