AI errors pose a significant challenge, hindering real-world applications. This work introduces a novel approach to cope with AI errors using weakly supervised error correctors that guarantee a specific level of error reduction. Our correctors have low computational cost and can be used to decide whether to abstain from making an unsafe classification. We provide new upper and lower bounds on the probability of errors in the corrected system. In contrast to existing works, these bounds are distribution agnostic, non-asymptotic, and can be efficiently computed just using the corrector training data. They also can be used in settings with concept drifts when the observed frequencies of separate classes vary. The correctors can easily be updated, removed, or replaced in response to changes in distributions within each class without retraining the underlying classifier. The application of the approach is illustrated with two relevant challenging tasks: (i) an image classification problem with scarce training data, and (ii) moderating responses of large language models without retraining or otherwise fine-tuning.

We are writing this editorial piece at the peak of the current Artificial Intelligence (AI) ‘spring’ as generative models quickly cross the bridge from the confines of academic and industry labs into our everyday lives. During times like this, one might be excused from forgetting how old the application of AI approaches in geography is. Geographers have been here before. About forty years ago, Smith (1984) wrote: AI techniques, if properly applied, should also allow researchers to spend a greater proportion of their time on creative thinking and less on technical drudgery. As with any set of tools, the techniques of AI cannot replace a hard-earned understanding of some phenomenon and will almost certainly be overvalued and misused by some practitioners. [Nevertheless], if used with care, the techniques of AI will prove of great benefit to such an applied, problem solving discipline as geography. (p. 157). It is in the subsequent issue of the same journal that we find Nystuen’s (1984) comment, suggesting that ‘[b]enefit to geography from such an alliance [with AI] is questionable considering that our own directions are murky enough’ (p. 358). Smith, in Nystuen’s view, should be ‘a little more critical in his appraisal of the scope of possible applications’ (Nystuen 1984, p. 359). The debate between Smith and Nystuen unfolded during the ‘AI spring’ of the 1980s, but the same hopes and concerns around a data-driven (rather than theory-driven) geography echo through the discipline’s history. From Openshaw’s (1992, 1998) work on AI tools for spatial modelling and analysis to Miller and Goodchild (2015) discussion of data-driven geography in the wake of big data, to the emergence of GeoAI (Janowicz et al. 2022) – primarily used as a shorthand for geospatial AI, encompassing the efforts towards creating spatially-explicit models in the era of deep learning. As detailed by Miller and Goodchild (2015), these ‘waves’ are evolutionary rather than revolutionary. These approaches are founded in abductive reasoning and foster the same discussions, tensions and shifts between nomothetic (law-seeking) and idiographic (description-seeking) knowledge that can be traced back to the very origins of the discipline. Traditional AI approaches have long been part of Geographical Information Science (GIScience), including research both on unsupervised learning approaches to geographical data mining (e.g. geodemographic classification and dimensionality reduction, see e.g. Miller and Han 2009) and supervised methods of inference (e.g. spatial autocorrelation and geographically weighted regression, see e.g. O'Sullivan and Unwin 2003). At the same time, each ‘wave’ is unique, and the current AI spring has again brought new challenges and opportunities. This special issue stemmed from a session organised at the Annual International Conference of the Royal Geographical Society (with IBG) in August 2021, which aimed to explore those challenges and opportunities with a particular focus on deep learning and human geography. The previous decade had seen unprecedented advances in image processing following the seminal paper on Alexnet (Krizhevsky et al. 2012), the emergence of large language models (LLMs) based on the transformer architecture (Vaswani et al. 2017), as well as the development of graph neural networks (Bruna et al. 2013, Hamilton et al. 2017). While those approaches to deep learning have found wide use in many aspects of GIScience and remote sensing (e.g. computer vision in geospatial applications), their application to human geography has been slower (Harris et al. 2017). Complementing the special issue introduced by Janowicz et al. (2020) on ‘Artificial intelligence techniques for geographical knowledge discovery’, this special issue focuses on GeoAI as a broader geographical AI and its applications in urban analytics (Liu and Biljecki 2022). The next section introduces the articles included in this special issue, while the final section contextualises the main themes emerging from those articles in the current, fast-paced landscape shaken by the emergence of foundation models (Bommasani et al. 2021).

In this paper we present theory and algorithms enabling classes of Artificial Intelligence (AI) systems to continuously and incrementally improve with a priori quantifiable guarantees – or more specifically remove classification errors – over time. This is distinct from state-of-the-art machine learning, AI, and software approaches. The theory enables building few-shot AI correction algorithms and provides conditions justifying their successful application. Another feature of this approach is that, in the supervised setting, the computational complexity of training is linear in the number of training samples. At the time of classification, the computational complexity is bounded by few inner product calculations. Moreover, the implementation is shown to be very scalable. This makes it viable for deployment in applications where computational power and memory are limited, such as embedded environments. It enables the possibility for fast on-line optimisation using improved training samples. The approach is based on the concentration of measure effects and stochastic separation theorems and is illustrated with an example on the identification faulty processes in Computer Numerical Control (CNC) milling and with a case study on adaptive removal of false positives in an industrial video surveillance and analytics system.