I LOVED this article! I myself am a big fan of Invisalign and am currently on a treatment plan with the SmileDirectClub. Would the Invsalign players you mentioned be competitors, or would they be potential customers and partners? From my understanding, 3D printing Invisalign aligners is patented technology that is unique to Align. I don’t think ClearCorrect, Candid Co and SmileClub actually 3D print their own aligners; some might not produce them at all. It might actually be more cost-effective for the Invisalign distributors like SmileDirectClub to purchase aligners from Align. Maybe Align could become the de-facto manufacturer for all Invisalign providers since it can win on price and speed due to its 3D printing technology and economies of scale.
I think this is one of the most exciting applications of 3D printing out there. Home construction is such a labor and time intensive endeavor, and 3D printing solves this problem incredibly well. I do, however, see some potentially negative implications for society should this technology become widespread.
First, 3D printed homes may have potentially devastating consequences on the economy. As home production becomes easier, home prices will also decrease. The vast majority of Americans have their wealth almost entirely tied into their homes – should real estate prices crumble, so would their wealth. This, in turn, might ruin lives and decrease overall consumption / spending and slow down the economy.
Moreover, as with all automation, 3D printing homes also threatens to eliminate an entire industry and class of jobs — hundreds and thousands of construction workers and others involved in the home construction industry might find themselves unemployed.
I wonder how the government might respond to 3D printing houses as well. Will certain cities artificially keep their prices high by enforcing pricing laws or regulating 3D house printing? Will there be a “prestige divide” between a 3D-printed house to a hand-constructed one, similar to that in the luxury goods industry? For example, a “handcrafted” handbag is seen as more intricate and valuable than one that is mass-produced.
This is such an important and heartbreaking issue – thanks for shedding light on it. I agree that the Gates Foundation. I haven’t ever done academic research before and am not sure how the system works. From my understanding, one measure of a paper’s success is the number of citations a paper receives. If I were to see a paper by author X, write a paper of my own and cite author X, wouldn’t that actually be a good thing? Or is it only rewarded under certain conditions — for example, do I also need to be published in a list of approved, related publications? Do I need to have certain credentials? It seems like from a citations perspective, making research open might actually be a good thing, provided that these papers are properly cited. If we can ensure faithful citations of papers, would the problem go away?
On the topic of profit sharing, the original researchers whose works were piggy-backed upon should definitely also have a share of the profits. I can see how this becomes a legal and logistical nightmare, though, and it’s unclear how much cut of the profit each party would get. What happens if one researcher builds upon another researcher’s work, who builds upon yet another’s, as is typically what happens in research? The complications are endless.
This was so fun and interesting to read! You mentioned that one way to encourage knowledge-sharing is allow people to form collaborative teams. I think that’s a great idea – naybe LEGO can also partner with MOOC’s to create instructional videos. I do, however, have some concerns about the collaborative model. First, I’m not sure if designing a LEGO set requires a diverse set of skills like software – it doesn’t seem very technical at all — anyone can figure out how LEGO’s work, and the main skill required is really just the design part. I do agree that it would certainly be more fun, and the ideas produced MIGHT be higher quality. Next, would a collaborative model result in fewer overall ideas submitted, and would the potential increase in quality offset the decrease in quantity, or is quality found in a large quantity of diverse ideas, as we see with design thinking? Third, team members might need to split the already paltry prize money / commission, which might lower incentives to participate.
I completely agree that the rigorous review process heightens barriers to submissions. Minted.com is a platform that crowdsources graphic design from a community of designers. The community votes on the best designs, and the winning designs are then printed onto a variety of mediums, such as greeting cards and even curtains. Minted, however, does not actually internally review these designs because they believe in the power of the crowd, which is, in essence, the spirit behind crowdsourcing. I wonder if LEGO can eliminate its internal review process completely and, like Minted and many other crowdsourcing platforms, believe in the power of the crowd.
Finally, I’m SO excited about what LEGO is doing with MIT Media Lab! Sounds like a project I would love to work on over the summer!
“When reflecting on BuzzFeed’s machine learning strategy two major questions are highlighted: can machine learning or AI ever fully replicate the creativity of human content producers? and how much financial risk should BuzzFeed take on to invest in machine learning programs?”
I also wonder how “creative” human content producers really are nowadays – so much content on the internet is just a rehash of something that’s already been done. So while ML might not be able to create sublime content, it might be good enough for 80% of what’s out there.
I was shocked when I learned that the machine-developed algorithm was biased against women and penalized resumes that included the word “women” and favored candidates who described themselves using terms more commonly found on male engineers’ resumes such as “executed” and “captured.”
I wonder if Amazon could keep the ML algorithm but manually remove that particular predictor variable, along with others that suggest similar types of biases.
One of the biggest (and relatively unsolved) challenges in machine learning today is summarizing and “reading between the lines”. Law cases have so many next-level inferences required that are not immediately captured in the words alone, and machines generally are terrible at identifying what’s important in a body of text. It might be some time before machines can contribute meaningfully in the ways you’re describing.
Another application of ML in the future might be scanning security tapes for similar faces – also a very hard problem, but with awesome implications!