This is such a fascinating concept, and I am glad you wrote about OpenIDEO. You bring up a good point about revolutionary ideas – and how crowdsourcing can break teams out of their usual thinking/ design processes. I would be curious to see how “open” the platform really is – who is able to contribute and what are their backgrounds? From what we have covered in class, it seems that true open platforms and crowdsourced ideas need to be open to people from various backgrounds and have very little overlap in terms of thought processes or preconceived ideas.
This is an interesting concept, and definitely something that gives a voice to consumers who feel that they have not been heard. However, my key concern is with the production process for cosmetics. Assuming consumers would like a certain product and it simply hasn’t been developed or is far too expensive to produce – will Volition still pursue it? Additionally, is Volition able to roll out this idea across various parts of the country – or even internationally?
This is a really interesting application of AM. I am curious to see if there is a way for Brooks Running to generalize certain runners that have ordered bespoke shoes into categories in order to make their product(s) available to the mass market (since it seems that they are not using AM to produce the actual shoe, but rather the prototype). Additionally, I see a key challenge being the customization. For example, if the shoe doesn’t fit (no pun intended), is the customer able to return the shoe to the retailer?
This is a great application of AM – and I am curious to see if there is a way to teach specific companies in developing nations a way to design their own AM processes so they are able to grow this technology and integrate it into their own production processes. I am also curious to see how the raw materials for each printed product are sourced and if this is potentially the key bottleneck in this production process (or if it is the technology that slows down production or advancement of this technology).
You make an interesting point here – and I would be curious to see if Facebook works with any other platforms in order to “bridge the (knowledge) gap” on certain key takeaways the way Palantir does. I am wondering if this is possibly also a legal issue that users can accept simply by logging in and accepting a “User Agreement” – or if this would counteract (and exclude) certain people who are most at risk/ would benefit the most from this type of AI. Additionally, there is another issue that Facebook will face – that of country-specific laws – which I believe will have a huge impact on how they proceed in the future.
You make a very good point about how AI and Machine Learning can be applied to healthcare and how one of the key challenges is applying it uniformly across different platforms (hospitals, labs, physicians offices, etc.). I would be interested to see if physicians are able to “dig into” the output that MySurgeryRisk generates – for example, if the algorithm spits out a positive outcome, would a physician be able to see (or even update) the different factors that go into the algorithm in order to make it more precise? I’d be curious to see if this is an option and how physicians and their inputs can improve the MySurgeryRisk algorithm over time.
This was an interesting piece on where you think Alibaba is going and how it is building out their capabilities and data analysis. I am particularly curious to see how the “New Retail Strategy” will pan out – especially since I am assuming a lot of these recommendations tie back to one of our initial discussions in class on how solid the input is with these algorithms. Do you think they will need designers or employees with a strong background in fashion to make recommendations – vs. basing recommendations on customers and their previous purchases. Maybe there is an option to assess how strong the incoming data is and how customers respond to these suggestions?
This is an interesting concept and something I’ve wondered about for a while. Their model is intriguing, but I think it is difficult to capture the true emotional benefit of buying behavior. A lot of purchase behavior is tied to psychology (or deep-rooted) thought processes that we may not be aware of. I’d be curious to see if they have looked into tying in psychologists from various backgrounds to build out initial consumer behavior trends and/or buying behavior. You also make a good point about “mixed effects modeling” which I do think is interesting because it ties in past behavior vs. (potential) future behavior and aspirations.
This is an interesting concept and I’d be curious to see how it pans out over the coming years. One thing I would like to see is how adaptable Wellio is to data put into the app. Will they try to loop in nutritionists or rely on data from their users. A lot of information is necessary, as you point out above, but the quality is definitely of the inputs is something I’d be concerned about as part of the Wellio team. Also, is Wellio going to optimize its recommendations for each person (based on only their data) or will they try to generalize recommendations on how it views its users across the board?