Thomas T. Tommerson III's Profile

Interesting read!

I’d be curious how your opinion would change in upcoming years with the rise of heads-up-displays on windshields for newer vintages of vehicles. Much like with cellphone use concerns, I think moving Waze from a mobile app to one hosted on the vehicle itself might shift the public safety concern discussion. I’d be curious to see what Google’s plans are with respect to automakers adding Waze to their cars’ operating systems, and if there’s potential for Waze to be pre-installed on any Google self-driving car in the future. If this becomes a staple feature, there could be a future where every car has Waze installed, and they in turn use live crowdsourcing to augment their autonomous driving capability.

Thanks for an entertaining read!

I wonder how the shift in distribution channels away from brick and mortar stores might be a benefit since Lego is leaning on DTC for product design. While it’s true that Toys’R’Us is likely a large contributor to Lego’s sales, a digital storefront could be well positioned to track purchasing data in real time and best position their products alongside consumer preferences in tandem with the crowdsourced design / themes. I’d be curious to see what % of contributors to their crowdsourcing purchase Lego online, and if their forums or communities for fans are hosted on the same site as their online purchasing.

Thanks for an entertaining read!

I think one of the key questions surrounding the value proposition of 3-D printing in space vs. sending objects up from Earth regards the required energy consumption (and required fuel) to send raw materials up into space. Part of the challenge in long-haul space missions, or even sending things into orbit, stem from the large amounts of physical fuel required to do so, and the fact that like anything else, this fuel has weight (which in turn requires energy to send up etc.). While the benefits of being able to print out replacement parts or backups for a long-haul flight like to Mars are clear, there is an issue around the availability of raw material. It would presumably cost as much or more to send blocks of materials up to space as it would be to carry backup parts, so is the 3-D printing of objects in space that valuable from a cost perspective? Unless you are able to gather the material in space, it’s hard to see the cost-benefits of this project, but perhaps I am being short-sighted.

Very interesting read! The first thing that actually comes to my mind in a future dominated by at-home production via 3-D printing is the potential for counterfeit merchandise. Given accessible materials and design, why not print my own Nikes at home rather than buy them at the store, in the same way people pirate software today? I think 3-D printing highlights the value therefore of material copyrights and protected design patents. You mention the increased ability of Nike to rapidly prototype and refine products, but a key limiting factor in design is the human input from professional players, and the testing required to iterate the designs. I’d be interested to see how AI can help with the designs, given an understanding of comfort and performance parameters.

A little self-promotional, but super-interesting read, Jon 🙂

What stands out to me is that you’re one of the few who acknowledge the capital-intensive nature of machine learning on top of the in-house labor and technical requirement. I believe that certain aspects of fashion can be predicted via tracking cycles (eg. suit cuts today resemble those of the late 1960’s), but I believe that human judgment is necessary on subjective matters like style. Touting machine learning as a planning tool might work well once you’ve picked up steam, but in early days it can be difficult to justify the cost. What are BOH’s methods for sourcing / buying consumer data? With a more liquid market of credit card purchase data, online viewership, etc, is that something that benefits the world (especially in terms of CSR like you mention) or harm it?

Thanks for an entertaining read! I think an important clarifications is that the software must still be trained initially (though supervised learning of classified images) to categorize images or define traits as it chooses, before the process can be run backwards. This creates an interesting dynamic by which trends in the initial data set may result in biases for how the program classifies objects on its own. For example, having come up with its own independent classification for what makes an animal “dog-like” or “cat-like”, the program might classify a dalmatian as a cat due to its spots. Perhaps that was a bad example, but this might indicate a use of this software in pointing out existing visual biases in existing data sets, since that’s what would be revealed when white noise is fed through and the machine tries to reiterate on these traits.