I really enjoyed learning about how General Assembly is using open innovation. How GA is constantly adapting to market changes and continuing to improve upon its curriculum are very noteworthy. You posed the question on competition, and I am concerned about this issue as well. I wonder how GA is able to maintain its open innovation platform and the amount of users willing to collaborate, should other players arise. As you mentioned, in-sourcing the content creation may be crucial towards ensuring the continuous development and refining of content; however, universities have many resources through research and studies that could out-compete such in-sourcing. I also wonder whether GA has considered partnering with such research universities to help promote open innovation while establishing additional resources that these universities could offer. Thanks!
I thought your article on how Dubai is investing in 3D printed cities was very innovative. I could see how 3D printed cities could be developed given that the additive manufacturing process is advantageous when customization is required. I could also see buildings portrayed as works of art and how the consumer or household could directly impact the design of its future home. However, you posed interesting thoughts on the challenges of 3D printed cities. For instance, I understand the fear that 3D printing may replace the workforce, but I wonder whether 3D printing could actually be beneficial. Given that the appropriate training is provided, I still believe that this process will require an extensive labor force to help design and work with the software to create the exact specifications required. However, I agree on many of the limitations on the types of materials that could be used, the durability issues, and the mass production restrictions that 3D printing faces. Thanks!
I really enjoyed your article on how 3D printing is assisting with disaster relief. Thank you for shedding light on a company that is paving the way for providing supplies in disaster-affected areas. I understand the challenge posed in your question, and I think that the major drawback for additive manufacturing is the inability to mass-produce. While customization and development of replacement parts are crucial for disaster relief as well, it would be a significant improvement if supplies could be produced more quickly and efficiently in cases where many supplies are needed. You mentioned educating locals, and I believe that by improving the skills gap with AM, products could be developed much more quickly, helping with the mass production issue. I wonder, also, how to lower the cost of 3D printing and the software associated with it to allow locals to experiment with this technology. Thanks!
I thought your article on how ML and AI are assisting with higher quality imaging and more accurate diagnoses for radiologists was extremely interesting. You posed challenging questions on the extent in which doctors may rely on ML and AI, and at what point, should a medical error occur, should the blame be placed on the machine versus the human. Hopefully, at the end of the medical analysis, given the higher quality imaging and predictive diagnosis, if a radiologist has conflicting thoughts, then second opinions could be acquired to assist with final recommendations. Ultimately, while ML and AI can be extremely reliable and an efficient method for analysis, I believe that the human is still responsible for recommending a certain procedure. Further, there may be other emotional or external factors that could impact the recommendation, and I believe that the doctor will be able to more appropriately assess such complex elements. Thanks!
I really enjoyed your article on how machine learning is helping the movie industry, specifically Fox, predict movie success via box office sales. You pose an interesting question on how to incorporate script elements into the machine learning process. I wonder if feeding the tone through recording of the script by the potential actors could assist with understanding how consumers would respond to the movie. Another concern I have related to your second question on predicting creative content is how actor preference or genre of movie is incorporated into the machine learning algorithm. Many consumers have very unique tastes with specific preferences, so I wonder how movie producers can assess their willingness to invest in specific actors based on the predictive success of the movie. I would think that certain, more famous actors would yield higher box office sales; however, perhaps a more detailed data point could be whether that actor won any recent awards. Thanks!