Feifei Shen

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On November 29, 2022, Feifei Shen commented on Insitro: Discovery New Medicines with AI :

Hi Patrick, thank you for your blog post! I believe this is an area where there is a great need to explore and deploy machine learning to streamline processes and time costs for the pharmaceutical industry. I’m curious if they are starting to look at potential partnerships with drug discovery companies. They seem to need large databases to be able to generate appropriate models for specific disease areas, and I wonder if they plan to focus on a specific class of diseases or if they want to start with broader terms.

On November 29, 2022, Feifei Shen commented on IKEA’s Leap Forward with Data and AI :

Hi Jiwon, thank you for your Blog! It was intriguing to learn how IKEA is using artificial intelligence and data not only for their decision making and forecasting, but also to improve their customer experience. It also reminded me of the example of Starbucks. They are both collecting a lot of customer data to make seasonal changes and improve the experience compared to their competitors. I’m curious to see how the data collection is adapted to the local context and how IKEA is implementing all the new digital and technological aspects into their traditional business model step by step.

On November 29, 2022, Feifei Shen commented on Plantix: Bringing Crop Science Machine Learning to Millions :

Hi Joseph, thank you for your blog. The topic of machine learning and agriculture is novel, but very interesting to me, and it’s really inspiring to see the potential of machine learning that can be applied to the diagnosis of crop diseases. I was wondering about the challenges you mentioned regarding phone signal, location and image accuracy. Do they have a team of agriculture experts to help correct the image data for diagnosis, as we learned in the VideaHealth case before, on the human support side? Or do they rely entirely on algorithms?

On November 14, 2022, Feifei Shen commented on Free but Not Equal? :

Hi Jiwon, thank you for your post! It’s disappointing that these hidden biases are really obvious, but when they try to train AI, no one really thinks about fixing the problem and even leaving the problem for the public.

On November 14, 2022, Feifei Shen commented on Forest full of trees :

Hi Kate, thank you for sharing these generated images. You suggested that narrowing the focus of AI learning could focus on interpretation and meaning rather than just facts and the representation of simple commands. I think learning to interpret like a real person can be difficult because different people may interpret the same thing slightly differently, especially when it comes to visualization.

On November 14, 2022, Feifei Shen commented on Concrete vs. Abstract? :

Hi Riya, thanks for sharing these generated images. I like that you are trying to engage the AI with abstract and concrete words to test its understanding. I think more training and iterations are needed to get the AI to understand abstract words. However, since abstract words may also be interpreted differently between people, the AI may never properly pick it up.

On November 2, 2022, Feifei Shen commented on Peloton’s Shift to Multidimensional Brand at the Right Timing :

Hi Jiwon, thank you for your post! We discussed in another class about how Peloton was expanding their business from hardware connected stationary bikes and treadmills. It was interesting to see how they were incorporating this connected fitness subscription on their virtual platform in order to provide better fitness experiences for people at home.I was wondering how this subscription platform and course could be further expanded to include a connection perspective with other subscribers, such as a social interaction perspective, in addition to personalization and connection with instructors on social media.

On November 2, 2022, Feifei Shen commented on ClassPass: Fitness Everywhere, for Everyone :

Hi Amy, thank you so much for your post! I enjoy reading about this new way of visiting different gyms, fitness studios, salons and spas in different locations. It is really convenient for people who often need to travel a lot but still want to stay healthy. However, I wonder if it is convenient and motivating enough for people who live in one place often enough that they want to get into a local gym nearby instead of traveling long distances to a new place to try a new class. Also, if they keep the same options every time with slow updates, does their loyalty remain as other studios are creating monthly passes as well?

On November 2, 2022, Feifei Shen commented on Win-win, creating a win-win from downstream to upstream in retail :

Hi Gigi, thank you for your post! I learnt about Win-win and it strategy of collecting data from fragmented, offline channels and chooses product categories with relatively low online sales penetration in order to avoid the competition with other tech giants and it also provide the huge space on scalability and sustainability. I also really appreciate the business model that win-win can provide mutual learning and benefit in order to build a sustainable foundation and trust relationship in the long run. I’m curious if a similar business model can be applied to other industries and if it will have a similar impact?

On October 4, 2022, Feifei Shen commented on Leveraging Big Data at Ibotta :

Hi Elizabeth, thank you for sharing the post! From your blog I learned about Ibotta and how it provides cash back for brand products in order to market the products. I’m surprised that the data generated from brand purchases can also be used for so many different things, such as market trends and shelf location validation. Since you are talking about receipt data generated without a brand partnership, I wonder if this also creates more opportunities for Ibotta to partner with in order to reach out to those potential brands. If the brand is a competitor of the company they are currently working with, what do they do with those data.

On October 4, 2022, Feifei Shen commented on Penguin Random House: Can it beat Amazon at its own game? :

Hi Nitya, thanks for the blog post. It’s really fascinating to see how Penguin Random House is using data to improve their reader experience, ensure visibility, and drive traffic to their own site. However, Amazon is a disruption to the traditional book market and it seems like a challenge to beat Amazon. Since PRH already has experience with the data. I’m curious if it would be a better option for those traditional book publishers, like PRH, to try to work with Amazon, or even with their Penguin Rewards system, to get more loyal customer data under a big database to attract more traffic to their own place?

On October 3, 2022, Feifei Shen commented on Airbnb: Anywhere is ̶h̶̶o̶̶m̶̶e̶  DATA. :

Hi LMB, Thank you for sharing this airbnb example. As an airbnb user, I can relate to how they have made the selecting process easier over time with different filters and recommendations. You pointed out how data analytics can benefit both customers and hosts to provide a better user experience. I think that is an important key to leveraging data. You also mentioned the limitations that make airbnb challenging in terms of data; however, I’m wondering if that’s how they’re trying to differentiate themselves from booking.com? I also curious about whether they are using data to differentiate themselves from other competitors in the market.