I’m curious whether 3d printing tech will prove up to the task of making complex objects like satellites, especially given the highly technical nature of their components and the exotic materials often involved in their production. Plus, since launch cost is by far the largest cost of a satellite, won’t the materials cost just as much as the finished product to get into orbit? Lots of challenges remain for this idea, I think.
I think General Mill’s R&D and acquired innovation can work synergistically if the R&D department takes fresh exogenous ideas and hones them to further the company’s specific goals and requirements. Often external ideas need only be the seed to point a specialized internal process in the right direction.
I’m really exciting to see the potential of adaptive 3d printing in industries like these to produce custom products, especially if they combine it with innovative imaging technology like Apple’s face ID. This would enable mass produced footwear with all the benefits of personalized custom orthotics!
I’d really like to skydive into the grand canyon, glide through the middle, then land on the back of a T-Rex and start charging around rodeo style.
But seriously, leveraging VR to recreate epic experiences is an awesome field that is only just starting to be explored. I love the concept and can’t wait to see where things go as the tech improves and people really start unleashing their imaginations.
The markets are one of the most complex man-made systems in existence. However, they are based on real relationships and rules, and thus eventually I think machine learning will exceed the best human judgment. As we learned in Finance though, alpha will disappear as soon as “everyone” has these algorithms, leading potentially to a market machine learning arms race not unlike the chess-playing computer arms race that still exists today.
I love the idea of holding prize contests to help solve critical tech milestones for an ambitious project like this. There’s always someone smarter you, and this method really leverages that truth to its fullest potential. A major challenge I can think of is misaligned incentives–unlike a government project that is trying to field a new piece of tech, multiple small agencies coming together to make a project like this happen are hindered by their individual interest to protect their own IP and profit as much as possible by its use. This portends a non-cooperative environment when contributors start being unwilling to give up their breakthroughs for an economically feasible cost.
Sal Paradise: That’s definitely an interesting question. I could see it being used for automatic traffic tickets, finding criminal suspects, and tracking people anywhere and everywhere they go. There’s definitely some major privacy / human rights issues associated with this kind of tech, and it will be interesting to see how it gets regulated in the future.
I would hope there’s an economic incentive to pass on R&D savings to the consumer. Though the problem is systemic, I also wish the barriers to new drugs were lower, as I think the harm they prevent through thorough testing is outweighed by the harm they FAIL to prevent by making new drugs so hard to get to market.
I think Tesla pursuing many parallel technologies is essential for its competitive advantage and maintaining an edge in the driving world. I like that Elon Musk thinks big and pushes for revolution over evolution. Though riskier, it makes for changes that truly add value for the consumer and drives innovation across the sector. I don’t think people will fully trust autonomy, though, until the tech proves itself not just safer than humans, but SIGNIFICANTLY safer.
I think it will be a very long time before computer algorithms can make dynamic decisions on the battlefield. The information is “fuzzy” and often ambiguous, requiring the best human intuition and judgment to avoid catastrophic mistakes. However, it could certainly be useful in lesser capacities! We often took radar-generated images of the ground and were required to manually pick out the bad guys from blobs of green pixels, which was sometimes extremely difficult to do. A trained algorithm would be excellent at this type of tasking, allowing the human to perform the task they are better suited for–qualitative judgment.