ChatGPT: Innovation and Prospects in the Digital Era
Vision for the future of AGI: building a safe and beneficial AGI
The OpenAI team's vision for AGI:
- We hope that AGI will empower humanity to thrive to the fullest extent possible in the universe. We don't expect the future to be a substandard utopia, but we want to maximize the good and minimize the bad, making AGI an amplifier for humanity
- We want the benefits, access and governance of AGI to be widely and fairly shared
- We want to be successful in navigating big risks. In the face of these risks, we acknowledge that things that seem right in theory are often stranger than expected in practice. We believe we must continually learn and adapt by deploying less functional versions of the technology to minimize "first time right" situations
Digital Era: Domain Innovation, Industry Transformation
book summary
Large pre-trained models are not good at summarizing; in the past, training the model via reinforcement learning from human feedback was found to help align model summaries with human preferences for short essays and articles; but directly judging summaries of entire books requires a lot of effort , because humans need to read an entire book, which takes a lot of time; to solve this problem, recursive task decomposition is used: a difficult task is procedurally broken down into simpler tasks; in this case, summarizing a long text Decomposed into summarizing several shorter paragraphs of text; recursive task decomposition compared to end-to-end training process
Speech Recognition
Whisper is an automatic speech recognition (ASR) system trained using 680,000 hours of multilingual and multitask supervised data collected from the web. We show that using such a large and diverse dataset improves robustness to accents, background noise, and technical language. In addition, it supports transcription in multiple languages, as well as translation of these languages into English. We are open sourcing the model and inference code as a basis for building useful applications and further research into robust speech processing;
education field
OpenAI trained a system to solve elementary school math problems with almost twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% of the problems of real children: a small subset of 9-12 year olds scored 60% on tests on our dataset, while our system scored 55% on the same problem
OpenAI built a neural theorem prover for Lean that learned to solve a variety of challenging high school Olympiad problems, including those from the AMC12 and AIME competitions, as well as two adapted from IMO
The Digital Age: Potential Threats and Prevention
Misuse of language models for false advertising
OpenAI researchers teamed up with Georgetown University's Center for Security and Emerging Technologies and the Stanford Internet Observatory to investigate how large language models can be misused for disinformation purposes. The collaboration includes a workshop in October 2021, bringing together 30 disinformation researchers, machine learning experts and policy analysts, culminating in a co-authored report based on more than a year of research. This report outlines how language models can be used to enhance the threats to the information environment posed by disinformation campaigns and introduces a framework for analyzing potential mitigations
Hazard Analysis Framework for Code Synthesis of Large Language Models
Codex is a large language model (LLM) trained on various code bases, whose ability to synthesize and generate code exceeds the previous state of the art. Despite the many benefits that Codex offers, models that can generate code at this scale have significant limitations, alignment issues, potential for abuse, and the potential to increase the rate of progress in technical areas that themselves can create instability effects or potential for misuse. However, such safety implications are unknown or remain to be explored. In this paper, we outline a hazard analysis framework built at OpenAI to discover the technical, social, political, and economic harms or safety risks that deploying a model like Codex may pose
To put it simply, it is to conduct risk assessment and analysis on the process of using artificial intelligence technology to generate code, so as to discover and solve potential risk problems. The framework can evaluate the generated code in terms of syntax analysis, context analysis, security analysis, etc., so as to determine the potential risks in the code and avoid problems in the actual application of the code. The application of this framework can help developers use language models to generate code more safely, and improve the quality and reliability of code