The growing interest in eXplainable Artificial Intelligence (XAI) has prompted research into models with built-in interpretability, the most prominent of which are part-prototype models. Part-Prototype Models (PPMs) make decisions by comparing an input image to a set of learned prototypes, providing human-understandable explanations in the form of ``this looks like that''. Despite their inherent interpretability, PPMS are not yet considered a valuable alternative to post-hoc models. In this survey, we investigate the reasons for this and provide directions for future research. We analyze papers from 2019 to 2024, and derive a taxonomy of the challenges that current PPMS face. Our analysis shows that the open challenges are quite diverse. The main concern is the quality and quantity of prototypes. Other concerns are the lack of generalization to a variety of tasks and contexts, and general methodological issues, including non-standardized evaluation. We provide ideas for future research in five broad directions: improving predictive performance, developing novel architectures grounded in theory, establishing frameworks for human-AI collaboration, aligning models with humans, and establishing metrics and benchmarks for evaluation. We hope that this survey will stimulate research and promote intrinsically interpretable models for application domains. Our list of surveyed papers is available at https://github.com/aix-group/ppm-survey.

The cost of information processing in physical systems calls for a trade-off between performance and energetic expenditure. Here we formulate and study a computation-dissipation bottleneck in mesoscopic systems used as input-output devices. Using both real datasets and synthetic tasks, we show how non-equilibrium leads to enhanced performance. Our framework sheds light on a crucial compromise between information compression, input-output computation and dynamic irreversibility induced by non-reciprocal interactions.

Random forest is a popular machine learning approach for the analysis of high-dimensional data because it is flexible and provides variable importance measures for the selection of relevant features. However, the complex relationships between the features are usually not considered for the selection and thus also neglected for the characterization of the analysed samples. Here we propose two novel approaches that focus on the mutual impact of features in random forests. Mutual forest impact (MFI) is a relation parameter that evaluates the mutual association of the featurs to the outcome and, hence, goes beyond the analysis of correlation coefficients. Mutual impurity reduction (MIR) is an importance measure that combines this relation parameter with the importance of the individual features. MIR and MFI are implemented together with testing procedures that generate p-values for the selection of related and important features. Applications to various simulated data sets and the comparison to other methods for feature selection and relation analysis show that MFI and MIR are very promising to shed light on the complex relationships between features and outcome. In addition, they are not affected by common biases, e.g. that features with many possible splits or high minor allele frequencies are prefered.

If you own an Amazon Echo, there's a chance that just reading that word triggered a pavlovian "bimm" in your mind. Or, if you have the wake sound disabled, maybe it's the timer alarm that makes you twitch if you hear it on a TV show (or someone else's speaker). Whatever you think of the sounds a smart speaker makes, none of them are accidental. They have all been meticulously designed to pull your attention or provide reassurance, depending on their goal. And the Echo could have sounded very different from how we know it today. The Echo series, in particular, has been instrumental in defining the smart speaker and the sounds we expect and (to avoid burned pizza) need it to make.

The current chapter aims at establishing a relationship between artificial intelligence (AI) and hardware security. Such a connection between AI and software security has been confirmed and well-reviewed in the relevant literature. The main focus here is to explore the methods borrowed from AI to assess the security of a hardware primitive, namely physically unclonable functions (PUFs), which has found applications in cryptographic protocols, e.g., authentication and key generation. Metrics and procedures devised for this are further discussed. Moreover, By reviewing PUFs designed by applying AI techniques, we give insight into future research directions in this area.