Joe Tung's Profile

Additive manufacturing in the medical field seems promising; however, you bring up a really good point regarding costs. As we try to contain costs in health care, what role will 3D printing play in medical device manufacturing? The quality of produced instruments is also very important to consider. It appears that smaller, more intricate, designs might be more difficult for additive manufacturing to produce.

This is fascinating. However, when human lives are potentially at hand, I think the threshold for trusting machine learning algorithm guidance should be set higher. As you mentioned, there are adversarial examples where machine learning algorithms may be tricked into giving answers contrary to what they might usually provide. Because machine learning logic is still a “black box,” it might be difficult to discern the rationale behind strategy recommendations. The ethics behind AI-driven decisions are also interesting to consider – who will be taking the blame when AI makes an ill-guided suggestion?

BMW appears to be ahead of the curve in additive manufacturing in the automobile space; it will be interesting to see how the company continues to disrupt traditional manufacturing processes. For example, will cars of the future be personalized to each individual’s preferences? Additive manufacturing seems to lower the cost of customizing different parts because machines can be reconfigured to print out a different design with relatively low cost. Having the ability to leverage just in time production with decreased inventory and work in progress transportation costs will also be a competitive advantage once the costs associated with 3D printing decreases.

I agree with you that by leveraging machine learning technology, bigger banks like JP Morgan appear to have a strong competitive advantage that smaller banking entities might not be able to afford. Perhaps the smaller banks might be able to promote the “human” or “service” aspect of their functions to better relate to customers, but I think ultimately the results will trump all other factors.

In terms of other front office disruptions that new technologies might be able to impact, it would be interesting to see if machine generated sales pitches are as effective. Quantifying effects and perhaps running A/B testings will be imperative to determine the effectiveness of these pitches.

I did not know that Netflix was letting viewers control the narrative of the show “Black Mirror” and allowing them to pick some of the endings for the 2019 season. I am curious to see whether this strategy increases viewer engagement – when I watch a movie, I don’t necessarily want to know or predict what happens. I like being surprised by unusual endings, and it seems that placing the ending in viewers’ control might be counterproductive to that.

More generally, I think it is great for Netflix to leverage big data and machine learning to better predict user preferences. However, by using past behavior to predict future preferences, Netflix will not be able to “surprise” users with new genres of movies that are completely unrelated in characteristics to movies watched in the past. Another potentially intriguing application of machine learning might be to track user behavior to monitor for multiple people using the same Netflix account.

This is a great overview of the company. I’m interested in learning more about how the product actually works and where it adds value – rare diseases are generally diagnosed with genetic testing and I would imagine that patients who use this product would still need to undergo confirmatory genetic testing. Rare diseases are also by definition rare, so I’m curious to see whether they can acquire enough images to improve accuracy and enough patients to stay sustainable.