The 5 Sampling Algorithms every Data Scientist need to know by @MLWhiz via @Medium

Here’s an intro to common sampling techniques.

Includes some sample Python code which makes it really easy to incorporate into your code (with some editing). Make sure you follow Rahul and give him lots of applause for helping you with this.

Open-endedness: The last grand challenge you’ve never heard of by Kenneth O. Stanley Joel Lehman and Lisa Soros via @OReillyMedia

While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.

This is a little bit of a long read but is worth the investment in time. A very interesting concept that I found fascinating. Something to think about.

China’s AI brain drain by Karen Hao via @techreview

China has pushed—successfully—to increase the number of Chinese AI researchers. But a new analysis shows that although the number of Chinese AI researchers has increased tenfold over the last decade, the majority of them live outside the country.

Impressive that they have a strategy but it could offer problems to organisations outside of China as they tempt people back to work in AI.

7 trends impacting commercial and industrial IoT data by Sastry Malladi via @infomgmt

Here's a look at seven top trends that are driving this space, from compute size to the value of true edge computing, to closed-loop edge to cloud machine learning.

Interesting article and very useful - worth a bookmark if you have any intention to use IOT.

No Time To Read AI Research? Topbots have summarised Top Papers From The Past Year by Mariya Yao via @topbots

Trying to keep up with AI research papers can feel like an exercise in futility given how quickly the industry moves. If you’re buried in papers to read that you haven’t quite gotten around to, you’re in luck. To help you catch up, Topbots have summarised 10 important AI research papers from 2018 to give you a broad overview of machine learning advancements in the past year.

This is incredibly useful and well worth using this important resource and saving yourself some valuable time.

Pay attention to the man behind the curtain by @quaesita via @topbots

Google’s chief decision scientist, Cassie Kozyrkov, talks about AI bias and how to handle it.

Great observations and well worth reading and thinking about in relation to your own work and the quality of what you are doing.

What 70% of Data Science Learners Do Wrong by Dan Becker via @kdnuggets

Lessons learned from repeatedly smashing his head with a 2-meter long metal pole for a college engineering course.

You definitely need to bookmark this article and make sure you can always get back to it for reference.