Training deep and complex machine learning (ML) models involves determining the best optimizer and then manually tuning its hyperparameters — a process that is both computationally intensive and time-consuming. Learning-to-learn (L2L) systems have recently emerged as a more efficient alternative to conventional human-engineered ML optimizers. A team from Google and…

The November release of ChatGPT garnered unprecedented public and media attention. OpenAI’s conversational large language model (LLM) was widely applauded for its ability to answer complex queries, generate correct computer code and coherent long-form essays, and even solve math problems. But might that last claim have been premature? In the…

ChatGPT’s ability to generate coherent and comprehensive essays on any topic in seconds has made it both a game-changing information resource and the bane of educators. …

AI’s evolution over the last decade has been incredible. While researchers might point to the successes of AlexNet or AlphaGo as milestones, the “wow” moments for the general public have come from prompt-based image generation models such as Stable Diffusion and, more recently, the power of ChatGPT. …

Climate change and extreme weather events have made weather and climate modelling a challenging yet crucial real-world task. …

From drug discovery and protein folding to tumour detection, AI is revolutionizing the biomedical and healthcare fields. Recent research into brain-computer interfaces (BCIs) has revealed their potential to restore rapid communication to people with paralysis by capturing neural activities evoked by attempted speaking actions and decoding these into text. …

Limit order books (LOBs) traditionally comprise instructions to buy or sell a given security at a specific price or better. The introduction of AI-powered trading systems has significantly impacted limit order book markets in recent years. While studies have shown that LOB prices can be predictable over short time periods…

Continual few-shot learning techniques enable AI models to learn from a continuous stream of tasks described by a small set of samples without forgetting their previously learned information. This learning paradigm is beneficial in real-world applications such as industrial robotics, where a deployed agent must learn in a dynamic environment…

Interpretability has emerged as a new buzzword and research focus in AI system development and deployment. Although deep learning models have achieved remarkable success in recent years, the question of how these models work remains underexplored and poorly understood — mainly due to a lack of ground truth information regarding…