This is a great article about how open innovation is a key lifeline for Amazon’s Alexa going forward. The author mentioned and described the university contest and different methods Amazon is using in order to promote Alexa platform. Very nicely written.
As far as entering emerging markets, it will have to certainly customize for the region and locality which it desires to enter. I doubt a “one size fits all” approach will work (since each region with it nuances are so different).
Amazon will have to also consider building an open innovation platform in other regions e.g India or China or Brazil, where this a young populace of people savvy in software and programming. Amazon will also have to educate consumers/corporate about the uses and advantages of Alexa and develop local partnerships (in regions and emerging markets where it desires to expand)
Very interesting article about the use of 3D printing for vending machines. Additive manufacturing is an interesting field and has potential to change the dynamics of this industry.
The author goes into detailed discussion about how 3D printing is useful for developing new distribution systems. The author does provide a great example about nail polish. A couple more examples could have further strengthened the argument. There is also a nice discussion on customization and the benefit offered by 3D printing in that regard.
From a constructive standpoint, I would have liked to see more details relating the exact economics of this vs traditional manufacturing and how this might affect the potential going forward in future. This would have made clear whether 3D printing has a potential or not, and if it does, what economics are required for that to be.
But overall, enjoyable reading, opened my eyes to the potential of 3D printing/other forms of manufacturing in the vending machine industry.
Great article about machine learning and its applicability in an HR setting. The author has done a fantastic job of explaining the two key themes (sentiment + theme clustering) and the value of such a system. This does also remind me of the Aspiring Minds case, where a software was built to connect job applicants to companies recruiting for talent.
The challenges that the author mentioned would probably have to be handled on a case by case basis. Each organization will have its own vernacular and jargon, and HR will have to work on getting a tailor made version of the product. Also there will be some errors, the hope is that the algorithm gets better with time and accuracy can increase over 95%.
For the machine learning part – I would have liked to see some more rigor and discussion around the three main components we learnt about (clustering, regression and classification). There could have also been an argument about the different types of learning (supervised, unsupervised etc)
Overall though an enjoyable read. I learnt a lot about machine learning and its potential applicability in an HR setting.
Great article about Japan’s largest online retailer for lifestyle products.
I really would be curious to understand how machine learning will take the company to the next level. The author mentions and argues that machine learning will help the company translate customer order specifications into corresponding manufacturing processes that balance design precision and speed. This does sound pretty remarkable – I would be curious to know exact examples as to how this will happen and by how much will the speed be improved.
The author also other competitors, such as Boston Stitch Fix, I would be curious if we were to benchmark and progress the exact areas where machine learning has helped this company (and correspondingly how the algorithm has learnt/improved over time)
This was a great article, opened by eyes to how machine learning can really potentially change the apparel industry, and have an effect on many aspects of the manufacturing chain.
Great article about Tesla and overview of the e-vehicle industry. I feel as though Tesla does follow a “closed innovation” policy when it comes to the actual battery that runs the car.
E-vehicle’s are mostly about the battery technology (which I think Tesla is very secretive about and does not share publicly). Tesla uses a lithium technology platform which is JV with Panasonic of Japan. I am given to understand that even among batteries, the Tesla battery, has the highest energy density. Once the battery of the car is obtained the rest of the car – is not very complex, since there is really no IC engine involved. E-vehicles have such few moving parts.
But overall, great article, well written, articulated and relevant discussions were made.
Great article about transport in London! I really liked the way the author has built up the argument, it does remind me a lot about the big “Red Bus” of London. It does open my eyes also to the way transportation must evolve, not only in London, but in many congested cities world over.
The author has mentioned details of machine learning, but has not mentioned as to why this technology is a must, and how this will span out in the short and medium term. What key aspects of machine learning will be important, how will it play a greater role, for CityMapper Smart Ride. What could be some of the limits of the technology as well?
However, great article, that really made me learn about how machine learning could potentially shape the transportation industry.
Very interesting reading about how Boeing has used 3D manufacturing, and used it effectively compared to rival Airbus. However while this is definitely an advantage, I had a couple questions that required food for thought.
1) The author recommends selling these components back to Airbus? Wouldnt this be a disadvantage, since Airbus then could use and replicate this technology, and develop this competence in house.
2) While Boeing has not had many customer issues, it did have huge delays in its 787 dreamliner. One of the Japanese airlines that used this aircraft had to ground the airplane, because of issues with Lithium Ion batteries.
3) Are additively manufactured components, structurally suitable for the aerospace industry, with all its regulations?