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.

In this post, we are going to see the basics of object detection in the computer vision, basics of famous object detection system YOLO (You Only Look once), and the installation procedure of the latest YOLO v4 in Ubuntu. This post is indented for absolute beginners who don't have much idea on implementing an objection detection system in Ubuntu Before discussing the object detection concepts, it will be good to start with the following concepts in computer vision. Object classification: This technique predicts the probability of different object categories( car, dog, cat, etc.) in an image, it essentially answers the question "What is in the picture?". It can only predict one category for one image. Object localization: This method can predict the probability of an object in the image along with its location in the image. This technique basically answers "What is in the picture and where it is?".

Machine vision, natural language processing, data analytics and other deep learning applications will propel global AI software revenues over the next five years via a growing list of industry segments spanning automotive and health care to financial services and retail. Market tracker Omdia forecasts AI software revenues will surge through 2025 to $126 billion, a 12-fold increase over a $10.1 billion industry in 2018. "The narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," said Keith Kirkpatrick, principal analyst with Omdia. "AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment for many years," Kirkpatrick added. Omdia estimates that more than half of AI revenues will be generated by machine vision and language applications, with deep learning deployments driving the AI market.

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.

CUDA stands for Compute Unified Device Architecture, and it's the reason popular deep learning libraries like Tensorflow & PyTorch are considered "GPU-accelerated". CUDA is Nvidia's programming platform that enables developers to leverage the full parallel processng capabilities of GPUs for deep learning applications. Almost all of the major deep learning libraries use CUDA under the hood, but it's not really something that most developers think about often. In this episode, I'll demo some progressively more complex CUDA examples by Nvidia to show you how using CUDA results in algorithmic speedups. We'll use Nvidia's profiler to clock speeds, then we'll analyze a pure-CUDA neural network by Sergey Bugrov to understand how a full neural pipeline on the GPU looks like.

Given the fast development of analysis techniques for NLP and speech processing systems, few systematic studies have been conducted to compare the strengths and weaknesses of each method. As a step in this direction we study the case of representations of phonology in neural network models of spoken language. We use two commonly applied analytical techniques, diagnostic classifiers and representational similarity analysis, to quantify to what extent neural activation patterns encode phonemes and phoneme sequences. We manipulate two factors that can affect the outcome of analysis. First, we investigate the role of learning by comparing neural activations extracted from trained versus randomly-initialized models. Second, we examine the temporal scope of the activations by probing both local activations corresponding to a few milliseconds of the speech signal, and global activations pooled over the whole utterance. We conclude that reporting analysis results with randomly initialized models is crucial, and that global-scope methods tend to yield more consistent results and we recommend their use as a complement to local-scope diagnostic methods.

Knowledge Base Completion has been a very active area recently, where multiplicative models have generally outperformed additive and other deep learning methods -- like GNN, CNN, path-based models. Several recent KBC papers propose architectural changes, new training methods, or even a new problem reformulation. They evaluate their methods on standard benchmark datasets - FB15k, FB15k-237, WN18, WN18RR, and Yago3-10. Recently, some papers discussed how 1-N scoring can speed up training and evaluation. In this paper, we discuss how by just applying this training regime to a basic model like Complex gives near SOTA performance on all the datasets -- we call this model COMPLEX-V2. We also highlight how various multiplicative methods recently proposed in literature benefit from this trick and become indistinguishable in terms of performance on most datasets. This paper calls for a reassessment of their individual value, in light of these findings.

Although Neural Differential Equations have shown promise on toy problems such as MNIST, they have yet to be successfully applied to more challenging tasks. Inspired by variational methods for image restoration relying on partial differential equations, we choose to benchmark several forms of Neural DEs and backpropagation methods on single image super-resolution. The adjoint method previously proposed for gradient estimation has no theoretical stability guarantees; we find a practical case where this makes it unusable, and show that discrete sensitivity analysis has better stability. In our experiments, differential models match the performance of a state-of-the art super-resolution model.