Alexa Elinor Walls

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Capacity Command Center: Machine Learning & Hospital Management at Johns Hopkins
Alexa Elinor Walls
Last modified on November 13, 2018 at 10:36 pm
A top US hospital is taking substantial steps in the use of machine learning to transform healthcare delivery

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On November 15, 2018, Alexa Elinor Walls commented on Sherwin-Williams: A Case Study of Open Innovation :

I love this example of new technology and approach applied in a 1.5 century-old company. The variety in the examples you give is interesting- from technology/features of coatings to colors and even containers, like the “Twist and pour” example. I wonder whether the adoption of “open innovation” is a singular strategic move by the company, or whether they have to integrated it many times and ways across the many divisions and applications.

On November 15, 2018, Alexa Elinor Walls commented on Unlocking the Power of Open Innovation in Disaster Relief :

Agree with the other commenters about what a compelling application of the technology this is, and equally agree with the concern you raise about unintended consequences- particularly given the acute vulnerability of this context. I think you do a great job articulating the role of crowdsourced data and mapping in coordination of delivery, and I wonder what it would take on the side of the intervening organizations (UN, ngos, military, government, etc) to use the platform effectively and dynamically to ensure that they don’t compound the issue of duplication and gaps by all looking at a map and rushing to where the gap in coverage is.

On another note, something that I always found particularly compelling about the use of crowdsourcing by crisis mappers was the concept of all of the volunteers who were able to contribute regardless of where in the world they were or how little or fragmented their available time may have been. I was struck by this format of sort of… micro- and remote volunteering, and with a skillset that we don’t traditionally associate with relief work. It lowered and moved the “bar” for contributing to disaster relief in a way that I think has such huge lessons for other fields. What if when we stood in line at spangler, instead of scrolling through instagram, we could hop on a crowdsource platform with a mission and spend those minutes chipping away at a problem?

On November 15, 2018, Alexa Elinor Walls commented on Symantec: Using Machine Learning to Improve Malware Research :

Such an interesting read. Do you think that with Symantec’s structural decision to transition their focus solely to cybersecurity- particularly selling off their Information Management component- is risky in that they might be putting all of their eggs in one basket, especially when, as you mention 1) security and privacy issues risks are so high and 2) it’s so hard to predict what the future dynamic will look like between attackers and cyberdefense?

User deleted this comment on November 16, 2018
On November 15, 2018, Alexa Elinor Walls commented on Symantec: Using Machine Learning to Improve Malware Research :

Such an interesting read. Do you think that with Symantec’s structural decision to transition their focus solely to cybersecurity- particularly selling off their Information Management component- is risky in that they might be putting all of their eggs in one basket?

On November 15, 2018, Alexa Elinor Walls commented on Enabling patients to get innovative treatments faster through machine learning at GSK :

The sheer scale of pharma- and therefore of of the opportunities like ML in this space- are astounding. I actually just read Patrick’s post about open innovation in drug discovery, and I’m curious whether (and if so, how) both of these transformative approaches could work together within a single company.

I’m glad that you call out data consolidation and structuring as a key pillar. It is such a simple but critically important point. Any company, whether they are currently leveraging AI or not, would be well advised to capture and store data in a way that will save them the painful retrofit down the road!

On November 15, 2018, Alexa Elinor Walls commented on DoD Additive Manufacturing: Pushing Supply Solutions to End Users :

WOW. There is so much here: IP concerns, security issues, logistical considerations, training needs… totally fascinating and very well presented. In so much of our history with technology, the DoD has served as the R&D and testing ground for what ultimately became mainstream technologies. I am wondering when the DoD ultimately does work through the issues that you raise, how might elements of this trickle down into a commercial context?

On November 15, 2018, Alexa Elinor Walls commented on Danko Arlington: The Future of Additive Manufacturing in the Aerospace Casting Industry :

Great read, Sean! I have always thought of this industry as so intensely risk-averse that I think of them as *very* late adopters. I didn’t realize how much the landscape has already embraced additive manufacturing! I’m curious reading this…what do the key competences look like for an aerospace casting house of the future? Comparing that to where Danko is now (and with consideration of their competitive landscape), what gaps do they need to proactively fill? On another note, will this this trend toward additive manufacturing in aerospace casting show up in any visible/tangible ways to average consumers?