Kevin Nyamweya

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Kevin Nyamweya
Last modified on November 13, 2018 at 8:05 pm
Can composers see themselves outdone by data scientists and computer programmers?

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On November 15, 2018, Erik Peterson commented on Open Innovation at the Bank of Canada :

This initiative sounds like a promising one if carried out in the right manner. There are a number of extremely talented macro economists, practitioners and other academics who could provide insights into the potential interventions that BoC can carry out and I believe that PIVOT should aim to focus on reaching out to these. While I would not be able to tease out exactly how it can do so, a framework that comes to mind is that of the innovation funnel. It could start by collating ideas from a multitude of research institutes, financial institutions and universities across the world and through an iterative process, filtering out those that make the most sense in the Canadian context.
The question of the areas of focus for PIVOT is important as innovation cannot be an end in itself. A good starting point to determine this would be BoC’s mandate as an ‘economic steward’ which is still a broad one. Historically, the role of central banks worldwide has been that of maintaining financial stability. At the end of the day, the key pillars for its success in implementing monetary policy is based on enhancing trust, reliability and consistency. Innovative measures could therefore be judged against its mandate as well as the 3 key pillars. In order to do so more informatively, perhaps the BoC can take stock of the evolution of its monetary policy regimes over time, taking note of the changing role of different meanings of financial stability over time. Moreover, they can extend PIVOT to look into how to address times of uncertainty as a central bank; moments which tend to be those of make or break. Any potential initiatives would demand an in depth analysis of the costs and benefits, the opportunities and risks of each.

On November 15, 2018, Erik Peterson commented on Crowd-sourcing the Secret of Life: 23andMe and Open Innovation :

At the risk of echoing all the sentiments above, I would just re- iterate that this is indeed a topic that needs to be addressed over the next few years as we do not have a clear answer to it just yet. If anything, I have so many questions. Beyond the potential concerns for the insurance industry and/ or finding potential partners, where would this leave us when it comes to choosing babies. With the rise of IVF and embryo screening for traits parents find ‘desirable’, what will our future world even look like? Beauty will be in the eye of the parent. As more genes associated with the likelihood of disease are uncovered, the possibility of a truly preventive medicine is within the grasp of many parents. But with that possibility come risks. How well will any one test deliver on its promise of a healthy child? Will parents feel obligated to use genetic testing without adequately understanding its benefits? What kinds of genetic tests will parents want? Indeed, recent findings suggest that an increasing number of parents using IVF are choosing embryos according to sex, and it’s possible to imagine them one day choosing embryos based on other nonmedical traits, such as hair color, height, or IQ. Would such choices reflect the less desirable aspects of our human nature?

On November 15, 2018, Erik Peterson commented on Using 3D Printing to Break the Iron Triangle :

This essay brings other innovations to mind such as the lightbulb which cost $850,000 to develop and a worker’s day wage to buy. It took 1,200 attempts to get to the perfect light bulb but over time and process improvement we now enjoy cheap lighting in our homes and streets. As you pointed out, one key benefit of 3D printing that we are already seeing is the acceleration of product development. This alone might not eliminate the iron triangle but it probably goes a long way to reducing it. I believe the only way to take it further would be to transform the way in which items are manufactured in the industry. Shifting the focus from designing large bulky parts into smaller parts that are used to build up the whole would not only make 3D printing doable but also be able to increase the rate of defect detection. Through this, the oil and gas industry can manufacture, stock and replace these parts as needed and as the oil and gas industry evolves, replace certain parts with those that have improved designs. This would have both the benefit of having the ability to make bespoke solutions for the industry as well as addressing challenges operators face in minimizing unscheduled downtime by maintaining large inventories of critical spare parts. Traditionally, it has been more cost-effective to overstock parts than to deal with extended downtime. Additive manufacturing can optimize asset maintenance by reducing warehouse stocks through on-demand printing and I believe the attendant savings have more impact given the historical volatility of oil prices.

On November 15, 2018, Erik Peterson commented on 3D printing in automotive industry :

There is already an electric car that was released into the market, LSEV, manufactured solely through 3D printing. Olli, a self driving car of the future is also set out to be manufactured through additive manufacturing. I do not see this trend stopping and therefore it is in Ford’s interest to embrace it and be at the forefront of pushing the frontiers. However, I believe the issues you point out are not necessarily interconnected and it is possible to be say, competitive in 3D printing but not in self driving systems (which lends itself more to machine learning). Ford should therefore partner up with machine learning automotive specialists that can help it think through how the two areas can be successfully linked. There are examples where machine learning is currently being used to solve the problem of printing accuracy and finding approppriate lattice positions/ support structures by using generative design and testing in the pre-fabrication stage, with the aim of improving printing efficiency and cost savings. This can be extended to broad segments defect detection and predictive maintenance in the case of self driving cars.

User deleted this comment on November 16, 2018
On November 15, 2018, Erik Peterson commented on Open Innovation at the Bank of Canada :

This initiative sounds like a promising one if carried out in the right manner. There are a number of extremely talented macro economists, practitioners and other academics who could provide insights into the potential interventions that BoC can carry out and I believe that PIVOT should aim to focus on reaching out to these. While I would not be able to tease out exactly how it can do so, a framework that comes to mind is that of the innovation funnel. It could start by collating ideas from a multitude of research institutes, financial institutions and universities across the world and through an iterative process, filtering out those that make the most sense in the Canadian context.
The question of the areas that PIVOT should be focused on is important as innovation cannot be an end in itself. A good starting point to determine this would be BoC’s mandate as an ‘economic steward’ which is still quite broad. Historically, the role of central banks worldwide has been that of maintaining financial stability. At the end of the day, the key pillars for its success monetary policy is all about trust, reliability and consistency. Innovative measures must therefore be could therefore be judged against this mandate as well as the 3 key pillars. In order to do so more informatively, perhaps the BoC can take stock of the evolution of its monetary policy regimes over time, taking note of the changing role of different meanings of financial stability over time. Moreover, they can extend PIVOT to look into how to address times of uncertainty as a central bank which tends to be make or break moments for these institutions. All these would demand an in depth analysis of the costs and benefits, the opportunities and risks of each of the measures that they aim to put forward.

On November 15, 2018, Erik Peterson commented on Google AI: Predicting Heart Disease in the Blink of an Eye :

The question you raise on hidden machine bias is an important one with many implications in not only the medical but the legal field as well. Who would be to blame in such instances? In the paper ‘Black Box Medicine’, Price notes that: “a large, rich dataset and machine learning techniques enable many predictions based on complex connections between patient characteristics and expected treatment results without explicitly identifying or understanding those connections.” However, by shifting pieces of the decision-making process to an algorithm, increased reliance on artificial intelligence and machine learning could complicate potential malpractice claims when doctors pursue improper treatment as the result of an algorithm error. I therefore agree with your choice of words in that AI merely ‘supports’ medical professionals rather than substitutes what they are currently doing. I would therefore believe the best course of action at the moment would be to focus on the complementarity of these tools to existing procedures rather than thinking about any potential substitution.

On your second point, I am pretty torn on this issue. There is a clear benefit from having a more reliant database to improve the algorithm for the greater societal good. However, it is not clear how these data can be used by the broader industry. It is not inconceivable to envision insurance companies being able to charge premiums based on the propensity towards having certain cardiovascular conditions which might have negative implications for consumers.

On November 14, 2018, Erik Peterson commented on Capturing Value Through Machine Learning, Shell Adapts The Era of Low Oil Prices :

I think it is interesting how Shell is able to use Machine Learning in the contexts of both worker safety and emissions reduction and can envisage other linkages to its overall supply chain. To your question, it would definitely be difficult to do so solely on this basis in the short term. The crux of machine learning is indeed the dataset as you point out and I believe setting out to create a comprehensible library of data (image/ audio recognition, harnessing the Internet of Things) would be one of the key steps to determine how impactful this initiative will be. That said, through a supervised or semi- supervised learning approach, I believe there are benefits in complementing the existing procedures with these new techniques of safety intervention.