Electric Sheep's Profile
I like the thought here how the population really has a say in the legal system. As many other posters have said above, I do worry about questions of access and the biases it will naturally introduce, especially to your point on the next step of crowdsourcing validation. I think there are underlying assumptions on rationality and expertise during a vote. I wonder if in certain countries where voting is perhaps more emotion-driven if this will work. For example, during election season in Indonesia there are historically campaigners that go around villages and give money to people to go and vote for them, which is essentially vote-buying. The fact that some of these practices prevail makes me concerned about potential manipulation, especially in an internet-based system where identity verification may still be difficult.
I think this issue of experts you brought up is also a valid point. I think the issue is two-fold – the experts to be crowdsourced from, and experts for validation purposes. My concern is that some countries may not have the right experts yet in place, especially for nascent industries (blockchain comes to mind), especially on the validation side. I think the balance between the experts outside the government and within will be very important as there could be a case where only ideas that agree with the current ruling power’s ideology gets pushed through.
Thank you for sharing this – I think there is a lot of potential for transient housing not only in disaster areas but also areas where land rights may be uncertain. An example of this will be the shanty towns that grow in developing nations such as the favelas in Brazil. New Story’s (and 3D printed housing in general) may be a solution where displacement does not necessarily equate having to start completely over which can be a game-changer for city planning for these vulnerable populations.
The main challenge I see is linked to this issue of access for communities, both in terms of pricing and physical access. Focusing on the physical access aspect (given that pricing should come down with scale), taking the extreme example of disaster zones, access to impacted areas may be extremely difficult. With this the question becomes how modular and transportable these houses can be to increase accessibility and flexibility, which I think may be a question New Story also faces as it scales even further.
Building on Raleigh’s comment, I do think some of the issues stemming from negative reviews could be due to pricing/ accessibility and it is still a proof of concept. To illustrate, the Futurecraft 4D retailed at USD300 and reseller prices are over USD1000 (based on the GOAT app). And this price is currently being paid for something that has not come to fulfill its promise of customization at a lower cost – right now the model comes in several colorways but are not customized for your feet.
Another point here is 3D scanning technology has been experimented with in the footwear industry – even in countries such as Indonesia there are brands (Mario Minardi) that offer bespoke shoes (made for the specs of your feet). For traditional dress shoes however, what has been found is that the definition of a good and comfortable fit is actually different for each individual. Some people like shoes that are a bit more loose, others on the tighter end. Bespoke dress shoes typically have the customer come in for multiple visits to try out the shoe to ensure it is right for them. While sneakers are generally more comfortable than dress shoes, I do think that this poses a challenge where the customer may be given a 3D printed shoe that according to their measurements should be perfect, but given their personal preferences are not the fit they are looking for. In essence, I think there is still a lot of art in the footwear industry that pure science cannot take over. Thus the bigger value in my opinion is in the lower cost and time of production coupled with customized designs but not necessarily in getting ‘the perfect fit’.
Interesting food for thought (pardon the pun)! I agree with your point that data is king in this AI-fueled environment and Yelp needs to find an edge where they can compete against the data and talent advantage many of these large players have. That is why, aside from Google and Facebook, I also worry about players like food delivery, especially integrated platforms (Uber and Uber Eats, Amazon recently launching Amazon Restaurants). The reasoning behind this is several folds. The first is Yelp’s touchpoint during the customer journey – they traditionally help in the selection portion and then the after-sales portion for the reviews. They have tried to expand to the occasion portion of the customer journey (which is more frequent), whether it be in delivery via Eat24 or Waitlists. My concern is that these segments are not a key source of strength yet for them – the food delivery space is very crowded and waitlists necessitates a change in behavior which will take time to develop. Uber and Amazon will have a stronger understanding of who the customers are given data collected from their other products (e.g. Uber will be better at geographic recommendations given commute patterns, Amazon perhaps knows price sensitivity) – the value to the restaurants in this case will be higher. Given these challenges, I find it interesting to see where Yelp decides to best leverage machine learning going forward and if they can carve a niche with a unique data set only they have.
Thank you for sharing this – I find the case study of a previously dominant tech industry giant is fighting to stay relevant quite interesting. To your question on defensibility, one of the areas of defensibility is keeping data proprietary – after all access to good training data is a key competitive advantage for machine learning, especially for computer vision where the data set is more complex and much of the time unstructured. As you have mentioned, aside from the Mobileye acquisition, they are building this defensible ‘moat’ through (hopefully exclusive) partnerships with manufacturers which is a step in the right direction. It will be interesting to learn more how these partnerships will develop in terms of data sharing. Will these suppliers strive to keep the data for themselves as a bargaining chip with other machine vision providers to get better terms? Or will there even be more fundamental issues with data right and privacy regarding the customer? This is especially important when recent developments such as the unauthorized access to Facebook’s user data stirred up such a controversy in recent years and automated vehicles may hold sensitive data such as workplace location and homes. In this case, these partnerships may go back to being primarily a hardware agreement instead of a potentially more lucrative one including data-sharing and creating more sophisticated automation.