How, Where is Machine Learning Evolving?

We were long used to analytics staying static in  method and application.   Well thats not the case, at least not yet.   Some good comments here in this excerpt:

Four ways machine learning is evolving, according to Facebook's AI engineering chief
Yangqing Jia, director of engineering for Facebook's AI platform team, on the changing field of machine learning.    By Nick Heath in TechRepublic

Machine learning is slowly changing the world — helping cars to "see" the world around them and virtual assistants to understand our questions and commands.

Driving forward machine-learning research are companies like Facebook, Google and Baidu — each of which are identifying new applications for the technology.

But how is the field of machine learning changing and what factors are shaping its future direction?

Yangqing Jia, director of engineering for Facebook's AI platform team, spoke about the changing nature of the field at the recent AI Conference presented by O'Reilly and Intel AI in London.

Training datasets are getting too big for humans to handle

In supervised learning, the system learns by example, typically by analyzing labelled data, for example, photos annotated to indicate whether they contain a cat.

The size of training datasets is often massive and continues to grow, with Facebook recently announcing it had compiled 3.5 billion public images from Instagram, labelling each image using attached hashtags.

"Data becomes a super important part in this AI ecosystem," said Jia.

"We know that lately, due to the internet era, we have a huge amount of data. That gives us a mass of data we can deal with."

The difficulty when datasets stretch to billions of images or videos is that manually labelling each one becomes too expensive and time-consuming.

"Data has become a gold mine but can we actually mine gold out of it?" said Jia.  ... "