The State of AI Report 2021 is now live!
This year, we have seen AI become increasingly pivotal to breakthroughs in everything from drug discovery to mission critical infrastructure like electricity grids.
Produced in collaboration with my friend Ian Hogarth, this year’s State of AI Report points to the real-world performance breakthroughs we’ve seen in NLP, computer vision and biology over the past 12 months.
While AI’s growing impact on society and the economy is now evident, our report highlights that research into AI safety and the impact of AI still lags behind its rapid commercial, civil, and military deployment. This, along with other prominent concerns of bias, gives us food for thought about how best to chart the progress of AI systems with rapidly advancing capabilities.
This year’s report looks particularly at the emergence of transformer technology, a technique to focus machine learning algorithms on important relationships between data points to extract meaning more comprehensively for better predictions, which ultimately helped unlock many of the critical breakthroughs we highlight throughout.
The report also sheds light on a watershed moment in the field of biology, where AI-first approaches continue to show their potential to entirely transform drug discovery and healthcare. I’m personally excited to see what’s next after the major breakthroughs with protein folding and the structure of RNA molecules.
We hope the report has something for everyone- from AI research to politics. Here are five key findings:
1. AI is stepping up in more concrete ways: AI is increasingly being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing calculations during pandemics. However, there are questions about whether the maturity of the industry has caught up with the enormity of its growing deployment. An increasingly data-centric, rather than model-centric, view of AI is emerging.
2. AI-first approaches have taken biology by storm: AI has enabled faster simulations of humans’ cellular machinery (proteins and RNA) which has the potential to transform drug discovery and healthcare.
3. Transformers have emerged as a general purpose architecture for machine learning: beating the state of the art in many domains including NLP, computer vision, and even protein structure prediction.
4. Investors have taken notice: We have seen record funding this year into AI startups, and two first ever IPOs for AI-first drug discovery companies, as well as blockbuster IPOs for data infrastructure and cybersecurity companies that help enterprises retool for the AI-first era.
5. China’s ascension in research quality is notable: China’s universities have rocketed from publishing no AI research in 1980 to the largest volume of quality AI research today.
The report is always a collaborative project and we’re incredibly grateful to Othmane Sebbouh - our star Research Assistant as well as our Contributors and Reviewers - all of whom played a part in making the report what it is.
We write this report to compile 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