The inherent risks associated with AI systems, especially in applications like autonomous driving and medical diagnosis, where errors can have severe consequences, should be handled carefully, keeping the risk factor under control. The key challenge lies in developing dependable models and ensuring their reliable execution, including innovative approaches to mitigate these risks effectively. Researchers from…
Large language models (LLMs) have revolutionized natural language processing, enabling groundbreaking advancements in various applications such as machine translation, question-answering, and text generation. However, the training of these models poses significant challenges, including high resource requirements and long training times due to the complexity of the computations involved. Previous research has explored techniques like loss-scaling…
Language models (LMs) have gained traction as aids in software engineering, where users act as intermediaries between LMs and computers, refining LM-generated code based on computer feedback. Recent advancements depict LMs functioning autonomously in computer environments, potentially expediting software development. However, the practical application of this autonomous approach still needs to be explored. Code generation…
ChatGPT – GPT-4 GPT-4 is the latest LLM of OpenAI, which is more inventive, accurate, and safer than its predecessors. It also has multimodal capabilities, i.e., it is also able to process images, PDFs, CSVs, etc. With the introduction of the Code Interpreter, GPT-4 can now run its own code to avoid hallucinations and provide…
Everything is online in the 21st century; almost everyone has a website or interacts with one daily. It is a necessity; hence, the websites use cookies and claim to improve the visitors’ browsing experience. However, we used the word claim to improve your browsing experience because some websites track the user’s IP address and geolocation…
The discipline of computational mathematics continuously seeks methods to bolster the reasoning capabilities of large language models (LLMs). These models play a pivotal role in diverse applications ranging from data analysis to artificial intelligence, where precision in mathematical problem-solving is crucial. Enhancing these models’ ability to handle complex calculations and reasoning autonomously is paramount to…
Integrating visual and textual data in artificial intelligence forms a crucial nexus for developing systems like human perception. As AI continues to evolve, seamlessly combining these data types is advantageous and essential for creating more intuitive and effective technologies. The primary challenge confronting this sector is the need for models to efficiently and accurately process…
Despite their significant contributions to deep learning, LSTMs have limitations, notably in revising stored information. For instance, when faced with the Nearest Neighbor Search problem, where a sequence needs to find the most similar vector, LSTMs struggle to update stored values when encountering a closer match later in the sequence. This inability to revise storage…
Cross-encoder (CE) models evaluate similarity by simultaneously encoding a query-item pair, outperforming the dot-product with embedding-based models at estimating query-item relevance. Current methods perform k-NN search with CE by approximating the CE similarity with a vector embedding space fit with dual-encoders (DE) or CUR matrix factorization. However, DE-based methods face challenges from poor recall because…
Transformers have taken the machine learning world by storm with their powerful self-attention mechanism, achieving state-of-the-art results in areas like natural language processing and computer vision. However, when it came to graph data, which is ubiquitous in domains such as social networks, biology, and chemistry, the classic Transformer models hit a major bottleneck due to…