"AI is not about replicating human intelligence but about finding patterns in data." - Andrew Ng, AI pioneer
Understanding training process
Training an AI involves stripping away specific prose to reveal abstract concepts. This process mimics how humans learn from text, transforming data into mathematical vectors that represent ideas instead of storing direct copies of the original material.
Copyright violation
Indian law defines a copy as a reproduction that is intelligible and capable of substitution. Since training data is converted into high dimensional space without storing text snippets, it does not meet the legal threshold for infringement during training.
Threat to human creators
Artists fear AI will produce content that rivals their own very quickly. This concern is valid, but applying copyright to the training cycle is a poor solution that misinterprets how models function and strips away the nuance of learning.
Shifting focus
If an AI system generates a response reproducing a substantial portion of a work, that is clear infringement. Legal remedies should focus on these specific outputs rather than the learning process to protect the rights of original authors effectively. This is a fine point worthy of judicial attention.
Protecting the future of learning
Extending copyright to the training cycle could inadvertently penalize human learning. Treating learning as reproduction undermines the principle that ideas and concepts are free for everyone to study, which is a distinction copyright law has always maintained.
Summary
The article explains that AI training is a form of learning rather than copyright theft. While the training process extracts abstract concepts without storing copies, the real legal risk lies in AI outputs that reproduce original works. Regulation should target these specific infringing outputs.
Food for thought
If we classify AI training as copyright infringement, should we also restrict human students from reading books to gain skills that might eventually compete with the original authors?
AI concept to learn: Neural network vectors
These are mathematical numbers representing underlying meanings within a multi dimensional space. This allows systems to capture the essence of data without needing to store a direct copy of the original source material. Thus, neural
network vectors are numerical representations of information inside
models. Words, images, sounds, or features are encoded as vectors so
neural networks can compute similarity, patterns, and meaning. Learning
adjusts these vectors to capture relationships, structure, and context
efficiently across high-dimensional spaces.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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