I don’t believe open Innovation will decrease NASA’s ability to develop more long term, complex projects (in opposition to the ones that can be tackled in “challenges”). I really like the idea of using open innovation to actually find and hire talent, and NASA being a company that needs to be at the edge of innovation all the time, it makes perfect sense. They can use the challenges to identify the best minds and make them a full time job offer, where they can focus on the long term research and development of more complex projects. Also, the way they started innovating within the Open Innovation space – allowing startups to use some of their patents at no cost until they are actually producing the products to sale, can also be a huge growth opportunity, as they can eventually make offers to acquire companies that develop very innovative and useful products
I believe Buzzfeed can actually use crowdsourcing to help validate the veracity of the information posted similar to the way it is done with blockchain for example – people get some kind of reward (can be status, discounts to partner’s merchandise or something in these lines) for verifying what is being posted on the platform, and the posts would gain an increasing rate of “reliability” as more people vouchered for it. Of course it is still subject to manipulation, but it is a way to start working on increasing the credibility of the platform in a cost effective way.
I really like the pioneer position VW’s is taking on using 3D printing more broadly in the automotive industry. I believe it can bring VW some competitive advantage that will definitely set them apart as innovative.
Regarding the question on who would be the target customers if people could design their own car, I can think of two main audiences: car enthusiasts – people that like cars as a hobby, who understand something about aerodynamics and mechanical engineering and would enjoy the opportunity to design their own vehicles, and people with some kind of disability, who could actually tailor the design of the car to better fit their needs
As mentioned in the article, it can be very difficult for a large manufacturer as Luxottica to change its whole production process at once. However, it is paramount that the company start working on the new process as it can really reduce production costs and enable new entrants to the market that, with a better cost structure, can actually end up performing better than Luxottica. I believe one approach that can be taken is to do it incrementally, starting with some brands that can capture more value from the customization process. For RayBan for example, you can already order a custom pair of glasses from their website (choose model, lenses, color, etc.) To include the option to actually customize the frame through 3D printing can bring great value to the customer and help improving brand perception and actually increase sales.
The two key challenges Chevron is facing regarding machine learning – 1) determining what data matters for decision-making and 2) physically and cost-effectively obtaining that relevant data – are actually challenges that virtually all companies entering the data analytics universe are facing. The same applies for the cyber-security risk. However, as we move to a more and more data driven business world each day, I believe companies have to focus on being ahead of its competitors to gain competitive advantage – or at least not to lose it – and using machine learning technology to do predictive maintenance can be a great source of competitive advantage for Chevron, so I believe the benefits here do out-weight the risks, specially when you consider the risk of falling behind its competitors.
I find the idea of using machine learning to evaluate the fair value of real state fascinating! The impact for the local governments and the tax payers can be huge and very positive, as stated in the article. I understand they still have a some refinement work to do on the model, but I wonder if we could extrapolate the use of this technology to help preventing events such as the 2008 housing market crisis for example. Back them, if there was an trustworthy algorithm indicating that there was a bubble and that the houses were actually overpriced, it could have been easier for the investors to understand how the mortgages were overvalued and that a market crash was imminent.
I found it very interesting learning about how machine learning can be used for such routine activities as dieting and exercising. People often think about the used of this technologies in a highly academic environment, but the applications in our daily life are endless.
Regarding the issue if data security, I believe it is a problem that eventually all companies will face – as we increase the availability of data online, both for storage purposes or sharing and crowdsourcing analysis and solutions, the risk of leakage and improper use of this information will increase, no matter how much we develop new security softwares and policies – given “hackers” will also develop new ways of breaching the security measures.
Of course developing new encryption methods is crucial, but I believe companies will have to face the trade-off of taking this risk versus the benefits this data availability can bring to their business and their clients