This year, new research collectives have open sourced breakthrough AI models developed by large centralized labs at a never before seen pace. By contrast, the large-scale AI compute infrastructure that has enabled this acceleration, however, remains firmly concentrated in the hands of NVIDIA despite investments by Google, Amazon, Microsoft and a range of startups.
Produced in collaboration with my friend Ian Hogarth, this year’s State of AI Report also points to an increase in awareness among the AI community of the importance of AI safety research, with an estimated 300 safety researchers now working at large AI labs, compared to under 100 identified in last year's report.
Small, previously unknown labs like Stability.ai and Midjourney have developed text-to-image models of similar capability to those released by OpenAI and Google earlier in the year, and made them available to the public via API access and open sourcing. Stability.AI’s model cost less than $600,000 to train, while Midjourney’s is already proving profitable and has become one of the leaders in the text-to-image market alongside OpenAI’s Dall-E 2. This demonstrates a fundamental shift in the previously accepted AI research dynamic that larger labs with the most resources, data, and talent would continually produce breakthrough research.
Meanwhile, AI continues to advance scientific research. This year saw the release of 200M protein structure predictions using AlphaFold, DeepMind’s advancement in nuclear fusion by training a reinforcement learning system to adjust the magnetic coils of a tokamak, and the development of a machine learning algorithm to engineer an enzyme capable of degrading PET plastics. However, as more AI-enabled science companies appear in the landscape, we also explore how methodological failures like data leakage and the ongoing tension between the speed of AI/ML development and the slower pace of scientific discovery might affect the landscape.
The report is a collaborative project and we’re incredibly grateful to Othmane Sebbouh, who made significant contributions for a second year running, and Nitarshan Rajkumar, who supported us this year, particularly on AI Safety. Thank you to our Reviewers and to the AI community who continue to create the breakthroughs that power this report.
We write this report to compile and analyze the most interesting things we’ve seen, with the aim of provoking an informed conversation about the state of AI. So, we would love to hear any thoughts on the report, your take on our predictions, or any contribution suggestions for next year’s edition.
Nathan and Ian
Co-authored in London (UK) by: