The natural language creation field is completely transformed by large language models (LLMs). Traditional fine-tuning approaches for responding to downstream tasks require access to the parameters of LLMs, which limits their use on potent black-box LLMs (like ChatGPT) that only give APIs. Because of this, recent research has focused heavily on prompting techniques that direct the generation results by offering many task-specific instructions and demonstrations, demonstrating that the prompt can considerably influence the resultant outcomes and thus necessitating careful design.  Althoug ..read more

The GPT model, which is the transformer architecture behind the well famous chatbot developed by OpenAI called ChatGPT, works on the concept of learning tasks with the help of only a few examples. This approach, called in-context learning, saves the model from fine-tuning with thousands of input texts and enables it to learn to perform well on different tasks using only task-specific examples as input. Fine-tuning the models for specific tasks can be very expensive as GPT is a “large” Language model with billions of parameters, and as all the model parameters need to be updated during fine-tu ..read more

There are rising worries about the potential negative impacts of large language models (LLMs), such as data memorization, bias, and unsuitable language, despite LLMs’ widespread praise for their capacity to generate natural-sounding text. It is challenging to validate (and rectify) such worries because of LLMs’ intricacy and developing capabilities. In this study, the authors present ReLM, a system for checking and querying LLMs with the help of conventional regular expressions. With ReLM, many language model evaluations may be formalized and made possible by simplifying complex evaluation me ..read more

Calendars, specifically Google Calendars, have both positive and negative aspects. For example, they can help plan gatherings, track time spent on individual tasks, and even keep in touch with pals. However, our schedule has the potential to balloon out of control quickly. Having nothing to go on but a sea of blue checkboxes on your weekly calendar might be very aggravating. This is why we’ve gathered here: to share the best calendar resources we’ve found. When you include that the average American now spends between 15 and 50 percent of their week in meetings, it’s easy to see why scheduling ..read more

A group of researchers from Google have recently unveiled StyleDrop, an innovative neural network developed in collaboration with Muse’s fast text-to-image model. This groundbreaking technology allows users to generate images that faithfully embody a specific visual style, capturing nuances and intricacies. By selecting an original image with the desired style, users can seamlessly transfer it to new images while preserving all the unique characteristics of the chosen style. The versatility of StyleDrop extends to working with entirely different images, enabling users to transform a children ..read more

Accurate segmentation of multiple objects is essential for various scene understanding applications, such as image/video processing, robotic perception, and AR/VR. The Segment Anything Model (SAM) was recently released, a basic vision model for broad image segmentation. It was trained using billion-scale mask labels. SAM can segment various objects, components, and visual structures in multiple contexts by using a sequence of points, a bounding box, or a coarse mask as input. Its zero-shot segmentation capabilities have sparked a quick paradigm change since they can be used in many applicatio ..read more

Apple has announced the release of Apple Vision Pro, a groundbreaking spatial computer that seamlessly integrates digital content with the physical world. This innovative device utilizes a fully three-dimensional user interface controlled by the user’s eyes, hands, and voice, offering unprecedented interaction. Powered by visionOS, the world’s first spatial operating system, Vision Pro allows users to engage with digital content as if it were physically present in their surroundings. Its design boasts an ultra-high-resolution display system with 23 million pixels across two screens, combined ..read more

In recent times, there has been significant progress in Natural Language Understanding and Natural Language Generation. The best example is the well-known ChatGPT developed by OpenAI, which has been in the headlines ever since its release. Though there has been incredible growth in the domain of Generative Artificial intelligence, the current large-scale AI algorithms still need to improve in achieving human-like visual scene understanding. Human beings can easily understand visual scenes, including recognizing objects, understanding spatial arrangements, predicting object movements, comprehe ..read more

In a revolutionary leap forward, generative retrieval approaches have emerged as a disruptive paradigm in information retrieval methods. Harnessing the potential of advanced sequence-to-sequence Transformer models, these approaches aim to transform how we retrieve information from vast document corpora. Traditionally limited to smaller datasets, a recent groundbreaking study titled “How Does Generative Retrieval Scale to Millions of Passages?” conducted by a team of researchers from Google Research and the University of Waterloo, delves into the uncharted territory of scaling generative retri ..read more

Neural networks have advanced quite significantly in recent years, and they have found themselves a use case in almost all applications. One of the most interesting use cases is the 3D modeling of the real world. We have seen neural radiance fields (NeRFs) that can accurately capture the 3D geometry of a scene by using normal, daily cameras. These advancements opened a whole new page in 3D surface reconstruction. The goal of 3D surface reconstruction is to recover detailed geometric structures of a scene by analyzing multiple images captured from various viewpoints. These reconstructed surfac ..read more