Artists may need to start competing with -- or embracing -- computer-made songs and soundtracks in the near future, if a new AI music generator shows any indication of what could come next for the music industry. Researchers at artificial intelligence lab OpenAI have released Jukebox, an open-source algorithm that can generate music, complete with lyrics, vocals, and a soundtrack. All the algorithm needs is a genre, an artist, and a snippet of lyrics, and Jukebox can create song samples that can be realistic and quite catchy. OpenAI's music generator runs on the same sort of machine-learning technology used to create deepfakes and employed by the slew of sites that popped up in 2019 generating fake memes, fake Airbnb listings, and fake cats. Jukebox produces its AI creations using artificial neural networks that train a computer to learn from an influx of data.

OpenAI today announced it will begin tracking machine learning models that achieve state-of-the-art efficiency, an effort it believes will help identify candidates for scaling and achieving top overall performance. To kick-start things, the firm published an analysis suggesting that since 2012, the amount of compute needed to train an AI model to the same performance on classifying images in a popular benchmark -- ImageNet -- has been decreasing by a factor of 2 every 16 months. Beyond spotlighting top-performing AI models, OpenAI says that publicly measuring efficiency -- which here refers to reducing the compute needed to train a model to perform a specific capability -- will paint a quantitative picture of algorithmic progress. It's OpenAI's assertion that this in turn will inform policy making by renewing the focus on AI's technical attributes and societal impact. "Algorithmic improvement is a key factor driving the advance of AI. It's important to search for measures that shed light on overall algorithmic progress, even though it's harder than measuring such trends in compute," OpenAI wrote in a blog post.

OpenAI recently launched Jukebox, a model that generates music with singing in the raw audio domain. As a generative model for music, Jukebox can handle the long context of raw audio using an autoencoder. Jukebox's autoencoder processes the audio files using a multiscale VQ-VAE to compress it to discrete codes and modeling those using autoregressive Transformers. Provided with a genre, artist, and lyrics as input, Jukebox can output a new music sample produced from scratch. This is a type of innovation that expands the boundaries of generative models to a new level.

Hyderabad: The field of Artificial Intelligence is moving forward in breakneck speed with major breakthroughs taking every passing day. Earlier this week on Wednesday, the Business Insider India website reported that a website known as Imgflip built a meme generator called'This Meme Does Not Exist', which harnesses the power of machine learning to generate new memes by using 48 most popular meme templates and creating new captions at the click of the mouse.On Thursday, OpenAI, a San Francisco-based research laboratory, unveiled Jukebox, a neural network that can create music, along with lyrics and vocals, as per a blog published on the research lab's official website. The researchers at the OpenAI lab trained multiple machine learning models that were fed with a dataset of over 1.2 million songs over made by combing through the web, which were then paired with their corresponding lyrics and metadata that includes the name of the artist, genre of the album, year of release, along with the playlist keywords linked to the song and the common moods. It then performs data augmentation by downmixing the right and left channels randomly to produce Mono audio.

We have taught computers to do some amazing and horrible things, as a species. But nothing summarizes both of these facets quite like a machine-learning-generated snippet of Kanye West rapping Eminem's "Lose Yourself" with what sounds like a mouthful of stockpiled quarantine Nutella. This is just one example of the thousands of cursed yet compelling song snippets generated by Jukebox, machine learning software developed by independent research organization Open AI and released to the world on Thursday. The fine details (which you can read in an accompanying paper) are complicated but the general idea is the researchers trained machine learning models capable of parsing music on audio from more than 1 million songs pulled from the web. From this fuzzy internal picture of what constitutes listenable music, Jukebox generates new songs in various genres and in the style of specific artists.

Old songs, new tricks: Computer-generated music has been a thing for 50 years or more, and AIs already have impressive examples of orchestral classical and ambient electronic compositions in their back catalogue. Video games often use computer-generated music in the background, which loops and crescendos on the fly depending on what the player is doing at the time. But it is much easier for a machine to generate something that sounds a bit like Bach than the Beatles. That's because the mathematical underpinning of much classical music lends itself to the symbolic representation of music that AI composers often use. Despite being simpler, pop songs are different.

Hyderabad: The field of Artificial Intelligence is moving forward in breakneck speed with major breakthroughs taking every passing day. Earlier this week on Wednesday, the Business Insider India website reported that a website known as Imgflip built a meme generator called'This Meme Does Not Exist', which harnesses the power of machine learning to generate new memes by using 48 most popular meme templates and creating new captions at the click of the mouse. On Thursday, OpenAI, a San Francisco-based research laboratory, unveiled Jukebox, a neural network that can create music, along with lyrics and vocals, as per a blog published on the research lab's official website. The researchers at the OpenAI lab trained multiple machine learning models that were fed with a dataset of over 1.2 million songs over made by combing through the web, which were then paired with their corresponding lyrics and metadata that includes the name of the artist, genre of the album, year of release, along with the playlist keywords linked to the song and the common moods. It then performs data augmentation by downmixing the right and left channels randomly to produce Mono audio.

I'm very glad that the article includes a "Limitations" section, because while to most untrained listeners (and even trained listeners), these samples seem miraculous, in reality what is happening is that this is simply a more-impressive version of what has already been available. Specifically, Jukebox is able to provide locally-coherent sounds, which are recognizable as "music", but over long-term horizons it loses large-scale structure. They mention this themselves, and rightly so. While this is very impressive, it is primarily just an exercise in how nice they are able to make their short-term "sentences" sound (to borrow an analogy from speech synthesis). However, the broader challenge of long-term structure and musical form (here an analogy might be novel-length narrative structure) remains an open problem.

A prominent approach is to generate music symbolically in the form of a piano roll, which specifies the timing, pitch, velocity, and instrument of each note to be played. This has led to impressive results like producing Bach chorals, polyphonic music with multiple instruments, as well as minute long musical pieces. But symbolic generators have limitations--they cannot capture human voices or many of the more subtle timbres, dynamics, and expressivity that are essential to music. A different approach[1] is to model music directly as raw audio. Generating music at the audio level is challenging since the sequences are very long.