FOSS Machine Learning News week 9-2020

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 AI Governance Database

This database is an information resource about global governance activities related to artificial intelligence. It is designed as a tool to help researchers, innovators, and policymakers better understand how AI governance is developing around the world.


2 The Future of Minds and Machines

There is an alternative vision for the future of AI development. By starting with people first, we can introduce new technologies into our lives in a more deliberate and less disruptive way. In The Future of Minds and Machines we introduce an emerging framework for thinking about how groups of people interface with AI and map out the different ways that AI can add value to collective human intelligence and vice versa.


3 A road map for artificial intelligence policy

The rapid development of artificial intelligence technologies around the globe has led to increasing calls for robust AI policy: laws that let innovation flourish while protecting people from privacy violations, exploitive surveillance, biased algorithms, and more. “You cannot just go to a computer lab and say, ‘Okay, get me some AI policy,’” he stressed. “This is a very complex problem,” Luis Videgaray PhD ’98, director of MIT’s AI Policy for the World Project, said in a lecture on Wednesday afternoon.

(MIT Reseach CS)

4 Self-Labelling via simultaneous clustering and representation learning

The idea is to define pretext learning tasks can be constructed from raw data alone, but that still result in neural networks that transfer well to useful applications. Instead, the novelty in our method lies in using a clustering approach that minimizes the same cross-entropy loss that the learning the network also optimizes. Learning from unlabelled data can dramatically reduce the cost of deploying algorithms to new applications, thus amplifying the impact of machine learning in the real world. In this paper, we develop a method to obtain the labels automatically by designing a self-labelling algorithm. Why do we want to train with labels? This is a chicken-and-egg problem: we require the labels to train the network, and we require the network to predict the labels.

(Visual Geometry Group)

5 Quantifying Independently Reproducible Machine Learning

We want to focus on the question of reproducibility, without wading into the murky waters of replication. What Makes a ML Paper Reproducible? At the same time, our process and systems must result in reproducible work that does not lead us astray. Peer review has been an integral part of scientific research for more than 300 years. The goal of this analysis was to get as much information as possible about things that might impact a paper’s reproducibility. How reproducible is the latest ML research, and can we begin to quantify what impacts its reproducibility?

(The Gradient)

6 A human-machine collaboration to defend against cyberattacks

The platform uses machine learning models to go through more than 50 streams of data and identify suspicious behavior. In addition to enterprise customers, the company began offering its platform to security service providers and teams that specialize in hunting for undetected cyberattacks in networks. The company has developed a closed loop approach whereby machine- learning models flag possible attacks and human experts provide feedback.

(MIT Reseach Innovation)

7 Safety Gym

We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. If the car is getting in too many fender-benders, you raise the fine until that behavior is no longer incentivized. Safety Gym To study constrained RL for safe exploration, we developed a new set of environments and tools called Safety Gym. By comparison to existing environments for constrained RL, Safety Gym environments are richer and feature a wider range of difficulty and complexity. In all Safety Gym environments, a robot has to navigate through a cluttered environment to achieve a task.


8 Very simple statistical evidence that AlphaGo has exceeded human limits in playing GO game

The AlphaGo than professional players and professional players than ordinary players shows the laying of stones in the distance becomes more frequent. In addition, AlphaGo shows a much more pronounced difference than that of ordinary players and professional players. Deep learning technology is making great progress in solving the challenging problems of artificial intelligence, hence machine learning based on artificial neural networks is in the spotlight again. In some areas, artificial intelligence based on deep learning is beyond human capabilities. It seemed extremely difficult for a machine to beat a human in a Go game, but AlphaGo has shown to beat a professional player in the game.


9 Lessons Learned from Developing ML for Healthcare

In addition to detecting known diseases, ML models can tease out previously unknown signals, such as cardiovascular risk factors and refractive error from retinal fundus photographs. This should be done throughout the process of developing technologies for healthcare applications, from problem selection, data collection and ML model development to validation and assessment, deployment and monitoring. Previous research indicates that doctors assisted by ML models can be more accurate than either doctors or models alone in grading diabetic eye disease and diagnosing metastatic breast cancer. How does the ML model help me in taking care of my patients? To reduce confusion, we have opted to refer to the (ML) validation set as the “tuning” set. Future outlook It is an exciting time to work on AI for healthcare.

(Google AI Blog)

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.