Artificial intelligence (AI) is reshaping the way we approach everyday tasks, simplifying processes, and unlocking new levels of efficiency. AI tools enhance productivity and offer innovative solutions to a wide range of challenges, from managing daily routines to improving communication and decision-making. Whether it’s automating repetitive chores, organizing schedules, or personalizing experiences, AI is becoming…
Designing antibodies with high specificity and binding affinity to diverse therapeutic antigens remains a significant challenge in drug development. Current methods struggle to effectively generate complementarity-determining regions (CDRs) responsible for antigen binding, especially the highly variable heavy chain CDR3 (HCDR3). These difficulties are mainly due to poor generalization of the already existing computational models to…
The field of neural network architectures has witnessed rapid advancements as researchers explore innovative ways to enhance computational efficiency while maintaining or improving model performance. Traditional dense networks rely heavily on computationally expensive matrix operations to encode and store information. This reliance poses challenges when scaling these models for real-world applications that demand extensive knowledge…
Since the release of BERT in 2018, encoder-only transformer models have been widely used in natural language processing (NLP) applications due to their efficiency in retrieval and classification tasks. However, these models face notable limitations in contemporary applications. Their sequence length, capped at 512 tokens, hampers their ability to handle long-context tasks effectively. Furthermore, their…
Large Language Models (LLMs) have become a cornerstone of artificial intelligence, driving advancements in natural language processing and decision-making tasks. However, their extensive power demands, resulting from high computational overhead and frequent external memory access, significantly hinder their scalability and deployment, especially in energy-constrained environments such as edge devices. This escalates the cost of operation…
Despite the transformative potential of large language models (LLMs), these models face significant challenges in generating contextually accurate responses faithful to the provided input. Ensuring factuality in LLM outputs is particularly critical in tasks requiring responses grounded in lengthy, complex documents, which form the basis for advancing their applications in research, education, and industry. One…
For education research, access to high-quality educational resources is critical for learners and educators. Often perceived as one of the most challenging subjects, mathematics requires clear explanations and well-structured resources to make learning more effective. However, creating and curating datasets focusing on mathematical education remains a formidable challenge. Many datasets for training machine learning models…
Autoregressive protein language models (pLMs) have become transformative tools for designing functional proteins with remarkable diversity, demonstrating success in creating enzyme families like lysozymes and carbonic anhydrases. These models generate protein sequences by sampling from learned probability distributions, uncovering intrinsic patterns within training datasets. Despite their ability to explore high-quality subspaces of the sequence landscape,…
The rapid development of Large Language Models (LLMs) has transformed natural language processing (NLP). Proprietary models like GPT-4 and Claude 3 have set high standards in terms of performance but often come with drawbacks such as high costs, limited accessibility, and opaque methodologies. Meanwhile, many so-called open-source models fail to fully embody the ideals of…
Natural Language processing uses large language models (LLMs) to enable applications such as language translation, sentiment analysis, speech recognition, and text summarization. These models depend on human feedback-based supervised data, but relying on unsupervised data becomes necessary as they surpass human capabilities. However, the issue of alignment arises as the models get more complex and…