We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

GPT-4 Technical Report

/pdf/explainable-ai-from-black-box-to-glass-box-2b7t7g0dsp.pdf

Explainable AI: from black box to glass box

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.

A Generalist Agent

We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.

/pdf/artificial-intelligence-and-jobs-evidence-from-online-1f2ef8l3.pdf

Artificial Intelligence and Jobs: Evidence from Online Vacancies

Artificial Intelligence and Marketing: Pitfalls and Opportunities

"The most important book I have read in quite some time" (Daniel Kahneman); "A must-read" (Max Tegmark); "The book we've all been waiting for" (Sam Harris) LONGLISTED FOR THE 2019 FINANCIAL TIMES AND MCKINSEY BUSINESS BOOK OF THE YEAR; A FINANCIAL TIMES BEST BOOK OF THE YEAR 2019 Humans dream of super-intelligent machines. But what happens if we actually succeed? Creating superior intelligence would be the biggest event in human history. Unfortunately, according to the world's pre-eminent AI expert, it could also be the last. In this groundbreaking book on the biggest question facing humanity, Stuart Russell explains why he has come to consider his own discipline an existential threat to our species, and lays out how we can change course before it's too late. There is no one better placed to assess the promise and perils of the dominant technology of the future than Russell, who has spent decades at the forefront of AI research. Through brilliant analogies and crisp, lucid prose, he explains how AI actually works, how it has an enormous capacity to improve our lives - but why we must ensure that we never lose control of machines more powerful than we are. Here Russell shows how we can avert the worst threats by reshaping the foundations of AI to guarantee that machines pursue our objectives, not theirs. Profound, urgent and visionary, Human Compatible is the one book everyone needs to read to understand a future that is coming sooner than we think.

Human Compatible: Artificial Intelligence and the Problem of Control

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Human Compatible: Artificial Intelligence and the Problem of Control