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This Week in Machine Learning: AI & Ethics, Brain Tumors, and Wildlife Protection

What has happened in the machine learning world over the last week? If you’re interested in the latest trends, check out the weekly roundup. News, analysis, interesting stories. Read, learn, and get inspired!

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

Weekly Roundup: June 17-29

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

» If AI is going to help us in a crisis, we need a new kind of ethics by Will Douglas Heavenarchive page on MIT Technology Review | June 24

Ethics is an important element in works on AI. Check this insightful interview with Jess Whittlestone from the Leverhulme Centre for the Future of Intelligence at the University of Cambridge on why we need a new kind of ethics for AI.

» IBM Research releases differential privacy library that works with machine learning by Jonathan Greig on TechRepublic | June 29

IBM released the open-source Differential Privacy Library. Its purpose is to allow experimentation, simulation, and implementation of differentially private models using a common codebase and building blocks.

» Machine Learning Tools Detect New Brain Cancer Cell Types by Jessica Kent | June 26

It’s well-known that ML can help in saving human lives. We’re still far from perfection, but this article shows that there’s a bright future ahead of us – researchers from Vanderbilt University leveraged unsupervised and automated machine learning techniques to analyze millions of cancer cells and identify new cancer cell types in brain tumors. The study can later help in the treatment of brain tumors.

⇒ Here’s the original research paper.

» Leveraging Temporal Context for Object Detection on Google AI Blog | June 26

What does machine learning have in common with wildlife and ecological monitoring? Read for yourself!

» 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!


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

10 mins read | Author Jakub Czakon | Updated July 14th, 2021

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. 

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