OpenAI's release of ChatGPT catalyzed a competitive environment for generative AI technologies, primarily focusing on foundational machine learning advancements. Despite the rapid growth and valuation of AI companies, significant challenges regarding data privacy, ownership, and model training methods persist. While some developers are cautious, others prioritize progress at the expense of legal and moral considerations. Solutions such as federated learning, which trains models without centralizing sensitive data, are being considered, yet these methods also present their own complexities and risks related to technological execution and data integrity.
Federated learning is a distributed (decentralized) ML technique that enables training models by moving the training process to where the data is, instead of collecting and moving the data to the central server.
Despite commercial success and wide adoption, the question of finding the best way to train these models - the legal, moral, and technical issues of data - remains an elephant in the room.
Some AI developers are painfully tiptoeing around data privacy and ownership challenges, while others (especially the big, powerful firms) simply ignore these issues, prioritizing 'innovation.'
Recently, many AI experts started to talk about federated learning, edge AI, and local AI as feasible alternatives for solving the sensitive data issues. However, these approaches have their own risks.
Collection
[
|
...
]