Machine learning is indeed could be a very useful in predicting what would the viewers like to see and what would resonate the most with them, consequently driving a revenue for the major content producers. However, one of the questions I have is how much it’s actually contribute into the development of the cinematographic art? By tailoring content according to the viewers preference, how much left to the director to create and express his/her own opinion, share the voice? Would this eventually lead to the end of such great directors as Filini, Tarkovsky etc?
Peter, this is a great article! Thanks for sharing!
For me, a more philosophical question is how does the platform help to form a well-rounded view to a person? If it only shows what you like, that means it will be biased significantly and ultimately person would not develop herself (ie only one political view, only one part of the story biased based on journalists opinion etc). I think it is the most scary part of AI – the moment when you don’t even realise that the news feed is just tailored to your preference, but you like it so much that you don’t bother to go outside and ask for other news/opinions etc.
It’s really interesting topic and application of machine learning algorithms. However, I would be curious to discuss how introduction of self-driving cars would change the industry. If the level of casualties decrease dramatically with self-driving cars, then would AXA even have a business at all?
Also, assuming AXA eventually would have data on every insured person in a real time, how would this change the nature of their business? With such data access most likely their estimation of probability of accident with any person would be so precise that AXA practically would not insure risky persons, because would know almost fo sure if the person is going to have any accident at some point or not.
In my opinion, I’d rather prefer machine learning algorithm to look at my posts/messages at facebook than a real person as it used to be. The algorithm is a tool and it does judge based on assumptions put into model. So, for me it’s more like a search engine through the posts/messages in a hunt for violation of community values, which is better in some sense than a real person would look through my posts. Moreover, the value it potentially could generate is enormous, even if it ever helps to safe only one life it is still worth it.
I agree with Zoey and believe that the key problem here is to get access to all the data from all the patients in order to provide a decent input to the machine learning algorithm to get a right output. Moreover, the algorithm would require approval/correction/disproval of type of decision /prediction it has made and knowing that in many way doctors could be subjective, I struggle with understanding of how the algorithm would learn what is right/what is wrong. Say, you run experiment in one clinic with limited number of patients and only one doctor “trains” the algorithm, then it will provide one type of output after some time. Same test but run un a different clinic guided by the doctor with the different opinion on some cases, could potentially lead to the different output from the algorithm after a while.
I think application of open-source development to mass-market products could be done to some extent, but not fully. First, development of a real product requires extensive R&D and investments of time and resources. What Volition does is just an open-source test of marketing concept. For instance, Jetset and Protect mask is not innovative type of mask or cream, it’s just a heavy moisturising cream with the pitch “for long flies”. In many ways this is what beauty companies call “innovation and product development” when in reality they just in a continuous search of perfect pitch for slightly modernised but old product. It’s hard to imagine though that industry will be disrupted through the open-source platforms, because true break-through most likely will come from professional chemists / biologist.
However, I do agree that open-source platforms are great to generate ideas for the next marketing concept and help to build awareness and initial enthusiasm around products which haven’t been produced.