This is a really cool application of AI. I see enormous potential in your point around partnerships with other companies looking to solve similar social problems using AI, for example with “precision agriculture” which is the technology that links sensor data on the ground to cloud service providers who analyze it. This could help in not only the food space to help farmers increase yield, but also in the space you mentioned around predicting and preventing natural disasters. I think the government will have to play a huge role to make sure the public policy environment’s rule will fit the transition to an AI future
The market for high performance athletic products, especially running shoes, is incredibly competitive at the moment. With this Adidas and Carbon partnership, Adidas has the advantage to be the first movers in the space of 3D printing. To be competitive, they need to target the right adopters, and that depends on whether they are after the serious runners or more casual all functioning shoe owners. The former group is a brand-loyal performance-driven group who care primarily about quality. If they are going after the latter group, I think Adidas has more room to operate to make this product a statement of novelty, with their campaign focused on the novel, “cool” aspect of the technology.
I agree that implications of machine learning in pathology and cancer care can be significant! Where I see this having a big impact is in the second opinion space, where today, many/most cancer patients when faced with a concerning diagnosis are suggested to receive a second opinion. The process of sending tumor samples and slides physically to different sites, let alone ensure there is enough tissue to run tests/review is painful and incredibly inefficient. Machine learning can not only help validate or quicken the diagnosis process, but the digital pathology platform for this can be used for so many applications beyond AI. To be able to share tumor slides digitally across institutions and even countries can be a saving activity when cancer treatment decisions and ultimately someone’s life is on the line.
This type of application of machine learning in the education has the potential to be incredibly powerful. I see the places that would benefit most from this application as the ones with the least amount of resources to obtain it (e.g. developing countries, inner cities, where the student/teacher ratio is unmanageably high for the classroom to be effective). After proof of concepts on these application, I would be curious to see if and how this capability will spread across education systems, through public taxes or private investments.
This is quite a relevant use case for AI. I am optimistic there is a lot of technology that can spot manipulated images or false stories, however, I agree with your concerns above. Practically to help the system learn, Factmata will need to create a very robust fact checking and validation algorithm. With the sheer number of and biased views in everyday media (even through reputable sources), I think a challenge will be understanding the subtle differences between fraud and biases, which even a human has difficulty deciphering.
I believe the answer to the last question of yours comes down to talent in recruiting. With the massive inequality in the demand and supply of data scientists, the winner of this game will be the one that is able to recruit and retain resources. Being under the large entity of Microsoft, LinkedIn right now has an advantage of attracting talent, and as more and more firms start moving towards a data-driven recruitment platform, I see this as the biggest challenge they must overcome for developing the best capability.