Marktechpost

MAY 8, 2024

Availability of training data: Deep learning’s efficacy relies heavily on data quality, with simulation environments bridging the gap between real-world data scarcity and training requirements.

Deep Learning 119

Deep Learning Neural Network Data Scarcity Natural Language Processing 119

Viso.ai

DECEMBER 18, 2023

Instead of relying on organic events, we generate this data through computer simulations or generative models. Synthetic data can augment existing datasets, create new datasets, or simulate unique scenarios. Specifically, it solves two key problems: data scarcity and privacy concerns.

Computer Vision 52

Computer Vision Neural Network Auto-complete Data Scarcity 52

Marktechpost

FEBRUARY 29, 2024

Data scarcity in low-resource languages can be mitigated using word-to-word translations from high-resource languages. However, bilingual lexicons typically need more overlap with task data, leading to inadequate translation coverage. This approach faces challenges with domain specificity and performance compared to native data.

Artificial Intelligence 99

Artificial Intelligence Artificial Intelligence Data Scarcity NLP 99

Marktechpost

APRIL 4, 2024

Such richness and diversity promise to significantly reduce the time and resources data teams spend on improving data quality, which has traditionally consumed up to 80% of their workload.

Artificial Intelligence 113

Artificial Intelligence Artificial Intelligence AI Modeling Data Scarcity 113

Unite.AI

MARCH 13, 2024

Ensuring data quality, addressing potential biases, and maintaining strict privacy and security standards for sensitive medical data are the major concerns. Data Availability and Quality : Obtaining high-quality, domain-specific datasets is crucial for training accurate and reliable DSLMs.

Large Language Models 204

Large Language Models Natural Language Processing LLM Data Scarcity 204