Back in 2016, Microsoft made some big headlines when they announced TayTweets: a chatbot speaking the language of teenagers. TayTweets could automatically reply to users' tweets and engage in conversations on Twitter. Tay was like a teenager, speaking a language resembling the English that we know, but um… somehow different. Tay was a little like a baby: it'd learn whatever you gave to it. It takes a human to make moral decisions about something being right or wrong, or about something being inappropriate or offensive.

When you think about customer service, what industries come to your mind? They deal with customers directly, so customer service is a huge part of their business. In manufacturing, however, the importance of customer service is often overlooked – which is a mistake as lost customers can mean millions of dollars in lost sales. AI solutions can analyze the behaviors of customers to identify patterns and predict future outcomes. Observing actual customers' behaviors allows companies to better answer their needs.

A few years back, most of our data was structured or textual. Nowadays, with the Internet of Things (IoT) a large share of the data is made up of images and videos. There's nothing wrong with that, and it may seem like there's no problem here, but the thing is that many of the systems utilizing machine learning or deep learning are trained in a supervised way, so they require the data to be labeled. The fact that we produce vast amounts of data every day doesn't help either; we've reached a point where there aren't enough people to label all the data that's being created. There are databases that offer labeled data, including ImageNet which is a database with over 14 million images.

Originally published at medium.com by @l.mokrzycki on April 22, 2018. General Data Protection Regulation comes to life in the European Union from 25th May and will strongly influence how Machine Learning products are developed. Without a doubt, it will increase the amount of work required for shipping ML project to productions, but on the other hand, it will be solid protection for the rights of users, which also includes us, creators of these projects. The rights that help us keep control over data that is collected about us and protect us from unfair results of fully autonomous systems. Now, a user will have a legal basis for access, rectify, transfer or deletion of his private data. With this post, I would like to propose best practices for ML models developers allowing them to reduce the amount of work and issues implied by the new law.

Think about a shop, a pharmacy, or even a gym open 24/7. We need places like that, we want services and products to be available whenever we need them but, at the same time, we know that some person has to work early in the morning, at night, or even during Christmas. E-commerce sites are available 24/7 but they don't require a shop assistant to be online all day round. There are chatbots that can do that. Simple questions are answers right away, more complicated ones are forwarded to the right specialist, while the customer receives some answer right now.