Stacy Tan's Profile
Super interesting piece! I have two questions after reading your post:
1) if the Fed does start to rely on an algorithm to set monetary policy, will the market (especially quant hedge funds) back-engineer the algorithm and try to take advantage of it?
2) I am afraid what the ML model would fail to incorporate is the element of fear and other investor behaviors. For example, in 2008, the financial crisis was not really caused by connectedness of banks or if some financial institutions won’t have enough capital to absorb the counter-party loss posted by Lehman’s failure. Instead, the entire financial system was brought down by contagion of fear and run on money market funds within days. So I am not sure if ML model based on data will be able to incorporate the behavior elements of the irrational market.
I enjoyed reading your post. I am curious about the cost of personalized device manufacturing, and I wonder if this is the reason that Medtronic is hesitate to embrace it with full force. Medical device industry competes on cost, and large companies like Medtronic enjoyed significant benefits of economics of scale. Will personalized additive manufacture add up cost per unit significantly so that it trumps the benefit of personalization, especially in the area of cardiac, spinal, musculoskeletal, neuromodulation, and diabetes.
I enjoyed reading your post. I have seen an increasing amount of news coverage about using ML to aid lending decisions. The core of each company’s algorithm is the access to data sets that are relevant to the borrower’s willingness and ability to pay. Some internet companies are starting to use personal data (including chat data, what kind of news someone read) in developing the algorithm, which I believe has gone too far and will eventually run into a regulatory wall. By then, the government is likely to put a stop on using personal data acquired to make credit review process easier.
The projection I outlined above answers part of the question you posted at the end of the article. Using ML for lending decision or credit review is more of a hype and hope, which is complicated by the regulatory barriers. Therefore, the risk of replacing labor in my opinion is possible but remote.
I enjoy reading your article, it features both sides – the huge opportunity and the potential risk – in an elegantly balanced way.
I think one area that FDA needs to act more quickly on issuing guidance is companion diagnostics for immuno-oncology therapies. One interesting company is called Gaurdant Health, they use machine learning algorithm to solve the noise problem in ctDNA. They are able to identify specific gene mutation (EGFR, KRAS, NRAS etc) that are the leading cause of a number of cancers (non-small lung cancer in particular). They are not yet in early detection or prediction, but more focused on companion diagnostic for therapy choice.
FDA is still no where in issuing guidance for relatively safe application of machine learning in diagnostics.
I enjoy reading this piece, it is well written, and here are some points to consider:
1) Toutiao’s AI-powered newsfeed creates the value for the company because it allowed higher advertisement fee charge. For example, businesses need to pay 4 times more to place their ads on Toutiao than on Baidu.com. Toutiao has the pricing power because it promises a higher click conversion rate as it is tailored to each user of its app. Every click, the amount of time spent on each news article, whether the user is using an iphone or a xiaomi phone, are inputs that help create the user portraits that helps more accurate advertisement targeting.
2) I think Toutiao’s strategy of globalization is not to replicate the same model as they have in China and copy it to the rest of the world, they have been active in acquiring international companies like musicly, which are more acceptable and accessible to the U.S. users