Consultants using ChatGPT-4 outperformed those who did not, by a lot [Link]
Consultants using GPT-4 completed 12% more tasks, executed tasks 25% faster, and achieved 40% higher quality results.
For the last several months, I been part of a team of social scientists working with Boston Consulting Group, turning their offices into the largest pre-registered experiment on the future of professional work in our AI-haunted age. Our first working paper is out today. There is a ton of important and useful nuance in the paper but let me tell you the headline first: for 18 different tasks selected to be realistic samples of the kinds of work done at an elite consulting company, consultants using ChatGPT-4 outperformed those who did not, by a lot. On every dimension. Every way we measured performance.
DeepMind’s cofounder: Generative AI is just a phase. What’s next is interactive AI. [Link]
DeepMind cofounder Mustafa Suleyman wants to build a chatbot that does a whole lot more than chat. In a recent conversation I had with him, he told me that generative AI is just a phase. What’s next is interactive AI: bots that can carry out tasks you set for them by calling on other software and other people to get stuff done. He also calls for robust regulation—and doesn’t think that’ll be hard to achieve.
Suleyman left DeepMind and moved to Google to lead a team working on AI policy. In 2022 he founded Inflection, one of the hottest new AI firms around, backed by $1.5 billion of investment from Microsoft, Nvidia, Bill Gates, and LinkedIn founder Reid Hoffman. Earlier this year he released a ChatGPT rival called Pi, whose unique selling point (according to Suleyman) is that it is pleasant and polite. And he just coauthored a book about the future of AI with writer and researcher Michael Bhaskar, called The Coming Wave: Technology, Power, and the 21st Century’s Greatest Dilemma.
DeepMind discovers that AI large language models can optimize their own prompts [Link]
In a new paper, researchers from DeepMind propose a new way: Optimization by PROmpting (OPRO), a method that uses AI large language models (LLM) as optimizers. The unique aspect of this approach is that the optimization task is defined in natural language rather than through formal mathematical definitions.
The researchers found that, on small-scale optimization problems, LLMs can generate effective solutions through prompting alone, sometimes matching or even surpassing the performance of expert-designed heuristic algorithms. However, the true potential of OPRO lies in its ability to optimize LLM prompts to get maximum accuracy from the models.
We present an approach to modeling an image-space prior on scene dynamics. Our prior is learned from a collection of motion trajectories extracted from real video sequences containing natural, oscillating motion such as trees, flowers, candles, and clothes blowing in the wind. Given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a per-pixel long-term motion representation in the Fourier domain, which we call a neural stochastic motion texture. This representation can be converted into dense motion trajectories that span an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping dynamic videos or allowing users to realistically interact with objects in real pictures.
How Silicon Valley doomers are shaping Rishi Sunak’s AI plans [Link]
Sam Altman’s Net Worth: How wealthy is the CEO of ChatGPT-maker OpenAI [Link]
The Executive Trying to Make Microsoft Smarter About AI [Link]
GPT-4 is not getting worse! [Link] RECOMMENDED
AI may outperform most humans at creative thinking tasks [Link]
China’s AI boom depends on an army of exploited student interns [Link] RECOMMENDED
I Saved 60 Hours of Coding With The “RJP” Prompting Technique [Link] RECOMMENDED
Using ChatGPT Custom Instructions for Fun and Profit [Link] RECOMMENDED
Prompt of the Day 🍭: Learning using the 80/20 principle
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