Combinatorial optimisation (CO) problems are among the hardest challenges in computer science, with real-world importance in logistics, transport, and energy systems. These problems involve choosing the “best” option from an astronomically large set of possibilities and are often NP-hard, meaning they become computationally intractable as the problem size grows. Neural solvers, advanced AI approaches designed… Read more »
Reinforcement learning (RL) has delivered some of AI’s most striking successes, from human-level Atari 1 play to world-class performance in Go2. Yet when applied to messy, real-world combinatorial optimisation (CO) problems such as energy grid management or autonomous logistics, even state-of-the-art RL systems can stall. Despite being trained to convergence, policies often hit a performance… Read more »
Multi-agent reinforcement learning (MARL) holds significant promise across domains such as autonomous driving, warehouse logistics, intelligent rail networks, and satellite alignment. Yet deploying MARL in the real world remains difficult. Training typically requires vast amounts of interactive data, which is both costly and potentially risky, particularly in safety-critical settings where trial and error is not… Read more »
Nucleotides are the fundamental units of DNA, and when linked together by a sugar-phosphate backbone, they form the strands that define our genome. Analysing the precise role of each nucleotide within these sequences is essential to understanding their influence on gene regulation and disease. However, the human genome contains around 3 billion nucleotides in a… Read more »
BioNTech hosted its annual AI Day at the Science Museum in London, the second event in their Innovation Series. The day brought together investors, analysts, media representatives and guests to explore how artificial intelligence could transform science and medicine. As part of BioNTech’s AI ecosystem, InstaDeep showcased the progress made across multiple fronts, from advanced… Read more »
Reinforcement learning (RL) is increasingly being applied to complex processes across science and engineering, with promising results in manufacturing, biology, and energy systems. By learning through trial and error, RL agents can optimise behaviour without explicit supervision1. Many of these processes are governed by differential-algebraic equations (DAEs). These combine time-dependent dynamics with algebraic constraints, making… Read more »
By Alex Graves The story of Bayesian Flow Networks (BFNs) took years to take shape. In 2010, I was working with autoregressive models and repeatedly ran into the same constraint: their fixed prediction order, in which a single variable is completely revealed at every step. This rigid structure felt unnatural compared to how we typically… Read more »
As AI continues to reshape our understanding of the biological landscape, DNA, RNA, and protein sequence models are rapidly emerging—each promising to be faster, more capable, and more accurate than the last. While genomic data is being generated at an astonishing pace, making sense of it remains a challenge. A key barrier is usability. Most… Read more »
Understanding molecular behaviour allows researchers to predict the physical and chemical properties of complex systems1, such as how a protein folds or how a drug binds to its target. These insights are critical across biology, chemistry, and materials science2, especially when experiments are costly, time-consuming, or difficult to scale. Yet molecular science has long grappled… Read more »
Antibodies play an important role in the adaptive immune response. By selectively recognising and binding to specific antigens—such as viruses or bacteria—they neutralise threats and are essential to both acute and long-term immunity. This ability to target a wide range of molecules has made antibodies indispensable to therapeutic development, fueling a market valued at $252.6… Read more »