AI agents have become essential tools for navigating web environments and performing online shopping, project management, and content browsing. Typically, these agents simulate human actions, such as clicks and scrolls, on websites primarily designed for visual, human interaction. Although practical, this method of web navigation poses limitations for machine efficiency, especially when tasks involve interacting…
Large Language Models (LLMs) have potential applications in education, healthcare, mental health support, and other domains. However, their accuracy and consistency in following user instructions determine how valuable they are. Even small departures from directions might have serious repercussions in high-stakes situations, such as those involving delicate medical or psychiatric guidance. The ability of LLMs…
Federated Learning is a distributed method of Machine Learning that puts user privacy first by storing data locally and never centralizing it on a server. Numerous applications have successfully used this technique, especially those requiring sensitive data like healthcare and banking. Each training round in classical federated learning involves a complete update of all model…
Retrieval-augmented generation (RAG) systems blend retrieval and generation processes to address the complexities of answering open-ended, multi-dimensional questions. By accessing relevant documents and knowledge, RAG-based models generate answers with additional context, offering richer insights than generative-only models. This approach is useful in fields where responses must reflect a broad knowledge base, such as legal research…
A major challenge in AI research is how to develop models that can balance fast, intuitive reasoning with slower, more detailed reasoning in an efficient way. Human cognition operates by using two systems: System 1, which is fast and intuitive, and System 2, which is slow but more analytical. In AI models, this dichotomy between…
Natural Language Processing (NLP) is a rapidly growing field that deals with the interaction between computers and human language. As NLP continues to advance, there is a growing need for skilled professionals to develop innovative solutions for various applications, such as chatbots, sentiment analysis, and machine translation. To help you on your journey to mastering…
One of the fundamental challenges in IR is that the classic systems are not designed to handle dynamic, multi-step tasks. Current IR frameworks rely on an immutable, predefined architecture that enables only single-step interactions; users must explicitly revise queries to get the desired results. Conventional models thus lag far behind as users increasingly request systems…
To determine if two biological or artificial systems process information similarly, various similarity measures are used, such as linear regression, Centered Kernel Alignment (CKA), Normalized Bures Similarity (NBS), and angular Procrustes distance. Despite their popularity, the factors contributing to high similarity scores and what defines a good score remain to be determined. These metrics are…
Accurate assessment of Large Language Models is best done with complex tasks involving long input sequences. Input sequence can exceed even 200,000 tokens in complex tasks such as repository analysis and information retrieval.LLMs, in response, have evolved, too, to accommodate context lengths of up to 1 million tokens. While examining the performance of capable LLMs…
Advanced AI technologies have brought about groundbreaking ways to deal with data. For many organizations and individuals, managing and analyzing data effectively has remained a persistent challenge. One of the core issues faced by users has been the time and technical know-how required to perform in-depth analyses of datasets. Moreover, tools for data analytics are…