- #FLOWSTATE WHOS TRYING TO GET LOST IN THE SAUCE GENRE MOVIE#
- #FLOWSTATE WHOS TRYING TO GET LOST IN THE SAUCE GENRE PROFESSIONAL#
Sometimes it can be toxic and suffocating as in the angrier strains of franchise fandom. The logical successor to that is not any single film critic today but the pluralistic critical response of the public via the Internet. Along with Susan Sontag, she treated as legitimate her very personal aesthetic response to art. Pauline Kael made famous a particular type of deeply subjective film criticism.
#FLOWSTATE WHOS TRYING TO GET LOST IN THE SAUCE GENRE MOVIE#
But taken as a group, they simulate that feeling of standing outside on the sidewalk after a festival screening, debating the movie with other film buffs.
#FLOWSTATE WHOS TRYING TO GET LOST IN THE SAUCE GENRE PROFESSIONAL#
Since many of the members are not professional critics, they don’t feel a need to conform to some standard review template. Sometimes what you want is a work that attracts you with equal force as it repels others., you can curate your own panel of people to follow and filter film reviews by their tastes. Similarly, often it's the movie that's divisive that I find most compelling. Some of my favorite restaurants and books don't rate highly on Yelp or Amazon. That's by design, but my aesthetic response to a film can't be mapped that way. Unlike Rotten Tomatoes or Metacritic The way those two sites compress the quality of film into a single numeric score has always been reductive. More and more, I’ve come to rely on the film buffs of Letterboxd to guide my film choices.
This doesn’t mean I rely exclusively on professional film critics. It’s even rarer for someone to be able to tie that to film craft given how visually illiterate our educational systems have left us. Something is almost always lost in translation to text. It’s a rare gift for someone to be able to express just how a film works on them given the subconscious and emotional nature of the medium. Some of my favorite reviews are pans of movies I loved, or vice versa. Instead, give me a review which can articulate why someone enjoyed a film or not. I’m not interested in terse recommendations like “this film is good” or “this film was terrible.” Given the individuality of aesthetic preferences, there’s little signal in a binary expression of one person’s preferences. Rather than a bug, this variance in taste is to be treasured. I doubt anyone will agree with all my movie choices below. Even people I consider to share many of my movie tastes will disagree with me vociferously on particular movies. I didn’t like Red Notice, but I can understand what types of metrics would lead Netflix to just splash it across every subscriber’s eyeballs.įilm is also a category in which we still haven’t fully understood the variation in people’s aesthetic preferences. It’s no surprise to me that Netflix seems largely to have given up on much of the work that came out of the Netflix Prize and instead focuses on using the massive funnel of its above-the-fold home screen real estate to push its latest original production. I’ve yet to see an algorithm that can just spit out a Wes Anderson-like movie. Machine learning algorithms have learned to write music that often sounds like specific composer and musicians. This makes it easier to generate a playlist of similar tracks even before gathering listener feedback. The ways that music tracks resemble each other feel easier to see with math. In many ways a TikTok is about as short a piece of media as could be designed that can be said to still tell a narrative (though maybe a dating app profile photo is even more concise). TikTok videos are even shorter than music tracks, but they often contain snippets of music tracks. People add songs to playlists or ask their streaming service to generate radio stations off of that track.Īs I’ve written before about TikTok, one of its most critical design choices was to full-screen videos, allowing it to gather really accurate signal from the viewer on each video. In music, you not only gather many more data points per hour because of the short duration of each track, but you gather feedback within each piece. Viewers generally provide a single point of feedback on a film, if they even choose to sample it: they either finish the movie or they don’t.
As a result, the frequency of feedback is much higher for music than film. Films are very long while music tracks only last a few minutes. In a category like music, people listen to their favorite tracks repeatedly. Some I might have never heard of had some critic or friend not written about them.įilm remains a difficult category for machine learning to crack. It is notable that none of my favorite Netflix movies this year came via their recommendations. My viewing output of is lower than usual but still much much higher than that of the median filmgoer.įilm is one category of media in which human recommendations still feel superior to algorithmic ones. A second year of the pandemic passed in which I didn’t attend any film festivals in person.
0 kommentar(er)
