Weekly ML drop #8

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.

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.

German retailer Otto allows algorithm to order their supplies
To shorter delivery times, Otto allows their Machine Learning based system to automatically resupply the stock, which led to shorter deliveries, fewer returns and less overall losses.

Nobody understands the Deep Learning
There are certain fields, where it is required, that algorithm used to find results must be explainable. Unfortunately, it’s close to impossible answer the question “what exactly caused this network to give this answer” given its complexity – hunders of layers with thousands of neurones. It also makes debugging and finding errors very hard.

AI can aquire biases against race, gender, etc.
There’s an old saying about computer systems: Garbage in, garbage out. It also works with ML systems. What kind of data we feed to learning algorithms, will impact how their models work. That’s why AI based on human generated data, can be not that democratic as some people promise.

Fast Drawing for everyone
This google post talks about the AutoDraw experiment, that figures out what you wanted to draw, and propose you a better representation of it. There are some limitations though. It won’t offer you a cat in similar shape, just bunch of predefined cats. And also a number of recognised objects is quite limited. Still, an interesting toy to play. And if you’re interested in science, behind it, there’s this article on research blog.

Google’s neural networks duel against each other
Most of today’s Machine Learning is so-called supervised learning. It does mean, that somebody needs to feed the algorithm with data, that’s supervised (for example labelled images). One overly simplified case could be that one network is generating cat pictures, and the other one is recognising cats and they get better by feeding data to each other.

I pick one or two videos every now and then that touches an interesting subject in AI and ML field. Sometimes it’s more scientific and the other it’s about real life applications.

In the future, everything will be smart
In this short video Kevin Kelly, author of “The Inevitable“, talks how AI will be a commodity, as electricity became in XIX century and we’re on the brink of another revolution.

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.

Deep Learning 101 from Intel
This one hour webinar goes trough basic concepts of Deep Learning and how those type of algorithms perform on Intel’s stack.

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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