In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations ($P^3$)- a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood of a peak maximum occurring at that location. The mapping between the spectrum and $P^3$ is achieved using MR-Ai, a physics-inspired deep learning neural network architecture, designed to handle multidimensional NMR spectra. Furthermore, we demonstrate that MR-Ai enables coprocessing of multiple spectra, facilitating direct information exchange between datasets. This feature significantly enhances spectral quality, particularly in cases of highly sparse sampling. Performance of MR-Ai and high value of the $P^3$ are demonstrated on the synthetic data and spectra of Tau, MATL1, Calmodulin, and several other proteins.

In the humanities, it is rarely possible to resort to proof. Humanities are not built on the formulation of hypotheses and their proof or refutation. It is a field where different ways of describing its material (e.g., artistic culture) compete [Harpham, 2013]. Therefore, the question of text authorship is so important for humanists; it remains one of the few questions in the humanities that can be formulated as falsifiable and sometimes verifiable hypotheses. This is an area where humanists find themselves in a situation very similar to that in which representatives of the sciences usually exist. Consequently, this is the rhetorical resource that humanists can use in the struggle for resources in science and for symbolic capital in the scientific field.

Smart signal processing approaches using Artificial Intelligence are gaining momentum in NMR applications. In this study, we demonstrate that AI offers new opportunities beyond tasks addressed by traditional techniques. We developed and trained several artificial neural networks in our new toolbox Magnetic Resonance with Artificial intelligence (MR-Ai) to solve three "impossible" problems: quadrature detection using only Echo (or Anti-Echo) modulation from the traditional Echo/Anti-Echo scheme; accessing uncertainty of signal intensity at each point in a spectrum processed by any given method; and defining a reference-free score for quantitative access of NMR spectrum quality. Our findings highlight the potential of AI techniques to revolutionize NMR processing and analysis. NMR spectroscopy is a powerful analytical technique of the peaks (Figure 1.a), which is not amenable for normal widely used to acquire atomic-level information about analysis.