Artificial intelligence and large language models (LLMs) are reshaping scientific research by enabling autonomous research assistants that can perform complex tasks such as literature review, hypothesis generation, and experimental design. A key emerging approach is the use of multi-agent systems, where specialised AI agents collaborate to achieve research goals. Multi-agent
Generative AI systems rely on vast quantities of text, images and other materials in their training. These models can generate new content, yet the copyright status of both the underlying training data and the outputs produced remains uncertain. Researchers and developers therefore need to understand the differing legal frameworks in
Prompt engineering, the deliberate crafting of inputs to guide large language models (LLMs) towards precise and effective outputs, is pivotal in harnessing AI capabilities across diverse applications, from research to creative tasks. Attention to prompts is essential because poorly constructed inputs can lead to inaccurate, irrelevant, or biased responses, wasting
Most contemporary LLMs employ a decoder‑only transformer architecture, which processes sequences in parallel via self‑attention. However, scaling dense transformers linearly in size increases computation and cost. Mixture‑of‑experts (MoE) approaches address this by activating only a subset of parameters per token. In the Switch Transformer, MoE routing
Beyond their underlying architectures, another crucial way to classify generative systems is by the type of data—or modality—they process and generate. Previously, such models were typically limited to a single modality (unimodal), meaning they were designed to process and generate only one type of content. A prominent example
Benchmarking is the practice of evaluating artificial intelligence models on a standard suite of tasks under controlled conditions. In the context of large language models (LLMs), benchmarks provide a common yardstick for measuring capabilities such as factual knowledge, reasoning, and conversational coherence. They emerged because the proliferation of new models
Neural networks represent one of the most significant developments in machine learning, offering computational models inspired by the biological neural networks of animal brains. As specialised models within the broader machine learning paradigm, neural networks have evolved from theoretical constructs to practical tools that drive modern artificial intelligence applications. The
