Ari Morcos

Ari Morcos, CEO of Datology and former research scientist at Google and Meta, discusses the vital but overlooked area of data curation in AI. He emphasizes that high-quality data is essential for developing efficient models, arguing that the focus should shift from scaling models to improving data quality. Morcos recounts his journey from neuroscience to understanding that as data scales, the importance of model architecture diminishes. He advocates for automating data curation to enable organizations to harness the full potential of AI effectively.

Ari Morcos, the CEO of DatologyAI and former researcher at DeepMind and FAIR, dives into the fascinating world of data and deep learning. He explores the nuances of data quality, emphasizing the distinction between hard and bad data points. The conversation touches on the evolution of image representation models and the critical role of data selection for model training. Ari also warns against the careless use of synthetic data and discusses how careful curation can boost model performance. Overall, it's a deep dive into optimizing data for smarter AI.

Explore the importance of data curation in AI models, challenges in data quality, removing types of data, relationship between data size and model size, choosing optimal data subset, future of data curation, impact on service providers. CEO of automated data curation platform shares insights. Estimating conceptual complexity algorithmically, automated data curation for ML training, exploring sector-specific approaches, optimizing model size and data size in ML.