OpenAI announced a new ChatGPT Health tab designed to handle medical questions in a dedicated, private space. The feature separates health chat history, offers encryption, multifactor authentication, and promises that health conversations will not be used to train the model. Users can link wellness apps such as Apple Health and MyFitnessPal. While the service is not intended for diagnosis or treatment, experts warn that the lack of HIPAA coverage could expose health data to inadequate protections. OpenAI says the tab is currently in beta and invites users to join a waitlist. Read more →

Microsoft is rolling out major updates to its consumer Copilot AI assistant, adding a group chat feature that supports up to 32 participants, an optional “real talk” mode that matches users' tone and adds wit, and a new voice‑mode character named Mico. The updates also enhance Copilot’s memory capabilities, letting users see and delete stored facts, and improve health‑related answers with trusted sources. While the changes launch in the U.S. consumer version first, Microsoft hints at future extensions to its business‑focused Microsoft 365 Copilot. Read more →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Read more →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Read more →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Read more →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Read more →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Read more →