Thanks for an inside look at Toyota and their approach to AM. From your article it seems that as the technology currently stands, it is not a viable solution yet for mass production, i.e. the economies of scale is not reached. For standard cars, I suspect the traditional technology will continue being important, but what are your thoughts on after-sale spare parts? Are there ways that Toyota can partner with its dealers to get spare parts in the hands of its consumers faster? Also the other aspect that they may already be exploring is around customization. Could AM help Toyota customize certain aspects of the car much quickly, thus helping them charge a premium for customization and adding it to their revenue stream?
It’s mind boggling to see where technology in the health care sector is moving. Along with pre-clinical trials, I suspect the company is also thinking of ways where it can move upstream to a point where trials on humans can be minimized. I do wonder though, is replicating a certain tissue enough if we truly want to understand the interaction the drug will have with not just that tissue, but other parts of the body? I am far from an expert in Biology, but how much should we be thinking about this interactions element. Furthermore, how can we make this technology more cost efficient?
Neighborly is doing some interesting work and it was fascinating to get your thoughts. I do share some of the concerns mentioned above around who are buying the bonds and can this give unequal power to wealthier residents to influence spend of money. Additionally, the Social Impact Bond (SIB) model may look attractive in the outset, but may end up financially hurting municipalities if they could have done the project by raising the money through taxation. I like your idea of having an open competition, though I wonder if such a thing would defeat the purpose of Neighborly as being a company that supports local investors to invest into their own community.
This article reminded me of the Watson case, especially around understanding linguistic rules and coherence. I am curious why they don’t use data sources such as newspapers or novels. As Alexander noted, there are different kinds of communication for different occasions and perhaps it might be useful for Grammarly to explore learning more from the best-selling novels of 2017. Is there a way these novels are written that make them particularly attractive–for example do they use similar structures around the composition of long and short sentences? What about alliterations? I think it would be a pretty valuable insight to know if certain rhetorical techniques stand out more to our generation and can be employed by Grammarly for its recommendations?
The role of machine learning in a high stakes environment is an interesting question. This past summer while traveling around Nepal I was surprised to learn that the first equipment a municipality buys with its budget is often a JCB or Caterpillar excavation machine. Often these municipalities run on a tight budget and are extremely far from the nearest service stations. Yet the excavators are extremely crucial especially during monsoon season when landslides are likely to occur. The ability to proactively detect the problem ahead of time and alert the owners would be truly useful in such geographies.
I do agree with you that Yelp has an opportunity to better leverage AI, especially since they have traditionally relied on crowd sourcing of data. As new restaurants open up and with the continued upward trend of travel, Yelp still has a competitive edge to be the go to app. I would have a hard time associating Uber purely as a review or recommendation app. While I do think that Google’s advancement in AI poses a threat to Yelp, especially since Google also controls the search engine, it can differentiate itself by having influencers on its site. For example, if I am able to follow on Yelp the food recommendations of chefs/celebrities I admire, I may be more likely to get on their platform.