Data is one of the most critical elements in building a large language model. However, existing systems either fail to customize a corpus curation pipeline or neglect to leverage comprehensive corpus assessment for iterative optimization of the curation. To this end, we present a pretraining corpus curation and assessment platform called Oasis -- a one-stop system for data quality improvement and quantification with user-friendly interactive interfaces. Specifically, the interactive modular rule filter module can devise customized rules according to explicit feedback. The debiased neural filter module builds the quality classification dataset in a negative-centric manner to remove the undesired bias. The adaptive document deduplication module could execute large-scale deduplication with limited memory resources. These three parts constitute the customized data curation module. And in the holistic data assessment module, a corpus can be assessed in local and global views, with three evaluation means including human, GPT-4, and heuristic metrics. We exhibit a complete process to use Oasis for the curation and assessment of pretraining data. In addition, an 800GB bilingual corpus curated by Oasis is publicly released.

Causal operators (CO), such as various solution operators to stochastic differential equations, play a central role in contemporary stochastic analysis; however, there is still no canonical framework for designing Deep Learning (DL) models capable of approximating COs. This paper proposes a "geometry-aware'" solution to this open problem by introducing a DL model-design framework that takes suitable infinite-dimensional linear metric spaces as inputs and returns a universal sequential DL model adapted to these linear geometries. We call these models Causal Neural Operators (CNOs). Our main result states that the models produced by our framework can uniformly approximate on compact sets and across arbitrarily finite-time horizons H\"older or smooth trace class operators, which causally map sequences between given linear metric spaces. Our analysis uncovers new quantitative relationships on the latent state-space dimension of CNOs which even have new implications for (classical) finite-dimensional Recurrent Neural Networks (RNNs). We find that a linear increase of the CNO's (or RNN's) latent parameter space's dimension and of its width, and a logarithmic increase of its depth imply an exponential increase in the number of time steps for which its approximation remains valid. A direct consequence of our analysis shows that RNNs can approximate causal functions using exponentially fewer parameters than ReLU networks.

And nowhere is that more true than in photography. I've certainly had fun with it on more than my share of photos. But the more I attempt to be a "serious" photographer, the less I want to rely on artificial intelligence to do my job for me. That's not to say it doesn't have its place. And AI certainly has its place in the world of art. A recent encounter with the "Neural filters" in Adobe Photoshop has me rethinking things a little, though.

Adobe's annual design and technology conference begins today, so the company is making updates across much of its software lineup as part of the fall event. When it comes to Photoshop, Adobe has a host of new features for desktop and iPad as well as an update on the progress of the web version. With additional tools for selections, Neural Filters, collaboration and working on a tablet, there could be something to make everyone's workflow a bit easier in the latest releases. First, Adobe has refined the Object Selection tool to improve the accuracy of automatic selections and expanded the list of items that Photoshop can recognize on its own. This builds on the selection abilities the company first brought to the app in 2020, allowing you to hover over an item in an image while Photoshop automatically detects and then selects it.

Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions. Previous deep learning based models neglect two issues that are critical for practical applications, namely high resolution (HR) image processing and model comprehensibility. In this paper, we propose a novel Deep Comprehensible Color Filter (DCCF) learning framework for high-resolution image harmonization. Specifically, DCCF first downsamples the original input image to its low-resolution (LR) counter-part, then learns four human comprehensible neural filters (i.e. hue, saturation, value and attentive rendering filters) in an end-to-end manner, finally applies these filters to the original input image to get the harmonized result. Benefiting from the comprehensible neural filters, we could provide a simple yet efficient handler for users to cooperate with deep model to get the desired results with very little effort when necessary. Extensive experiments demonstrate the effectiveness of DCCF learning framework and it outperforms state-of-the-art post-processing method on iHarmony4 dataset on images' full-resolutions by achieving 7.63% and 1.69% relative improvements on MSE and PSNR respectively.

Adobe's next downloadable Neural Filter for Photoshop, which is called'Photo Restoration' is about to be announced, and we've been given an exclusive first look. We attended last night's launch party and got a closer look at the Photo Restoration Neural Filter in action – it's designed for processing old photos, and essentially is a fast, AI-powered way to make them look brand new by removing imperfections and grain. And we do mean fast – the Photo Restoration Neural Filter doesn't really do anything that Photoshop couldn't already do, but the difference is simply that it does it in seconds, with just a single click. Taking the grunt work out of image editing, Photo Restorations will turn removing scratches, noise and other imperfections from old photos – a task that could easily take hours – into the work of a moment. This kind of wizardry is par for the course with Neural Filters, and why Photoshop CC ranks so highly in our guide to the best AI photo editing software.

This article is part of our series that explores the business of artificial intelligence. Like every year, Adobe's Max 2021 event featured product reveals and other innovations happening at the world's leading computer graphics software company. Among the most interesting features of the event is Adobe's continued integration of artificial intelligence into its products, a venue that the company has been exploring in the past few years. Tickets to TNW 2022 are available now! Like many other companies, Adobe is leveraging deep learning to improve its applications and solidify its position in the video and image editing market. In turn, the use of AI is shaping Adobe's product strategy.

Perhaps we spoke a tad too soon. Adobe has extended the AI-enhanced capabilities of its Photoshop image editing software to include instant sky replacement, some super-easy selection tools and some early Neural Filters, including emotion editing. It all rolled out today in an update for Creative Cloud subscribers. Probably the most useful to experienced image editors will be the new selection tools, which use Adobe's Sensei AI algorithms to improve the software's ability to quickly draw selection lines around complex objects, in particular where tricky hair, complex backgrounds and objects that blend into the background somewhat. Sky replacement is a very neat feature that does a pretty amazing job of intelligently selecting the sky in your image and dropping in one of a couple dozen standard alternatives – or letting you upload your own sky images to get exactly the look you want.

Artificial intelligence is changing the world of image editing and manipulation, and Adobe doesn't want to be left behind. Today, the company is releasing an update to Photoshop version 22.0 that comes with a host of AI-powered features, some new, some already shared with the public. These include a sky replacement tool, improved AI edge selection, and -- the star of the show -- a suite of image-editing tools that Adobe calls "neural filters." These filters include a number of simple overlays and effects but also tools that allow for deeper edits, particularly to portraits. With neural filters, Photoshop can adjust a subject's age and facial expression, amplifying or reducing feelings like "joy," "surprise," or "anger" with simple sliders.

Artificial intelligence is changing the world of image editing and manipulation, and Adobe doesn't want to be left behind. Today, the company is releasing an update to Photoshop version 22.0 that comes with a host of AI-powered features, some new, some already shared with the public. These include a sky replacement tool, improved AI edge selection, and -- the star of the show -- a suite of image-editing tools that Adobe calls "neural filters." These filters include a number of simple overlays and effects but also tools that allow for deeper edits, particularly to portraits. With neural filters, Photoshop can adjust a subject's age and facial expression, amplifying or reducing feelings like "joy," "surprise," or "anger" with simple sliders.