Tag: Reliability

OpenAI, the company behind the AI chatbot ChatGPT, faces a libel lawsuit after providing false information about a legal case. The incident highlights the challenges involved in developing advanced AI models that balance creativity and accuracy. Researchers have identified the issue of hallucination, where AI models fabricate information, as a significant problem in the industry. OpenAI will focus on refining the ChatGPT model to reduce such incidents in the future.

OpenAI proposes a new strategy to combat AI models' tendency to produce false or fabricated information called process supervision. Instead of rewarding only the final correct conclusion, AI models are rewarded for each correct reasoning step, improving accuracy in multi-step domains. Their approach could lead to more human-like thinking and better explainable AI.

A talk-radio host in Georgia files defamation lawsuit against OpenAI's ChatGPT. The host claims that the AI generated fake legal complaints falsely accusing him of embezzlement, which caused damages to his reputation. This highlights concerns about AI-generated content's credibility and risks involved. As AI continues to advance, ethical and responsible mechanisms must develop to ensure its use appropriately.

Dynatrace offers a way for businesses to monitor OpenAI ChatGPT, a leading generative AI model, to ensure reliable and cost-effective service delivery. With Dynatrace, enterprises can collect, visualize and alert on OpenAI API consumption, latency, and stability information. The AI technology also helps optimize performance whilst providing valuable root cause analyses.

Contextual AI raises $20 million in seed funding to develop next generation large language models for enterprises. Their new text-generating AI technology, based on retrieval augmented generation, aims to improve reliability, compliance and governance issues in LLMs. Their focus on accuracy, reliability and traceability targets the enterprise market with a much more integrated solution. RAG technology allows LLMs to develop context-aware responses for specific business applications, with the majority of funding dedicated to product development.