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AI that works beyond the lab must survive changing conditions, unusual inputs, resource limits, and long-term maintenance—not just perform well in a controlled experiment. In a 30 September 2026 interview with The AI Journal, software engineer and researcher Prajval Mohan explains how that goal connects his work in reinforcement learning, computer vision, digital pathology, and scalable systems.
What makes an AI problem worth pursuing?
Mohan says he looks for a combination of practical importance, meaningful technical difficulty, and the opportunity to build something useful. That perspective links projects as different as path planning, roadside safety, digital pathology, and systems for the mortgage industry.
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He describes the common thread as building practical systems in which reliability, efficiency, and real-world constraints matter. It is his account of how he chooses problems, rather than a universal formula for deciding which AI research deserves attention.
Why is real-world reinforcement learning difficult?
In reinforcement learning, an agent learns by taking actions and observing their consequences. Mohan points to a tension that becomes especially consequential outside a controlled setting: exploration may reveal better actions, but a poor action can be costly or unsafe.
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Exploration has a real cost
Restricting an agent’s opportunities to explore may reduce the risk of bad outcomes, but can also limit the quality of the solution it learns. The balance depends on the environment and the consequences of acting; the interview does not quantify a best exploration strategy.
Training conditions may not last
A policy that works during training can become unstable when conditions change or an unexpected event occurs. Mohan also identifies practical limits on computation, available information, and the time available to make decisions. These are engineering concerns he describes, not evidence that every reinforcement-learning deployment encounters the same failure modes.
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The interview says Mohan’s path-planning work led to a method called “Iterative SARSA,” but it does not identify a paper, repository, algorithmic description, or evaluation results. The name alone is not enough to establish how the method works or how it compares with alternatives.
What does digital pathology show about building computer vision systems?
Digital pathology applies image-analysis methods to digitized tissue slides. It makes the practical stakes of computer vision clear: a useful system needs more than a promising accuracy result; its behavior and limitations must also be understandable to the people assessing it.
Mohan discusses his work with Slideflow, including model ensembles, out-of-distribution detection, deep ensembles, hyper-deep ensembles, and adversarial training. Those specific contributions are reported in the interview. The project and paper establish his association with Slideflow and describe the framework’s wider capabilities, but do not independently enumerate every contribution attributed to him in the interview.
What Slideflow provides
The Slideflow project repository describes an open-source deep-learning framework for digital pathology. Its listed capabilities include image-processing tools, uncertainty quantification, and explainability.
A 2024 paper, “Slideflow: deep learning for digital histopathology with real-time whole-slide visualization,” lists Mohan among its authors. The paper describes a library for digital-pathology deep learning and whole-slide visualization, and reports availability through GitHub, PyPI, and Docker Hub. Its authors report extracting whole-slide tiles at 40× magnification in 2.5 seconds per slide; that figure is a paper-reported result under the stated conditions, not a general guarantee across hardware or workloads.
How should AI research results reflect deployment?
Mohan recommends reporting two views of performance: what a system achieves under ideal conditions, and what can realistically be maintained when cost, scale, and ongoing upkeep are considered. The distinction helps readers see both a system’s experimental potential and the practical conditions attached to using it.
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He also says engineers should ask whether others can reproduce a result, whether it can be implemented reliably, and how it behaves under unusual inputs, system failures, heavier workloads, and changing conditions. These are recommendations from his interview, not a claim that one standardized evaluation framework has been established.
The AI Journal interview with Prajval Mohan was published on 30 September 2026. The technical framing and recommendations above are Mohan’s views as presented in that interview; the Slideflow capabilities and performance figure are described by the project and paper sources.
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