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This is because AI chatbotssteallift information from thoroughly researched articles and generate curated and precise responses to queries.
As you may know, AI chatbots like ChatGPT andMicrosoft Copilotheavily rely on copyrighted content for their responses.
AI-generated content is often riddled with hallucinations which impacts the quality of responses.
Interestingly, OpenAI CEO Sam Altman admittedit’s impossible to develop ChatGPT-like tools without copyrighted content.
The ChatGPT maker argued that copyright law doesn’t forbid training AI models using copyrighted material.
When you launch Copilot in Windows 11, you’ll find a disclaimer indicating “Copilot uses AI.
Semantic entropy helps identify AI hallucinations, but requires more computing power.
Check for Mistakes.”
According to anew study, a group of Oxford researchers have seemingly found a way around this critical issue.
Prof. Yarin Gal says:
Getting answers from LLMs is cheap, but reliability is the biggest bottleneck.
In situations where reliability matters, computing semantic uncertainty is a small price to pay.
According to Former Twitter CEO Jack Dorsey:
“Don’t trust; verify.
You have to experience it yourself.
And you have to learn yourself.
Dorsey adds thateverything will soon feel like a simulationas AI models and chatbots become more sophisticated.
But our new method overcomes this.”
Semantic entropy can determine the difference in the meanings of the outputs generated.
Then you compare the different answers with each other.
This is different from many other machine learning situations where the model outputs are unambiguous."
The research was conducted on six models, including OpenAI’s GPT-4.
The only downside of semantic entropy is that it requires more computing power and resources.
