Oliver Badenhorst

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On November 14, 2018, Oliver Badenhorst commented on Foxconn: Large Scale Manufacturing in the Machine Learning Age :

Thanks for the perspective, Cong. I think you raise good points on the workforce-related challenges of Foxconn making such a radical change to its business model. I would anticipate that it will be easier to deal with the need for specialised technicians than it will be to handle the negative repercussions of potentially significant workforce reductions, at least in the short term. It’s far more attractive to be a worker being asked to upskill, or a new hire brought onboard to build-out new capabilities, than it is to be the worker being let go. I’m interested in your thoughts on how you think a nation’s social safety net and industrial relations architecture will affect the character of its adoption of disruptive technologies such as machine learning. I can easily envisage the strong role of unions and labour bodies in nations such as Germany standing as an impediment to the rapid adoption of these kinds of advancements.

On November 14, 2018, Oliver Badenhorst commented on 23 & Who?: Deep Research into our Genetic Code :

Thanks very much for the fascinating contribution, John. I’m curious as to whether you believe it will be possible in the near-to-medium future for an individual to have any reasonable expectation of genetic privacy, beyond even whether they should have such an expectation. With recent developments using genetic databases, and advancements in techniques allowing researchers (or law enforcement) to identify individuals many genetic steps removed from the matched DNA, my intuition is telling me that privacy is a fast-fading dream. This is before we even consider malicious data extraction or inappropriate data usage from these databases. Should we be worried? or should we simply accept that genetic privacy was a relic of the pre-genetic age and attempt to ameliorate the worst impacts of such a new world?

Thanks very much for the contribution, Jayant. One (of the many) potential challenges I see to the adoption of cryptocurrencies and alternative financial technologies is the chicken and egg problem they face in climbing the adoption curve. As you note, when asset valuations are extremely volatile, such as they have been for cryptocurrencies, potential users will be reticent to come onboard, but without new users it’s hard for a platform to achieve the critical mass necessary to reduce that volatility. It is simple enough for traditional fiat currencies to cross that chasm–they have the coercive power of the state mandating their adoption–but alternative payment mechanisms will have to cross that chasm all on their own. Whether they can do it and how quickly will be a fascinating process to watch.

On November 14, 2018, Oliver Badenhorst commented on The future of energy: forecasting the weather? :

Thanks for the contribution, Lori. I’m curious about your point that increasing the accuracy of weather predicition will enable power providers such as NextEra to better close the gap between reserve power and demand. My previous understanding was that power production would be dynamically adjusted as grid demand rose and fell (e.g. gas plants being turned on during periods of peak demand). What additional options are opened up if you have greater foreknowledge as to when those spikes will occur? Does it change the power generation source you use (e.g. using a plant that takes longer to spin up rather than gas), or is it some other mechanism that provides a benefit?

On November 14, 2018, Oliver Badenhorst commented on Additive Manufacturing at GE Aviation :

Nice work, mate. I share your concerns about materials costs posing a potentially significant barrier to mainstream adoption and sustainability of additive manufacturing. Anecdotally, with all the talk I hear about other manufacturers following GE in investing heavily in the space, increased demand for the inputs and complements to additive manufacturing should, all else be equal, further drive up their prices. Unless those technologies mature at a similar pace, they could easily become the bottleneck hindering further growth in the space. Did you get a sense of the direction of those cost curves or technological development during your research?

On November 14, 2018, Oliver Badenhorst commented on Football and Chess: How Machine Learning Can Improve Playcalling in the NFL :

Thanks for the article, mate. I will also be interested to see how the increasing rollout of machine learning technology affects competition, both in sport and in other fields–it brings to mind comparisons to the introduction of game theory to commercial and strategic decision-making in the 60s and 70s.

I also agree with your point that algorithms are limited in their ability to predict future plays based on historical data, but I suspect that the consequence of that limitation is that the ideal decision-making unit will be a combination of both human and machine intelligence. Given that inputs are forever-changing (e.g. the coach may sense quarterback is simply having an off day) and opponents are responding to a team’s decisions in real-time, I can’t see a world where the judgment fans have come to value so highly is not still being exercised. That being said, it’s almost certainly going to have to be the next generation of coaches who bring this skillset to life–I have a hard time believing that old dogs will be able to learn drastically new tricks.