From Possible to Probable AI Models
The article discusses the challenges of building reliable AI systems, emphasizing the distinction between what is possible and what is probable. It highlights that while generative AI can produce a wide range of outputs, the reliability of these outputs is often low. The author argues for a deeper understanding of probability theory to improve AI consistency in production environments.
- ▪Generative AI can produce a variety of outputs, but not all are reliable or useful.
- ▪The article emphasizes the difference between possible outcomes and probable outcomes in AI systems.
- ▪Hallucinations in AI occur when models generate plausible but low-probability outputs.
Towards Data Science files mainly under ai. We currently carry 104 of its stories.
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/from-possible-to-probable-ai-models/ |
| Publication time | Wed, 20 May 2026 12:00:00 +0000 |
| Retrieval time | 2026-05-20T12:10:02.408Z |
| Last seen | 2026-05-20T12:10:02.408Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | JzRW2XgVoHt4 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Artificial Intelligence From Possible to Probable AI Models The real challenge in building reliable AI Sara A. Metwalli May 20, 2026 7 min read Share Image by Roberto Lee Cortes from Pexels. Over the past couple of years, I have been involved in many conversations about generative AI (and you probably have, too!). These conversations varied in focus, from ones with the general public about the use of AI to ones with more technical people about the accuracy of models. Regardless of who I am conversing with, people are often fascinated and curious about what models can do. Can an LLM write a functional kernel driver? It can. Can it write a song about how much you love your cat? It sure can. Can a diffusion model generate a photo-realistic image of a medieval astronaut? It can.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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