Historical milestone · 2014
Neural Turing Machines
Reviewed through September 18, 2026
2014 · Historical milestone
Neural Turing Machines
- Era
- 2010s
- Theme
- AI paradigms & knowledge representation
- Evidence form
- Preprint + synthetic tasks
- School / paradigm
- Differentiable memory / connectionist computation
- Institution / context
- Google DeepMind
- Researchers
- Alex Graves; Greg Wayne; Ivo Danihelka
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Coupled a neural controller to differentiable external memory and trained it on copying, sorting, and associative recall.
Result / historical claim. Showed that gradient-based systems could learn simple algorithms and content-addressed memory operations.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: AI paradigms and knowledge representation.
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Preprint + synthetic tasks
Limitation / debate. Tasks were synthetic; training stability, scaling, and robust generalization remained open.
Source status. The source link below is the verified link our reviewed topic research already carries for this milestone.
Reproduce
A reproduction tutorial is linked only when one exists for this exact record.
A reproduction tutorial is not yet available for this entry. The closest reviewed material is AI paradigms and knowledge representation.
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.