It is interesting to consider that neural networks are inspired by the complex biological processes underlying human thought and perception. These machine learning models mimic the way biological neurons process inputs within their specific receptive field. The synapse of a neuron applies a specific weight to each input, and then an activation function is used to determine whether the aggregated input is sufficient to activate the neuron and propagate the signal. Science has progressed to the point that we understand the underlying mechanisms of this biological process, but the sheer amount of neurons and synapses in the system makes it difficult to fully understand how the pieces all come together.
I believe artificial neural networks do emulate the basic processes of human thought, but our current understanding of the human brain is so limited that I am not surprised that artificial neural networks are just as esoteric.
Facebook’s use of wild data to train its image recognition models is an interesting and somewhat risky decision. User-generated hashtags often cover a range of topics that relate to the image content in an abstract sense, such as an emotion that the image evokes or something personal to the user. From my experience, it seems like people do not usually tag concrete objects or details within photos, and these would be more generalizeable and of interest for accessibility applications. I find it surprising that the accuracy of Facebook’s image recognition models improved with the use of wild data, but this is a promising opportunity for the company to access a larger pool of data.
The issue of user privacy is very sensitive, especially given Facebook’s recent and widely publicized scandals. Users will likely react strongly to any perceived breach in privacy, so Facebook will need to anticipate these challenges and talk to its audience early to control the message.
The Genome Project is a tremendous opportunity for innovation in precision medicine and for research into understanding human health within the context of the whole person. However, this does raise several critical issues surrounding privacy and ethics. To your point, contributors to The Genome Project are made aware of the implications of their decision; however, this information could potentially be used to identify individuals, and while the contributors may have agreed to this, their families and descendants may not be willing to make this information available to the world. As they share genes with this individual, these people are relevant stakeholders, and yet they may not have a choice in the matter. This decision then continues to affect the family for generations to come.
In the near term, as long as the researchers make a reasonable effort to preserve the anonymity of the data collected and commit to this project’s scientific integrity, I do not see genetic discrimination becoming an issue.
The main barrier I see to open innovation initiatives in shaping U.S. policy is inertia on the side of the policymakers themselves, rather than the difficulty of building an efficient open innovation platform. Back-end efficiency, as you put it, may be the only practical application of open innovation given the partisanship of governmental bodies. Systems such as petitioning and lobbying are, in some sense, a version of open innovation–albeit a more diluted and perhaps inefficient version–and it seems difficult to conceive of a system in which ideas that are any less publicized and well-funded would gain traction. I agree with you however that the process of shaping public policy needs to be improved, and it would be interesting to see whether the government is open to implementing crowdsourced solutions.
Additive manufacturing’s benefits in the research and development phase are very compelling and, in Medtronic’s case, added a great deal of value to the ideation and prototyping process. I agree with Howard Hughes’s point above that the capital investment for 3D printing technology is relatively high, but to your point, this technology holds the potential to revolutionize organ implants. This possibility is certainly an attractive and important prospect to keep in mind, but this level of innovation will take a long time to become first feasible and then applicable. The healthcare industry is very risk averse, and the legal and ethical implications of this are complex, so the time horizon may well extend much longer than 10 years–at which point, additive manufacturing might already have become obsolete.
I agree with the other members of this discussion that additive manufacturing is a key technology for the medical device industry; however, this technology seems more suited to the research and development phase rather than the production phase. 3D printing is already widely used for rapid prototyping in the medical devices industry, which leads me to disagree with the notion that it currently lends Stryker a competitive advantage. I would be interested in seeing whether the company applies this technology in some unique way–whether that be at a lower cost, shorter lead time, or other innovative application–in order for additive manufacturing to truly be a differentiating factor for the firm. Scaling this process for production will likely become more efficient over time, but whether this is a worthwhile investment for Stryker in its current state depends on whether it also innovates the additive manufacturing process itself, instead of just using it as a tool to build its product pipeline.