History
9 entries
| Entry | Summary | As of |
|---|---|---|
| AI wintershistorical period | AI history includes periods when funding and public confidence contracted after ambitious expectations went unmet. The phrase groups distinct episodes, so causal claims should name the country, institution, and time period being discussed. | 2026-07-13 |
| Enterprise operational AIhistorical period | Enterprise AI platforms increasingly connect models to governed data, tools, evaluations, and workflows. Palantir AIP exemplifies a shift from isolated chat toward operational systems built on permissions and organizational context. | 2026-07-13 |
| Public adoption of generative AIhistorical period | Conversational and generative products brought model interaction to a broad public audience in the 2020s. Product release histories show rapid iteration in interfaces, tools, model routing, and access tiers alongside base-model progress. | 2026-07-13 |
| Symbolic AI and early programshistorical period | Early AI research emphasized symbolic representations, search, theorem proving, and explicit rules. This tradition treated intelligence as structured manipulation as well as learning, a contrast that still shapes hybrid systems. | 2026-07-13 |
| The Dartmouth workshop and the name AIhistorical period | The term artificial intelligence was used in the proposal for the 1956 Dartmouth summer research project. The proposal framed intelligence as aspects of learning and intelligence that could in principle be precisely described for machines. | 2026-07-13 |
| The Transformer erahistorical period | After 2017, Transformer architectures spread from translation into language, vision, speech, and multimodal systems. Attention-based scaling changed the dominant training stack without making recurrence or convolution obsolete for every task. | 2026-07-13 |
| The deep-learning revivalhistorical period | Large datasets, accelerator hardware, and effective training methods drove renewed success for deep neural networks. AlexNet's 2012 ImageNet result became a visible milestone, though it rested on decades of earlier neural-network work. | 2026-07-13 |
| The expert-systems erahistorical period | Expert systems encoded domain knowledge as rules and inference procedures for constrained applications. Their maintenance and knowledge-acquisition costs helped expose limits of hand-built symbolic knowledge at scale. | 2026-07-13 |
| The statistical machine-learning turnhistorical period | Late twentieth-century AI increasingly emphasized probabilistic modeling and learning from datasets. Evaluation on held-out data created a different engineering loop from manually specifying every decision rule. | 2026-07-13 |