I enjoyed reading the piece, definitely a very interesting angle.
I agree with the limitation the author has raised, especially around regulatory limitation as well as data access. Regulatory limitations and data issues will definitely limit the ability of lenders to solely rely on machine learning derived-decisions. I also wonder how much manipulation borrowers in emerging markets can make to secure lending if it is solely vetted by an algorithm.
In certain markets, I do agree ML can help with those lending decisions. I believe Ant Financials (Alibaba subsidiary) applied ML and built a platform as well as their own credit rating at least on the consumer front, they then have the service of “Huabei” to provide financial solutions on consumer purchases. The scores “Zhima Xinyong” are now widely accepted at various other services since the majority of the people in China has it and it provides some benchmark. So it is another interesting example to check out if you are interested.
Good article overall and I enjoyed reading your perspective!
Extremely well written and thought-provoking piece! Really enjoyed reading it.
The author mentioned “Deep learning, however, can provide more accurate and faster “nowcasts” of key economic indicators given vast quantities of consumer and financial data available today” – my question is how would deep learning necessarily be able to pick out the relevant data that is leading indicators versus lagging ones? The ML relies on data as well as algorithms so not sure the Fed will have the right input in terms of numbers or parameters to look at.
Also, the capital market doesn’t have that long of a history and I would if the data size is enough for the ML requirement to predict different cycles. It can clearly do better than human but how would a Fed’s ML compete with a D.E Shaw or Citadel?
When I think about the Feds and its decisions, I also think there’s a lot of macro-economics conditions and policy-making influences so I do wonder how much help ML can provide.
Nevertheless, a great piece and I enjoyed reading it!
This is a great article about Machine learning in the education space. I particularly liked how clear and structured this paper is.
The graphic at the very beginning made it very clear about how ML can be applied in education space and the author elaborated on education operations and learner acquisition. It is clear that he has a great understanding of the industry as well as the specific company and it was interesting to learn about the methods they obtained clients.
I also like his suggestions regarding Learning Efficacy. I liked the comparison and ‘lessons learned’ from ‘VIP Kid’ in China since that company has realized tremendous success in the past few years. Seeing the children of my friends using it and improving in language skills was amazing and I am looking forward to seeing how UpGrad can make a difference in higher education.
As a loyal Chanel fan, I’m both excited to hear about the innovations and aren’t surprised at the slow development cycle. I am surprised similarly to you about their choice of product for innovation – Mascara. As I imagine the face products to have a much higher profit margin. Or going beyond Chanel beauty, I expect once the development cycle speed up and they have a better relationship/technology matured, Chanel can move this into their couture line. The 3D printing can fit different body curves as well as tailored requests. That is where I see the purposes of customization.
Overall great read and a great topic to study!
I appreciate the topic as I also looked at how the Gates Foundation is using Open Innovation and solving some of the harder open-ended challenges. I also enjoyed reading your perspective as you dive deeper into this specific example of a clean toilet.
It was really interested seeing pictures of Gates with a beaker of human feces in Beijing during the Reinvent Toilet Expo in Beijing, and it was a huge step forward for the world in thinking about clean toilets. We, living in cities, often time thinks that the toilet isn’t an issue but more than 50% of the people around the world does not have access to clean, reliable toilet. It is a major problem in the rural area for the sewage system or water, thus the virus isn’t eliminated. Gates’ solution to this is similar to when he developed PC through Microsoft – developing singular independent toilet system that doesn’t rely on networks.
Happy to discuss more and enjoyed reading your paper.