Baker Flagg's Profile

Fascinating concept for collecting the data that feeds machine learning – thanks for researching! I would be very interested to know what demographic information they collect from users and how that factors into their creation of target markets and efforts to eliminate bias. Do they use demographic characteristics (socio-economic status, religious identification, race, etc) to construct these markets and/or treat them as possible sources/determinants of biases (conscious or unconscious)? It seems like this kind of data would need to be included and considered in their processes in either case, but this in turn leads to additional security, privacy, and regulatory considerations.

Thank you for explaining the regulatory considerations for AM and allowable materials – I am a neophyte in this particular area and found this article extremely educational. AM certainly has what seems to be an unassailable position as the most optimal method for use in R&D due to its ability to produce multiple variations of an item relatively quickly using only one machine and requiring no expensive and time-consuming re-tooling. Do you believe that traditional manufacturing processes have “plateaued” in terms of their ability to improve and produce more complicated assemblies/parts? If this is the case, then eventually transitioning to full AM certainly seems desirable. If there is still room for improvement with traditional processes, a switch to AM for full production may be less advantageous.

Definitely agree with you that the best next iteration will be a blend of human expertise and appropriately-crafted ML. I may have chosen the wrong word in “intuition” to describe what humans have that ML programs lack to date. It seems that one of the biggest limitations of ML, for now, is it cannot – to use an in-vogue section phrase – “see around corners” (predicting the effects of regime changes, political trends, zeitgeists, thorny hypotheticals, etc). Humans still hold the upper hand here with regard to long-term predictions, but can still greatly benefit from the partnership with ML.

Great overview clearly explaining the operating method and significance of this ML application. The risk of data fragmentation and regulation/privacy you identify has come up across several ML-related posts. Specific to this case, do you see this application of ML to EHRs requiring any adjustment of or modifications due to HIPAA?

Fascinating article – good job outlining how uncertainties in certain surgeries and trauma situations necessitate large inventories. Interested to get a sense of how quickly prototypes in J&J’s “center of excellence” can produce an item – are they anywhere near a fabrication time that could enable completion of a customized item during surgery – applying Just-In-Time inventory to the operating theater?

Thanks for highlighting JPM’s blended internal/external source approach to ML platforms. I’d be really interested to learn more about how that process is currently being coordinated – specifically how these ventures are identified, monitored, and then on-boarded. JPM released a research paper on the applications of big data and ML in finance that opined data scientists with no financial experience or insight developing finance ML applications could be a risky proposition – will be interesting to see how (or if) JPM tries to follow its own guidance on that matter.

You are spot on in your question – is it possible for the IFRC to make a system that is sufficiently accessible to be broadly useful but capable of identifying and filtering false or erroneous requests? The military often uses satellite imagery or drones to corroborate intelligence reports – perhaps a similar approach could be used to gather more information about unusual/suspicious aid requests (either in cooperation with the military for larger disasters or using smaller commercial drones at the agency/group level)? Additionally, there may be an opportunity for enhancing coordination between responders with a kind of “tactical overlay” package only available to recognized relief agencies that uses geotagging to show the exact location of relief assets relative to the resource requests. This could facilitate effective and efficient coverage of a geographic area between several different agencies or response groups.