Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories. Due to the lack of supervision, previous GCD methods face significant challenges, such as difficulty in rectifying errors for confusing instances, and inability to effectively uncover and leverage the semantic meanings of discovered clusters. Therefore, additional annotations are usually required for real-world applicability. However, human annotation is extremely costly and inefficient. To address these issues, we propose GLEAN, a unified framework for generalized category discovery that actively learns from diverse and quality-enhanced LLM feedback. Our approach leverages three different types of LLM feedback to: (1) improve instance-level contrastive features, (2) generate category descriptions, and (3) align uncertain instances with LLM-selected category descriptions. Extensive experiments demonstrate the superior performance of \MethodName over state-of-the-art models across diverse datasets, metrics, and supervision settings. Our code is available at https://github.com/amazon-science/Glean.

Software engineer Vishnu Mohandas decided he would quit Google in more ways than one when he learned the tech giant had briefly helped the US military develop AI to study drone footage. In 2020, he left his job working on Google Assistant and also stopped backing up all of his images to Google Photos. He feared that his content could be used to train AI systems, even if they weren't specifically ones tied to the Pentagon project. "I don't control any of the future outcomes that this will enable," Mohandas thought. "So now, shouldn't I be more responsible?" Mohandas, who taught himself programming and is based in Bengaluru, India, decided he wanted to develop an alternative service for storing and sharing photos that is open source and end-to-end encrypted.

In the age of powerful diffusion models such as DALL-E and Stable Diffusion, many in the digital art community have suffered style mimicry attacks due to fine-tuning these models on their works. The ability to mimic an artist's style via text-to-image diffusion models raises serious ethical issues, especially without explicit consent. Glaze, a tool that applies various ranges of perturbations to digital art, has shown significant success in preventing style mimicry attacks, at the cost of artifacts ranging from imperceptible noise to severe quality degradation. The release of Glaze has sparked further discussions regarding the effectiveness of similar protection methods. In this paper, we propose GLEAN- applying I2I generative networks to strip perturbations from Glazed images, evaluating the performance of style mimicry attacks before and after GLEAN on the results of Glaze. GLEAN aims to support and enhance Glaze by highlighting its limitations and encouraging further development.

Rapid advancements in AI are heralding a new generation of powerful tools--including the ability to quickly surface knowledge across a business. Glean, a firm established by Google search engineers and other industry veterans, possesses considerable expertise in this area. AI News caught up with Arvind Jain, CEO and Founder of Glean, to hear more about how the company is using AI to surface workplace knowledge and supercharge productivity. AI News: Can you tell us about Glean and its goals? Arvind Jain: Glean is solving perhaps the most urgent problem in today's workplace: helping people find and access the information they need to do their best work.

We're on a mission to bring people the knowledge they need to make a difference in the world. Glean was founded by a seasoned team of former Google search and Facebook engineers, who wondered why we don't have an easier way of finding what we need at work. In our personal lives, we have tools to help us find pretty much whatever we need. Why don't we have that at work? And that was the beginning of Glean.

As language models get more complex, they also get more expensive to create and run. Some companies are locked out. ALVIN QI, WHO works at a search startup called Glean, would love to use the latest artificial intelligence algorithms to improve his company's products. Glean provides tools for searching through applications like Gmail, Slack, and Salesforce. Qi says new AI techniques for parsing language would help Glean's customers unearth the right file or conversation a lot faster.

Calvin Qi, who works at a search startup called Glean, would love to use the latest artificial intelligence algorithms to improve his company's products. Glean provides tools for searching through applications like Gmail, Slack, and Salesforce. Qi says new AI techniques for parsing language would help Glean's customers unearth the right file or conversation a lot faster. So Glean uses smaller, less capable AI models that can't extract as much meaning from text. "It is hard for smaller places with smaller budgets to get the same level of results" as companies like Google or Amazon, Qi says.

It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences. Although many existing robotics studies use a forward model framework, there are generalization issues with high degrees of freedom. The current study shows that the predictive coding (PC) and active inference (AIF) frameworks, which employ a generative model, can develop better generalization by learning a prior distribution in a low dimensional latent state space representing probabilistic structures extracted from well habituated sensory-motor trajectories. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound, while goal-directed planning is accomplished by inferring latent variables for maximizing the estimated lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data by setting an intermediate value for a regularization coefficient. Furthermore, comparative simulation results show that the proposed model outperforms a conventional forward model in goal-directed planning, due to the learned prior confining the search of motor plans within the range of habituated trajectories.