Toby Johnson

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On November 15, 2018, Toby Johnson commented on Earth to everyone: we need your help! (Open innovation at NASA) :

Great piece! I’m used to reading about open innovation at younger companies or those in the tech industry where crowdsourcing/open-sourcing is just part of the business, so I haven’t thought about the cultural requirements needed before. While I agree that there may be some retention risk of existing talent, I believe the open innovation contests serve as a great recruitment funnel and would ultimately be positive for the team. Similar to your point about NASA only having to pay for proven solutions, by hiring successful contest participants, NASA already has a strong filter for performance. Bringing this talent in house would hopefully improve the collaborative culture and increase the likelihood that winning solutions would actually be implemented.

On November 15, 2018, Toby Johnson commented on Printing the Future of Helicopters with Bell :

Thanks Ryan for sharing this! It’s really exciting to see an example of where additive manufacturing is actually opening up new opportunities (e.g., materials that weren’t previously possible, refurbishing old helicopters) instead of just replacing old ways of doing things as an advertising gimmick. However, as you mentioned, it seems to be mostly used for prototyping at this stage. I wonder how additive manufacturing actually fits into massive scale assembly in the future. Will it be mostly used in a job shop set up for limited component subassembly? Or can it actually replace big parts of traditional assembly lines?

On November 15, 2018, Toby Johnson commented on ZOZO’S AMBITION: CAN YOU QUANTIFY “COOL”? :

Very cool concept! I think applying machine learning to optimizing mass customization in clothing is a huge competitive advantage. It’s crazy that we can customize cars and furniture to our liking, but most everyone buys their clothes generic. However, I question how well machine learning can determine new product design in fashion where new trends can’t be extrapolated from historical data. At most, I think machine learning can help provide a snapshot of what’s popular today. As such, I agree with your point that they should continue to work alongside designers who can act as the “training data” for future trends.

On November 15, 2018, Toby Johnson commented on Chanel has a magic wand for beautiful eyelashes, thanks to 3D printing :

Thanks for sharing this fascinating use case of 3D printing! I can definitely see the benefits of rapid prototyping with something like a mascara wand where the shape can really impact the performance. However, I question what the benefits would be in most other makeup products where the main performance differentiator comes from the product formulation (e.g., color, texture, staying power), which isn’t addressed through 3D printing. Chanel has obviously invested a lot in this new 3D printing facility, but I wonder if it’ll end up mostly as an advertising gimmick or will actually pay off in other products?

Super interesting read! As a frequent Amazon user, I am always amazed how packages are shipped out within hours of submitting an order. I think you bring up a good point that Amazon will need to tap into cutting edge researchers to solve its problems so it can’t fully rely on internal R&D, but I question if this contest is actually bringing in new solutions or is mostly a recruiting tool as you mention. Crowdsourcing is great for novel ideas, but this contest requires building an actual robot so is much more time (and capital!) intensive. Would contest participants who are working on this part-time actually be able to deliver better solutions than a full-time R&D team? Either way, I agree in the long-term hiring and retaining this talent is the real asset.

On November 14, 2018, Toby Johnson commented on Airbnb: Utilizing Machine Learning to Optimize Travel :

Really interesting to hear how AirBnB is using machine learning, especially thinking ahead to photo classification and NLP to make the listings more relevant and useful to users. I’m a bit surprised of their use of personalized search given that there can’t be that many data points per user since booking a trip is an infrequent occurrence. I would also be concerned that the preferences could change significantly based on the trip (e.g., big group vs. romantic getaway). Similarly, for your recommendation to power search rank based on similar reviews, you first need the user to have written multiple reviews. I think your suggestion of expanding into the trip ideation process would be a great way to solve this issue because you’ll be able to collect more information earlier on about the type of trip and on the user.