Goto

Collaborating Authors

Fine Arts


AIhub monthly digest: April 2020 – ethics, music, education and Westworld

AIHub

Welcome to our April 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent conferences and events, and much more. In this edition we cover a diverse range of topics including AI ethics, education, music, GPT-Neo, and Westworld. Marija Slavkovik wrote this very interesting retrospective on the AAAI symposium on implementing AI ethics. The aim of the symposium was to "facilitate a deeper discussion on how intelligence, agency, and ethics may intermingle in organizations and in software implementations." Another ethics conference on the horizon is the AAAI/ACM conference on artificial intelligence, ethics, and society, scheduled for 19-21 May.


Should every B.A. include some AI?

#artificialintelligence

Colby College is a private liberal arts school located in southern Maine. You can take classes in art history, chemistry, music, all the staples, and now the school is adding artificial intelligence to the list. Colby is among the first liberal arts colleges to create an artificial intelligence institute to teach students about AI and machine learning through the lenses of subjects like history, gender studies and biology. The college received a $30 million gift from a former student to set up its new institute. This, of course, comes as the world is grappling with ethics and AI and how to build a moral foundation into algorithms.


Understanding and Creating Art with AI: Review and Outlook

arXiv.org Artificial Intelligence

Recent advances in machine learning have led to an acceleration of interest in research on artificial intelligence (AI). This fostered the exploration of possible applications of AI in various domains and also prompted critical discussions addressing the lack of interpretability, the limits of machine intelligence, potential risks and social challenges. In the exploration of the settings of the "human versus AI" relationship, perhaps the most elusive domain of interest is the creation and understanding of art. Many interesting initiatives are emerging at the intersection of AI and art, however comprehension and appreciation of art is still considered to be an exclusively human capability. Rooted in the idea that the existence and meaning of art is indeed inseparable from human-to-human interaction, the motivation behind this paper is to explore how bringing AI in the loop can foster not only advances in the fields of digital art and art history, but also inspire our perspectives on the future of art. The variety of activities and research initiatives related to "AI and Art" can generally be divided into two categories: 1) AI is used in the process of analyzing existing art; or 2) AI is used in the process of creating new art. In this paper, relevant aspects and contributions of these two categories are discussed, with a particular focus on the relation of AI to visual arts. In recent years, there has been a surge of interest among artists, technologists and researchers in exploring the creative potential of AI technologies. The use of AI in the process of creating visual art was significantly accelerated with the emergence of Generative Adversarial Networks (GAN) [56].


How to Convert More Leads for AI Products and Services

#artificialintelligence

Last month we ran a podcast series on the AI in Industry podcast on the theme of "Advancing Your Career in the Era of AI", with a focus on how non-technical professionals can become more valuable in the market, and can become involved in AI projects and initiatives, without ever learning to code. I received twice as much feedback on this series as any other series we've ever run on the podcast - which surprised me. It surprised me because I think about everything on Emerj.com as being useful for nontechnical professionals. We've built our editorial calendar and our products around the needs of nontechnical professionals who want to make the most of their careers, but this recent series spoke to that topic directly. But hitting directly on the theme of "Advancing Your Career in the Era of AI" clearly hit a cord. For that reason, I've decided to release a three-part video and article series on that same topic, breaking down the lessons that were most important for me - and sharing a bit of my own story going from small-town martial arts instructor to international AI speaker and strategist.


Defense Innovation Unit Teaching Artificial Intelligence To Detect Cancer - Eurasia Review

#artificialintelligence

The Defense Innovation Unit is bringing together the best of commercially available artificial intelligence technology and the Defense Department's vast cache of archived medical data to teach computers how to identify cancers and other medical irregularities. The result will be new tools medical professionals can use to more accurately and more quickly identify medical issues in patients. The new DIU project, called "Predictive Health," also involves the Defense Health Agency, three private-sector businesses and the Joint Artificial Intelligence Center. The new capability directly supports the development of the JAIC's warfighter health initiative, which is working with the Defense Health Agency and the military services to field AI solutions that are aimed at transforming military health care. The JAIC is also providing the funding and adding technical expertise for the broader initiative.


Artificial Intelligence Can't Deal With Chaos, But Teaching It Physics Could Help

#artificialintelligence

While artificial intelligence systems continue to make huge strides forward, they're still not particularly good at dealing with chaos or unpredictability. Now researchers think they have found a way to fix this, by teaching AI about physics. To be more specific, teaching them about the Hamiltonian function, which gives the AI information about the entirety of a dynamic system: all the energy contained within it, both kinetic and potential. Neural networks, designed to loosely mimic the human brain as a complex, carefully weighted type of AI, then have a'bigger picture' view of what's happening, and that could open up possibilities for getting AI to tackle harder and harder problems. "The Hamiltonian is really the special sauce that gives neural networks the ability to learn order and chaos," says physicist John Lindner, from North Carolina State University.


Teaching Artificial Intelligence to diagnose COVID-19

#artificialintelligence

The new dataset contains more than 1,000 anonymised sets of chest CT scans. This expands on the earlier database of CT studies of patients with laboratory-confirmed infection created by scientists at the Diagnostics and Telemedicine Centre. The data set aims to inform AI to diagnose COVID-19. The dataset is the largest to date, and all CT studies in the dataset have a special marking made according to the classification, which reflects the manifestation of pathological abnormalities of COVID-19 in the lung tissue based on the chest computed tomography. According to experts at the Diagnostics and Telemedicine Center, a database with CT scans converted into the'research' Neuroimaging Informatics Technology Initiative (NIFTI) format is intended for developing artificial intelligence algorithms.


Few-Shot Class-Incremental Learning

#artificialintelligence

The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets.


Few-Shot Class-Incremental Learning

arXiv.org Machine Learning

The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets.


AI • Robots • Automata in Art - Art Appreciation

#artificialintelligence

What is the difference between humans and robots? What are the dangers of artificial intelligence? I'll discuss when AI began and how it relates to art. Author Jeff Krimmel states, "AI began with the calendar and abacus." The word'automata' (pleural for automaton) is from the Greek word meaning'self-acting'.