Tomas Musich's Profile
Really enjoyed the piece. Closed innovation makes a lot of sense when you have a position of total leadership and access to resources plus data. This can be very powerful to sustain and even increase your competitive advantage. I think open innovation just has a different use case and is very helpful when you do not possess the capabilities, financial resources, a culture of innovation, and/or time to develop new technologies to keep being competitive. For me, one common example is when we see companies setting up corporate venture funds to invest with more agile startups to jointly develop new technologies/products. This also works through minority investments + later acquisitions.
I share the concerns of the mismatch between the speed of regulation and with innovation. I also fear that regulation could also get push backs from existing players, delaying, even more, the permissions to Organovo. In connection with the company’s need to generate revenue, I would strongly argue against going into cosmetics because I think it will dilute its mission and harm its credibility. I think there are probably many other product lines to develop leveraging on the existing platform (other organs skin, etc). Another way could be to sign contracts with existing companies to develop other organs development. This would not only advance them in terms of product development but also secure a flow of revenues from the partnership.
Love the idea of making the drug development cycle faster to develop better drugs that can reach patients that can´t wait faster. One of the concerns I do have about the current process you describe is how the company can secure a constant supply of human cells to “feed” the printer (I guess partnerships with academic and medical institutions will become more crucial). My other concern is around regulation and if there exists a scenario in the future were this could replace human testing in the FDA drug development cycle. Overall, I think this technology is amazing and has a huge potential for the industry. Great essay!
Great essay. Truly believe that healthcare has a lot to benefit from ML, especially in a context where costs keep rising and medical attention becomes a “luxury” good for people with financial stress. Mike pointed out an interesting aspect in his comment above which is where do we get the data from. Thinking ahead in the future, there will be a great opportunity to capture more relevant data in an automatic way with the widespread of wearables and the increasing sensors added to smartphones. This will add a lot of relevant automated input that could be used by health providers to prevent and/or act faster in emergencies. But I do see a big challenge in terms of privacy (as you also mentioned in your essay) not only on how we are going to keep that info secure but also on how much consumers are willing to share with health providers.
Very interesting article. I’m actually a (heavy) user of Grammarly since I’m not a native English speaker. I do share your concern of how much the company should rely on crowd-sourced data and the constraint of having the recommendations checked by paid manual editors. That said, I do believe there are contexts in which grammar mistakes (or informal choices) do not matter that much such as informal communications, chats, etc. But there are other types of situations in which even one mistake or informality has a great cost (academic writing, cover letters for a job, to mention a few). Grammarly does offer in its platform the option of choosing your intent, style, and audience, and I believe that it should try to invest to get the error rate close to zero at least in a formal context of writing. Overall, I think the product is great and it is evolving greatly due to ML and the spread of use among users.
As you mentioned in your blog post the dangers of the vicious bias loop in the algorithm are quite considerable. Effectively, if we feed biased data to the machine, it will output biased recommendations. But putting this aside for one moment, I´m also concerned that if we assume that past performance can always predict future tastes, we are not giving an opportunity to the scenario in which a consumer’s preference actually change (maybe because she/he is at a different stage of her/his life and prioritizes different things). I think the algorithm should also have some sort of adjustment considering the lifetime of the user to avoid this loss in quality of prediction.
I share the belief that AV will be a staple of our transportation system, and very probably dominate it. My biggest concern is about the capacity of the algorithm to make tough ethical challenges such as for example taking a self-inflicting damage driving decision to avoid crushing and killing more people (choosing the “lesser” evil/bad outcome). And although this can get subjective (what is the lesser bad outcome), the image recognition functionalities should be working at 100% with absolutely no chance of mistakes (google in the near past has been having trouble with image recognition https://www.theregister.co.uk/2017/04/19/cloud_vision_api_defeated_by_noise/). That said, I think one of the benefits of the model is that it will increase car utilization, thus reducing the number of cars on the streets and the levels of pollutions caused by cars.
Although Amazon can definitely be a threat in the future, I think that it has played its bet on food by acquiring Whole Foods last year. Considering the size of the deal, and the work to be done in terms of integration with Amazon, push for amazon fresh, etc. I think that Kraft Heinz should be more worried at startups disrupting specific high-value categories (e.g Just with its plant-based mayo: https://www.justforall.com/en-us/products/consumer/mayo). One way to defend against this type of disruptive competitors in terms of speed of development is to have a corporate venture fund from which Kraft can invest in companies doing interesting innovations and potential buy them in the future (way of diversifying R&D and speeding up innovation process). As a matter of fact, Kraft announced a month ago the launch of a $100M corporate venture fund to invest in tech companies doing interesting stuff in the food space: http://ir.kraftheinzcompany.com/news-releases/news-release-details/kraft-heinz-announces-launch-100-million-venture-capital-fund
I think machine learning makes a lot of sense for this type of business model but, as you pointed out, there is a considerable risk when fashion companies try to use past preferences to predict future tastes. I would also be concerned about how the algorithms are factoring the differences of style between different locations, and the shift in styles from season to season (I wonder if this would make the learning part of the algorithm back to moment zero). Because of this, I believe that the concept of augmented human makes a lot of sense when deciding future decisions of dress purchases and predictions of rentals.
Where I do believe machine learning can have a lot of impact is on supply chain management, specifically on the estimation of returns, time adjustments for cleaning the dresses, and period of time that one thinks the dress will be amortized (how many rentals before the dress losses all its value).
One last thought is that if RTR pushes more and more brick and mortar spaces it will lose its capacity to gather more data (doesn´t have a history of clicks, for example) and train the algorithm to make better predictions.