Remove Continuous Learning Remove Data Scarcity Remove Data Science
article thumbnail

Innovations in AI: How Small Language Models are Shaping the Future

Pickl AI

Challenges and Limitations Despite their advantages, Small Language Models face challenges such as limited generalisation, data scarcity, and performance trade-offs, which necessitate ongoing research to enhance their effectiveness and applicability. Their narrow focus can limit their applicability in more generalised scenarios.

article thumbnail

Deep Learning for Medical Image Analysis: Current Trends and Future Directions

Heartbeat

Challenges and Limitations Despite the tremendous progress made in deep learning for medical image analysis, several challenges and limitations persist. Recognizing and addressing these issues is essential to ensure the responsible and practical application of deep learning models in healthcare. References Dylan et al.

article thumbnail

Predicting the Future of Data Science

Pickl AI

Summary: The future of Data Science is shaped by emerging trends such as advanced AI and Machine Learning, augmented analytics, and automated processes. As industries increasingly rely on data-driven insights, ethical considerations regarding data privacy and bias mitigation will become paramount.