Anthropic, a leading generative AI startup, has announced that it would not employ its clients' data to train its Large Language Model (LLM) and will step in to safeguard clients facing copyright claims.
Anthropic, which was established by former OpenAI researchers, revised its terms of service to better express its goals and values. The startup is setting itself apart from competitors like OpenAI, Amazon, and Meta, which do employ user material to enhance their algorithms, by severing the private data of its own clients.
The amended terms state that Anthropic "may not train models on customer content from paid services" and that Anthropic "as between the parties and to the extent permitted by applicable law, Anthropic agrees that customer owns all outputs, and disclaims any rights it receives to the customer content under these terms.”
The terms also state that they "do not grant either party any rights to the other's content or intellectual property, by implication or otherwise," and that "Anthropic does not anticipate obtaining any rights in customer content under these terms."
The updated legal document appears to give protections and transparency for Anthropic's commercial clients. Companies own all AI outputs developed, for example, to avoid possible intellectual property conflicts. Anthropic also promises to defend clients against copyright lawsuits for any unauthorised content produced by Claude.
The policy complies with Anthropic's mission statement, which states that AI should to be honest, safe, and helpful. Given the increasing public concern regarding the ethics of generative AI, the company's dedication to resolving issues like data privacy may offer it a competitive advantage.
Users' Data: Vital Food for LLMs
Large Language Models (LLMs), such as GPT-4, LlaMa, and Anthropic's Claude, are advanced artificial intelligence systems that comprehend and generate human language after being trained on large amounts of text data.
These models use deep learning and neural networks to anticipate word sequences, interpret context, and grasp linguistic nuances. During training, they constantly refine their predictions, improving their capacity to communicate, write content, and give pertinent information.
The diversity and volume of the data on which LLMs are trained have a significant impact on their performance, making them more accurate and contextually aware as they learn from different language patterns, styles, and new information.
This is why user data is so valuable for training LLMs. For starters, it keeps the models up to date on the newest linguistic trends and user preferences (such as interpreting new slang).
Second, it enables personalisation and increases user engagement by reacting to specific user activities and styles. However, this raises ethical concerns because AI businesses do not compensate users for this vital information, which is used to train models that earn them millions of dollars.
