# Use cases motivating new directions in Raven **URL:** https://discuss.ocaml.org/t/use-cases-motivating-new-directions-in-raven/16977 **Category:** Community **Created:** [July 15, 2025, 6:15pm UTC](https://discuss.ocaml.org/t/use-cases-motivating-new-directions-in-raven/16977 "2025-07-15T18:15:59Z") **Posts on this page:** 1 **Page:** 1
### Author: ![mostafatouny](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ocaml.org/mostafatouny/32/5826_2.png) [@mostafatouny](https://discuss.ocaml.org/u/mostafatouny) #### Post date: [July 15, 2025, 6:15pm UTC](https://discuss.ocaml.org/t/use-cases-motivating-new-directions-in-raven/16977/1 "2025-07-15T18:15:59Z")
Hello, Raven was [recently announced](https://discuss.ocaml.org/t/announcing-raven-scientific-computing-for-ocaml-alpha-release/16913) for modern scientific computing in OCaml. Combining provable guarantees with machine learning started to get witnessed in both research and industry, but not matured yet for everyday production. Since type-driven programming is more native in logic-based avenues, it is worthwhile to highlight these for future directions. ## Probabilistic Models _Story._ A company decided to design a simple score for assessing loyal customers to be targeted for promotions. The score should be adapted according to the feedback they’ll get from consumers. _ML Limitation._ The score must be completely interpretable / explainable for their feedback assessment. Modern ML models do not offer that. _Decision._ Adhoc rules are implemented in a SQL query to compute the scores. _Probabilistic Models._ A fully interpretable probabilistic model could be designed, considering the rules the team hypothesized. Think of a graph whose edges denote a rule alongside a probability. If the client is a female, then with 70% a chance, it takes that vertex. each client traces a path, to predict whether a client is recommended for a promotion. _Why OCaml?_ Many probabilistic models are based on [logical axioms](https://en.wikipedia.org/wiki/Probability_axioms), and thereby we can infer using logic. A stakeholder would appreciate a guarantee that no more 20% of clients will get at least 50% discount. I saw Probability theory formalism in type theory as in [Kachapova’s paper](https://thescipub.com/abstract/jmssp.2018.209.218). It is in Coq theorem prover (implemented in OCaml). I am not aware of any extention of those fronteirs, from theorem proving to type-driven probabilistic model design. Knowledge-based systems seems to be dead in all modern languages. ## Learning-augmented Algorithms _Story._ A ride-hailing company decided to design a new matching algorithm to ensure fairness among all demanding areas. Serving a segment of customers at the expense of others causes bad feedback. _ML Limitation_. The data is biased, and thereby any machine learning model shall be biased as well. The company rejects any learning-from-data as it does not conform with its policies. _Decision._ The engineering team found an open source matching engine, and after some testing and tuning, deployed it. _Learning-augmented Algorithms._ A matching algorithm could be designed ensuring fairness among demanding areas, yet preferring profitable clients within each area. Think of binary search but with choosing the pivot element using a predictive blackbox. If prediction accuracy is 100% then we find the target after one comparison. If accuracy is bad, then we are not worse than the worst-case scenario of binary search, finding the target in O(log). _Why OCaml?_ Since fairness has an [axiomatic logical foundation](https://en.wikipedia.org/wiki/Arrow%27s_impossibility_theorem#Background), expressing logical properties is in favour of OCaml in property testing. Partial function application enables a clean algorithm parametrization. I am not aware of any library for _learning-augmented algorithms_ in any language. ## Neuro-symbolic AI _Story._ A legal consultancy company is designing a chatbot. It has strict policies the chatbot should follow. _ML Limitation._ Text generation could hallucinate. It is more problemetic in sensitive domains like legal consultancy. _Decision._ Retrieval-augmented generation, where related trusted sources are retrieved, guided by them the text is generated. _Neuro-symbolic AI._ Instead of just retrieving, a symbolic engine could query the user some questions, and reason and infer new conclusions using logic. Based on these conclusions, the text generation process is guided. _Why OCaml?_ It is more native to express symbolic components in terms of types. I am not aware of any library in any language for building symbolic components using types. ## Bonus. Data Engineering Data engineering is critical for any scientific computing work. Functional programming is already recognized for that. See [TU Delft’s course](https://online-learning.tudelft.nl/courses/functional-programming-for-big-data-processing/). ## Discussion We are calling out for the community, to try similar case studies. - Start with adhocs or imperative programming integration techniques in any language. - If promising, look for architectural patterns in research. - Estimate whether OCaml’s ecosystem is of an added value. Contributing to OCaml is not a hobbyist’s decision. It should be driven by a business model, and that is what we are trying to figure now. Multi-disciplinary volunteers are needed, across research, engineering, and business. I am happy to learn from your feedback and suggestions.