This is a super interesting read on additive manufacturing and its potential in the aviation industry! It seems that the short-term investment for training, materials, regulatory support will be high for a company like Boeing but the long-term pay-off is high. It seems like Boeing needs to generate buy-in from its investors for this long-term payoff and act quickly before Airbus or similar companies establish a first-mover advantage that Boeing can’t overcome.
However, I also wonder about the broader ethical implications of AM in our future. It seems that by hiring new employees trained in AM, this could also displace employees and traditional factory manufacturing workers that cannot be easily re-trained to accommodate AM or simply aren’t needed for AM. As we move towards more automation, how much responsibility do corporations have towards these displaced workers?
Great article Justin! I think the idea of open innovation for a huge problem like the opioid epidemic is really interesting. But aside from trying to address the opioid epidemic effectively, I wonder what goals HHS are exactly trying to accomplish through open innovation. Specifically, what value does open innovation provide to HHS/the opioid epidemic that simply funding solutions created by first responders/the community doesn’t provide? Is it that people don’t normally collaborate together and this provides them a chance to come up with better and more creative ideas? Or is HHS trying to create awareness and buy-in from the country through idea generation/implementation? I think this fundamentally shifts whether idea selections should be internal or external.
I think your point about exploitation vs exploration is also a great one and the tension here is as you mentioned–HHS doesn’t seem to have taken a side about which direction they want to head in. This drastically could reduce the potential success of projects they choose, and I agree that there should be a clear overarching direction.
This is a pretty exciting company given its potential for improving rework and production quality! The data sharing/security risk question you pose is a thought-provoking one, and an additional tension I see about sharing information is that as they give their client companies more information about what is going wrong, companies may eventually improve their processes to an error rate of zero. Consequently, it seems that essentially as Sualab helps companies improve, they also decrease their own client pool and business.
In addition, you mentioned the blackbox problem that they face where they don’t actually know what the algorithm is “thinking.” I think this article posed an interesting way to interpret the blackbox problem–essentially breaking down the machine learning model into more interpretable decision trees: https://hamsabastani.github.io/interp.pdf
This is a really interesting article and a trend in an industry that I (like the other commenters) find a bit surprising!
You mentioned that some of the toys and Aristotle use machine learning and essentially collect data about the preferences of the child. In addition to privacy concerns for a vulnerable group, I wonder if the preferences of a young child are entirely relevant for Mattel. If his/her parents don’t approve, the child’s preferences are essentially entirely ignored. I think this is a case where the overall mission of Mattel is a bit confusing. Is it trying to use machine learning to positively influence early childhood somehow? Is it simply to scrape tons of data? It isn’t clear what role machine learning plays aside from the novelty aspect.
I do think though that the toy industry can still benefit from technology and machine learning. One thought that comes to mind is creating educational and adaptive toys with AI/ML since it would be engaging for the child but also would generally elicit a positive reaction from parents.
Patrick, great job highlighting the gap that private-public partnerships can fill and the importance of this call to action by open innovation!
My main concerns are that this type of open innovation may skew in favor of pharmaceutical companies’ interests. I worry academic researchers would be incentivized to shift their research to target drug development at the expense of academic basic science research. As this article below outlines well (1), some of our most effective drug and therapeutic discoveries (crispr, ACE inhibitors, etc) have been because of exploratory science mostly through researching biological pathways. Already, there is a lot of pressure for deliverables in academic medicine, and a private pharmaceutical partnership may skew that pressure even further at the expense of exploratory science. In addition, if private pharmaceutical companies have vested financial interests in certain types of drug development (certain rare diseases, etc), I worry that all academic partners would then be incentivized to shift their research to those fields, directly at the expense of research in other areas. My main question is how can we encourage these partnerships/open innovation while being careful not to hinder scientific discoveries outside of pure drug development?
Bernie, this is a really cool topic and you did a great job distilling it down to the key tensions and concepts! Obviously this is a great way to eventually reduce the emotional and monetary costs of organ transplants, but my main concerns are that Organovo is not anywhere near that end goal yet. If the idea right now is for the 3D liver to replace current in vitro assays for pharmaceutical testing, the Organovo liver has to really show some significant promise since the current method of using cell lines is currently relatively cheap. In addition, as you mentioned, the Organovo liver is correlated to clinical results but has only been tested with 3 drugs. Based on the Organovo website you cited, the clinical results are fairly broad (“toxic” vs “nontoxic”, etc) so with a sample size of 3 drugs, I’m not convinced that the 3D liver is a great alternative to current methods. With all this said, I’m not sure this is really a sustainable business model unless the science/technology can really push ahead very, very quickly in the coming years.