In addition to today's launch of OpenAI's GPT-4, which is now confirmed to be the GPT model running in Bing, Microsoft also announced the stable version of its Edge web browser will now include the new Bing AI chatbot in a sidebar. The feature was first introduced at Microsoft's AI press event in February but was previously only available as a developer preview, not a public release. With today's official unveiling of GPT-4, Microsoft is shipping the feature, which it calls the "Edge Copilot," in the stable version of its Microsoft Edge browser. The update reimagines the concept of the sidebar, which previously hosted Edge's "Discover" feature to provide users with context about the page they're visiting. Now the new sidebar will offer an AI chatbot instead.

The GPT-4 upgrade paves the way for a more advanced version of the ChatGPT technology that mimics human intelligence. OpenAI, a firm backed by Microsoft Corp and Alphabet Inc's Google, announced on Tuesday that it is releasing a strong AI model called GPT-4. In a blog post, OpenAI, the company behind the viral chatbot ChatGPT, described its latest technology as "multimodal," meaning it can respond to picture and text cues when asked to generate content. ChatGPT Plus subscribers and developers can use the text-input option after joining a waitlist while the image-input capability is still testing. The long-awaited release shows how more office workers may turn to constantly developing AI for more and more activities and how technology firms compete for a piece of the AI market.

Earlier this month, when LinkedIn started seeding "AI-powered conversation starters" in people's news feeds to boost engagement on its platform, the move saw more than little engagement of its own, none of it too positive. But the truth of the matter with LinkedIn is that it's been using a lot of AI and other kinds of automation across different aspects of its platform for years, primarily behind the scenes with how it builds and operates its network. Now, with its owner Microsoft going all-in on OpenAI, it looks like it's becoming a more prominent part of the strategy for LinkedIn on the front end, too -- with the latest coming today in the areas of LinkedIn profiles, recruitment and LinkedIn Learning. The company is today introducing AI-powered writing suggestions, which will initially be offered to people to spruce up their LinkedIn profiles, and to recruiters writing job descriptions. Both are built on advanced GPT models, said Tomer Cohen, LinkedIn's chief product officer.

Nearly four months after OpenAI stunned the tech industry with ChatGPT, the company is releasing its next-generation version of the technology that powers the viral chatbot tool. In a blog post on Tuesday, OpenAI unveiled GPT-4, which the company says is capable of performing well on a range of standardized tests and is also less likely to "go off the guardrails" with its responses, as some users have previously experienced. OpenAI said the updated technology passed a simulated law school bar exam with a score around the top 10% of test takers; by contrast, the prior version, GPT-3.5, scored around the bottom 10%. GPT-4 can also read, analyze or generate up to 25,000 words of text, and write code in all major programming languages, according to the company. OpenAI described the update as the "latest milestone" for the company.

An entire team responsible for making sure that Microsoft's AI products are shipped with safeguards to mitigate social harms was cut during the company's most recently layoff of 10,000 employees, Platformer reported. Former employees said that the ethics and society team was a critical part of Microsoft's strategy to reduce risks associated with using OpenAI technology in Microsoft products. Before it was killed off, the team developed an entire "responsible innovation toolkit" to help Microsoft engineers forecast what harms could be caused by AI--and then to diminish those harms. Platformer's report came just before OpenAI released possibly its most powerful AI model yet, GPT-4, which is already helping to power Bing search, Reuters reported. In a statement provided to Ars, Microsoft said that it remains "committed to developing AI products and experiences safely and responsibly, and does so by investing in people, processes, and partnerships that prioritize this."

PwC said it partnered with AI startup Harvey for an initial 12-month contract, which the accounting and consulting firm said will help lawyers with contract analysis, regulatory compliance work, due diligence and other legal advisory and consulting services. PwC said it will also determine ways for tax professionals to use the technology. It said its access to Harvey's technology is exclusive among the Big Four professional services firms. Harvey is built on technology from OpenAI, the Microsoft Corp-backed startup that on Tuesday released an upgraded version of its AI sensation ChatGPT. Harvey received a $5 million investment last year in a funding round led by the OpenAI Startup Fund.

A study was conducted to prove AI software could be used to translate and generate illustrations without any human intervention. This was done with the purpose of showing and distributing it to the external customer, Pratham Books. The project aligns with the company's vision by leveraging the generalisation and scalability of Machine Learning algorithms, offering significant cost efficiency increases to a wide range of literary audiences in varied geographical locations. A comparative study methodology was utilised to determine the best performant method out of the 3 devised, Prompt Augmentation using Keywords, CLIP Embedding Mask, and Cross Attention Control with Editorial Prompts. A thorough evaluation process was completed using both quantitative and qualitative measures. Each method had its own strengths and weaknesses, but through the evaluation, method 1 was found to have the best yielding results. Promising future advancements may be made to further increase image quality by incorporating Large Language Models and personalised stylistic models. The presented approach can also be adapted to Video and 3D sculpture generation for novel illustrations in digital webbooks.

Topology Optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP can be computationally expensive and get stuck in local minima, limiting their applicability to complex or large-scale problems. Learning-based approaches have been developed to accelerate the topology optimization process, but these methods can generate designs with floating material and low performance when challenged with out-of-distribution constraint configurations. Recently, deep generative models, such as Generative Adversarial Networks and Diffusion Models, conditioned on constraints and physics fields have shown promise, but they require extensive pre-processing and surrogate models for improving performance. To address these issues, we propose a Generative Optimization method that integrates classic optimization like SIMP as a refining mechanism for the topology generated by a deep generative model. We also remove the need for conditioning on physical fields using a computationally inexpensive approximation inspired by classic ODE solutions and reduce the number of steps needed to generate a feasible and performant topology. Our method allows us to efficiently generate good topologies and explicitly guide them to regions with high manufacturability and high performance, without the need for external auxiliary models or additional labeled data. We believe that our method can lead to significant advancements in the design and optimization of structures in engineering applications, and can be applied to a broader spectrum of performance-aware engineering design problems.

ODIN is an innovative approach that addresses the problem of dataset constraints by integrating generative AI models. Traditional zero-shot learning methods are constrained by the training dataset. To fundamentally overcome this limitation, ODIN attempts to mitigate the dataset constraints by generating on-demand datasets based on user requirements. ODIN consists of three main modules: a prompt generator, a text-to-image generator, and an image post-processor. To generate high-quality prompts and images, we adopted a large language model (e.g., ChatGPT), and a text-to-image diffusion model (e.g., Stable Diffusion), respectively. We evaluated ODIN on various datasets in terms of model accuracy and data diversity to demonstrate its potential, and conducted post-experiments for further investigation. Overall, ODIN is a feasible approach that enables Al to learn unseen knowledge beyond the training dataset.

Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality images that are faithful to a given reference text, known as a "prompt". These systems have immediately received lots of attention from researchers, creators, and common users. Despite the plenty of efforts to improve the generative models, there is limited work on understanding the information needs of the users of these systems at scale. We conduct the first comprehensive analysis of large-scale prompt logs collected from multiple text-to-image generation systems. Our work is analogous to analyzing the query logs of Web search engines, a line of work that has made critical contributions to the glory of the Web search industry and research. Compared with Web search queries, text-to-image prompts are significantly longer, often organized into special structures that consist of the subject, form, and intent of the generation tasks and present unique categories of information needs. Users make more edits within creation sessions, which present remarkable exploratory patterns. There is also a considerable gap between the user-input prompts and the captions of the images included in the open training data of the generative models. Our findings provide concrete implications on how to improve text-to-image generation systems for creation purposes.