Abstract: An Artificial Intelligence (AI) driven content generation system is configured to interact with and inject user inputs into the decision-making process to navigate the user through the boundless creative space offered by content generation algorithms powered by AI. A content generation application may generate an initial collection of artifacts (e.g., multimedia) for exposure to a user, which may be generated based on user input, context awareness, or randomly generated. Responsive to user selection of one of the initial artifacts, a subsequent collection of artifacts is generated based on characteristics of the selected artifact. The generative process is recursive in that the generative application and the user continue to interact until the user decides to use one of the generated artifacts. The generative process enables the user to navigate the virtually infinite creative space of artifacts which may be generated by AI algorithms.

Abstract: An Artificial Intelligence (AI) driven content generation system is configured to interact with and inject user inputs into the decision-making process to navigate the user through the boundless creative space offered by content generation algorithms powered by AI. A content generation application may generate an initial collection of artifacts (e.g., multimedia) for exposure to a user, which may be generated based on user input, context awareness, or randomly generated. Responsive to user selection of one of the initial artifacts, a subsequent collection of artifacts is generated based on characteristics of the selected artifact. The generative process is recursive in that the generative application and the user continue to interact until the user decides to use one of the generated artifacts. The generative process enables the user to navigate the virtually infinite creative space of artifacts which may be generated by AI algorithms.

Abstract: A content server stores video and audio, and other media containing songs. One or more seed songs associated with seed lyrics and a client device are obtained. A seed feature vector characterizing the seed lyrics is obtained. A song lyric corpus including candidate feature vectors characterizing candidate song lyrics of candidate songs is accessed. Song lyric features are stored in the song lyric corpus to facilitate identification of candidate lyrics most similar to the seed lyrics. The candidate feature vectors in the song lyric corpus may be reduced-dimension versions of high-dimensional feature vectors quantifying characteristics of the song lyrics. One of the candidate songs is selected according to a measure of similarity between the seed feature vector and one of the candidate feature vectors corresponding to the selected candidate song. A song recommendation including the selected candidate song is generated and provided to the client device associated with the song.

Abstract: Embodiments relate to censoring audio data. A censoring system receives audio data including a song tag and amplitude data as a function of time. The amplitude data represents spoken words occurring over a duration, as well as non-spoken word sound overlapping with some of the spoken words during the duration. The system accesses a set of song lyrics and processes the set of song lyrics and the amplitude data together to identify timestamps in the amplitude data. These timestamps indicate a time during the duration when one of the words from the lyrics begins in the amplitude data. The system compares the words in the set of song lyrics to a blacklist and adjusts the amplitude data at the timestamps of blacklisted word occurrences to render the audio at the blacklisted words incomprehensible. The system outputs the adjusted amplitude data.