Adobe has launched special versions of Photoshop, Express, and Acrobat that integrate directly with ChatGPT for streamlined creative workflows. PCWorld reports these integrations allow users to edit images, create designs, and manage PDFs within the chatbot interface across desktop, web, and iOS platforms. The tools enable tasks like background blurring, brightness adjustments, PDF conversion, and design creation using Adobe's extensive creative libraries through conversational commands. Adobe has launched special versions of Adobe Photoshop, Adobe Express and Adobe Acrobat for ChatGPT . This gives users access to a wide range of features directly in the popular chatbot at no cost, including image editing and the ability to convert text to PDF files. Here's how Adobe described the new ChatGPT integration: Accessing Adobe's apps in ChatGPT is as simple as typing the name of the app followed by an instruction. For example, to blur the background of an image with Photoshop, users can type: "Adobe Photoshop, help me blur the background of this image." ChatGPT then automatically surfaces the app and uses contextual understanding to guide the user through the action. To learn more about how to get started with Adobe apps for ChatGPT, read here .

Do you want allegedly useful "artificial intelligence" features in your face in every single service and tool you use, constantly, unceasingly, and demanding you pay more for it? The latest perpetrator is Adobe, who's now raising the price of its priciest Creative Cloud plans next month and justifying it by bundling in a bunch of generative AI tools. The Creative Cloud All Apps plan is being renamed Creative Cloud Pro, because apparently tools that cost hundreds of dollars a year and aren't available as full purchases aren't for "professionals" unless they're paying the maximum amount. If you're in the US, Canada, or Mexico, and if you're currently subscribed to All Apps, you'll be moved over to the Pro plan starting on June 17th… with a price bump from 60 per month to 70 per month for standard, yearly-subscribed users in the US. Month-to-month prices will jump from the already-sky-high 90 per month to 105 per month.

Acrobat Pro's comprehensive PDF features show why it's still the editor against which all others are judged. Editor's note: This review was updated December 9, 2024 to reflect the addition of AI Assistant and current pricing. Adobe created the PDF two decades ago and its PDF editor has continued to rule the category, despite what many users felt was its exorbitant price. But a couple of years back, Acrobat adopted a cloud subscription model that now makes it more affordable for folks without an enterprise budget. Acrobat Pro is composed of three components: Acrobat, which allows you to perform a variety of editing functions on your PDFs on desktop and mobile devices; Adobe Document Cloud, which lets you create and export PDF files, as well as store and send files and collect electronic signatures; and Acrobat Reader, which enables you to read, print, and sign PDFs.

Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.

Question answering (QA) has become an important application in the advanced development of large language models. General pre-trained large language models for question-answering are not trained to properly understand the knowledge or terminology for a specific domain, such as finance, healthcare, education, and customer service for a product. To better cater to domain-specific understanding, we build an in-house question-answering system for Adobe products. We propose a novel framework to compile a large question-answer database and develop the approach for retrieval-aware finetuning of a Large Language model. We showcase that fine-tuning the retriever leads to major improvements in the final generation. Our overall approach reduces hallucinations during generation while keeping in context the latest retrieval information for contextual grounding.

AI Assistant in Adobe Acrobat, now in beta, is a new generative AI–powered conversational engine deeply integrated into Acrobat workflows, empowering everyone with the information inside their most important documents. As the creator of PDF, the world's most trusted digital document format, Adobe understands document challenges and opportunities well. Our continually evolving Acrobat PDF application, the gold standard for working with PDFs, is already used by more than half a billion customers to open around 400 billion documents each year. Starting immediately, customers will be able to use AI Assistant to work even more productively. All they need to do is open Acrobat on their desktop or the web and start working.

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models.