Another day, another deepfake: but this time they can sing. New research from Imperial College in London and Samsung's AI research center in the UK shows how a single photo and audio file can be used to generate a singing or talking video portrait. Like previous deepfake programs we've seen, the researchers uses machine learning to generate their output. And although the fakes are far from 100 percent realistic, the results are amazing considering how little data is needed. Getting a bit wackier, why not have everyone's favorite mad monk, Grigori Yefimovich Rasputin, belting out the Beyoncé classic'Halo'?

Breakthroughs in neural networks--a type of machine learning that vaguely imitates the structure of neurons in the brain--have given rise to a form of the technology called generative AI that can do everything from imitate photorealistic images and abstract art to composing music or writing. As the cultural debate around AI-fueled art begins to heat up, we're looking back on what kind of work has actually come out of the initial experiments in this space. Here are six examples of AI's use in creative processes that offer a sense of the current state of the technology and a hint at its larger potential: Google's DeepDream computer vision software, first released in 2015, turns any image into an abstract hallucinogenic version of itself by finding and enhancing certain patterns within the image. While the system might have little practical use for creative professionals on its face, it represented an early foray into the type of AI-generated art that has come to proliferate the open-source community. One of the most important breakthroughs in AI art also came from then-Google AI researcher Ian Goodfellow in a 2014 paper in which he formalized the structure for something called a generative adversarial network, a key tool in AI-created content.

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. The conventional approach, for both context-aware and standard models, is to retrieve items and apply the constraints as independent operations. The order in which these two steps are executed can induce significant problems. For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i.e., less popular items). As a result, the additional information that the constraint brings about user intent may not be accurately captured. In this paper we propose integrating the information provided by the contextual constraint into the similarity computation, by merging constraint application and retrieval into one operation in the embedding space. This technique allows us to generate high-quality recommendations for the specified constraint. Our approach learns constraints representations jointly with the user and item embeddings. We incorporate our methods into a matrix factorization model, and perform an experimental evaluation on one internal and two real-world datasets. Our results show significant improvements in predictive performance compared to context-aware and standard models.

In this paper we explore techniques for generating new music using a Variational Autoencoder (VAE) neural network that was trained on a corpus of specific style. Instead of randomly sampling the latent states of the network to produce free improvisation, we generate new music by querying the network with musical input in a style different from the training corpus. This allows us to produce new musical output with longer-term structure that blends aspects of the query to the style of the network. In order to control the level of this blending we add a noisy channel between the VAE encoder and decoder using bit-allocation algorithm from communication rate-distortion theory. Our experiments provide new insight into relations between the representational and structural information of latent states and the query signal, suggesting their possible use for composition purposes.

News and social media move financial markets. Bloomberg is one of the largest producers of news in the world and we ingest millions of news stories every day from over 70,000 external news feeds and social media such as Twitter. This data keeps our clients informed, and our team's insights help make sense of it for our customers. Bloomberg's Artificial Intelligence (AI) group: researchers and engineers who have a passion for solving complex problems. Our charter: to extract and identify relevant, meaningful, tradable, and actionable information (such as pricings, earnings, recommendations and major events) from data (including news, web, social media, and structured data) in real-time.

What is happening in the field of evolutionary and genetic algorithms today? Are there any cutting edge scientific projects in terms of AI/AGI? I'd very much appreciate it if someone could link me the relevant websites, researches, papers regarding the subject along with respective books or monographs. I'm just trying to find things out and getting back on track.

If you haven't hopped aboard the smart speaker train, now is as good a time as any, as there are two relatively inexpensive ones on the market today--the Google Home Mini and the Amazon Echo Dot. If you're looking for a device that can play music, give you the weather, and operate your smart gadgets, you may have been eyeballing these small-but-mighty smart speakers. The question is, though, which one is better? How do the features compare? There are distinct differences in how each speaker--the Echo Dot, left, and Google Home Mini--lights up when given a command.