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This Week in Machine Learning: AI & Economy, ML Engineers, Criminal Justice, and More

Algorithms and numbers are all around us, we just don’t realize how much it affects our daily lives or even actions. What has happened in the machine learning realm over the last week? If you’re interested in how machine learning is changing our world, make sure to check out our weekly roundup. News, 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 30 – July 6

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

» The impact of machine learning and AI on the UK economy by David Bholat on Vox.eu | July 2

An interesting read on how machine learning and AI are changing the economy and financial systems. Can AI predict the financial crisis? How will the labor market and global economy look like? Read. if you’re interested in how the financial sector is functioning in the age of advanced algorithms. 💰

» Why every company will have machine learning engineers soon by Caleb Kaiser on Towards Data Science | July 1
We all know machine learning engineers are sough-after experts. More and more companies decide to hire them. Quite a concise and interesting read for the followers of the latest trends in AI world. Do you already have ML engineers in your team? 👨‍💻

» Fairness in Machine Learning – The Case of Juvenile Criminal Justice in Catalonia by Marius Miron, Songül Tolan, HUMAINT project, European Commission, Joint Research Centre on Re-Work Blog | June 1

It turns out machine learning can also help in criminal justice. Check this interesting read to see how. Fascinating! ⚖

» What is scientific machine learning and how will it change aircraft design? on Aerospace Testing | July 2

Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at the University of Texas, Austin discusses an emerging field of computing that could revolutionize how aircraft are designed. 🛫

» Roadmap to Machine Learning: Key Concepts Explained by Oleksii Kharkovyna on Towards Data Science | July 1

As the title says, key concepts explained. You probably know all (or most) of them, but it’s a quick read with a nice list of the most important concepts.

» My Invisalign app uses machine learning and facial recognition to sell the benefits of dental work by Veronica Combs on TechRepublic | July 2

ML transforms even the dental sector. Read how! 🦷

» 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. 

Continue reading ->

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