jack schlossberg

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One Fish, Two Fish: How Machine Learning Can Enhance Fisheries Management
jack schlossberg
Last modified on November 13, 2018 at 6:52 pm
Overfishing has decimated global fish stocks and threatens food security for billions down the line, but machine learning helps regulators meet this urgent challenge with new capabilities and better science.

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On November 14, 2018, jack schlossberg commented on From gaming to healthcare: Microsoft’s approach to visualizing cancer :

Such a great piece.

Project InnerEye clearly stands to deliver huge benefits to patients and doctors, not only by speeding up diagnoses, but saving costs, too, and both points are well-researched and articulated here.

What I’m left wondering is a question common to any application of AI to matters of great moral or ethical significance: even if InnerEye achieves near perfect accuracy, will doctors and patients ever be comfortable relying on it when a life is on the line? Will doctors have to double-check its analysis, and if so, how does that limit the efficiencies it delivers?

On November 14, 2018, jack schlossberg commented on Open Innovation at NASA: Impact in Culture :

Cool piece.

The open innovation platform has already produced new capabilities and better science for NASA, and it seems smart for the agency to expand it in the future. The cultural concerns raised are duly noted and insightful. In addition to those, i am left wondering how government classification will limit the ability for open innovation to help solve the classified projects NASA is working on? Will open innovation be limited to only those projects that are public knowledge, or can NASA figure out a way to utilize the platform for more strategic and secretive programs?

On November 14, 2018, jack schlossberg commented on Is Addition the New Subtraction at Lockheed Martin? :

Very interesting read!

Exploring AM’s ability to improve Lockhead’s prototyping capabilities was an unexpected and much more interesting approach than simply summarizing the material savings cost benefits. Given that this technology is so new and the concerns noted above about its structural integrity, prototyping seems like the avenue most appropriate to explore for Lockhead.

This piece made me curious about what companies are in the best position to develop the future of AM. Is it Lockhead? Maybe, they have the industry expertise and RD budget. Or will their size, history and institutional inefficiencies prevent them from making the types of innovations that a startup might?

On November 14, 2018, jack schlossberg commented on Adidas Lives its Slogan, Impossible is Nothing, with 3-D Printed Sneakers :

Great piece!

Clearly, 3D printing stands to disrupt retail in immeasurable ways. It seems smart for Adidas to invest in this technology, but the question presented by the author about Carbon’s ability to later change its pricing/business model seems really significant. Once Carbon or another 3D printing company figures out how to produce sneakers at scale, i don’t see why this wouldn’t level the playing field for all shoe companies. How should companies create a strategy for the future, assuming 3D printing becomes a reality, to remain competitive? For an apparel company like adiddas, won’t their design and brand equity remain the most important value add?

On November 14, 2018, jack schlossberg commented on Teachers Trust Teachers: The Rise of Open Educational Resources :

This is a thoughtful and compelling essay, one i very much enjoyed reading. The pen is mightier than the sword, as the author well knows.

TPT is exciting because it not only solves a problem that seems not just urgent but unjust. Its strength is that it allows teachers to help each other, rather than relying on the public education infrastructure that underserves them.

Scale is clearly something that TPT has to figure out in the coming years, but i also wonder how it controls for quality. Are higher-quality materials priced higher? If it is able to scale and there is money to be made, how can the platform avoid the same dynamic that has lead to the problem of underfunded school systems in the first place?

On November 14, 2018, jack schlossberg commented on Using Machine Learning for Crime Prediction :

This essay rules.

PredPol clearly has significant benefits for police departments that are constantly facing budgetary constrains and resource limitations. It seems like it could help any police force in a big city to plan their routes more effectively. I’m wondering, though, how applicable it is for smaller cities with even less funding. Take Flint, MI, for example. I imagine the department has a pretty good idea of where and when crimes are most frequent, but still struggle to stay on top of it because they are stretched too thin.

As you say, the ethical questions associated with this technology are serious — racial bias especially. New York City’s legislation seems like a great first step. Two thoughts come to mind. First, how much bias and injustice would cities be willing to tolerate from an AI algorithim if the overall impact would produce a more just police presence than exists today in its absence? Second, how might the right to privacy be used to challenge the legality of this tool or limit its dataset?