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clcorcoran
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Wow! What an awesome idea for making housing more affordable, by helping to reduce the costs of materials and labor. To your question about who should be involved in whether these materials and practices should be used to create affordable housing, I think it’s very important to get grassroots support from the community of individuals that would be most likely to live in these homes. Of course, improved affordability is very important to them, but so is having a home that feels . . . homey. What part of the home construction process can be 3D printed with very little impact (e.g., hidden infrastructure of a home), and what components need to be sourced from traditional supplies?
As to the point about labor, I’m not sure. Because there is such a shortage of affordable housing today, if we’re able to significantly increase the affordability of materials, we may be able to build 2x as many homes, keeping a constant number of laborers employed (if 1/2 the labor is needed on a 3D printed home).
Awesome article! Very well written, and a great subject. I wonder if there is an opportunity to create a competitor firm that is more responsive to user ideas, with a tighter turnaround time (less time, maybe with less volume). Lego obviously has enormous brand value, is a trusted children’s toy manufacturer, and produces innovative block-sets. But, maybe a start-up could crowdsource lego-compatible ideas, and produce them in a faster timeframe. This distributed design creation coupled with 3D printing could make de-scaled, small-batch specialty designs much faster than the Lego process. This brings up lots of legal questions: to what extent is the size and shape of Lego blocks protected intellectual property? What are the regulations and liabilities surrounding the production of children’s toys?
I think the question you pose (and addressed in this last comment) about when to engage a hackathon team to address a technical issue is great. Doing some research on crowdsourcing for my own post, it seems that crowdsourcing fits best when creativity is needed, and internal solutions are more successful when it is a purely technical challenge. I guess the question of whether or not to use a team of outside innovators is still kind of case-specific: is it just a matter of iterating and improving on a current process that’s hitting a roadblock? Or, does the entire process need to be redesigned? Depending on the resources required to run the competition (staff; prizes) VHO might consider using an annual hackathon for difficult issues; the creative, crowdsourced solutions may not always work, but after being reviewed and updated by the internal technical team, they may be able to improve the speed of innovation. This boils down to a cost : benefit problem, but it may make sense to continue the hackathons, with the added benefit of keeping the original teams engaged.
Better forecasting of wind availability is amazing technology — as utilities can better forecast the supply of renewable sources, less fossil-fuel baseload will be required. As Lingxi mentioned, the Internet of Things can also have a huge impact on the way that consumers use energy, by delaying appliance use to off-peak time, and cycling A/C demand during critical peaks. I wonder how much of the current problem is due to lack of technology, and how much of it is due to behavior: do households do laundry in the mornings because it’s an efficient use of time, or is it because they’re managing their work clothes with JIT delivery? Similarly, is the dishwasher being run after dinner (in which case it can be delayed), or is being run in advance of dinner, because clean cookware is needed? There’s probably some variation and some households for which significant behavior change will be needed in order to realize the benefits of machine-learning based IoT shifted load.
Wow, what an awesome idea. It’s interesting to think about the way that machine learning may impact the skillsets of individuals beyond the task at hand. Take, for instance, the feedback you are providing to graders who include cursory comments like “good job” at the end of an assignment. You are forcing them to be more critical, more analytical, in their feedback. Though the machine is doing this with the explicit goal of improving the UpGrad product, it could also result in encouraging the graders, generally, to be better critical thinkers. Traditionally, formal feedback has been dispersed and uncommon. Machine learning is providing constant feedback and may lead to faster individual improvement.