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ML

Weekly ML drop #6

I’ve become more and more interested in machine learning during last year. This is my way of collecting and sharing interesting reads on the topic I stumble upon. Those posts are published each Friday, they are divided into few categories and the format is constantly evolving.

Accidentally this week, I also have a theme. Most articles are related to impact on workplaces and how companies work.

News
In this part, I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about interesting applications,  predictions and controversies that AI causes.

Axa is using ML to predict big car accident with 78% accuracy
In this article on Google Platform blog, the case of a big insurance company is discussed. What technology was used and how it worked.

How automation impacts economy?
This post discusses potential outcomes of more and more automation in modern economies.

Artificial intelligence could dramatically improve the economy and aspects of everyday life, but we need to invent ways to make sure everyone benefits.

How AI is transforming workplace
Discussion of multiple aspects of workplace and how machine learning and other AI-related technologies may change them.

How AI is changing the way companies are organised
This article brings up some examples how introducing ml tools may change how companies are structured and how internal communication is performed.

Machine learning used to smart compression of videos
Netflix uses new machine learning based algorithm to compress video scene by scene for better results.

Opinions of 17 experts, how worried we should be about AI


Learning materials

Here I’m sharing material for learning ML that I found useful – online courses, blogs, books etc. This is usually rather technical stuff.

In-depth, non-technical guide to machine learning
This five-part article goes through terms and techniques used in machine learning in human-friendly language. Very good first contact with anything machine-learning related.


This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.

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