News & Comment

Self-driving laboratories that integrate robotic production with artificial intelligence have the potential to accelerate innovation in biotechnology. Because self-driving labs can be complex and not universally applicable, it is useful to consider their suitable use cases for successful integration into discovery workflows. Here, we review strategies for assessing the suitability of self-driving labs for biochemical design problems.

This issue of Nature Computational Science features a Focus that highlights both the promises and perils of large language models, their emerging applications across diverse scientific domains, and the opportunities to overcome the challenges that lie ahead.

Strong barriers remain between neuromorphic engineering and machine learning, especially with regard to recent large language models (LLMs) and transformers. This Comment makes the case that neuromorphic engineering may hold the keys to more efficient inference with transformer-like models.

Large language models (LLMs) are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to address the complexity that has hindered the study of collectives and raise possible risks that warrant new methods.

While leading tech companies race to build ever-larger models, researchers in Brazil, India and Africa are using clever tricks to remix big labs’ LLMs to bring AI to billions of users.

We reflect on ongoing topics and discussions at conferences in the field of computational chemistry.

The adoption of generative artificial intelligence (AI) code assistants in scientific software development is promising, but user studies across an array of programming contexts suggest that programmers are at risk of over-reliance on these tools, leading them to accept undetected errors in generated code. Scientific software may be particularly vulnerable to such errors because most research code is untested and scientists are undertrained in software development skills. This Comment outlines the factors that place scientific code at risk and suggests directions for research groups, educators, publishers and funders to counter these liabilities.

We provide our recommendations on effectively writing computational science manuscripts.

We discuss different strategies to improve the writing of titles and abstracts.

Many humanists are skeptical of language models and concerned about their effects on universities. However, researchers with a background in the humanities are also actively engaging with artificial intelligence — seeking not only to adopt language models as tools, but to steer them toward a more flexible, contextual representation of written culture.

Decision-making inherently involves cause–effect relationships that introduce causal challenges. We argue that reliable algorithms for decision-making need to build upon causal reasoning. Addressing these causal challenges requires explicit assumptions about the underlying causal structure to ensure identifiability and estimatability, which means that the computational methods must successfully align with decision-making objectives in real-world tasks.

We provide recommendations on how to write an effective presubmission enquiry.

The use of generative artificial intelligence (AI) in healthcare is advancing, but understanding its potential challenges for fairness and health equity is still in its early stages. This Comment investigates how to define fairness and measure it, and highlights research that can help address challenges in the field.

Nature Computational Science is calling all researchers who develop and use generative models for molecular design and discovery to publish with us.

In honor of the 100th birthday of Margaret Dayhoff, we spotlight her footprint in the field of bioinformatics.

We reflect on what science tells us about the importance of diversity.

Autonomous synthesis laboratories promise to streamline the plan–make–measure–analyze iteration loop. Here, we comment on the barriers in the field, the promise of a human on-the-loop approach, and strategies for optimizing accessibility, accuracy, and efficiency of autonomous laboratories.