S.Finch

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Applying Machine Learning for the Common Good – Is it Always Worthwhile?
S.Finch
Last modified on November 13, 2018 at 2:16 pm
How do we thoughtfully use data to increase efficiency for the greatest amount of people in a sector that has historically and exclusively been driven by human judgement? Where is human oversight non-negotiable?

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On November 14, 2018, S.Finch commented on Babel Street: The Cacophony of Social Media is the New Newsroom :

I thought the content in this piece was accurate and thoughtful. I have used Babel Street technology before and can attest to the speed, efficiency, and general common good that it provides. So, thank you for the holistic view point. The last question you raised is critical and has been a central debate in policy, dating back to 2003 and the release of the Patriot Act. U.S. policy has simply not caught up with technology yet. As AI advances, the federal government’s response lags behind. Regardless, I believe it won’t be until some type of standard (law/policy) is set that this fundamental, and arguably philosophical, question can be answered.

What I found particularly compelling about your piece was your underlying point regarding speed at the tradeoff of human judgement. While at first glance this may seem enticing, I am hesitant to believe it will be albeit of profiling. I wonder in what ways you can restructure the learning to only raise topics of a certain confidence level – much like Watson. To that point, I wonder at what threshold of crime, crisis, or general situation, utilizing this technology makes sense and does not infringe on citizen’s rights.

User deleted this comment on November 15, 2018
On November 14, 2018, S.Finch commented on Babel Street: The Cacophony of Social Media is the New Newsroom :

thought the content in this piece was accurate and thoughtful. I have used Babel Street technology before and can attest to the speed, efficiency, and general common good that it provides. So, thank you for the holistic view point. The last question you raised is critical and has been a central debate in policy, dating back to 2003 and the release of the Patriot Act. U.S. policy has simply not caught up with technology yet. As AI advances, the federal government’s response lags behind. Regardless, I believe it won’t be until some type of standard (law/policy) is set that this fundamental, and arguably philosophical, question can be answered.

What I found particularly compelling about your piece was your underlying point regarding speed at the tradeoff of human judgement. While at first glance this may seem enticing, I am hesitant to believe it will be albeit of profiling. I wonder in what ways you can restructure the learning to only raise topics of a certain confidence level – much like Watson. To that point, I wonder at what threshold of crime, crisis, or general situation, utilizing this technology makes sense and does not infringe on citizen’s rights.

On November 14, 2018, S.Finch commented on Block by Block: Harnessing Open Innovation at The LEGO Group :

I really enjoyed this post. It was nostalgic, interesting, and gave insight to the ways that LEGO has stayed relevant for decades. Your question on over-reliance on open innovation is a thought-provoking one. Given the success of open innovation thus far, and given the main users of the product, children, I think it will be key to LEGOs success moving forward. To answer it directly, I do not believe that they are over reliant, so long as internal teams continue to sort and analyze with future priorities and trends toward web-based content in mind. As you’ve noted, LEGO has brilliantly utilized the knowledge of the customers they care most about in a genuine and authentic way. Where I think LEGO can improve/continue progressing, is through thoughtful and intentional success metrics within their open innovation structure. Is it always cost effective? Are the ideas sourced and used radically different from strategy teams? I think structured evaluations of the process are key to solidifying the points you noted about internal guardrails.

On November 14, 2018, S.Finch commented on 3D Printing and Humanitarian Aid, Will the US Learn from Past Disastrous Response? :

This is an incredible and thoughtful use of additive manufacturing – and a well done piece! I am confident in solar energy to ensure sustainability and a power source. I really enjoyed your emphasis on economic stability and investment in the local communities. I believe this has been missing from both the narrative and the strategy of developed countries providing disaster relief for the last 2 decades. To your question, and as great as an idea as this may be, I fear for it’s viability. That is rooted in questions of access to the technology, and the immediate needs during disaster relief. On the latter point, from my experience, immediate needs (which would presumably be those filled by this fast acting 3D printing) are items which would could NOT be printed through this technology. As you mentioned, these items include food, water, diapers, medicine etc. However, I do not believe this makes the technology obsolete. I would simply argue that it is more applicable in the 5-10 days after a disaster strikes. On the first and more important point, given that disaster relief is often political, and often strikes countries with lower socioeconomic populations, I worry the presentation of 3D printers is altruistic, due to both the lack of access printers, and inability to access, ship, and utilize these printers in disaster zones.

On November 14, 2018, S.Finch commented on LEGO: Leveraging the Building Blocks of Open Innovation :

To you second question, I think that LEGO has to target both parents and adults. As shown through your thoughtful research on nostalgia, the population of young adults and adults that still hold tight to their childhood favorites, and in fact still find a great deal of value in these items, is higher than ever. Allowing for continued open innovation and responses, and continuing to target this group, will increase brand loyalty for generations.

As I think through the next generation of parents and toys, I think that LEGO has to adjust to trends within parenthood in order to stay relevant. Parenting at large is moving towards a world where toys must serve some type of educational purpose. Parents want to capitalize on all of the time their child has. Thus, it would be critical for LEGO to bridge the gap between their web based presence and their material toys. In order to do this, I think they can leverage their commitment and footprint in open innovation and allow for parents to collaborate with their teams to deliver the best, most beneficial lego toys moving forward.

On November 14, 2018, S.Finch commented on Will 3D Printing Change the Future of Homebuilding and End Global Homelessness? :

In thinking through the questions posed, I question the applicability and viability of this idea. In my opinion, making an impact on the homelessness crisis would be extremely difficult. For one, this would require real estate and capital from the government (or some other willing body) to build such structures. Secondly, homelessness comes with a plethora of other extraneous issues (job stability, lack of government documents, social and family stability and support etc). While a home is a critical piece to the puzzle, in order to solve for homelessness at large, other key actions need to be taken.

To the question about revolutionizing homebuilding more generally, I don’t know that additive manufacturing is the most efficient vehicle at this time. Given houses’ complex nature (utilities, basements etc) and the presumable lack of printers able to carry out this task (and at scale), I would guess that this is more of a niche market and will fall into the same category that ‘tiny homes’ are currently.

User deleted this comment on November 15, 2018
On November 14, 2018, S.Finch commented on Babel Street: The Cacophony of Social Media is the New Newsroom :

I thought the content in this piece was accurate and thoughtful. I have used Babel Street technology before and can attest to the speed, efficiency, and general common good that it provides. So, thank you for the holistic view point. The last question you raised is critical and has been a central debate in policy, dating back to 2003 and the release of the Patriot Act. U.S. policy has simply not caught up with technology yet. As AI advances, the federal government’s response lags behind. Regardless, I believe it won’t be until some type of standard (law/policy) is set that this fundamental, and arguably philosophical, question can be answered.

On November 14, 2018, S.Finch commented on Spotify is better at picking your music than you are, but will they always be the best? :

I believe there are absolutely opportunities for Spotify to continue advancing. This piece really drove me to think about machine learning’s intersection with the human psyche and human emotions. I agree with Petra’s point above, that the ‘preferences’ are limited due to the scope of songs the listener are exposed to. I also argue that Spotify seems to provide the sought-after model, and perceives to have a competitive edge, because there is no best alternative. Music is emotional and often creates an emotional connection between the listener and the song. Furthermore, human’s taste song by song is often particular. While they may enjoy a few specific genres, their enjoyed songs per album listed may vary dramatically. As machine learning progresses to better understand emotions and fickle human preferences, I think Spotify will have to adapt and incorporate these to keep any competitive edge.

User deleted this comment on November 14, 2018
On November 14, 2018, S.Finch commented on Spotify is better at picking your music than you are, but will they always be the best? :

I believe their are absolutely opportunities for Spotify to continue advancing. This piece really drove me to think about machine learning’s intersection with the human psyche and human emotions. I agree with Petra’s point above, that the ‘preferences’ are limited due to the scope of songs the listener are exposed to. I also argue that Spotify seems to provide the sought-after model, and perceives to have a competitive edge, because there is no best alternative. Music is emotional and often creates an emotional connection between the listener and the song. Furthermore, human’s taste song by song is often particular. While they may enjoy a few specific genres, their enjoyed songs per album listed may vary dramatically. As machine learning progresses to better understand emotions and fickle human preferences, I think Spotify will have to adapt and incorporate these to keep any competitive edge.