Proteins, essential macromolecules for biological processes like metabolism and immune response, follow the sequence-structure-function paradigm, where amino acid sequences determine 3D structures and functions. Computational protein science AIms to decode this relationship and design proteins with desired properties. Traditional AI models have achieved significant success in specific protein modeling tasks, such as structure prediction and…
Pre-trained vision models have been foundational to modern-day computer vision advances across various domains, such as image classification, object detection, and image segmentation. There is a rather massive amount of data inflow, creating dynamic data environments that require a continual learning process for our models. New regulations for data privacy require specific information to be…
A fundamental challenge in advancing AI research lies in developing systems that can autonomously perform structured reasoning and dynamically expand domain knowledge. Traditional AI models often rely on implicit reasoning processes, which limit their ability to explain decisions, adapt across domains, and generalize relational patterns. These shortcomings hinder their applicability to complex scientific problems that…
Reinforcement learning (RL) has fundamentally transformed AI by allowing models to improve performance iteratively through interaction and feedback. When applied to large language models (LLMs), RL opens new avenues for handling tasks that require complex reasoning, such as mathematical problem-solving, coding, and multimodal data interpretation. Traditional methods rely heavily on pretraining with large static datasets.…
Bagel is a novel AI model architecture that transforms open-source AI development by enabling permissionless contributions and ensuring revenue attribution for contributors. Its design integrates advanced cryptography with machine learning techniques to create a trustless, secure, collaborative ecosystem. Their first platform, Bakery, is a unique AI model fine-tuning and monetization platform built on the Bagel…
The development of TTS systems has been pivotal in converting written content into spoken language, enabling users to interact with text audibly. This technology is particularly beneficial for understanding documents containing complex information, such as scientific papers and technical manuals, which often present significant challenges for individuals relying solely on auditory comprehension. A persistent problem…
Tokenization, the process of breaking text into smaller units, has long been a fundamental step in natural language processing (NLP). However, it presents several challenges. Tokenizer-based language models (LMs) often struggle with multilingual text, out-of-vocabulary (OOV) words, and inputs like typos, emojis, or mixed-code text. These issues can reduce model robustness and add complexity to…
It can significantly enhance LLMs’ problem-solving capabilities by guiding them to think more deeply about complex problems and effectively utilize inference-time computation. Prior research has explored various strategies, including chain-of-thought reasoning, self-consistency, sequential revision with feedback, and search mechanisms guided by auxiliary verifiers or evaluators. Search-based methods, particularly when paired with solution evaluators, leverage additional…
Artificial Intelligence has made significant strides, yet some challenges persist in advancing multimodal reasoning and planning capabilities. Tasks that demand abstract reasoning, scientific understanding, and precise mathematical computations often expose the limitations of current systems. Even leading AI models face difficulties integrating diverse types of data effectively and maintaining logical coherence in their responses. Moreover,…