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Rogan slammed over 2011 video of him laughing about forcing female comedians to have sex
Link to tweet
https://www.the-sun.com/news/1016817/joe-rogan-video-laughing-joey-diaz-forcing-female-comedians-oral-sex/amp/
TRIGGER WARNING
In the recently resurfaced 2011 clip from 'The Joe Rogan Experience' podcast, Rogan seems to find his friend's recollection hilarious, and sends him doubling over in laughter and throwing his hands back in delight.
57-year-old Diaz continues: "You think I’m f****** kidding? Yeah, you’ve got to suck my d*** to get up to the Belly Room", referring to a popular LA comedy club.
“How many girls did you have do that?” Rogan asks.
“Oh, 20,” Diaz flippantly responds as Rogan laughs and claps his hands.
"I’ll make a call for you. That’s the f****** gateway into coming to Hollywood, everybody knows that," he adds.
Diaz recalls more of the disturbing details, saying: "I had this little blonde open mic chick that used to come up to the comedy store with a 20 and take me up to the Belly Room and suck my d*** then go do her set .... She was tremendous.
"She finally freaked out when she got to Hollywood she was beautiful and when she left she had dirty f****** nails."
*snip*
26 replies
= new reply since forum marked as read
= new reply since forum marked as read
What’s behind the Spotify Algorithm? From NLP over content based filtering to collaborative filtering.
https://towardsdatascience.com/uncovering-how-the-spotify-algorithm-works-4d3c021ebc0
In 2008 Spotify started changing the world around music by introducing music streaming. Since then music on CDs and DVDs has left all our lives and the music industry changed a whole lot. Nowadays, Spotify is the biggest player (with 365 Mio. users and 165 Mio. subscribers) in the music streaming market but has to maintain its position between American giants like Apple (Apple Music), Amazon (Amazon Music), and Google (YouTube Music). To do so, two peer groups have to be in the focus of Spotify: artists and users. To deliver the best service to them, there is one thing at the heart of Spotify: Algorithms & Machine Learning. The better Spotify understands the users and the greater the customer experience is, the more users can be convinced, converted to paying customers, and held as customers. In other words, data and algorithms are Spotify’s opportunity to not be crunched between Apple, Amazon, and Google and so far they do a pretty good job. So, let’s take a closer look at how the Spotify algorithm works, but first the basics:
Spotify Home Screen
At the center of the Spotify, recommendation system is the home screen which is peppered with many customized playlists and recommendations. The recommended playlists include Discover weekly, B Side, Release Radar, your mixtapes, and many more. Other sections of the cleverly arranged home screen are Jump Back In, Recently Played, or Recommended for today. The home screen is created and curated by an AI called BaRT (“Bandits for Recommendations as Treatments”), which will be explained in detail further. Spotify’s sections like Jump back in are also called Shelves.
Next to these recommended playlists, there are the sections Recommended songs (automatic playlist continuation) and you might also like below each playlist and each album. So, you can find almost on every Spotify screen a customized section. To recommend music to customers and to predict fitting songs on and off the home screen, Spotify has to rely on data. So let’s take a look at the available data sources. The data gathered and used by Spotify is quite extensive. At Spotify, almost everything is tracked. You might have experienced it gently: Spotify’s year in review. In this section you can see the minutes you listened to over the year, all genres, preferred artists, newcomers, top playlists, favorite podcasts, and honestly, this is only the tip of the iceberg. In the case of Spotify, most users are okay with being tracked because they benefit from a great user experience, nice stats, and top-notch recommendations.
Now let’s take a more detailed look at the data gathered. Let’s start with the artists. Of course, Spotify stores all data entered by the artists: song names, description, genre, images, lyrics, and song files. Next to this sort of data entered from the “provider side”, Spotify gathers and tracks the data of the counterpart, the consumers. This data comprises consumers listening history, skipped songs, how often a song has been played, playlists stored, music downloaded, social interactions such as shared playlists or sharing music, and more variables. Next to these two sources of “internal” data, Spotify probably also uses external data such as articles, blog posts, or other text data about songs or artists-related topics.
Spotify’s Acquisitions
2014 Spotify acquired Echonest, a MIT related start-up for music intelligence, for 100 million dollars. Echonest had already 2014 more than 1 billiard of data about songs & artists. In 2012, the Echonest founder Brian Whitman said that their system is scoring 10 million music-related websites every single day to analyze what is trending and what is going on on the music market. Next to Echonest, Spotify strengthened its M&A strategy and acquired in 2015 Seed Scientific, a data science and analytics consultancy to gain knowledge and expertise in-house. Another major acquisition on their AI path was in 2017 when Spotify incorporated Sonalytic, an audio detection startup. Audio detection you might know from Shazam or similar apps that help you identify songs from which you do not know the name. Furthermore, audio detection systems can also be used for the prevention of copyright violations. Spotify uses audio detection to enhance personalization in playlists and songs, to “match songs with compositions and to improve its publishing data system”. All of the mentioned acquisitions have been and are the fundament for Spotify to build its sophisticated recommendation system.
snip
https://towardsdatascience.com/uncovering-how-the-spotify-algorithm-works-4d3c021ebc0
In 2008 Spotify started changing the world around music by introducing music streaming. Since then music on CDs and DVDs has left all our lives and the music industry changed a whole lot. Nowadays, Spotify is the biggest player (with 365 Mio. users and 165 Mio. subscribers) in the music streaming market but has to maintain its position between American giants like Apple (Apple Music), Amazon (Amazon Music), and Google (YouTube Music). To do so, two peer groups have to be in the focus of Spotify: artists and users. To deliver the best service to them, there is one thing at the heart of Spotify: Algorithms & Machine Learning. The better Spotify understands the users and the greater the customer experience is, the more users can be convinced, converted to paying customers, and held as customers. In other words, data and algorithms are Spotify’s opportunity to not be crunched between Apple, Amazon, and Google and so far they do a pretty good job. So, let’s take a closer look at how the Spotify algorithm works, but first the basics:
Spotify Home Screen
At the center of the Spotify, recommendation system is the home screen which is peppered with many customized playlists and recommendations. The recommended playlists include Discover weekly, B Side, Release Radar, your mixtapes, and many more. Other sections of the cleverly arranged home screen are Jump Back In, Recently Played, or Recommended for today. The home screen is created and curated by an AI called BaRT (“Bandits for Recommendations as Treatments”), which will be explained in detail further. Spotify’s sections like Jump back in are also called Shelves.
Next to these recommended playlists, there are the sections Recommended songs (automatic playlist continuation) and you might also like below each playlist and each album. So, you can find almost on every Spotify screen a customized section. To recommend music to customers and to predict fitting songs on and off the home screen, Spotify has to rely on data. So let’s take a look at the available data sources. The data gathered and used by Spotify is quite extensive. At Spotify, almost everything is tracked. You might have experienced it gently: Spotify’s year in review. In this section you can see the minutes you listened to over the year, all genres, preferred artists, newcomers, top playlists, favorite podcasts, and honestly, this is only the tip of the iceberg. In the case of Spotify, most users are okay with being tracked because they benefit from a great user experience, nice stats, and top-notch recommendations.
Now let’s take a more detailed look at the data gathered. Let’s start with the artists. Of course, Spotify stores all data entered by the artists: song names, description, genre, images, lyrics, and song files. Next to this sort of data entered from the “provider side”, Spotify gathers and tracks the data of the counterpart, the consumers. This data comprises consumers listening history, skipped songs, how often a song has been played, playlists stored, music downloaded, social interactions such as shared playlists or sharing music, and more variables. Next to these two sources of “internal” data, Spotify probably also uses external data such as articles, blog posts, or other text data about songs or artists-related topics.
Spotify’s Acquisitions
2014 Spotify acquired Echonest, a MIT related start-up for music intelligence, for 100 million dollars. Echonest had already 2014 more than 1 billiard of data about songs & artists. In 2012, the Echonest founder Brian Whitman said that their system is scoring 10 million music-related websites every single day to analyze what is trending and what is going on on the music market. Next to Echonest, Spotify strengthened its M&A strategy and acquired in 2015 Seed Scientific, a data science and analytics consultancy to gain knowledge and expertise in-house. Another major acquisition on their AI path was in 2017 when Spotify incorporated Sonalytic, an audio detection startup. Audio detection you might know from Shazam or similar apps that help you identify songs from which you do not know the name. Furthermore, audio detection systems can also be used for the prevention of copyright violations. Spotify uses audio detection to enhance personalization in playlists and songs, to “match songs with compositions and to improve its publishing data system”. All of the mentioned acquisitions have been and are the fundament for Spotify to build its sophisticated recommendation system.
snip
