Radical Open Innovation News week 12-2019

Welcome to our weekly selection of digital innovation news. Created using our opinionated automated selection algorithm with a twisted text rank summary creator. We present some top innovation news items to get you thinking, debating and take action in order to make our world even better.

1 Harnessing Organizational Knowledge for Machine Learning

Rather than labeling training data by hand, Snorkel DryBell enables writing labeling functions that label training data programmatically. In this work, we explored how these labeling functions can capture engineers’ knowledge about how to use existing resources as heuristics for weak supervision. Rather than hand- labeling training data to do this, in Snorkel DryBell users write labeling functions that express various organizational knowledge resources (A), which are then automatically reweighted and combined (B). This technique learns by observing the matrix of agreements and disagreements between the labeling functions’ outputs, taking into account known (or statistically estimated) correlation structures between them. We used these organizational knowledge resources to write labeling functions in a MapReduce template-based pipeline.

(Google AI Blog)

2 The Only Way Manufacturers Can Survive

Even today, although most manufacturers are beginning to flirt with digital technologies, not one has successfully pulled off a digital transformation. However, while waves of technology — automation, additive manufacturing, AI — are washing over the corporate world, redefining the nature of work and productivity, there are no playbooks and few best practices for manufacturers’ digital transformation. To start, CEOs must react quickly to weak signals, but over time they should develop an overarching vision for digital transformation. Manufacturers believe in continuous improvement, digital companies in constant innovation. We’re both convinced that executing a digital transformation is not only the most complex but also the most critical challenge that any manufacturer faces today.

(MIT Sloan Management Review)

3 The Apache Way to Sustainable Open Source Success

The Apache Way advances sustainable Open Source communities: everything we do is Open Source so all kinds of users can benefit from our experience. As Open Source software continues to grow in importance, it seems appropriate to reflect upon the ongoing success of The Apache Software Foundation (ASF) as it approaches its 20th anniversary. The Apache Way of community-driven development continues to gain momentum despite the compounding challenges of building software in the greater Open Source ecosystem. Open Source software can be digitally copied and reused in an unlimited number of ways. As a US 501(c)(3) public charitable organization, The Apache Software Foundation helps individuals and organizations understand how Open Source at scale works in a highly competitive market.

(Apache Foundation)

4 Agile Is Not Enough

The company creates an agile team of developers, designers, and an initiative owner who understands customer behavior and can make decisions about focus and priorities. Integrating development, maintenance, and support activities at the agile team level helps eliminate unnecessary handoffs while establishing end-to-end accountability. But agile teams can stumble as they interact with and depend on others, so it becomes a matter of anticipating and mitigating these choke points in the organization. This means triaging different kinds of work and building agile teams that combine the right mix of internal and external talent. Three impediments in particular work against agile in most organizations: rigid architecture, poor talent management, and lack of a product mindset.

(MIT Sloan Management Review)

5 The Responsible Machine Learning Principles

A practical framework to develop AI responsibly. The 8 principles of responsible ML development provide a practical framework to support technologists when designing, developing or maintaining systems that learn from data. The Responsible Machine Learning Principles are a practical framework put together by domain experts.
Their purpose is to provide guidance for technologists to develop machine learning systems responsibly.


6 Iodide: Data science in your browser

Iodide (from Mozilla) is tool designed to make Web-based scientific communication and computation more accessible to scientists by bringing them a low friction iterative workflow in a familiar environment influenced by tools such as Jupyter, R-Studio, and the MATLAB IDE. We believe that the Web provides an unparalleled platform for communicating scientific ideas with easily shareable interactive data visualization and accessible beautiful write up, but that modern Web development seems slow, cumbersome, and unapproachable to most scientists. Iodide stands alongside other excellent tools for exploring and prototyping JavaScript, and it may be used just for prototyping and experimenting with web programming if desired.


The Radical Open Innovation weekly overview is a brief overview of innovation news on Digital Innovation and Management Innovation from all over the world. Your input for our next edition is welcome! Send it to [info] at [bm-support]dot[org]