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This Week in Machine Learning: Human-Machine Interaction, AI Limitations, and Neuralink

Machine learning and artificial intelligence help to discover new things, push the boundaries, and sometimes even save people’s lives. What can we do with the help of algorithms and what is still ahead of us? Find out in our Weekly Roundup.

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

Weekly Roundup: 25-31 August

» 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. 🙂

» IBM has built a new drug-making lab entirely in the cloud by Karen Hao on MIT Technology Review | August 28

Even among scientists, the trend of remote work has gained popularity. IBM has built a new chemistry lab called RoboRXN in the cloud. It combines AI models, a cloud computing platform, and robots to help scientists design and synthesize new molecules while working from home. 🧪

» Human meets AI: Intel Labs team pushes at the boundaries of human-machine interaction with deep learning by Tiernan Ray on ZDNet | August 29

An interesting interview with Lama Nachman about his work as the lead of the research lab called the Anticipatory Computing Lab at Intel Labs and the research they do at the intersection of humans and AI.

Machine learning and AI can help humans with disabilities. An inspiring story worth checking out! ♿

» Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings by Daniel Freedman and Ehud Rivlin, Research Scientists, Google Health on Google AI Blog | August 28

ML brings a lot of hope for people with colorectal cancer (CRC), which is a global health problem and the second deadliest cancer in the United States, resulting in an estimated 900K deaths per year. Here, the authors present the Colonoscopy Coverage Deficiency via Depth algorithm, or C2D2, a machine learning-based approach to improving colonoscopy coverage that can help save lives. 🏥

» Elon Musk’s Neuralink is neuroscience theater by Antonio Regalado on MIT Technology Review | August 30

Are you excited about Elon Musk’s Neuralink? So are we, but… should we? Read this objective insight into the famed invention to learn what to expect.

» Six Limitations of Artificial Intelligence As We Know It on Mind Matters | August 27

AI, as everything in the world, has its limitations. What are they? Check it out yourself in this interesting discussion between Larry L. Linenschmidt of the Hill Country Institute and Walter Bradley Center director Robert J. Marks. The focus is on why we mistakenly attribute understanding and creativity to computers.

» How Machine Learning Is Changing Medicine And Healthcare? by Robert Krajewski on Ideamotive | August 27

One of the biggest applications of ML can be found in science that is dedicated to helping people with disabilities and illnesses. How it affects researches? Read the article and find it out (examples included). 🥼

» 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

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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