Every time we ask Siri to set a reminder, we use Google Translator or we click on a banner, there are deep learning technologies playing behind the scenes. But for these to work properly, they need to be trained with a huge amount of quality data.
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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.
Machine learning algorithms require a great amount of numeric data to work properly. Real people, however, do not speak to bots using numbers, they communicate through the natural language. That’s the main reason why chatbot developers need to convert all these words into digits so that those virtual assistants can understand what users are saying. And here is where word embeddings come into play.
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.
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.