This Week in Machine Learning: Led Zepbot, Love, and Art

Posted August 5, 2020

Our world hides many secrets, some of which we could never be able to discover. But that’s what we have AI for! In this weekly roundup, you’ll learn about how algorithms and machine learning can help discover new things!

Here are the best picks from the last week from the world of machine learning. Enjoy the read!

Weekly Roundup: July 28 – August 3

» Neptune.ai blog – as always, make sure to visit our blog to find out interesting and in-depth articles on machine learning from the last week. 🙂

» Scientists use AI to predict what makes a successful relationship by Thomas Macaulay on The Next Web | July 29

If you rely on algorithms when it comes to romantic relationships, you’ll like this one. 💘 This project led by Western University used machine learning to directly quantify and compare the predictive power of many variables that purportedly shape romantic relationship quality among 11,196 romantic couples.

👉 Here’s the original research

» Recent advances give theoretical insight into why deep learning networks are successful
by Sabbi Lall, Massachusetts Institute of Technology on TechXplore | July 28

A group of MIT researchers recently reviewed their contributions to a better theoretical understanding of deep learning networks, providing direction for the field moving forward. Check out more in this interesting read!

👉 And here’s the original paper

» Not even scientists can tell these birds apart. But now, computers can by Erik Stokstad on Science Magazine | July 28

Algorithms can open possibilities for studying many other bird species and behaviors thanks to convolutional neural networks. Check it out! 🐦

» Man creates new Led Zeppelin song using the power of artificial intelligence by Fraser Lewry on Classic Rock Magazine | July 28

Now this one’s interesting! If you’re a fan of Led Zeppelin, you should definitely check it out – Mountain Man by Led Zepbot. 🎸 That’s what happens when you put Led Zeppelin’s lyrics through algorithms. Just listen for yourself 😉

» UK ditches visa algorithm accused of creating ‘speedy boarding for white people’ by Thomas Macaulay on The Next Web | August 4

We’re for equality! 👩👩🏿 The UK is scrapping a controversial algorithm used in visa applications following allegations that it discriminates against certain nationalities.

» This AI can discover the hidden links between great works of art by Daphne Leprince-Ringuet on ZDNet | July 29

The researchers created MosAIc, an algorithm that looks for links that humans wouldn’t have thought of. And when it seems humans know everything about art, the AI brings a surprise! 🖼

» KDnuggets™ News of the week with top stories and tweets of the past week, plus opinions, tutorials, events, webinars, meetings, and jobs.

» Old but gold, the reliable Reddit thread on ML for more news on machine learning. There’s always something for everyone – tips, tricks, hacks, and more news.


That’s all folks! I hope you found something of interest in this weekly roundup. Don’t forget to check our blog for more inspiring articles.

👉 Came across an interesting ML article? Or maybe you wrote one yourself and would like to share it with other people? Let us know, we’ll spread the news in our weekly roundup!


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ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Jakub Czakon | Posted November 26, 2020

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

Continue reading ->