FOSS Machine Learning News week 13-2020

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 Designing AI Systems With Human-Machine Teams

Automatic language translators are based on preset rules of grammar and meaning, and are therefore closed; AI systems learn by accumulating terms and colloquialisms. Just as machines learn from humans, humans can acquire insights from algorithms. This will enable AI systems to shift from one configuration to another depending on the environment and human factors. Although the AI systems may have enough stored and processed data to make educated guesses, the risk of something bad happening can’t be overlooked. To know when such interventions are required, the AI system needs to have a level of transparency.

(MIT Sloan Management Review)

2 Using Machine Learning to Detect Design Patterns

This blog post explains why design patterns matter and reports promising results of an experimental use of machine learning (ML) to detect design patterns in source code. An important component of evaluating software quality attributes at scale is the ability to efficiently identify these design approaches in source code known as design patterns. The quality of software can make or break a program budget. Quality attributes such as reliability, security, and modifiability are just as important as making sure the software computes the right answer. The ability to evaluate software is critical both for software developers and for DoD program managers who are responsible for software acquisitions.

(Software Engineering Institute)

3 System trains driverless cars in simulation before they hit the road

A simulation system invented at MIT to train driverless cars creates a photorealistic world with infinite steering possibilities, helping the cars learn to navigate a host of worse-case scenarios before cruising down real streets. For that reason, there’s usually a mismatch between what controllers learn in simulation and how they operate in the real world. After successfully driving 10,000 kilometers in simulation, the authors apply that learned controller onto their full-scale autonomous vehicle in the real world. Some computer programs, called “simulation engines,” aim to imitate these situations by rendering detailed virtual roads to help train the controllers to recover. But the learned control from simulation has never been shown to transfer to reality on a full-scale vehicle.

(MIT Reseach CS)

4 Massively Scaling Reinforcement Learning with SEED RL

Handling two completely different tasks on one machine (i.e., environment rendering and inference) is unlikely to utilize machine resources optimally. The SEED RL architecture is designed to solve these drawbacks. We demonstrate the performance of SEED RL on popular RL benchmarks, such as Google Research Football, Arcade Learning Environment and DeepMind Lab, and show that by using larger models, data efficiency can be increased.

(Google AI Blog)

5 Signal processing is key to embedded machine learning

Signal processing is key to embedded Machine Learning. When we hear about machine learning – whether it’s about machines learning to play Go, or computers generating plausible human language – we often think about deep learning. However, that does not mean that machine learning is a magic button you can press to add intelligence to your deployment.


6 Teaching an algorithm what it means to be fat

Overweight individuals, and especially women, are disparaged as immoral, unhealthy, and low class. These negative conceptions are not intrinsic to obesity; they are the tainted fruit of cultural learning. We extract schemata about obesity from New York Times articles with word2vec, a neural language model inspired by human cognition. Our findings reinforce ongoing concerns that machine learning can encode, and reproduce, harmful human biases.


7 Delivering information and eliminating bottlenecks with CDC’s COVID-19 assessment bot

The bot, which utilizes Microsoft’s Healthcare Bot service, will initially be available on the CDC website. Microsoft is helping with this challenge by offering its Healthcare Bot service powered by Microsoft Azure to organizations on the frontlines of the COVID-19 response to help screen patients for potential infection and care. The Healthcare Bot service is a scalable Azure-based public cloud service that allows organizations to quickly build and deploy an AI-powered bot for websites or applications that can offer patients or the general public personalized access to health-related information through a natural conversation experience. In a crisis like the COVID-19 pandemic, it’s not only important to deliver medical care but to also provide information to help people make decisions and prevent health systems from being overwhelmed.


8 NLP Frameworks

Natural language processing (NLP) is a field located at the intersection of data science and machine learning (ML). It is focused on teaching machines how to understand human languages and extract meaning from text. Using good open FOSS NLP software saves you time and has major benefits above using closed solutions. In the FOSS ML Guide you find an opinionated list of top FOSS NLP frameworks.


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.