Great article. As a civil engineering myself I find 3D printing to be a very innovative approach to construction that may challenges the industry faces today. However, if the goal is to use this technology to tackle global homelessness, we should think about how it fits the reality of developing countries where this problem is more severe. As mentioned in the post, 3D printing is highly intensive in capital and in some regions of the world it may be just unavailable today. Also, the long term endurance of this structures is yet to be tested. Behavior of concrete structures highly varies depending on molding, setting and other conditions during the construction process. If the idea is to provide a long term solution to the poor, these homes should prove minimum standards towards fire, flooding, earthquake and other potential environmental hazards. Even just aging in a humid weather can end up corroding the structure of an entire building. In regions where wood is sufficiently available (and affordable), wooden structures remain one of the most convenient and efficient construction methods in terms of performance. Maybe 3D printing proves a better solution in desert or deforested regions?
Echo WJB’s comment on the idea of making this tool available to the individuals, not just the police. It is certainly very interesting application of machine learning and trying to solve a very relevant problem in today’ society, but its impact would be much higher if applied to emerging countries where violence and crime levels are much higher. Think of Latin America, where not only crime levels are high but corruption may also affect the effectiveness of local police forces enforcing the law. Empowering individuals to prevent their own exposure to crime and violence seems an interesting alternative in these countries.
No doubt those huge HP metal printers are very expensive equipment. Scaling up this technology would require huge upfront capital investment. However, considering the fast pace this industry is evolving, is it smart for BMW to make a mega investment in 2018 printers if next year’s version will most likely be far superior? Like buying a laptop, after a couple of years you will buy a new one to keep up with the increased computing power. It seems hard to find the right trade off between investing for a long-term manufacturing facility vs retaining flexibility to go for the latest technology available. Maybe something like a car-leasing scheme could work — e.g. you return your printer back to HP every X years in exchange of the latest one available?
Very interesting post. Considering the how significant are the global loses incurred by companies when unable to anticipate and manage disruptions in their supply chains, the use of machine learning to try interpret demand patterns seems a creative solution to improve efficiency of the whole system. Given natural disasters and environmental events account for a large share of the disruption causes, maybe using machine learning to anticipate these natural events would position supply chain managers one step ahead. However, that might require analyzing a complete different set of databases and raises the question of whether supply chain managers should be really engaging in weather forecasting? Then why not modelling the entire global economy to anticipate its up and downs and their impacts in your supply chain. As any optimization exercise, precisely defining the scope of the problem machine learning is trying to solve is key for its own success.
Great post. Improving metallurgical recovery rates remains a key challenge to the mining industry. That 10% is a big hole in the sack if we consider the enormous capital and time effort to find, develop and run a large scale Barrick-style gold mine. Incorporating machine learning seems today a must-have complementary tool for engineers to try maximize recoveries, especially when treating more complex orebodies or using unconventional extraction techniques (e.g. sulfide leaching). One thought — would the algorithms work better if they were feed with data from multiple mining operations and not just the one they are trying to optimize for? Maybe the algorithms could “train” better if learning also from patterns found in similar gold mines in their respective districts or in other parts of the world. Barrick as an operator of multiples sites globally could then find “data synergies” with this approach.
Awesome post. I am a big fan and user of Spotify — actually I almost did my essay on the same topic. One thing Spotify could make to improve their algorithms is finding a way to “filter out” the cases when you are asked by a friend to play a random song — which you may not like — but ends up impacting the intelligent feed Spotify generates for you. For example, I mostly listen to rock music but when working out I like to switch to more electronic stuff. More drastically, sometimes I use Spotify to play songs to my kids when driving my car. As a result, I get this random “recommendations” from Spotify that have nothing to do with the music I actually like. I though maybe their algorithms could identify these outliers and avoid messing my cautiously curated recommendations list from Spotify…