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3D-aware Generative Adversarial Networks (GANs) have made remarkable advancements in generating multi-view-consistent images and 3D geometries from collections of 2D images through neural volume rendering. However, despite these advancements, a significant challenge has emerged due to the substantial memory and computational costs associated with dense sampling in volume rendering. This limitation has compelled 3D GANs…

The communication between the doctor and the patient is critical to providing effective and compassionate care. A medical interview is “the most powerful, sensitive, and versatile instrument available to the physician,” according to studies. It is thought that clinical history-taking accounts for 60-80% of diagnoses in certain contexts.  Advancements in general-purpose large language models (LLMs)…

In advanced computing, the focus intensifies on creating more efficient data processing techniques. The modern world, increasingly reliant on data for decision-making, demands methods to swiftly and accurately interpret vast and complex datasets. This field’s significance spans diverse sectors, from healthcare to finance, where understanding data leads to insightful and impactful decisions. The crux of…

In 3D scene generation, a captivating challenge is the seamless integration of new objects into pre-existing 3D scenes. The ability to modify these complex digital environments is crucial, especially when aiming to enhance them with human-like creativity and intention. While adept at altering scene styles and appearances, earlier methods falter in inserting new objects consistently…

Navigating the intricate coding landscape often presents developers with a recurrent challenge – the disruptive back-and-forth between their code and external language models. This process involves a tedious dance of copying, pasting, and editing, leading to a fractured coding flow. While some developers have explored the use of ChatGPT during coding, the constant context-switching required…

Reinforcement learning (RL) has applications in various fields, and one such important application can be found in aligning language models with human values. Reinforcement learning from Human Feedback (RLHF) emerges as a pivotal technology in this alignment field. One of the challenges pertains to the limitations of reward models that serve as proxies for human…

Much of the modern Artificial Intelligence (AI) models are powered by enormous training data, ranging from billions to even trillions of tokens, which is only possible with web-scraped data. This web content is translated into numerous languages, and the quality of these multi-way translations suggests they were primarily created using Machine Translation (MT). This research…

Object detection plays a vital role in multi-modal understanding systems, where images are input into models to generate proposals aligned with text. This process is crucial for state-of-the-art models handling Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). OVD models are trained on base categories in zero-shot scenarios but must predict both…

Privacy concerns have become a significant issue in AI research, particularly in the context of Large Language Models (LLMs). The SAFR AI Lab at Harvard Business School was surveyed to explore the intricate landscape of privacy issues associated with LLMs. The researchers focused on red-teaming models to highlight privacy risks, integrate privacy into the training…

In artificial intelligence, the challenge arises when multiple AI agents need to work together seamlessly to tackle complex tasks. This collaborative intelligence is essential for building intelligent assistant platforms, automated customer service ensembles, or multi-agent research teams. Existing solutions, like Autogen and ChatDev, have their strengths, but they come with limitations, such as complex programming…