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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With state-of-the-art models achieving high performance on standardbenchmarks, contemporary research paradigms continue to emphasize generalintelligence as an enduring objective. However, this pursuit overlooks thefundamental disparities between the high-level data perception abilities ofartificial and natural intelligence systems. This study questions the TuringTest as a criterion of generally intelligent thought and contends that it ismisinterpreted as an attempt to anthropomorphize computer systems. Instead, itemphasizes tacit learning as a cornerstone of general-purpose intelligence,despite its lack of overt interpretability. This abstract form of intelligencenecessitates contextual cognitive attributes that are crucial for human-levelperception: generalizable experience, moral responsibility, and implicitprioritization. The absence of these features yields undeniable perceptualdisparities and constrains the cognitive capacity of artificial systems toeffectively contextualize their environments. Additionally, this studyestablishes that, despite extensive exploration of potential architecture forfuture systems, little consideration has been given to how such models willcontinuously absorb and adapt to contextual data. While conventional models maycontinue to improve in benchmark performance, disregarding these contextualconsiderations will lead to stagnation in human-like comprehension. Untilgeneral intelligence can be abstracted from task-specific domains and systemscan learn implicitly from their environments, research standards should insteadprioritize the disciplines in which AI thrives.

Beyond Interpretable Benchmarks: Contextual Learning through Cognitive and Multimodal Perception