Probabilistic Programming Defended: Learning Yields Compact Symbolic Program Libraries, Not Numeric Circuits
xuanalogue · x · 2026-09-15
xuanalogue pushes back on the objection that engineering AI would require hand-specifying everything: the traditional probabilistic programming argument is that learning still happens, but what gets learned are compact higher-order symbolic representations — libraries of probabilistic programs and their parameters — rather than numeric circuits like neural networks. A technical exchange in the ongoing 'grow it vs. engineer it' AI paradigm debate.
Related event: Debate: should AI be engineered via probabilistic programming or grown(4 posts)→
More from Research
- OpenAI model's PDE proof compiles in Lean; researcher likens fuss to whining over Perelman — RexDouglass · 2026-09-15
- Phillip Isola highlights a non-mainstream AI route: RL from scratch via ultra-fast simulators — AjdDavison · 2026-09-15
- Cutting AI verifier reading cost: top-50 retrieval kept just 2 of 8 minority evidence items — iMiguelmars · 2026-09-15
- Cutting AI verifier reading cost: top-50 retrieval kept just 2 of 8 minority evidence items — iMiguelmars · 2026-09-15
- SSAD2026 talk covers autonomous driving 3D perception, from LiDAR self-supervision to multi-sensor distillation — abursuc · 2026-09-15
- The Principles of Diffusion Models: 522-page book PDF released ahead of MIT Press 2027 print edition — RichmanRonald · 2026-09-15