Blog » General » This Week in Machine Learning: AI & Life in Space, Hackers, and Unpredictability of AI

This Week in Machine Learning: AI & Life in Space, Hackers, and Unpredictability of AI

Machine learning has a great impact on our world, even on our daily decision. It changes the way we work, think, and make decisions. It helps us achieve more and reach out for things we never thought we could reach. It’s undoubtedly one of the biggest inventions of humankind. But it also changes and evolves.

What has happened in ML over the last week? Here are the best picks. Enjoy the read!

Weekly Roundup: July 7-13

» 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. 🙂

» AI Seeks ET: Machine Learning Powers Hunt for Life in the Solar System by Mark Anderson on IEEE Spectrum | July 10

NASA team pioneers AI algorithm to help Mars rover and Titan drone drill for astrobiological evidence. AI may help us in our search for life in space. For all the lovers of space and algorithms. 🛰 👽

» Unpredictability of Artificial Intelligence by Roman Yampolskiy on Hacker Noon

Although this article dates back to July 1st, it’s worth mentioning. In this paper, the author, Professor of Computer Science, AI Safety & Cybersecurity Researcher, discusses the Unpredictability of AI. He proves that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know the terminal goals of the system.

» How to Choose a Machine Learning Technique by Yulia Gavrilova on Serokell | July 8

Read this concise guide if you’re looking for the best ML technique for your work. 

» AI 50 Founders Predict What Artificial Intelligence Will Look Like After Covid-19 by Kenrick Cai on Forbes | July 10

The coronavirus has a large impact on the world. Also on the AI. How will it evolve in the post-COVID-19 world? Check it out yourself!

» A new way to train AI systems could keep them safer from hackers on MIT Technology Review | July 10

“One of the greatest unsolved flaws of deep learning is its vulnerability to so-called adversarial attacks. When added to the input of an AI system, these perturbations, seemingly random or undetectable to the human eye, can make things go completely awry.” 👩‍💻 For more, read this short and interesting article. 👨‍💻

» 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!


READ NEXT

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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