AI
Delivering nuanced insights from customer feedback
Using GPT-3 to deliver fast, nuanced insights from customer feedback.
Jan 4 · 00:00OpenAI
5,786 press releases in English indexed under ai.
Using GPT-3 to deliver fast, nuanced insights from customer feedback.
Fine-tuning GPT-3 to power and scale done-for-you video creation.
Lilian Weng works on Applied AI Research at OpenAI.
Abstract While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in
We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
Abstract Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may
We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques. Larger models are better at self-critiquing, with scale improving critique-writing more than summary-writing. This shows promise for using AI systems to assist human supervision of AI systems on difficult tasks.
Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.
Abstract We show that a GPT‑3 model can learn to express uncertainty about its own answers in natural language—without use of model logits. When given a question, the model generates both an answer and a level of confidence (e.g. "90% confidence" or "high confidence"). These levels map to probabilities that are well calibrated. The model also remains moderately calibrated under distribution shift, and is sensitive to
We’ve released new versions of GPT-3 and Codex which can edit or insert content into existing text, rather than just completing existing text.
We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.