FOSS Machine Learning News week 15-2020

Welcome to our biweekly selection of Free and Open machine learning news. Created using our own opinionated selection and summary algorithm. FOSS machine learning is crucial for everyone. Machine Learning is a complex technology. So keep it simple.

1 A Step Towards Protecting Patients from Medication Errors

In our next phase of research, we will examine under which circumstances these models are useful for finding medication errors that could harm patients. To mitigate these challenges, one can imagine a system more sophisticated than the current rules-based error alerts provided in standard electronic health record software. Even for ‘false negatives’ — cases where the medication ordered by doctors did not appear among the top-25 results — the model highly ranked a medication in the same class 42% of the time. With the widespread use of electronic health records, it may now be feasible to use this data to identify normal and abnormal patterns of prescriptions. In an initial effort to explore solutions to this problem, we partnered with UCSF’s Bakar Computational Health Sciences Institute to publish “Predicting Inpatient Medication Orders in Electronic Health Record Data” in Clinical Pharmacology and Therapeutics , which evaluates the extent to which machine learning could anticipate normal prescribing patterns by doctors, based on electronic health records.

(Google AI Blog)

2 Designing Ultra Low Power AI Processors

However, not all challenges to achieve the best architecture for an ultra-low power AI processor are unique to AI, Collins said. “How much functionality do you want within a certain power envelope? All three of those things need to align. Many of the existing AI processors aren’t all that much different from other types of processors. Otherwise, you end up with software people designing hardware, and that doesn’t go well, or hardware people designing software, and that gets even worse. AI chip design is beginning to shift direction as more computing moves to the edge, adding a level of sophistication and functionality that typically was relegated to the cloud, but in a power envelope compatible with a battery.

(Semiconductor Engineering)

3 Bluetooth signals from your smartphone could automate Covid-19 contact tracing while preserving privacy

A team led by MIT researchers and including experts from many institutions is developing a system that augments “manual” contact tracing by public health officials, while preserving the privacy of all individuals. Health officials begin contact tracing to contain infections, asking you to identify people with whom you’ve been in close contact. In other circumstances, public health officials could request that this person get tested if they were noticing a cluster of cases. This broad set of mobile apps is under development by a team led by Ramesh Raskar of the MIT Media Lab. These signals represent random strings of numbers, likened to “chirps” that other nearby smartphones can remember hearing.

(MIT Reseach)

4 Karate Club

Karate Club is an unsupervised machine learning extension library for NetworkX. Karate Club consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. Karate Club is GPL3 licensed FOSS software. So in the next update this great machine learning API framework will be added to the open ML Framework collection in the Free and Open Machine Learning Guide. The paper with all information on Karate Club can be found here.

(Karate Club)

5 Foundations of Data Science

While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems. With this in mind we have written this book to cover the theory we expect to be useful in the next 40 years, just as an under-standing of automata theory, algorithms, and related topics gave students an advantage in the last 40 years. This great book can be downloaded to be used by everyone!

(Download the book)

6 RFBNet: Custom Object Detection training with 6 lines of code

Making computer vision easy with Monk, low code Deep Learning tool and a unified wrapper for Computer Vision. In this experiment, we created a custom object detection using RFBNet with just basic programming skills without even knowing the architecture and PyTorch framework.


7 A Collection of Variational Autoencoders (VAE) in PyTorch

A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. VQ VAE uses Residual layers and no Batch-Norm, unlike other models. All the models are trained on the CelebA dataset for consistency and comparison. The architecture of all the models are kept as similar as possible with the same layers, except for cases where the original paper necessitates a radically different architecture


8 Neuromorphic Chips: The Third Wave of Artificial Intelligence

While quantum computing has made major strides, neuromorphic is still in its lab stage, until recently when Intel announced its neuromorphic chip, Loihi. Hence hardware and software experts have come up with two solutions: Quantum Computing and Neuromorphic Computing. This may indicate the third wave of Artificial Intelligence. Hence it is necessary to bring major design transformation with improved performance that can change the way we view computers. The second generation was populated by using deep learning networks to analyze the contents and data that were largely concerned with sensing and perception.


9 The Vital Role Of Big Data In The Fight Against Coronavirus

He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. In Europe and America, privacy considerations for citizens are of bigger concern than they are in China, yet medical researchers and bioethics experts understand the power of technology to support contact tracing in a pandemic.


The FOSS Machine Learning News Blog is a brief overview of open machine learning news from all over the world. Free and Open machine learning means that everyone must be able to develop, test and play and deploy machine learning solutions. Read and share the FOSS ML Guide! And remember:You are invited to join the Free and Open Machine Learning open collaboration project.