A side‑by‑side test of Google’s Gemini and OpenAI’s ChatGPT shows Gemini delivering deeper detail and clearer guidance across tasks such as career summaries, email drafting, and medical advice. Gemini links sources, avoids fabricating facts, and offers multiple options with context, giving it an edge over ChatGPT in the evaluated scenarios. Leer más →

OpenAI released details of an internal stress‑test aimed at measuring political bias in its chatbot models. The test, conducted on 100 topics with prompts ranging from liberal to conservative and charged to neutral, compared four models—including the newer GPT‑5 instant and GPT‑5 thinking—to earlier versions such as GPT‑4o and OpenAI o3. Results show the GPT‑5 models reduced bias scores by about 30 percent and handled charged prompts with greater objectivity, though moderate bias still appears in some liberal‑charged queries. The company says bias now occurs infrequently and at low severity, while noting ongoing political pressures on AI developers. Leer más →

A new paper from OpenAI examines why large language models such as GPT‑5 and ChatGPT continue to produce plausible but false statements, known as hallucinations. The authors explain that pretraining encourages models to predict the next word without distinguishing truth from falsehood, leading to errors on low‑frequency facts. They also argue that current evaluation methods reward correct answers regardless of confidence, prompting models to guess rather than express uncertainty. The paper proposes redesigning scoring systems to penalize confident mistakes, reward appropriate uncertainty, and discourage blind guessing, aiming to reduce hallucinations in future AI systems. Leer más →

A new paper from OpenAI examines why large language models such as GPT‑5 and ChatGPT continue to produce plausible but false statements, known as hallucinations. The authors explain that pretraining encourages models to predict the next word without distinguishing truth from falsehood, leading to errors on low‑frequency facts. They also argue that current evaluation methods reward correct answers regardless of confidence, prompting models to guess rather than express uncertainty. The paper proposes redesigning scoring systems to penalize confident mistakes, reward appropriate uncertainty, and discourage blind guessing, aiming to reduce hallucinations in future AI systems. Leer más →

A new paper from OpenAI examines why large language models such as GPT‑5 and ChatGPT continue to produce plausible but false statements, known as hallucinations. The authors explain that pretraining encourages models to predict the next word without distinguishing truth from falsehood, leading to errors on low‑frequency facts. They also argue that current evaluation methods reward correct answers regardless of confidence, prompting models to guess rather than express uncertainty. The paper proposes redesigning scoring systems to penalize confident mistakes, reward appropriate uncertainty, and discourage blind guessing, aiming to reduce hallucinations in future AI systems. Leer más →

A new paper from OpenAI examines why large language models such as GPT‑5 and ChatGPT continue to produce plausible but false statements, known as hallucinations. The authors explain that pretraining encourages models to predict the next word without distinguishing truth from falsehood, leading to errors on low‑frequency facts. They also argue that current evaluation methods reward correct answers regardless of confidence, prompting models to guess rather than express uncertainty. The paper proposes redesigning scoring systems to penalize confident mistakes, reward appropriate uncertainty, and discourage blind guessing, aiming to reduce hallucinations in future AI systems. Leer más →

A new paper from OpenAI examines why large language models such as GPT‑5 and ChatGPT continue to produce plausible but false statements, known as hallucinations. The authors explain that pretraining encourages models to predict the next word without distinguishing truth from falsehood, leading to errors on low‑frequency facts. They also argue that current evaluation methods reward correct answers regardless of confidence, prompting models to guess rather than express uncertainty. The paper proposes redesigning scoring systems to penalize confident mistakes, reward appropriate uncertainty, and discourage blind guessing, aiming to reduce hallucinations in future AI systems. Leer más →