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How Spotify Beat Apple, Amazon, and Google Using Machine Learning
Harvard
Last modified on November 13, 2018 at 5:21 pm
In an era in which Apple, Amazon, and Google are dominating the technology world, Spotify has succeeded in besting all of them in the music streaming industry. The question is then, how is Spotify able to fend off its major [...]

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On November 14, 2018, Harvard commented on Repair Time Reductions On Submarines Through Additive Manufacturing :

A. Ham – Thanks for the essay. I think the environment in which submarines operate (out in the sea) is very well suited to having the AM technology on board in case parts break during operation. When parts break down during operation, submarines can easily reproduce repair parts without having to go back to land to have the parts repaired. This should significantly expand the operation horizon for submarines.
As you mentioned, as AM and AI are widely used across industries including the Navy, it would be important that we develop workforce who are skilled to be able to understand the new technologies and integrate them into company (organization) operations. For the Navy, these operators may not need to have the full in-depth knowledge of these technologies, but at least working knowledge of how to operate the AM machines. To prepare well for these upcoming changes, the Navy can consider having a separate but centralized command (across the Navy) where they focus on the new upcoming technologies (AI and AM included). Within this new command, they would have full knowledge of what new technologies are emerging and which technologies can be used most appropriately across different units / teams within the Navy.

On November 14, 2018, Harvard commented on Alibaba turns to Open Innovation to harness the power of millions of tech developers around the world :

Tatiana – Thank you for the essay. One thought I had regarding sharing seller data to external parties (in addition to confidentiality) is whether Alibaba is compromising its competitive advantage by sharing these massive data that Alibaba is accumulating. Alibaba can leverage all its data to understand its consumers and sellers better, which I consider to be a very important strategic asset to the company. I think there is a trade-off between sharing Alibaba’s proprietary data vs. achieving innovation through Open Platform. One potential solution is to limit Open Platform to be within Alibaba (only Alibaba’s employees can participate), though this may limit the benefit of having a wide group of people contribute to innovation.
You also raise an interesting point regarding the reduced need for labor. I personally think that companies would still need specialists who know their companies very well, even though majority of innovation is done externally through open innovation. Though there will be a large pool of freelancers participating in open innovation projects of tech companies, I still think that there will be needs to hire company specialists and the impact on the overall labor market from open innovation would be limited.

On November 14, 2018, Harvard commented on How can a traditional conglomerate (aka a global business producers) can be innovative? :

Masato Nakamura – Thanks for the essay. It was interesting to read that Mitsubishi Corporation is striving hard to innovate through Open Innovation, as my expectation for such traditional Japanese companies was that they would be very rigid, traditional, and not open to changes. I think Mitsubishi is doing a great job trying to stay ahead of its competition through Open Innovation.
I think there are numerous benefits for VC (as well as Mitsubishi) to partner with each other. Mitsubishi, as one of the largest conglomerates in Japan, likely has massive scale and power across all the industries they operate in. VCs can benefit from Mitsubishi’s wide network and resources to better source new ideas and look for opportunities. Mitsubishi should even consider having their own VC arm, where they can look for new innovations and technologies that can help the company.
While reading the essay, I had two questions come up to my mind that I wanted to pose to you:
1. Instead of limiting the Open Innovation participants to Mitsubishi employees, do you think it’s possible to open it up to public and anyone can participate with good ideas?
2. Given the very hierarchical nature of Japanese culture, do you think this works against the Open Innovation projects that Mitsubishi is doing right now? And if so, what are some of the ways to mitigate this risk?

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On November 14, 2018, Harvard commented on How can a traditional conglomerate (aka a global business producers) can be innovative? :

Masato Nakamura – Thanks for the essay.

On November 14, 2018, Harvard commented on The Growing Market for Identifying Fake IDs :

Courtney H – Thanks for this interesting read. While Jumio’s goal of using machine learning to detect identity fraud sounds like an innovative idea, I wonder if the company’s business model is too susceptible to any technological disruption. As you mentioned in your essay, facial recognition system could easily eliminate the need to have any physical IDs, where people’s identities are confirmed just using their face. Now that we have this functionality in our hand (iPhone), I suspect that the technology will be very widely used in no time. And, as you mentioned, 10 years down the road, there could be new identity verification technologies that may make Jumio’s services obsolete. Given this, Jumio should strive hard to integrate their machine learning capabilities into any new identity verification systems that could arise.
One additional concern I had was what if people figure out how Jumio does its identity verification through machine learning and try to game the system to trick the machines into believing that fake documents are real? I think there is a risk that people will figure out how to trick the machines and again, Jumio should stay on top of its technologies to prevent this machine-fraud from happening.

On November 14, 2018, Harvard commented on Chanel’s Foray Into 3D Printing :

Arting – Thanks for the interesting read. While I was reading the essay (and before I got to the last paragraph), I had exactly the same question as your question #1. With 3D printing, can Chanel keep its luxury brand image and cachet? For example, people love buying handbags that are “made in Italy / France,” which is why ultra-luxury brands insist on manufacturing their handbags in Europe vs. outsourcing to Asia, where they can manufacture at a fraction of the cost in Europe. In the case where Chanel expands 3D printing to its apparel and handbags, I do wonder how consumers would react to “made by 3D printing” tag on their products.
On the counterfeit point, I think it concerns all industries where 3D printing can be used. 3D printing will lower the bar for people to create counterfeit products, which will become a problem especially for branded consumer goods companies. I agree with you that companies who are venturing out into 3D printing should consider any potential consequences of having their products being copied easily by 3D printing.

On November 13, 2018, Harvard commented on Foxconn: Large Scale Manufacturing in the Machine Learning Age :

C. Xu – Thank you for the thoughtful essay. It was interesting to read how machine learning can be adopted into the manufacturing industry. I think you raise a valid point around the need to have skilled operators who understand how machine learning works and can apply it to the solve the challenges an organization faces. Lack of skilled workers may work as a hindrance against wide usage of machine learning not just in the manufacturing industry but in any other industries where machine learning can be used. To avoid this issue, we should try to have more programs dedicated to teaching people about machine learning.
It was also surprising to know that Foxconn will be investing ~$350m into AI R&D over the next few years. Even for Foxconn, this is not a small investment and it would be interesting to see how the investment goes. At the end of the investment, Foxconn may prove to be a pioneer in machine learning in the manufacturing space. Or, in the worst case, it may prove that the manufacturing industry is not “ripe for machine learning disruption.”