I disagree with some of the comments above; part of the beauty of LEGO (at least, in parents’ eyes) is that it is a totally offline, screenless activity that builds fine motor skills. I think it’s possible to continue to crowdsource ideas for LEGO products while still keeping the product 100% physical. I hope LEGO continues to learn from children and build products that interest them – it will save LEGO both time and money.
This makes so much sense! I’m particularly interested that the Marine Corps is trying to get IP from defense contractors – that would save the federal government so much money and time, and help fix the government procurement process. Hopefully the government would also be able to develop more innovative capabilities in-house that could feed these new technologies.
This seems like an incredibly smart way to ensure that aid dollars are being used efficiently. It also seems like a more sustainable solution than our current aid process – supplies could be printed only as needed, so that they aren’t wasted. Furthermore, as you note, locals could be trained to operate the machines, cutting out much of the wasteful overhead associated with our current development aid system. I imagine that as batteries and other ways of storing energy get smaller and cheaper, powering these machines in remote locations will get more cost-effective.
While Dreambox sounds like a fantastic company with potential to add significant value to the classroom, I worry about over-reliance on machines to teach skills that would otherwise be taught in a classroom. In addition to hard skills, children rely on their teachers and peers to learn social skills, group dynamics, and coping mechanisms. Like Colm, I think Dreambox could be a wonderful tool to add to a teacher’s portfolio, but it should not replace a teacher entirely.
I think it’s great that NASA is willing to crowdsource some R&D ideas – I’m sure it’s significantly cheaper, faster, and likely more effective than exclusively sourcing ideas from a small cohort of highly specialized individuals. However, one of the reasons that specialists come to work at NASA is that they have access to sensitive projects that aren’t otherwise publicly available. How can NASA balance working on cutting-edge projects that may have real significance to national security with opening up the R&D process?
I wonder how the AI algorithm deals with stories or articles that stretch the truth, but may not be outright falsehoods. Even humans are often divided on whether an article is intentionally misleading or simply exaggerates a certain perspective. I wonder if, like Watson, Facebook’s algorithm is also able to predict how “certain” it is that the article is truly fake news.
Given that the current algorithms seem to be able to identify species accurately, I wonder if machine learning could also help scientists identify how fish migration patterns are changing as the oceans warm.
To your point about GFW’s dataset being incomplete – the algorithm is only as effective as the data that it is fed, so if ships continue to refuse to cooperate in certain areas of the world, it could skew the efficacy of the algorithm.
This seems like a perfect application for machine learning – I think your point about the completeness of the data is really important. Data cleanliness seems to be a critical issue for a lot of applications that use data about public infrastructure or other projects. And I hope it means we all spend less time in traffic in the future!