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.

We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at https://jukebox.openai.com, along with model weights and code at https://github.com/openai/jukebox

Our effort is toward unifying GAN and DRL algorithms into a unifying AI model (AGI or general-purpose AI or artificial general intelligence which has general-purpose applications to: (A) offline learning (of stored data) like GAN in (un/semi-/fully-)SL setting such as big data analytics (mining) and visualization; (B) online learning (of real or simulated devices) like DRL in RL setting (with/out environment reward) such as (real or simulated) robotics and control; Our core proposal is adding an (generative/predictive) environment model to the actor-critic (model-free) architecture which results in a model-based actor-critic architecture with temporal-differencing (TD) error and an episodic memory. The proposed AI model is similar to (model-free) DDPG and therefore it's called model-based DDPG. To evaluate it, we compare it with (model-free) DDPG by applying them both to a variety (wide range) of independent simulated robotic and control task environments in OpenAI Gym and Unity Agents. Our initial limited experiments show that DRL and GAN in model-based actor-critic results in an incremental goal-driven intellignce required to solve each task with similar performance to (model-free) DDPG. Our future focus is to investigate the proposed AI model potential to: (A) unify DRL field inside AI by producing competitive performance compared to the best of model-based (PlaNet) and model-free (D4PG) approaches; (B) bridge the gap between AI and robotics communities by solving the important problem of reward engineering with learning the reward function by demonstration;

Recent work in adversarial attacks has developed provably robust methods for training deep neural network classifiers. However, although they are often mentioned in the context of robustness, deep generative models themselves have received relatively little attention in terms of formally analyzing their robustness properties. In this paper, we propose a method for training provably robust generative models, specifically a provably robust version of the variational auto-encoder (VAE). To do so, we first formally define a (certifiably) robust lower bound on the variational lower bound of the likelihood, and then show how this bound can be optimized during training to produce a robust VAE. We evaluate the method on simple examples, and show that it is able to produce generative models that are substantially more robust to adversarial attacks (i.e., an adversary trying to perturb inputs so as to drastically lower their likelihood under the model).