Radical Open Innovation News week 37-2019

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 Using machine learning to estimate risk of cardiovascular death

They then assigned a label — i.e., whether or not the patient died of cardiovascular death — to each set of adjacent heartbeats. Within the first 15 minutes of a patient experiencing an ACS, there was enough information to estimate whether or not they would suffer from cardiovascular death within 30, 60, 90, or 365 days. RiskCardio’s high-risk patients — patients in the top quartile — were nearly seven times more likely to die of cardiovascular death when compared to the low-risk group in the bottom quartile. Then, they measured how much more likely a patient would suffer from cardiovascular death as a high-risk patient when compared to a set of low-risk patients. With that in mind, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient’s risk of cardiovascular death.

(MIT Reseach CS)

2 Giving Lens New Reading Capabilities in Google Go

Lens uses Google Translate’s neural machine translation (NMT) algorithms, to translate entire sentences at a time, rather than going word-by-word, in order to preserve proper grammar and diction. When users point their camera at text they don’t understand, Lens in Google Go can translate and read it out loud. If you want to try out these features for yourself, they are available today via Lens in Google Go. These reading features become more contextual and useful when they are paired with display. Lens uses CNNs to detect coherent text blocks like columns, or text in a consistent style or color.

(Google AI Blog)

3 You Can’t Afford to Please Everyone

I like to think about what happens at the margins: If you have five versus six cashiers, how does that affect customer wait times? You may want to cut down on the staffing costs, but scaling back staffing affects customer wait times. Are there other sectors where the idea of waiting is key? Ward: Wait times continue to be an important area of research for many organizations. Although giving customers what they want — and as rapidly as possible — is certainly a worthy goal for service organizations, Ward notes that businesses can’t always afford to do this. In the past 10 or 20 years, emergency departments across the country have been getting more and more crowded and wait times have been increasing.

(MIT Sloan Management Review)

4 How Discord moderators build innovative solutions to problems of scale with the past as a guide

How do moderation teams overcome the challenges caused by new technological environments? This expansion into Discord introduced a range of challenges especially for the moderation teams of large communities. This is exactly the case for the teams of volunteer moderators who work to regulate content and protect online communities from harm. The work will be presented in Austin, Texas at the _ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW’19) _in November 2019. In Discord, this social aspect also made moderation work much more difficult.

(Planet Ubuntu)

5 Collaborate Smarter, Not Harder

With collaboration analytics, we can begin to shed light on who needs to collaborate with whom about what, what types of collaboration yield particular results, and how collaboration affects employee satisfaction, performance, and attrition. An algorithm ingested all this collaboration data and revealed which associates were in a position to compare pairs of other associates. Collaboration analytics can uncover silos across capabilities that — if better integrated — could spur innovation and translate creative ideas into production-ready offerings. A leader in the secondary mortgage market, employed a “passive data” collaboration analytics engine that enabled its analytics team to easily identify opportunities for streamlining. It’s also possible to extract collaboration data from existing digital sources, such as meeting and email data, as a by-product of other behaviors.

(MIT Sloan Management Review)

6 Welcome to the Future of Cloud Native Java

Today, with the release of Jakarta EE 8, we’ve entered a new era in Java innovation!

(EclipseFoundation)

7 Microsoft Icecaps

With natural language processing rapidly increasing in popularity, more and more tools have become available to the public to build large systems. Some of these tools are intended for general-purpose NLP, while others focus on specific domains such as language modeling and text generation. However, few are designed to target conversational scenarios and the specific needs they entail.

Microsoft Icecaps was created to offer researchers and developers an open-source toolkit with a focus on conversational modeling. With a design emphasizing flexibility, modularity, and ease of use, Icecaps empowers users to build customized neural conversational systems that produce personalized, diverse, and informed responses.

(Icecaps)

8 Building ages in the Netherlands

All 10 million or so buildings in the Netherlands. Building heights and date of construction from 3D BAG (Basisregistratie Adressen en Gebouwen) data.

(Building ages)

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]