Artificial intelligence (AI), often recognized as machine intelligence, is an area of computer science that emphasizes developing and managing …

Artificial Intelligence is the simulation of human intelligence processes by machines, especially by a computer system.

… of multiple scientific research fields and modern technological innovations such as robotics, artificial intelligence and machine learning.

Explanations are crucial parts of deep neural network (DNN) classifiers. In high stakes applications, faithful and robust explanations are important to understand and gain trust in DNN classifiers. However, recent work has shown that state-of-the-art attribution methods in text classifiers are susceptible to imperceptible adversarial perturbations that alter explanations significantly while maintaining the correct prediction outcome. If undetected, this can critically mislead the users of DNNs. Thus, it is crucial to understand the influence of such adversarial perturbations on the networks' explanations and their perceptibility. In this work, we establish a novel definition of attribution robustness (AR) in text classification, based on Lipschitz continuity. Crucially, it reflects both attribution change induced by adversarial input alterations and perceptibility of such alterations. Moreover, we introduce a wide set of text similarity measures to effectively capture locality between two text samples and imperceptibility of adversarial perturbations in text. We then propose our novel TransformerExplanationAttack (TEA), a strong adversary that provides a tight estimation for attribution robustness in text classification. TEA uses state-of-the-art language models to extract word substitutions that result in fluent, contextual adversarial samples. Finally, with experiments on several text classification architectures, we show that TEA consistently outperforms current state-of-the-art AR estimators, yielding perturbations that alter explanations to a greater extent while being more fluent and less perceptible.

The 3 machine learning methods had similar performance in differentiating between the dementia, MCI, and CN groups.

Machine learning and artificial intelligence have become more … With extensive memory that allows machines to process several things at once, …

… artificial intelligence, the Internet of Things, and machine learning. … the SLM solution will use Solukon’s flagship machines for different …

The prospect that artificial intelligence can soon produce long-form … out to developers who created a machine learning algorithm trained on the …

However, with this potential also comes uncertainty and fear. The rise of AI has sparked debate about the potential negative effects on employment and privacy, as well as the ethical implications of creating machines that can think and act like humans. It is important to remember that we have been through this before. The introduction of the printing press, for example, had a profound impact on society and the economy, but it also sparked fears about the loss of jobs and the spread of misinformation. Despite these concerns, the printing press ultimately proved to be a transformative technology that paved the way for many of the advancements we take for granted today.

On Thursday, a pair of tech hobbyists released Riffusion, an AI model that generates music from text prompts by creating a visual representation of sound and converting it to audio for playback. It uses a fine-tuned version of the Stable Diffusion 1.5 image synthesis model, applying visual latent diffusion to sound processing in a novel way. Created as a hobby project by Seth Forsgren and Hayk Martiros, Riffusion works by generating sonograms, which store audio in a two-dimensional image. In a sonogram, the X-axis represents time (the order in which the frequencies get played, from left to right), and the Y-axis represents the frequency of the sounds. Meanwhile, the color of each pixel in the image represents the amplitude of the sound at that given moment in time.