This is meant as the main place for us to exchange exciting news about these technologies, such as ML (Machine Learning), DL (Deep Learning), AI, Quantum computing, HPC and more.

I'll make a start with this news article:
Rensselaer Team Aims to Pave Way for Robust AI in Medical Imaging (Source: HPC Wire)
Rensselaer Polytechnic Institute is the very same university handling the BOINC project Milkyway@Home btw.

Extract

• currently researching the potential of AI, machine learning, and deep learning in biomedical imaging
  
• potential to advance image reconstruction, image quality, computer-aided diagnosis, and image-guided surgery
  
• developing AI techniques that are robust enough to guard against deep learning glitches without interfering with tasks
  
• detect adversarial images, correct those discrepancies, and improve the quality of information the AI system produces
  

AI algorithms uncannily good at spotting your race from medical x>rays

... They were surprisingly accurate. The worst performing was able to predict the right answer 80 per cent of the time, and the best was able to do this 99 per cent, according to the paper.

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Artificial Intelligence may diagnose dementia in a day (BBC):
The algorithm can identify patterns in the scans even expert neurologists cannot see and match them to patient outcomes in its database. [...] In pre-clinical tests, it has been able to diagnose dementia, years before symptoms develop, even when there is no obvious signs of damage on the brain scan.

Racial profiling/classification from x-rays sounds a bit scary if you ask me :)

Interesting way to save computation cycles or pondering?
https://arxiv.org/pdf/2107.05407.pdf

Now in 3D - Deep learning techniques help visualize X-ray data in three dimensions (ScienceDaily): Processing of 2D images is easy and can be done on a smartphone nowadays. Now, scientists might have found a new way of handling 2D data to interpret it as a fully modelled 3D representation using AI. This may be the key to turning X-ray data into visible, understandable shapes at a much faster rate. A breakthrough in this area could have implications for astronomy, electron microscopy and other areas of science dependent on large amounts of 3D data.

Here's the corresponding paper: Rapid 3D nanoscale coherent imaging via physics-aware deep learning

https://bdtechtalks.com/2021/08/19/machine-learning-research-pitfalls/

GARBAGE IN, GARBAGE OUT
Dodgy Data Makes AI Less Useful
The problem, according to the MIT research, is that these data sets often have errors in the human-created labels. They calculated that 3.4 percent of the data labels they examined were either flat-out wrongly labeled or were questionable in some way. (Source)

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Machine-learning models vulnerable to undetectable backdoors: new claim

Boffins from UC Berkeley, MIT, and the Institute for Advanced Study in the United States have devised techniques to implant undetectable backdoors in machine learning (ML) models.

Their work suggests ML models developed by third parties fundamentally cannot be trusted.

In a paper that's currently being reviewed – "Planting Undetectable Backdoors in Machine Learning Models" – Shafi Goldwasser, Michael Kim, Vinod Vaikuntanathan, and Or Zamir explain how a malicious individual creating a machine learning classifier – an algorithm that classifies data into categories (eg "spam" or "not spam") – can subvert the classifier in a way that's not evident.

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