Understanding how models process and interpret time series data remains a significant challenge in deep learning to enable applicability in safety-critical areas such as healthcare. In this paper, we introduce Sequence Dreaming, a technique that adapts Activation Maximization to analyze sequential information, aiming to enhance the interpretability of neural networks operating on univariate time series. By leveraging this method, we visualize the temporal dynamics and patterns most influential in model decision-making processes. To counteract the generation of unrealistic or excessively noisy sequences, we enhance Sequence Dreaming with a range of regularization techniques, including exponential smoothing. This approach ensures the production of sequences that more accurately reflect the critical features identified by the neural network. Our approach is tested on a time series classification dataset encompassing applications in predictive maintenance. The results show that our proposed Sequence Dreaming approach demonstrates targeted activation maximization for different use cases so that either centered class or border activation maximization can be generated. The results underscore the versatility of Sequence Dreaming in uncovering salient temporal features learned by neural networks, thereby advancing model transparency and trustworthiness in decision-critical domains.

As AI art generators take the world by storm, some people wonder if it should count as art at all. The technology is still evolving and has some wrinkles to iron out, which means there are indeed flaws to consider alongside the incredible artwork a good artificial intelligence can produce. Let's explore the issue by breaking down the definition of art and whether or not AI-based work fits within that umbrella. Starting with the etymology of the word, Merriam-Webster's definition states that "art" stems from the Latin word "ars", which means, among other things, acquired skill, craftsmanship, and artistic achievement. Today, there's little consensus between philosophers and artists as to what real art is.

On March 11, 2021, the auction house Christie's sold a work by an American graphic designer, Michael Winkelmann, a.k.a. Beeple, for a colossal $69 million, making it the third most expensive work ever sold by a living artist. The work, Everydays: The First 5000 Days, is a nonfungible token, or NFT. It's a computer file that cannot be exchanged, copied, or destroyed, which gives the purchaser proof of authenticity. It lives online in a virtual space--an immaterial space--in a blockchain, a secure digital public ledger.

What if computers had the ability to dream? Google's innovative DeepDream software is turning Artificial Intelligence neural networks inside out to comprehend how computers think. When a bunch of artificial brains at Google began producing surreal images from otherwise standard photos, engineers contrasted what they saw to dreamscapes. Their image-generation method was termed Inceptionism and the code that powered it was called DeepDream. Wikipedia says "DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images."

By increasing this creative interpretation you can produce dream-alike imagery. Neural Networks act like our brain in the case of Pareidolia: it looks for familiar patterns, which derive from datasets they were trained on. The example above (a screen from my presentation on the AI Meetup Frankfurt, November 2019) demonstrates how our brain recognizes a face in the rock formations of Cydonia region on Mars. A user Nixtown transformed Da Vinci's Mona Lisa by continuous DeepDream iterations -- and AI recognizes weird patterns. Often our brain recognizes patterns or objects which aren't there.

Sign in to report inappropriate content. This video was produced using a convolutional neural network that finds and enhances patterns in images. It creates a dream-like hallucinogenic appearance in the deliberately over-processed images How does it work?

We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical details of automatic art generation and the interaction between AI and people, as both artists and the audience of art. We introduce two algorithms for generating 3D point clouds and then discuss their actualization as sculpture and incorporation into a holistic art installation. Specifically, the Amalgamated Deep-Dream (ADD) algorithm solves the sparsity problem caused by the naive DeepDream-inspired approach and generates creative and printable point clouds. The Partitioned DeepDream (PDD) algorithm further allows us to explore more diverse 3D object creation by combining point cloud clustering algorithms and ADD. Keywords Partitioned DeepDream, Amalgamated DeepDream, 3D, Point Cloud, Sculpture, Art, Interactive Installation, Creative AI, Machine Learning Introduction Will Artificial Intelligence (AI) replace human artists or will it show us a new perspective into creativity? Our team of artists and AI researchers explore artistic expression using Machine Learning (ML) and design creative ML algorithms to be possible co-creators for human artists. In terms of AIgenerated and AIenabled visual artwork, there has been a good amount of exploration done over the past three years in the 2D image area traditionally belonging to the realm of painting.

The original dream of research in artificial intelligence was to understand what it is that makes us who we are. Because of this, artificial intelligence has always been close to cognitive science, even if the two have been somewhat far apart in practice. Functional AIs have tended to do best at quickly finding'good-enough' approaches to problems that are easy to state but whose solutions are difficult or tedious to describe explicitly. A more modest definition of artificial intelligence might read as'computer programs that can learn how to perform tasks rather than require specific hardwired instructions.' It turns out this encompasses a lot--think language processing in Amazon's Alexa, or Google's AlphaGo--and AI has recently even been able to produce art.

"AI art", or more precisely art created with neural networks, has recently started to receive broad media coverage in newspapers (New York Times), magazines (The Atlantic), and countless blogs. Combined with the ongoing general "AI hype" and multiple recent museum and gallery exhibitions, this coverage has produced the impression of a new star rising in the art world: that of machine-generated art. It has also led to the popularization of an ever-growing list of philosophical questions surrounding the use of computers for the creation of art. This brief article provides a pragmatic evaluation of the new genre of AI art from the perspective of art history. It attempts to show that most of the philosophical questions commonly cited as unique issues of AI art have been addressed before with respect to previous iterations of generative art starting in the late 1950s. In other words: while AI art has certainly produced novel and interesting works, from an art historical perspective it is not the revolution as which it is portrayed.

What does the world look like to AI? Researchers have puzzled over this for decades, but in recent years, the question has become more pressing. Machine vision systems are being deployed in more and more areas of life, from health care to self-driving cars, but "seeing" through the eyes of a machine -- understanding why it classified that person as a pedestrian but that one as a signpost -- is still a challenge. Our inability to do so could have serious, even fatal, consequences. Some would say it already has due to the deaths involving self-driving cars. New research from Google and nonprofit lab OpenAI hopes to further pry open the black box of AI vision by mapping the visual data these systems use to understand the world.