Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor. In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable. Experiments on 4 darkweb social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture. Our code is publicly available at : https://github.com/gayanku/NBC-Softmax

StyleGAN has achieved great progress in 2D face reconstruction and semantic editing via image inversion and latent editing. While studies over extending 2D StyleGAN to 3D faces have emerged, a corresponding generic 3D GAN inversion framework is still missing, limiting the applications of 3D face reconstruction and semantic editing. In this paper, we study the challenging problem of 3D GAN inversion where a latent code is predicted given a single face image to faithfully recover its 3D shapes and detailed textures. The problem is ill-posed: innumerable compositions of shape and texture could be rendered to the current image. Furthermore, with the limited capacity of a global latent code, 2D inversion methods cannot preserve faithful shape and texture at the same time when applied to 3D models. To solve this problem, we devise an effective self-training scheme to constrain the learning of inversion. The learning is done efficiently without any real-world 2D-3D training pairs but proxy samples generated from a 3D GAN. In addition, apart from a global latent code that captures the coarse shape and texture information, we augment the generation network with a local branch, where pixel-aligned features are added to faithfully reconstruct face details. We further consider a new pipeline to perform 3D view-consistent editing. Extensive experiments show that our method outperforms state-of-the-art inversion methods in both shape and texture reconstruction quality. Code and data will be released.

We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly outperforms more complex error-detection mechanisms proposed in previous work. To this end, we contribute a novel method for introducing realistic, human-originated label noise into existing crowdsourced datasets such as SNLI and TweetNLP. We show that this noise has similar properties to real, hand-verified label errors, and is harder to detect than existing synthetic noise, creating challenges for model robustness. We argue that human-originated noise is a better standard for evaluation than synthetic noise. Finally, we use crowdsourced verification to evaluate the detection of real errors on IMDB, Amazon Reviews, and Recon, and confirm that pre-trained models perform at a 9-36% higher absolute Area Under the Precision-Recall Curve than existing models.

ChatGPT, the machine–learning fueled chatbot, has been widely acclaimed for its ability to interact with users and generate "human-like" text.

According to MSU, machine learning is a form of artificial intelligence that can help make simulations faster and more powerful.

The RELEX solution, powered by AI and machine learning, will support JYSK in managing and improving the outcomes of its increasingly complex …

Ammaar Reshi was playing around with ChatGPT, an AI-powered chatbot from OpenAI when he started thinking about the ways artificial intelligence could be used to make a simple children's book to give to his friends. Just a couple of days later, he published a 12-page picture book, printed it, and started selling it on Amazon without ever picking up a pen and paper. The feat, which Reshi publicized in a viral Twitter thread, is a testament to the incredible advances in AI-powered tools like ChatGPT--which took the internet by storm two weeks ago with its uncanny ability to mimic human thought and writing. But the book, Alice and Sparkle, also renewed a fierce debate about the ethics of AI-generated art. Many argued that the technology preys on artists and other creatives--using their hard work as source material, while raising the specter of replacing them.

Michael Huppe is President & CEO of SoundExchange, an adjunct music law professor, published author, frequent contributor and lecturer. In the not-too-distance future, when a new recording artist seizes the spotlight with hit songs, a huge social media following and sold-out venues, it won't be a human being. It'll be a performer whose lyrics, melodies and voice are solely created by artificial intelligence (AI). We're already seeing hints of this with virtual artists such as metaverse avatars, hybrid performers that rely on a combination of AI and human talent. Beyond music, there's also been the emergence of AI products that create realistic digital images based on a natural language sentence provided by the user.

It involves the use of various tools and techniques to extract insights from datasets.

It is a transformer – or a machine learning model – that processes and understands sequential data, such as natural language text.