Julie Gaffney

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My concerns are less for competition on the crowdsourcing itself (i.e. can Pfizer keep a competitive advantage in the world of crowdsourcing?) and more with what might arise from opening the gates to data and access to information. I could see a possibility where granting access to massive and valuable internal data sets might allow for a new entrant to get vital information. On the flip side, this could really help accelerate development in the pursuit of treating and curing devastating diseases.

I would think about crowdsourcing in a focused avenue like through university research departments. This would allow for Pfizer to have a little better handle on how the data might be used while getting insights from a talented, decentralized network.

On November 15, 2018, Julie Gaffney commented on Crowdsourcing as the Future of Secret Cinema :

In some sense, this feels like an outgrowth of the long-running and diffuse interactive experience of The Rocky Horror Picture Show. That cult following seemed to spur the growth of “quote-alongs” run by theaters like the Alamo Drafthouse and even into the bigger types of events highlighted by the entertainer. I think as a business owner of Secret Cinema, it’s important to frame competition as a “share of attention” where it is competing against not just traditional film or direct competitors, but also sporting events, music performances or even streaming at home.

It’s certainly possible to generate original content by a crowd. Improv comedy has in some sense also done that for years. However, it might be difficult to scale in a traditional business sense as no script and performance will be the same – but therein lies the charm of it all.

On November 15, 2018, Julie Gaffney commented on 3D Printing Better Surgery at the Mayo Clinic :

Two things stick out to me here:

(1) 3-D printing will probably be better served by an outsourced or centralized center as described here due to time and cost factors. I think economics are currently challenging to make a 3-D printing machine operation effective, so focusing on maximizing utilization should help ease some of that burden, and

(2) because 3-D printing is at this point best suited for intricate parts that would otherwise need a lot extrusion or other work, one option might be to use additive manufacturing for complex joints like the knee.

As mentioned above, coupling machine learning with the doctor’s discretion is a great opportunity to accelerate progress in this arena. Much of the progress in neonatal care has been done the hard way through trial and error, so this provides a great way to pull both pull insights from past experiences and accelerate future learnings. Thanks for highlighting this potential, KMA!

Thanks for the post, Sterling. I think if AECOM is comfortable with how additive manufacturing can impact the residents and structural integrity and avoid the ‘danger zone’, it should proceed with these projects. I see two large potential benefits in doing so: (1) AECOM can help provide more affordable supply to a high demand market, hopefully alleviating some of the housing pressure across the world, and (2) AECOM can use its learning to help guide the formation of intelligent and experience-based regulation. By taking the lead and pioneering this arena, AECOM can provide value on a number of fronts across consumers, technology and government.

On November 13, 2018, Julie Gaffney commented on An A.I. Odyssey: How Fox is Using Machine Learning to Market Movies :

Thanks to Mr. Gauthier for sharing some great thoughts on this subject! In contrast to certain studios taking massive swings with a fewer number of films (a la the Marvel canon), I am curious to see how production companies with a different model can utilize machine learning.

For example, Jason Blum at Blumhouse Productions supports a larger number of films that are less expensive to create. By doing things like focusing on scripts with minimal set changes and compensating big time actors with more variable compensation, Blumhouse limits the downside risk on each film while pursuing massive upside (e.g. “Paranormal Activity”). This ends up creating a portfolio of many small bets that looks more like that of a VC shop than a typical studio or buyout shop.

It would be really interesting to see if machine learning can help Blumhouse refine its understanding even further of what makes a “successful movie.” Perhaps machine learning can help Blumhouse process a massive number of scripts at the ‘top of the funnel’ so that the company can see an even wider set of investment opportunities. This might allow scripts to hit the final screen that might not have otherwise made it if the screenwriter couldn’t get the right meeting.