I like the approach of keeping fees low or zero to list on Valve’s Steam platform. High costs would keep smaller developers off the platform. I realize that Valve has to balance low cost with high quality, so I could see a scenario where Valve lends its developers to smaller firms for advice/assistance on creating really great games. Nvidia has a similar program that embeds their software engineers with companies working on machine learning applications, where those companies may not have the level of expertise necessary to achieve their objectives. VR has been a popular trend for a while but hasn’t had a big impact and I would love to see that happen.
I must admit I found the use of a specialized cryptocurrency to be odd, as there are plenty of good payment networks with good old money they could just use to achieve their objectives. That being said, I do like the decentralized aspect of paying individuals to find recyclable goods and depositing them at certified collection points where they can be exchanged for food, water and phone charging. Recycling is now not just an environmental issue, it is also a geopolitical one as China has sharply reduced the amount of recycling and garbage imported into the country. In the past, China was a major depot for recycling plastics and waste materials, and now many countries, the US included, must develop more innovative approaches to deal with their own environmental waste. There are both large challenges but also substantial revenue opportunities for businesses that can develop novel ways of managing recycling.
I like the idea of these tools being used to augment caregivers abilities to diagnose changes In their patients. It’s clear that we are not doing enough to use data and analytics to monitor our elderly patients. It is clear for my own experiences with my grandparents in nursing homes, That data and attentiveness are two things that the industry could sorely use. I am however a bit leery of generating all of this data, and the questions that ensue about who owns, retains, and gets to use this data. I think we would all feel uncomfortable if we knew that our data was not deindividualized, Or that this information was used to violate privacy. I agree with the comment above regarding HIPAA compliance. I would draw a parallel with the use of genetic data in medical care- (https://www.genome.gov/27026050/president-bush-signs-the-genetic-information-nondiscrimination-act-of-2008/), and the GINA act of 2006, which prevents discrimination on the basis of one’s genetic data. I think a similar standard should apply to nursing home and other sensitive health data.
I cannot tell you how much mind numbing data sifting one does as a Wall Street analyst! I would love the idea of tools supplied by S&P to make transcripts, financial data and company reports more accessible, data driven, and understandable. I am curious about the application of machine learning tools to investing. That has been a super popular field for a while, but I think investors broadly are just beginning to understand the implications of this. To your point around risks of a completely automated system, I think of something like the 2010 Flash Crash, where a number of automated trading bots malfunctioned and caused a substantial and rapid decline in securities prices, an event which took years to fully understand and disentangle. Great piece!
I agree with the thoughts above that physicians should not be experts in performing three dimensional printing, I view this medical service as a specialist technician’s job within a hospital or care facility, where the technicians produce the biomaterials and the physicians are responsible for surgery and healthcare after delivering the printed organs.
I am concerned with the long lead times and expensive development costs, particularly with respect to medical applications, which leads me to think they should focus on one or two high impact applications of the software. Great read!
I really enjoyed reading this article. I remember watching the movie Prometheus a number of years ago and wondering why humanity couldn’t build something like their artificially intelligent surgical robotics system, where the system rapidly diagnosed and performed surgical intervention. My biggest question is simply technical efficacy, and my guess is that this will take a long time to resolve. I think you are quite right that data and practice is a good way to ‘feed’ this system, I just wonder if you could get close enough to the ‘real thing’ to have the learning apply. I do like your proposals for no downside risk to the hospital system that is trying the device. Great read!
It makes sense to me to bring in the best and brightest to work on difficult transportation problems, which is why I think Walker should bring in contest winners and participants full-time. That being said, I don’t think there is any reason why Hyperloop should run this experiment only once- there will certainly be additional engineering challenges along the way, and as a comment above attested, there is always someone smarter, more creative, more insightful into addressing these complicated transportation challenges. Bringing additional problem solvers on board can only help, given the not only difficult engineering challenges but the formidable government obstacles to be overcome to make this program a reality.
I love the idea of applying bug hunting to government programs and providing bounties for hackers who find vulnerabilities. I wondered about Ash Carter’s quote above- particularly the security through obscurity part. Do you think there is a risk with adverse selection where the government does not ask for hacker feedback/bounties on more sensitive programs because of their bias towards obscurity, and so low level systems are well reviewed but more mission critical ones are not? Or that bureaucracies may not even ask for feedback on their programs for a fear of losing credibility or career risk?
I thought this technology could be really compelling for high end performance footwear and rapid prototyping. I could envision a scenario where a long distance ultramarathon team takes data generated while running and uses it to make tweaks to the digital files for the shoe and print new versions of the shoe during the race, adapting to the conditions on the go. Or imagine printing a new version of your cleats at halftime when it has started to rain or snow that have additional grip and support. I thought the cost breakdown was really interesting as well- had no idea that retailers took half the cost of the shoe. Very nicely written.