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Machine Learning at American Water
flamingunicorn
Last modified on November 13, 2018 at 12:24 am
The largest American water utility American Water (NYSE:AWK) is uniquely positioned to be at the forefront of technological change within the water utilities industry. Machine learning could bring massive operational efficiency improvements to the company, but will require strong commitment [...]

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On November 15, 2018, flamingunicorn commented on Straight to the Source: Open Innovation at Buzzfeed :

Hi! Thanks for the thought provoking post. I think moving towards more credibility through crowdsourced news is difficult, because Buzzfeed reporters just don’t have the access to the same resources as journalists at CNN/Economist/FT would. Additionally, I think crowdsourcing poses credibility issues that the traditional news outlets don’t have. The source does not really matter when I’m looking at cat pictures or rumours about celebrities, but will matter a great deal when I am, for example, presenting an opinion to senior management. If Buzzfeed wants to become more serious of a news source I think they will have to move away from crowdsourcing and therefore to some degree overthrow the current model of their success.

On November 15, 2018, flamingunicorn commented on Betabrand: Too Much Open Innovation? :

Hi Aparna! Thanks for the interesting article – I had never heard of Betabrand before. From my perspective they actually have a fairly solid business model that gets more robust over time. I don’t think they will lose their appeal for as long as they can draw in the designers that continue coming up with products that their customer base love. What Betabrand truly has is a unique process that they’ve kept on refining over the years and a process for sourcing projects that are exciting and drives their sales. I think such businesses are extremely hard to build because you need a degree of scale to make the unit economics work, however, once set in motion this innovation machine can just continue producing for as long as the customer wants overlap with what the open innovation process creates.

On November 15, 2018, flamingunicorn commented on When Chanel trades sewing machines for 3D printers :

Thanks for the thoughtful article! I think moving towards 3D printing for a (comparatively) low volume designer like Chanel makes a lot of sense. The cost efficiencies for doing 3DP vs. mould injection are massive at the scale of a couple of units, and it permits a lot more creativity. These efficiencies however become liability at a larger scale (cost per unit actually increases with scale, since you need to buy more printers), so I do believe that small volume businesses that produce pieceworks will be the ones benefitting most from this technology. Having built prototypes using 3D printers, I am, however, concerned about the quality of 3D printed products, which tends to be considerably poorer than you’d experience with a mould injection product. If Chanel manages to get the quality to a point of mould product, I think 3D printing, given the time it takes to print a product (could be days!!), assemble it (mostly manual) and cost (not cheap at all!) will actually be Chanel’s best differentiator against mass produced clothes. I think 3DP will remain a niche for high end design, because they’re not trying to achieve economies of scale. A widespread use in design however with current unit economics is unlikely to happen.

On November 14, 2018, flamingunicorn commented on Medtronic – missing out on a personalized medicine opportunity, or appropriately cautious? :

Your post really made me think a lot. Ultimately I think Medtronic is not being cautious at all, but are acting appropriate to their core market. The killer use case of 3D printing is prosthetics and other biomechanical implants, which as far as I know is not a key expertise of Medtronic. In the field they are playing in – implantable electronics, bioelectronics and surgical instruments, 3D printing adds very little value in manufacturing since the products need not be individualised to the human physiology – at least not from a biomechanics point of view.

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On November 14, 2018, flamingunicorn commented on Chanel’s Foray Into 3D Printing :

I think moving towards 3D printing for a (comparatively) low volume designer like Chanel makes a lot of sense. The cost efficiencies for doing 3DP vs. mould injection are massive at the scale of a couple of units, and it permits a lot more creativity. These efficiencies however become liability at a larger scale (cost per unit actually increases with scale, since you need to buy more printers), so I do believe that small volume businesses that produce pieceworks will be the ones benefitting most from this technology. Having built prototypes using 3D printers, I am, however, concerned about the quality of 3D printed products, which tends to be considerably poorer than you’d experience with a mould injection product. If Chanel manages to get the quality to a point of mould product, I think 3D printing, given the time it takes to print a product (could be days!!), assemble it (mostly manual) and cost (not cheap at all!) will actually be Chanel’s best defence against counterfeit products.

On November 14, 2018, flamingunicorn commented on Machine Learning in Radiology: Threat or Opportunity? :

I agree with you saying that AI replacing radiologists is overblown. It’s a bit like saying that autopilot in planes will replace pilots – something that is unlikely to happen anytime soon even though most planes spend the majority of time in autopilot mode. A critical thing to remember is that radiologists are the ones training the algorithms, so in fact, we as a society ought to invest in the development of superior radiologists who could spot existing and new pathologies with superior accuracy so that we can have better algorithms for mainstream use.
As for the regulators, CDS (clinical decision support) is still a point of learning and evolution. CDS is currently bundled under Medical Device classification, but the guidance on whether a piece of medical software is a CDS or not is example/case based and in my opinion does not provide a clear enough criteria. If I’m correct Zebra Medical Vision was the first to have an FDA approved/CE marked radiology CDS that was approved this year under the medical device classification. As of now, new versions/retrained models of CDS do not have any fast track approval and must go through a new review process. Hopefully that at least is bound to change in the future as the technology becomes more mature and regulators have figured out the best way to harness the fact that ML based CDS has the potential to improve over time.

On November 13, 2018, flamingunicorn commented on Can Ford survive the disruption in mobility after enjoying Henry Ford’s business model for 100+ years? :

I’ve been watching OEM battles with deep interest, and the more I think about it the less I am convinced that Ford’s and GMs approach of getting into AV through acquisitions will prove to be successful. My primarily concern is the lack of in-house expertise and the furious war for talent. In this specific talent pool they’re competing with companies that software engineers really want to work for – Waymo, Apple and Tesla being the most obvious examples.
An alternative approach to buying expertise is a partnership – FCA and Waymo being the most obvious example. FCA provides fleet for Waymo and are already testing in multiple cities in the US. Now this approach has its downside – whether FCA will end up taking the AV market ultimately hinges on Waymo’s success. Which model – partnerships or acquisitions, or perhaps some combination of both will prove to be the fastest to market and ultimately the most financially sustainable in the long run only time will tell.

On November 13, 2018, flamingunicorn commented on BenevolentAI: Can artificial intelligence drive the next generation of drug development? :

Hi JP, I think the innovation in drug discovery using AI is extremely challenging, given the current data available for drug development. In addition to selection bias of trial data you already mentioned, a key issue in current BenevolentAI approach is the lack of negative results. The fact that scientists/clinical trials rarely if ever report negative results leads to massive survivorship bias that hypothetically at best would produce drugs with similar/same mechanisms of action and at worst waste precious resources by leading people into a search field of molecules that have already been proven not to work. I’m really curious to see how this field will develop in the future as it becomes less historic data dependent and more simulation driven. I think that a lot of exciting possibilities will emerge facilitated by the exponential growth in processing power and the possible arrival of quantum computing.

On November 13, 2018, flamingunicorn commented on Partners Healthcare and Machine Learning: Building Efficiencies in Diagnostics and Data Interpretation :

CDSS has a massive promise to improve access and quality of care, especially in countries with fewer experienced and qualified physicians per capita. Having invested and sat on boards in multiple CDSS projects I can attest that interoperability, i.e. inability to scale the technology across a network of hospitals that each have a unique IT system is the bottleneck to scale. Sadly, it’s the smaller and resource constrained hospitals [that arguably would benefit the most] that today lack the resources for integrating these IT solutions into their workflow. Unless we move to a truly standardised HCIT protocol, I doubt that it’s going to change.
Regulatory approval pathway and business model are still at their nascent stage. The initial FDA guidance paper shown a lack of appreciation and understanding for CDSS and its role in clinical workflow. I expect there to be many iterations in regulations over the coming years. The lack of clear regulatory pathway and aforementioned scalability issues have held companies and investors back from CDSS despite the great promise. I do think that that’s changing in the right direction and Partners as well as a couple of European hospitals are really leading the way, since they have the IT infrastructure, sponsorship and academic resources necessary for these projects to happen.
As for the natural language processing of EHR notes – it’s done already in some hospitals, but a scribing style of radiologists within hospitals can vary widely and therefore NLP models may not be as straightforward to transfer as purely image and demographics based systems. Even image based systems may not be – I’ve talked to some physicians who concur that phenotypic differences across regions might decrease the accuracy of a CDSS that’s trained on a single hospital data. I know that there have been efforts across the board to test model transfer across sites and geographies, and I’d be watching this space to see how it pans out.