English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Simple rules can generate complexity and human can also learn fast with rules. Can machines learn in a similar way? Can artificial intelligence be independent of statistics? Machine learning is growing rapidly but models are poorly interpretable and depend on statistics. We propose a method by iteration based on causality which is the real one exists in the system. It is composed of fixed goals and basic rules called DNA rules. These DNA rules can be obtained from the definition and is not statistical rules. The causality in rules promises the process to be precise, because the potential attractor of the system is deterministic, because it is subjected to the rules although the system is complex especially under uncertain interference. Such a model not only works well in the traditional deterministic systems like the stable point and limited circle but also can work in some seemingly random and systems which are considered to be stochastic systems. The model is taken to play a game and it makes the machine learns fast and adaptively, and it is also interpretable with the causality and independent from the amount of data for it is based on causal iteration. It learns and even predicts the seemingly random interference in the game. We found such a model is adaptable, and it works well even in out-of-sample situations. The model is compared with an LSTM network in prediction a seemingly random sequence, the result shows the causality-based model also works well. We think that it may help to solve some problems hard for the traditional statistical method and become an enrichment for the current models.

A kind of artificial intelligence model based on nature without statistic