Jake Meiner's Profile
Great post! I also find myself wrestling with Hill’s point above – ultimately, the value of validated information collected through clandestine operations will likely reign supreme in the Intelligence Community. Similar to how machine-learning-augmented human decision making works, then, I find myself wondering what the best applications for Open Innovation are in a space so dominated by a need for clandestine action and response. In the case of IARPA’s contest to identify emerging geopolitical trends from the masses, for instance, at what point do you draw the line between “this information is good to share with the masses” and “this information, when shared publicly, poses an additional threat to national security?” Is there an additional risk that, by making trendspotting an open-source effort, we run the risk of providing additional motivation or information to so-called “lone-wolf” operators, thereby undermining the end-goal of the Intelligence Community? I also find myself struggling to see how the IC can overcome Andrew’s point above re: racial and cultural biases in reporting suspicious activity. Are we, as a society, prepared for unregulated reporting from the masses?
First off, I’m a massive Disney fan, so thank you so much for sharing this piece. I tend to be a bit optimistic when it comes to beloved Disney, but I think that (while surely profit maximizing), this technology will aim to deliver Disney’s main goal of its parks – to instill joy and ensure the most magical experience for its guests. I’m particular interested in how Disney will best utilize Tink to drive a better in-park experience for its guests. Given the wealth of data now available through MagicBands, Tink has the opportunity to create truly bespoke experiences for Disney guests that highlight the things that visitors with similar profiles enjoy most. Combined with your fantastic idea on customized daily itineraries, Tink could have the power to drive traffic to the right areas of the park at the right times for maximum magic. I also really enjoy your points about Disney using AI/ML to put characters at the most heavily trafficked areas. In a world where the “magic” comes from those special interactions with adored characters, Disney has the opportunity to build lifetime fans and visitors, all thanks to the beauty of data.
Really great piece – thank you for sharing. Like Mike notes above, I’d be very intrigued to see how scalable the roll-out of Vulcan is globally to developing markets, and how ICON considers its work with New Story relative to their other “clients.” Given that AM technology is already on the expensive side (and I’d imagine that, given the sheer size of the technology required to build a house, is even more expense in the case of Vulcan), I’m curious how New Story will be able to secure the funding required by Vulcan to bring this to the markets where it is needed most. Which government agencies or NGOs might be willing to work with both New Story and ICON to make this a more accessible technology? As you note, AM might have adverse effects on the labor markets in these developing areas where affordable housing is most in demand. Would this prevent New Story and ICON from finding the partners they need to bring this to life as well?
Fantastic piece. I’m particularly intrigued by your question around whether Toyota should trust its supply chain to vet new AM technologies, or should move towards a vertical integration model. Given the need, as you point out, to seriously consider migrating to AM in the long-term, I wonder if there’s a third option here. Perhaps Toyota should consider partnering with its supply chain to develop technological capabilities “approved” or “vetted” by Toyota itself to sustain its supply chain in the longer term. This way, Toyota still only maintains responsibility for 25% of vehicle content, but holds greater sway over it’s Tier I Supply Base. Of course, a partnership such as this would depend on Toyota’s willingness to consider long-term contracts and obligations with these suppliers – but this trade-off may be worthwhile for Toyota to use with its best-in-class suppliers given the high up-front costs of developing AM. I’m curious to see how AM continues to develop as the traditional shops in injection molding begin to age out their workforce.
Great piece. I agree with your take on the need for both machine learning and human to best determine product assortment. I’m glad to see that RTR is already doing this in a way – as you point out, RTR is interpreting supervised learning outputs with the aid of stylists to determine product assortment. In an industry where ratchet effects can quickly change the course of what’s in vogue, the “stylist’s touch” will remain critical to ensure that RTR remains relevant and appealing to its core consumer. Ultimately, I don’t think that machine learning can completely replace the creative visionary for this very reason – fashion is too personal and too tactile (with too unpredictable a life cycle) to fully entrust to the machines. I do remain curious, though, about whether these ratchet effects will be as important at RTR, as formalwear and women’s dress fashion hasn’t seen as drastic a change as streetwear or sportswear has.