entity · derived
Deep learning
Derived node: assembled mechanically from the claims carrying deep-learning. A roster, not an adjudicated definition.
Every claim under this term
- 3409548-003 : Current hedge fund trading technology is hard coded by humans, whereas deep learning systems can be given a simple command and derive a result from data on their own; this is the operative difference
- 3409548-028 : Whether deep learning can identify particular features of a stock that would be profitable remains contested rather than settled.
- 4796714-007 : The black box character of deep learning models is a governance failure and not merely a technical inconvenience: opacity obstructs debugging, obscures bias detection and mitigation, and prevents comp
- 4855607-002 : The computational cost of improving deep learning performance scales so badly that halving the error rate is estimated to require over five hundred times more computational resources, which raises a s
- 4855607-003 : Deep learning models inadvertently learn and amplify whatever biases exist in their training data, so the composition of the training corpus, not the architecture, is the source of unfair or discrimin
- 4855607-004 : Deep learning models adapt to changes in data distribution far less readily than human learning does, which limits their reliability once the operating environment diverges from the training data.
- 4855607-027 : Because annotating large datasets is labor intensive and expensive, smart contracts that reward community members with tokens for annotation are needed to sustain a steady flow of high quality labeled
- 4941807-008 : The opacity of deep learning models obstructs debugging, obscures the detection and mitigation of bias, and prevents comprehension of how AI decisions are reached.
- 5541658-004 : The rapid adoption of deep learning and predictive analytics in law was driven by two enabling conditions: the increasing availability of digitized legal data and the computational power to process it