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This Week in Machine Learning: Meena, ML Finds New Antibiotic and More

Every day brings new opportunities. A week brings even more. To make sure you’re getting the most out of it, we’ve gathered a list of trending articles of the week – everything about Data Science, AI, tech and machine learning. 

Here goes a dose of the latest news, discoveries, and inspiring stories. There is something for everyone. Enjoy your read!

This Week in Machine Learning

KDnuggets™ News of the week with top stories of the past week.

Also, if you’re interested in machine learning in natural language understanding, make sure to check the article on KDNuggets by Jesus Rodriguez, Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything

> Artificial intelligence yields new antibiotic by Anne Trafton | MIT News Office February 20, 2020

Extremely fascinating news for all scientists working in the field of medical and biological engineering. The article shows how machine learning can help in saving people’s lives – MIT researchers used a machine-learning algorithm to identify a drug called halicin that kills many strains of bacteria.

> Stargazing with computers: What machine learning can teach us about the cosmos by Shannon Brescher Shea, | Argonne National Laboratory February 20, 2020

An interesting insight into how machine learning can help discover new objects in our universe by analyzing huge amounts of data.

For all the enthusiasts of cosmos and machine learning who like to look into the stars. 🌌

> Magic Quadrant for Data Science and Machine Learning Platforms by Gartner

The report already has two weeks but the internet still talks about it. Make sure to check it out!

> Building Machine Learning Models by Integrating Python and SAS® Viya® by Kris Stobbe February 19, 2020

For all of you working in Python. This article demonstrates how you can predict the survival rates of Titanic passengers with a combination of both Python and SAS using SWAT.

> Alibaba designs new AI tool to diagnose coronavirus; it’s 96% accurate by Alexandru Micu

Read how the world’s largest retailer and e-commerce company, the Chinese-based Alibaba Group, is throwing its technical know-how into the fight against the coronavirus outbreak.

> Expert-augmented machine learning – collective authorship February | PNAS (Proceedings of the National Academy of Sciences of the United States of America) 18, 2020

A research paper on expert-augmented machine learning (EAML)

> Check out the wonderful and reliable Reddit thread on ML for more news on machine learning.

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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