TomB

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On November 14, 2018, TomB commented on Open Innovation at the Bank of Canada :

I really like the idea of using open innovation at the BoC. I do agree that cybersecurity as a topic for one of their first three challenges is not what I would have expected, but feel that a diverse set of ideas on the topic can help the BoC internal cyber team to build stronger protections against threats. I do not think that open innovation can replace internal innovation and controls, but believe it serves as a great complement to internal initiatives, and requires less resources and capital. In today’s environment of rapid advancement and innovation, I think that the BoC should focus their next few PIVOT challenges on evaluating the best-practices of international central banks. I think evaluating other economies, their history of bank policies, and the public/stock market reactions to various policies, and especially how those banks are innovating in today’s environment, could spark new innovation within the BoC across a variety of topics from cybersecurity to communication to policies.

In this highly competitive environment, I think it is critical for Unilever to try to identify promising brands and acquire them earlier in their lifecycles at more reasonable valuations. Leveraging The Foundry and Unilever Ventures is a good way to do this, providing both a benefit to the startups of mentorship, brand name, marketing advice/support, and logistics support while reaping the financial, open innovation, and synergistic benefits of the startups. [1] A lot of large companies, such as Alibaba, Rakuten, Google, etc are forming internal venture capital arms to compete in this space, and thus it is very difficult to differentiate yourself and win deals.

[1] http://www.unileverventures.com/.

On November 14, 2018, TomB commented on IBM Watson for Cybersecurity Takes Center Court at Wimbledon! :

It was great to read about the success of IBM QRadar and Watson, both in terms of speed (60x faster than human analyst) and preventing any breaches on the Wimbledon website. [1] I hope that all companies are taking the necessary steps to protect their own and their consumer’s information, as we have seen the terrible implications of cyber attacks on the reputations of Target, Under Armour, Yahoo, Equifax, and others, where consumer trust is hard to gain and earn back. I do feel that the vast majority of companies today are taking this threat very seriously, and there have been a lot of startups being backed by venture capital firms that attempt to address cybersecurity issues. The big challenge I see with machine learning and artificial intelligence to tackle this issue is that while machines are excellent at processing and analyzing historical data, the most threatening cyber attacks are unprecedented, and therefore likely a challenge for machines to detect based on historical information. I am also concerned about heightened personal cybersecurity for individuals, as today, most people store all their personal information on their personal computers and phones, and most individuals don’t have the means to purchase/attain personal cyber protection.

[1] https://www.ibm.com/case-studies/wimbledon-2017.

Great article! Given that Align only holds a 10% market share of the orthodontic appliance market, I think there is massive whitespace for growth. [1] However, they need to move incredibly quickly, as Danaher, 3M, and other large conglomerates have deep pockets and are likely highly attracted to a product that is highly complementary to their existing product portfolio and that they can charge $3,000-$8,000 for and make for a fraction of the price, achieving high margins. Given that 40 of Align’s patents expired in October, I think they need to immediately diversify their supplier base and try to lock in long-term contracts. [2] With regards to your first question, I think it will be hard to maintain the pace of innovation that led to their success thus far, as additive manufacturing is decentralizing. However, I do think they can use their existing products and apply learnings to adjacent technologies. For instance, they can start working on dentures and teeth implants, where they work closely with top dental researchers to advance innovation in the overall dental field. This being said, I think there is a lot of room for growth with their existing technology, and they should focus on maximizing their returns on Invisalign while secondarily innovating in new areas.

[1] https://www.forbes.com/sites/michelatindera/2018/05/02/bracing-for-competition-cheaper-challengers-enter-invisaligns-1-5-billion-market/#4832fb1a2392.
[2] https://www.forbes.com/sites/michelatindera/2018/05/02/bracing-for-competition-cheaper-challengers-enter-invisaligns-1-5-billion-market/#4832fb1a2392.

On November 14, 2018, TomB commented on Google AI: Predicting Heart Disease in the Blink of an Eye :

In 2014, approximately 12 million Americans were misdiagnosed (~1 out of every 20 patients) and ~50% of those misdiagnoses resulted in real harm. [1] While I do worry about the risk of hidden machine bias and potential incorrect diagnosis in the field of medicine, I would hope that the use of machine learning and AI will improve the misdiagnosis rate through continuous improvement as more and more samples are added to the studies, creating a stronger, more accurate feedback loop. I do think that machine learning and AI are capable of transforming the healthcare industry and delivering simpler, more accurate diagnoses and treatments, but I think it is critical to retain human contact in the healthcare industry, as interaction with highly trained doctors brings patients comfort when undergoing trying or stressful times.
With regards to your second question, I would be willing to share my health data with Google to help improve their algorithms and contribute to better, simpler, more accurate healthcare diagnoses and treatments. However, I would only do this if Google kept everything I shared with them highly confidential and it was on a no-names basis, as this information is highly personal.

[1] https://www.cbsnews.com/news/12-million-americans-misdiagnosed-each-year-study-says/.

On November 14, 2018, TomB commented on Google AI: Predicting Heart Disease in the Blink of an Eye :

In 2014, approximately 12 million Americans were misdiagnosed (~1 out of every 20 patients) and ~50% of those misdiagnoses resulted in real harm. [1] While I do worry about the risk of hidden machine bias and potential incorrect diagnosis in the field of medicine, I would hope that the use of machine learning and AI will improve the misdiagnosis rate through continuous improvement as more and more samples are added to the studies, creating a stronger, more accurate feedback loop. I do think that machine learning and AI are capable of transforming the healthcare industry and delivering simpler, more accurate diagnoses and treatments, but I think it is critical to retain human contact in the healthcare industry, as interaction with highly trained doctors brings patients comfort when undergoing trying or stressful times.
With regards to your second question, I would be willing to share my health data with Google to help improve their algorithms and contribute to better, simpler, more accurate healthcare diagnoses and treatments. However, I would only do this if Google kept everything I shared with them highly confidential and it was on a no-names basis, as this information is highly personal.

[1] https://www.cbsnews.com/news/12-million-americans-misdiagnosed-each-year-study-says/.
Evaluating patient health using AI and machine learning introduces the risk of hidden machine bias, which may result in incorrect diagnoses. How can Google use AI and machine learning to support medical professionals in their clinical work without relying too heavily on the machine to determine important patient information?
Machine learning in health care requires a significant amount of data from healthy and unhealthy patients to improve the accuracy of the algorithm. Would you be willing to share your health data with companies like Google in order to support the development of machine learning algorithms for applications in health care?

This is incredible! Not only does Contour Crafting reduce the cost to build a home by 1/5 and completes the process in ~1 day, it also reduces waste, minimizes accidents at home construction sites, and limits imperfections in the structure that would require more capital and time to fix in the future [1]. As with most other 3D printing applications, a concern is the 10.3 million construction jobs in the US and more abroad that will be displaced with jobs that require higher education and engineering expertise. [2] This is a big concern given rising inequality in the US and beyond. [3] Overall, I think that this is an amazing invention and a great application of 3D printing. I do wonder how expensive the machines are to produce and what the economics are to Contour Crafting. In any case, the government should be willing to subside some of the costs given the extreme social and environmental benefit. Thank you for posting!

[1] http://contourcrafting.com/building-construction/.
[2] https://www.statista.com/statistics/187412/number-of-employees-in-us-construction/.
[3] https://www.cnbc.com/2018/07/19/income-inequality-continues-to-grow-in-the-united-states.html/