James Eckfeldt

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On November 13, 2018, Thomas Shelby commented on Ford: Using Machine Learning To Humanize Vehicles :

Very interesting article. I believe that his technology’s biggest application would be regarding safety. Imagine a car that could detect if a driver is having a heated argument, or falling asleep, and could consequently deploy a safety mechanism to take over and avoid an accident. Sounds like a winner for everyone to me. However, to what extent will machines ever be able to do this effectively? What if I’m driving someone to the hospital in an emergency? Would the car assess my stress level and facial expression as “danger” and take over? You touch on a really interesting point, which is the extent to which we want to surrender control to machines. It’s a slippery slope that we don’t know much about and that could have catastrophic implications.

On November 13, 2018, Thomas Shelby commented on Are Algorithms the Hiring Managers of the Future? :

Even though I agree that this is a great tool to assess candidates general skill set and narrow down the number of applicants, I still think that Pymetrics has a long way to go in the candidate selection and assessment industry. First, it seems that they are providing a general aptitude test, something that lots of companies already do. Yes, maybe their algorithms are better at matching and predicting future success for candidates, but I don’t see a highly differentiating feature yet. Second, they are completely leaving past experience out of the picture. For positions where past experience is not relevant (i.e. entry level), this could be a great tool. However, for more senior positions, as a hiring manager, I would not rely on this tool to screen out candidates for me. What if the candidate had a bad day and scored low in a couple of parts of the test? I would advise Pymetrics to develop algorithms that could also take into account or screen candidates’ resumes for relevant experience.

I believe that the Task Rabbit acquisition by IKEA will be great for both in the long run and don’t think that Task Rabbit’s identity will be impacted in any way whatsoever. First of all, I’m positive that almost no one knows about this and it IKEA has not advertised anywhere in Task Rabbit’s webpage. Moreover, I would argue that Task Rabbit’s main value proposition is around democratizing access to handymen specifically. Even though “taskers” can also provide other services such as delivery or grocery shopping, the vast majority of services offered by the company are related to classic handyman tasks such as moving, furniture assembly, repairs, painting, installations, etc. This said, even if the association with IKEA was publicly made to everyone, I believe that there would be no significant impact since IKEA is a brand intricately related to the handyman business.

On November 13, 2018, Thomas Shelby commented on How Additive Manufacturing Can Improve Access to Adequate Housing :

Even though I believe that 3D printed homes for $4000 are a great step and advancement in addressing the world’s “homelessness” issue, I believe that its final impact will end up being limited for the time being. The reason for this is simple: Land property value. The real issue worldwide is the steep cost of land property, which is why so many people don’t have a place to live in. Moreover, another limitation I see is access to water, sewage, electricity and other utilities. This all costs money, adding up to the final cost of setting up a home, and access to basic utilities is extremely limited in developing countries.

On November 13, 2018, Thomas Shelby commented on Machine Learning – In Theatres Now :

As a movie buff, I’m kind of wary when it comes to machine learning’s role in predicting future hits in the industry. I believe that movie hits are a result of creative screenwriting and the ability to tell a story (no matter what it is) in an engaging way, so in my opinion past data can be used as another tool to assess historical viewer preferences but will not be very useful in making predictions about future blockbusters. For this reason, I think that its applicability would be more valuable on the marketing and distribution strategy side, rather than on the content creation side.

On November 13, 2018, Thomas Shelby commented on Spotify: Can machine learning drive content generation? :

Not surprisingly, Spotify already expanded beyond music and into video. And not only song music videos, but also music-related documentaries, “behind the scenes” and a “best advice” channel featuring essential mantras from the biggest artists in the industry. In my opinion, this better positions them as the authority in music content and was a logical step forward given the infrastructure, platform, and resources they already had in place. Who knows if video will be a successful play, but at least they’ll be able de further collect data from users and learn from it.