Interesting topic and a good article on it.
A couple of thoughts on the matter.
First, I’d be interested in how to set the tasks for ML that would actually relevant for the customer? True, safety issues are worth dealing with, but what if the customer actually tackles them in a very efficient way already without any ML algorithms? Additionally, CAT would also need to provide training packages for the clients not accustomed with new interfaces and ML implications.
I believe, another challenge for CAT would be to find a way to get all the data from client experience to enhance the database and train ML. Although clients would be likely willing to help the company to improve its performance, there still remains a threat that dissatisfied customer, who actually have the most interesting additions to the dataset, would be less willing to share info about failures. This is especially true for the remote areas without any internet connection so that sharing the data would incur additional cost to the customer.
I’d be interested to know more about ML applications for other urban areas. Would it make sense to try to apply the same algorithms to Boston city or at least learn what tasks could be efficiently solved by ML in this context? Would it be reasonable to create a national agency aiming to create effective and easily scalable solutions for urban areas with a similar profile?
Talking about the risks of ML technology here I would be worried about ever-changing patterns in the city as people tend to constantly change and adapt their behavior to new rules, regulations, etc. Along the same lines, for the particular example about restaurants – wouldn’t it be possible for malevolent restaurant owners to game the system?
I believe the estimates of open innovation efficiency, in this case, could be biased upwards since the challenges presented for crowdsourcing ideas might be easier and less complicated in the first place as otherwise, they would be too important to test for open innovation. The fact that NASA traditional R&D was less creative and less successful with their solutions can just point to the fact that engineers deemed these problems unimportant and not deserving enough effort, both mentally and timewise.
However, I believe that crowdsourcing of ideas even for “toy problems” may have a very substantial impact on the breadth of ideas funnel at the very first stage. This approach of widening the ideas arena rather then increasing competition inside the arena could actually be well taken by entrenched engineers as they would be simply given free food for thoughts without compromising their professional superiority and alleviating most the security concerns.
Very thoughtful and interesting article!
I’d be interested to know how, if at all, AM technologies are utilized by SpaceX and more broadly by other high-performance industries, such as sports cars, aviation, navy etc? Could there be an opportunity to take some ready and tested techniques from these areas? Conversely, I think it could make sense to look for applications of some NASA technologies to less extreme and more consumer-oriented industries?
To the safety concern point, I think unmanned flights or small open space experiments could help mitigate this risk to some extent. In general, I think it’s important to bear in mind that, although safety cannot be compromised in any way, postponing some AM applications could actually imply a use of less reliable and riskier traditional technologies only because they have well-known threats and limitations.
You touched an important point on the costs of 3D printing for construction applications and 20-year period to reach profitability seems discouraging. My guess would be that 3D-printing in the construction industry is at a significant disadvantage due to lack of economies of scale. Another concern would be that 3D-printers maintenance and logistics costs would make its application economically unsustainable even in the long-term.
In addition, on the safety risk mitigation, I would be interested in potential new risks that additive manufacturing would create. Somebody still has to deliver the printers on site, service them, and finally, still do construction jobs even with additive manufacturing outputs.
Good piece on crowd-sourcing in an unusual and complex context.
In addition to your question on “can we trust the crowd what it’s right or wrong” I would point out that there could be different answers for different styles and purposes of communication. For example, informal texting could have very different golden standards than formal press releases. Therefore Grammarly should not only focus on collecting crowd-sourced data but also on sorting and marking it, which could be actually done by both, users and ML.
Another difficulty I can foresee with crowd-sourcing grammar is that it could make user experience slightly more troublesome and therefore lead to opting-out eventually. Currently one of the Grammarly advantages is smooth implementation into web browsers and it’s very easy to ignore suggestions and the plug-in doesn’t require any input from users. But once users are asked to clearly mark styles, paragraphs, beginnings and ends of the essays they could be annoyed by pop-up windows and/or often useless suggestions and consider switching off Grammarly altogether.
Thanks for your comment. Your point on IBM Watson is spot on – they actually partnered with another huge gold miner, Goldcorp: https://blog.goldcorp.com/2017/03/03/ibm-watson-gaining-new-exploration-insights-through-artificial-intelligence/ The only downside I see in this type of partnership is that it usually doesn’t provides a complete buy-in from the company’s employees which is essential for this type of disruption.