Daichi Ban's Profile

Thank you for sharing this post! I do not know much about military given my background, but machine learning and military defense is an interesting topic. Given UK or any other countries do not have budgets like US or China, I imagine building up machine learning capabilities would be crucial. At the same time, partnership with private sectors may be difficult given the confidential issue. It might make sense for the government to work with universities on machine learning as part of research topic to use their capabilities as well as non-profit identity. Another idea is to use best practice by other nations such as Israel. Based on Alex’s presentation during flag day, I remember that Israel has a strong technology industry, which works closely with the military. The model they are using may be applicable for UK or other countries which do not have enough budget, yet struggling to leverage machine learning potential.

Interesting reading! I do not have much knowledge about the industry, but if machine learning can shorten the lead time of drug development to market, that would be very good. If machine learning could be used for drug development and discovery, I believe the biopharmaceutical industry’s business model needs to be changed to speed up testing and getting approval from regulators. Time to test drugs may not be able to be reduced, but companies need to be creative while maintaining health issues. At the same time, educating and working closely with regulators to influence them is important as approval tends to be a lengthy, burdensome process. Overall, I am hoping that machine learning could disrupt the industry to reduce the lead time without sacrificing the quality of drug testing/development.

Interesting topic for equity research and technology! I would argue that research staff could do 1) more coverage of companies (such as small-mid cap) and industries, 2) spent more time analyzing areas where technology cannot use such as interviews with management, supply chain etc. As more investors with different risk profile increase, more coverage and deep insights, which normal investors cannot obtain would be valued. The best thing about AI, machine learning etc is that you can cut down time to prepare data and information drastically. I believe RBC could use this type of technology not only research division, but also other divisions such as investment banking where large amount of data and information needs to be gathered and analyzed. I heard that big banks such as JP Morgan is thinking about this type of transformation. Their aim is to try to fee up associates from manual work/data crunching, and let them spend more time with clients. I believe this type of transformation would also help the industry increase potential talent pools as well.

Interesting article. Thanks for sharing! On your question of industry-wide collaboration on data platforms, I would argue that it would be beneficial for areas that include new technology, large CAPEX, and high risk. The ROMEO is a perfect example since if you have a project with that size and risk for the offshore wind projects, it makes sense to have a consortium to diversify the risk. However, areas such as onshore wind and photovoltaic where the technology is mostly established and the industry is getting mature, I would argue the less benefit of industry-wide collaboration. Another point to think about is the country you are trying to operate. If the country has a less experience on onshore wind/photovoltaic, it could make sense to have partnerships vertically as the costs/risks could be substantial. Overall, I like the data sharing/industry collaboration to renewable energy space since it is still a risky emerging industry where lots of innovation, cost reduction and ultimately, environmental friendly solutions could occur.

Thanks for sharing your thoughts. I found the article interesting since I had not thought about using technology to manage regulatory risk from financial institutions’ perspectives. I believe areas such as fraud, money laundering could be potentially benefit based on technology like big data, AI etc. If financials institutions like banks could use their internal/external customer data, online data, google and found a trend/tendency of such activities. For example, if there is a cash transfer from one country to another where the address of the bank account seems to be suspicious. At my prior company, there was a fraud case where my company mistakenly sent the money to the fake bank account. It turned out that the supplier my company intended to send money to did not exist in that address. You could easily see that if you google map. If bank could use the technology to prevent such cases, it would be a big competitive advantage vs other banks since they could build trust with customers better.

Thanks for sharing this post. I happened to have the same topic, and found it interesting to see that the recommendation was somewhat different from mine. I liked your idea of applying for different business units like power and transportation. However, the question is how much benefit they could get from margin perspective, not just revenue as you pointed out given the cash constraints they have. I believe sharing the resources and facilities as well as focusing on consulting work would be more feasible to better utilize their current capabilities without spending too much. Still, the question is how they can get ahead of the competition where there are tons of companies spending money in this field. One potential way to resolve this is to have a JV structure with external firms to bring in cash while keeping additive innovation technology in-house. Another way could be to dispose the additive completely/outsource it. Whatever path they choose, I believe additive is a technology that GE cannot avoid so interesting to see which path the company takes going forward.