My company[1] is a similar service for the artisan/premium beverage industry (focusing on coffee, beer, and spirits). We use machine learning and data science to understand what individuals and populations taste in a product, what attributes they're selecting for, and what they like and dislike in the product.
We then use that data to monitor their products in real time for quality control problems (flaws, taints, contamination, and batch variations), and create production and flavor profile optimizations.
I think Shazam (and my company) are offering a valuable service to artists and creators, who never had access to these types of optimizing and feedback tools before. At some level, music and cooking is an art - but its also a business, and the art is based on appreciation and consumption; and thus feedback, assessment, and optimization are critical.
I believe in the future, both music and high end drinks will be created on a near individual level - that they will be targeted and modified and optimized for very small groups and demographics. I believe that is a good thing for everyone; better products, higher quality, made for me, and that its tools like [1] and Shazam that are making this possible.
I don't know... what you're talking about is something like how Netflix used their data to motivate shows like House of Cards and Orange is the New Black, which they "knew" in some sense would have mass appeal. The masses are easy to please because you can just average over their individual tastes and capture huge swaths of the general population (see also: political parties, popular music, "middle-brow" chain restaurants), but it's much more difficult to get an accurate prediction for a single person. If you zoom in close enough, everyone becomes an outlier.
Also, creating art is not as simple as entering the infinite realm of possible songs (or paintings, or meals or...) and just choosing a set of parameters. Again, a broad set of parameters, sure. I'm going to write a rock song. I'm going to cook a boeuf bourguignon. But how to capture the weird unsystematic idiosyncrasies of any given individual, and do so in a way that doesn't feel soulless and artificial?
No, Netflix uses collaborative filters (not feature learning) for recommending shows, and does not collect feature data on individual "consumer" preference (at least not publicly).
What my company does uses sensory and preference data at the individual level, and projects for demographics and populations. Good food doesn't feel fake. Good drinks don't feel fake. In the long run, few products will be made by averaging tastes for the mass market - that's what coke and pepsi are.
Comments
My company[1] is a similar service for the artisan/premium beverage industry (focusing on coffee, beer, and spirits). We use machine learning and data science to understand what individuals and populations taste in a product, what attributes they're selecting for, and what they like and dislike in the product.
We then use that data to monitor their products in real time for quality control problems (flaws, taints, contamination, and batch variations), and create production and flavor profile optimizations.
I think Shazam (and my company) are offering a valuable service to artists and creators, who never had access to these types of optimizing and feedback tools before. At some level, music and cooking is an art - but its also a business, and the art is based on appreciation and consumption; and thus feedback, assessment, and optimization are critical.
I believe in the future, both music and high end drinks will be created on a near individual level - that they will be targeted and modified and optimized for very small groups and demographics. I believe that is a good thing for everyone; better products, higher quality, made for me, and that its tools like [1] and Shazam that are making this possible.
[1] www.Gastrograph.com
I don't know... what you're talking about is something like how Netflix used their data to motivate shows like House of Cards and Orange is the New Black, which they "knew" in some sense would have mass appeal. The masses are easy to please because you can just average over their individual tastes and capture huge swaths of the general population (see also: political parties, popular music, "middle-brow" chain restaurants), but it's much more difficult to get an accurate prediction for a single person. If you zoom in close enough, everyone becomes an outlier.
Also, creating art is not as simple as entering the infinite realm of possible songs (or paintings, or meals or...) and just choosing a set of parameters. Again, a broad set of parameters, sure. I'm going to write a rock song. I'm going to cook a boeuf bourguignon. But how to capture the weird unsystematic idiosyncrasies of any given individual, and do so in a way that doesn't feel soulless and artificial?
No, Netflix uses collaborative filters (not feature learning) for recommending shows, and does not collect feature data on individual "consumer" preference (at least not publicly).
What my company does uses sensory and preference data at the individual level, and projects for demographics and populations. Good food doesn't feel fake. Good drinks don't feel fake. In the long run, few products will be made by averaging tastes for the mass market - that's what coke and pepsi are.
He isn't talking about "recommending shows," he is talking about how Netflix used data science to determine what shows to produce