All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. I wait to meet Xavier Niel in a room that feels fitting for one of France's richest men. Niel is the original French internet mogul, of the generation before founders wore T-shirts to the office. His team wears suits; he arrives in a classic white shirt.

There is no doubt that machine learning is one of the major driving forces behind most of the advanced techs and gadgets we have today. Whether it is your smart home device or that newly bought self-driving car yours, ML is playing a vital role not only advancing gadgets but is also changing the way people interact with machines. No doubt, it is one of the hottest technologies in the world. However, people almost forget that there is something called Machine Teaching that also plays a significant role in all the ML use cases. We all have heard a lot about ML, which is also seen as a subset of artificial intelligence.

Everyone uses the term artificial intelligence (AI) generically, and they mix it up with Machine Learning, Data Science, and Predictive Analytics. The definitions have changed over time, and everyone seems to inherently assume that they know what they're talking about when they say AI. From my perspective, Artificial Intelligence is essentially the ability of machines to perform those targeted functions that you normally associate with a human being. These are things that humans do naturally, like reasoning, learning, and interacting with the environment. Anything that falls in that realm right now is AI.

Artificial Intelligence is the fourth industrial revolution after the invention of steam power, electricity, electronics and software. And Bottos will be the key driving force for this revolution as the data becomes the core competitive power in AI development.

As part of Artificial Lawyer's continuing series about emerging legal tech markets around the world, today's guest post about Belgium is brought to us by Tom Pieters, a lawyer at Belgian law firm AdvoDender. He is also a member of COMMIT (Committee on IT) and A.I.-D.I. (Committee on Artificial Intelligence and Digital Innovation) of the Flemish Bar Association. On October 27, 2016 Artificial Lawyer published a guest article by Dutch lawyer Niek van de Pasch on the quickly growing legal tech scene in Holland. It was a very interesting read, but it only added to my growing sense of unease, a feeling that started upon my discovery of Dutch Legal Tech in February 2016. I have been interested in all things tech for a very long time.

Slow feature analysis (SFA) is a method for extracting slowly varying driving forces from quickly varying nonstationary time series. We show here that it is possible for SFA to detect a component which is even slower than the driving force itself (e.g. the envelope of a modulated sine wave). It is shown that it depends on circumstances like the embedding dimension, the time series predictability, or the base frequency, whether the driving force itself or a slower subcomponent is detected. We observe a phase transition from one regime to the other and it is the purpose of this work to quantify the influence of various parameters on this phase transition. We conclude that what is percieved as slow by SFA varies and that a more or less fast switching from one regime to the other occurs, perhaps showing some similarity to human perception.