This is fascinating. One advantage of opening this kind data and innovation up to the public is that previously overlooked people can participate in this process. It may have been previously strictly the realm of the medical profession to help innovate in medicine and treatment, but opening up this to coders, mathematicians, philosophers…etc. can potentially tap overlooked expertise and perspectives that can lead to more innovation. Very exciting future of these kinds of projects!
The thing that most interests me is how data privacy will play a role in this model as more data sources are opened up for private use. More data is needed to get better insights, but how will governments protect data from being used against them, and how will future sources of data about citizens be anonymized for the protection of the citizens. What will the people think about having corporations searching through and analyzing information about their daily activities.
I believe the tipping point for 3D printing will be the speed of producing parts at a large scale. Today’s application of 3D printing is mostly for prototyping new components, which are then used to create injection molds and production parts at scale. I see a future where 3D printers are able to produce components at a much faster rate, approaching the time it takes to create components today, but without the need for additional tooling and injection molding required today. When that happens, 3D printing can begin the transition from the contemporary production process to a new 3D printing led process.
This is a very interesting paradox. Slack was built to foster communication across the organization. However, more communication can increase the “noise” within an organization and ironically decrease the flow of important information to the people who need it. The risk with adding machine learning to the mix is that information the machine does not deem important could be filtered out and overlooked. While you can make the case that this will happen once and then the machine will learn not to make that mistake again, there is the risk that the information is critical and if overlooked could cause serious harm to the company. Overall, machine learning looks promising in this regard, but it doesn’t come without risks.
I agree with Michael. Customized footwear is a major opportunity for two reasons. With 3D printing we are approaching a point where many products can be customized to your own preferences, which will shift the expectation for products towards customization wherever possible. I expect that the customizations will be focused on functional improvements, which will tailor the sole of the shoe to your foot and walking/running form, as well as cosmetic, which will be designing the color and shape of the shoe based on personal preference. Both of these offer a short-term premium for shoe companies, but may become table-stakes over the long-run.
While I understand why Caterpillar would want to enter into the Machine Learning space, do you think they can maintain high levels of performance over the long-term? It clearly makes sense in the development of better-performing machinery, as you mention. But I wonder if they have a sustainable competitive advantage in the broader use of Machine Learning for customer data and user experience, neither of which are core competencies. If Cat wants to go deep on Machine Learning, I believe they should build out this capacity as you recommend. Otherwise, I see this as a short-term, opportunistic advantage that will not have long-term viability at Caterpillar.
Very interesting. While I understand the benefits of using the untapped 90% of 3D printers, I wonder how sustainable this business is in the long run. As 3D printing becomes more advanced and automated, will the competitive advantage of the distributed network evaporate as manufacturers purchase their own 3D printers and have them run autonomously 24/7/365? It seems to me that major manufacturers would rather purchase their own printers and control the entire process rather than leverage a distributed network of printers with potential quality assurance issues. It will be interesting to see how 3D Hubs does!
Very interesting. My main concern with AI-driven diagnoses and applications in medicine is how the patient will react to the use of a machine. Patients already decry the evaporation of bedside manner in the medical care experience, with doctors’ noses buried in charts and laptops when the patient actually wants calm, assured attention. Will the expanded use of AI make it even easier for doctors to interact with their patients even less, further degrading the experience? My solution would be that if doctors become more accurate and efficient with AI and Machine Learning assistance, they should not be given additional patients. Rather, the extra time should be spent getting back to the basics of healthcare: patients.