In the Pixar movie Up, a cartoon dog called Dug sports a magical collar of sorts that can translate his barks and whines into fluent human speech. Elsewhere in the real world, very well-trained dogs can be taught to press buttons that produce human speech for simple commands like "outside," "walk," and "play." Humans have always been fascinated by the potential to communicate with the animals that they share the world with, and recently, machine learning, with its ever more advanced capabilities for parsing human speech, has presented itself as a hopeful route to animal translation. An article in the New York Times this week documented major efforts from five groups of researchers that looked at using machine-learning algorithms to analyze the calls of rodents, lemurs, whales, chickens, pigs, bats, cats, and more. Typically, artificial intelligence systems learn through training with labeled data (which can be supplied by the internet, or resources like e-books).

Jason Allen used Midjourney to create AI-generated art. It came in first at the Colorado State Fair fine arts competition art competition. The intense reactions were immediate. An AI-Generated Artwork Won First Place at a State Fair Fine Arts Competition, and Artists Are Pissed! Congrats to Sincarnate! Hopefully this can lead to a designated category for AI art? https://t.co/582iA3wyGb

Kizuna AI plays games, cooks, and even answers her viewers' questions. She sings, too, moonlighting as a pop star with millions of views on her music video. Kizuna AI is like any other influencer on YouTube, except she's not real. But it's not strictly accurate to say she's a standalone, completely digital entity born from the chaos of the internet. Kizuna AI is voiced by a human actress and produced by a character designer, 3D modeler, and an anonymous team.

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over traditional analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as aliasing, which can occur when an analog image is converted to digital form. Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic compression methods, such as the discrete cosine transform and adaptive Huffman coding.

AI creation, such as poem or lyrics generation, has attracted increasing attention from both industry and academic communities, with many promising models proposed in the past few years. Existing methods usually estimate the outputs based on single and independent visual or textual information. However, in reality, humans usually make creations according to their experiences, which may involve different modalities and be sequentially correlated. To model such human capabilities, in this paper, we define and solve a novel AI creation problem based on human experiences. More specifically, we study how to generate texts based on sequential multi-modal information. Compared with the previous works, this task is much more difficult because the designed model has to well understand and adapt the semantics among different modalities and effectively convert them into the output in a sequential manner. To alleviate these difficulties, we firstly design a multi-channel sequence-to-sequence architecture equipped with a multi-modal attention network. For more effective optimization, we then propose a curriculum negative sampling strategy tailored for the sequential inputs. To benchmark this problem and demonstrate the effectiveness of our model, we manually labeled a new multi-modal experience dataset. With this dataset, we conduct extensive experiments by comparing our model with a series of representative baselines, where we can demonstrate significant improvements in our model based on both automatic and human-centered metrics. The code and data are available at: \url{https://github.com/Aman-4-Real/MMTG}.

This study explores the association between music preferences and moral values by applying text analysis techniques to lyrics. Harvesting data from a Facebook-hosted application, we align psychometric scores of 1,386 users to lyrics from the top 5 songs of their preferred music artists as emerged from Facebook Page Likes. We extract a set of lyrical features related to each song's overarching narrative, moral valence, sentiment, and emotion. A machine learning framework was designed to exploit regression approaches and evaluate the predictive power of lyrical features for inferring moral values. Results suggest that lyrics from top songs of artists people like inform their morality. Virtues of hierarchy and tradition achieve higher prediction scores ($.20 \leq r \leq .30$) than values of empathy and equality ($.08 \leq r \leq .11$), while basic demographic variables only account for a small part in the models' explainability. This shows the importance of music listening behaviours, as assessed via lyrical preferences, alone in capturing moral values. We discuss the technological and musicological implications and possible future improvements.

Lyricists must write concretely enough to be evocative, yet vaguely enough to allow each listener his personal interpretation. The nineteen-sixties and seventies saw an especially rich balance struck between resonant ambiguity and massive popularity -- aided, as many involved parties have admitted, by the use of certain psychoactive substances. Half a century later, the visions induced by those same substances offer the closest comparison to the striking fruits of visual artificial-intelligence projects like Google's Deep Dream a few years ago or DALL-E today. Only natural, perhaps, that these advanced applications would sooner or later be fed psychedelic song lyrics. The video at the top of the post presents the Electric Light Orchestra's 1977 hit "Mr. Blue Sky" illustrated by images generated by artificial intelligence straight from its words.

The minstrel show has returned, riding on the apocalyptic horses of artificial intelligence, social media, and NFTs. FN Meka, a rapper created by artificial intelligence who gained TikTok fame through viral short music videos, exists. This fact itself is unfortunate. More unfortunate is that the artificial construct was temporarily signed to Capitol Records. The company dropped FN Meka in response to complaints from Industry Blackout, an activist organization of Black professionals in the entertainment industry, who accused the creators of engaging in racist stereotypes and a modern version of blackface.

NVIDIA -Embedded AI and Deep Learning for Intelligent Devices. The growing adoption of ADAS technology by OEMs and the rising demand for enhanced …

Artwork created by artificial intelligence (AI) was awarded a first place blue ribbon and a $300 prize after winning the digital category at the Colorado State Fair Fine Arts Competition, but human creators are furious about the news - with one saying the world is'watching the death of artistry unfold.' The AI artwork, dubbed Théâtre D'opéra Spatial, was submitted by Jason Allen, the president of a Pueblo-based gaming company Incarnate Games, who said he used Midjourney to make the stunning scenes that appear to combine medieval times with a futuristic world. Midjourney is an AI program that creates images from textual descriptions. Allen announced his win on Discord, an instant messaging social platform where he goes by the name Sincarnate, which then spread to Twitter where users shared their distaste that artwork generated by AI was chosen over those created by a human - one user stated'that's pretty f*cking sh*tty.' The Colorado resident appears to not be bothered by the criticism, as Allen notes on Discord how Twitter users are against AI-generated art, but are also'the first to throw the human under the bus by discrediting the human element.'