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Training Robots to Curate Individualized News
North
Posted on November 13, 2018 at 1:54 pm
Social news provider Newstag is addressing issues of user engagement and content aggregation through deployment of machine learning algorithms

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On November 15, 2018, North commented on From Idea to 3-D in a Day: UPS and Fast Radius :

Thank you for a very interesting article, super interesting to read about how a small company managed to partner with a behemoth like UPS! I do share your concerns about 3D printing never really reaching “mass production” status, with the main issue being that printing will (most likely) always be a slower process than batch manufacturing through casting which offers less precision, but also a lower ability to customize products. I think that where 3D printing will first come into the mass market is where there is a need for customization, or where there is large variation in the type of products being manufactured, e.g. “unique” spare parts for cars that have been out of production for a while.

On November 15, 2018, North commented on FEWER PILOTS IN THE COCKPIT—MORE OPPORTUNITIES FOR MACHINES :

Thank you for shining some light on an interesting topic! Per your last question on how comfortable customers would be with the reduced number of pilots in the planes, I think this is a question of time – as we are increasingly exposed to machine controlled products, we will likely acclimatize to this “new normal”. In addition, I think part of the fear can be overcome just by having the possibility to remote-control the aircraft if the machines fail.

On November 15, 2018, North commented on Does Additive Manufacturing Pose a Threat to Gun Control? :

Thank you for a great, but scary, article! I fully share your worries about gun control in a world with additive manufacturing. So far we have, luckily, not seen any larger issues, but it is already possible to “print” a gun that can be used to fire several shots. The main issue here is that it will be very difficult to control and regulate, there are already blueprints for guns circulating on the internet, and anyone can buy or build from scratch, a 3D printer. I believe the only way to try to avoid this issue is to increasingly educate, and further control, the gun market in general – if no one carries a gun, no one else will have a strong enough incentive to get one. This is however a difficult path, and I’m not sure that it will be enough to solve the issue. Overall, I can only echo GSGoldstein above – I’m scared!

On November 15, 2018, North commented on Predicting your casualties – how machine learning is revolutionizing insurance pricing at AXA :

Thank you for a great article on a very interesting subject! Telematics is probably the future of auto insurance, and as you mention is increasingly used by major insurance providers. As to your question about ethics, this is a very interesting topic in the insurance world in general – since one of the main reasons for insurance to exist is to collectivize the risk, more information is not always good. If you are perfectly able to predict the risk of any one customer being in an accident, why would you not charge the full cost that you are expecting to that one customer? This can have fatal consequences especially in medical insurance, where someone with a high risk of cancer might be denied, or priced out of, any kind of medical insurance. In my mind this is an area that needs to be regulated, and quite heavily so, to avoid significant costs to society.

On November 15, 2018, North commented on Personalized Styling at Scale: What is the right balance between machine and human input? :

Great article! StichFix really has an interesting approach, and I believe that there is definitely a role for machine learning in the fashion world. However, you pose an interesting question – what is the right balance between data and human? We have recently seen Gucci turning around, without the use of data, but instead focusing on a “brilliant” head designer. It’s likely that data will come in to the mainstream more and more, but that the high end “innovative” side of fashion will not be as disrupted, as that end of the fashion industry is more akin to art than to products – something that is difficult to imitate with data, since it is by definition backwards looking.

On November 15, 2018, North commented on Can Machine Learning predict, prevent and diagnose Diabetes effectively? :

Thank you for an interesting essay! Per your first question, you dive in to the issue of ML still being a “high touch” way of diagnosing diabetes due to the need to train the algorithm. While this is definitely true, this issue should become less relevant going forward as a function of the algorithms getting better – as this should reduce the number of “inconclusive” diagnosis, where doctors need to step in and make a call. Hopefully this should be enough to make it almost self-sufficient in terms of labor, but there might be other issues I am missing.