The automotive industry is uniquely positioned to truly take advantage of machine learning, which at it’s core requires a tremendous amount of data in order to work well. Most automotive companies have access to a huge amount of data, of course with their customers’ consent, and can use data from customers to feed their machine learning technology. In discussing the application of machine learning in cars, many are often frightened by safety implications. However, I believe machine learning channel and in fact improve safety by enabling the machine to think more like a human being, which can’t be possible through data collected from customers driving normal cars every day. Once again I very much enjoyed reading this article
Very interesting article and I enjoyed learning about the applications within the space context. While I believe in the costs benefits of this kind of technology particularly when I applied to areas of manufacturing that can be tremendously costly, I am worried about the quality of production today.
I’m thinking about space exploration, one must remember that in most countries, it is generally funded through public funds. The risk of failure or error is high and until quality can be assured, there will understandably be resistance to this innovation.
3-D printing is a double edge sword. On one hand, as the author describes, it’s can not only speed up the product development process, but can also reduce costs (ie, 10% at Nike). However, I do not believe the quality of 3-D printing today justifies it’s widespread use.
One might argue that the use of 3-D printing in the production and design of sneakers and running shoes is perfect. After all, a shoe does not need to be absolutely perfect, especially if Nike’s lower costs translate into savings for the consumer. My worry is more related to the use of 3-D printing in creating products that must be absolutely perfect, four instance in healthcare for medical devices.
I look forward to seeing the impact of 3-D printing on the Broadmoor retail market
I enjoyed learning about how a company in Brazil uniquely approached innovation through joint ventures, partnerships, employee-led idea generation and through venture-capital. The author describes how innovation was partly acquired through AGV with a start up, enabling the company to tap into the knowledge of innovative biotech.
However, the most powerful takeaway for me was how idea generation and innovation can be bottom him up as a post to top down. Bottom up idea generation not only tops into the full talent pool of a company to generate innovation, but also energizes and encourages employees to be innovative in their thinking. This key insight is critical for myself as I shape my own leadership style and the Razien model can serve as a role model for similarly large organizations that seek to innovate.
Well written article and I enjoyed reading about the applications within the legal profession. I agree with the author that technology assistance document reviews can prove to be quite helpful, particularly for certain use cases. For instance, this method can be quite useful when reviewing standardized, easy to understand contracts such as credit contracts. However, as the author points out, there are limitations to the use of this technology today; would an executive truly let the review of a merger document be handled by a machine as opposed to a trusted lawyer?
Despite my hesitations with the broad application of this technology, I believe there are tremendous suicidal benefits particularly for lawyers. It is commonly known that the legal profession is particularly cumbersome, with paralegals and lawyers having to go through many hundreds of pages of documents in great detail. Technology assisted document reviews can tremendously reduce the amount of time time lawyers spend on low-value added work, focusing on high value tasks.
Can innovation be acquired? I believe yes. General Mills strategy of acquiring innovation and knowledge through acquisition can be an effective tool for rather large organizations that are too large to truly innovate. As others have pointed out, this strategy is not unique to General Mills, but has also been pursued by others including Kraft.
The issue that I see in a large, want to stab list organization purchasing a much smaller agile and innovative company is the negative impact on innovation post acquisition. For instance, when Walmart acquired Jet.com, innovation slowed and Jet.com stopped growing at its historically impressive rate. While I do believe that large companies that acquire innovation do benefit, it remains to be answered whether acquisition also simultaneously stifles innovation.
Well-written article and raises a fascinating application of 3D printing. As the author points out, 3D printing applied to the housing industry can enable reduced building costs through standardization and automation, which can lower the barrier to home ownership with tremendous societal benefits. At the same time, I believe 3D printing can impact the housing industry beyond just lower-cost homes, including enhancing home customization and improving building planning. For instance, one can better incorporate floor heating, piping, electric planning of outlets and wiring through 3D printing because of the huge amount of design and planning required; compared to traditional building, 3D printing centralizes design and building whereas a traditionally built home may have several sub-contractors, the pitfalls of which have been well-documented. Some questions remain to be answered: How will developers react to this kind of disruption? Does location/topography prevent the use of additive manufacturing techniques? What is needed to quickly scale this technology?
Well-written article and raises interesting arguments on both sides of the use of 3D printing in the automotive industry. The mobility industry must become more affordable to improve quality of life globally, and certain applications of this technology may enable change. For instance, the use of 3D printing for repairs (e.g., producing spare parts locally using blueprint design vs. shipping parts from Germany) can reduce costs. However, safety is also critical and I’m still concerned by the quality of 3D printed products.
Captivating story to Back of House and a fascinating concept. Machine learning, at its core, works best in situations where historical and live data provides for largely accurate predictions of patterns. I would be curious to see how well machine learning can predict fashion–is historical data relevant in predicting future trends? Does ‘history repeats itself’ in the world of fashion? If machine learning truly works in fashion, it can be used to predict quantity of order placements on items using machine learning to predict expected popularity. At the same time, given the significance of celebrity influencers as the article points out, one may proceed with an algorithm that counts the number of times a set of celebrities is photographed wearing a particular newly released item, in order to take advantage of the time lag between the time x when a celebrity is seen wearing an item and time y when a mass of consumers seek to purchase that item.
Thank you for reading! Yes, I too am curious about how they’ll tackle multiple users who have different addresses, but use the same Amazon account. Which address will they ship to–perhaps they can use the IP address of the person who looked up the product?
Thank you Jonathan!