Knowledge Retrieval systems have been prevalent for decades in many industries, such as healthcare, education, research, finance, etc. Their modern-day usage has integrated large language models(LLMs) that have increased their contextual capabilities, providing accurate and relevant answers to user queries. However, to better rely on these systems in cases of ambiguous queries and the latest…
The rapid advancements in artificial intelligence have opened new possibilities, but the associated costs often limit who can benefit from these technologies. Large-scale models like GPT-4 and OpenAI’s o1 have demonstrated impressive reasoning and language capabilities, but their development and training remain financially and computationally burdensome. This creates barriers for smaller organizations, academic institutions, and…
Large language models (LLMs) have become crucial tools for applications in natural language processing, computational mathematics, and programming. Such models often require large-scale computational resources to execute inference and train the model efficiently. To reduce this, many researchers have devised ways to optimize the techniques used with these models. A strong challenge in LLM optimization…
Artificial Intelligence (AI) has made significant strides in various fields, including healthcare, finance, and education. However, its adoption is not without challenges. Concerns about data privacy, biases in algorithms, and potential job displacement have raised valid questions about its societal impact. Additionally, the “black box” nature of many AI systems makes it difficult to understand…
Developing Graphical User Interface (GUI) Agents faces two key challenges that hinder their effectiveness. First, existing agents lack robust reasoning capabilities, relying primarily on single-step operations and failing to incorporate reflective learning mechanisms. This usually leads to errors being repeated in the execution of complex, multi-step tasks. Most current systems rely very much on textual…
Large reasoning models are developed to solve difficult problems by breaking them down into smaller, manageable steps and solving each step individually. The models use reinforcement learning to enhance their reasoning abilities and develop very detailed and logical solutions. However, while this method is effective, it has its challenges. Overthinking and error in missing or…
Developing effective multi-modal AI systems for real-world applications requires handling diverse tasks such as fine-grained recognition, visual grounding, reasoning, and multi-step problem-solving. Existing open-source multi-modal language models are found to be wanting in these areas, especially for tasks that involve external tools such as OCR or mathematical calculations. The abovementioned limitations can largely be attributed…
The rapid growth of digital platforms has brought image safety into sharp focus. Harmful imagery—ranging from explicit content to depictions of violence—poses significant challenges for content moderation. The proliferation of AI-generated content (AIGC) has exacerbated these challenges, as advanced image-generation models can easily create unsafe visuals. Current safety systems rely heavily on human-labeled datasets, which…
Artificial Intelligence (AI) is revolutionizing how discoveries are made. AI is creating a new scientific paradigm with the acceleration of processes like data analysis, computation, and idea generation. Researchers want to create a system that eventually learns to bypass humans completely by completing the research cycle without human involvement. Such developments could raise productivity and…
GANs are often criticized for being difficult to train, with their architectures relying heavily on empirical tricks. Despite their ability to generate high-quality images in a single forward pass, the original minimax objective is challenging to optimize, leading to instability and risks of mode collapse. While alternative objectives have been introduced, issues with fragile losses…